Reviewerly/exHarmony huggingface dataset:
Performance Comparison Analysis of Fixed and Solar-Tracker Installed Panel at PV System

Abstract Energy generation by conventional energy has been an issue in many countries due to more demands and less supply and getting lesser by the years. This condition urges all the countries in the world to search for other alternatives of energy generation and the desired one is the one with unlimited sources. This unlimited source or self-renewable is called renewable energy. PT. Pertamina (Persero) RU-III Plaju as a state-owned enterprise is committed taking the active role in supporting the government in the campaign of using renewable energy in generating electricity. PT. Pertamina (Persero) RU-III Plaju installed a 3 x 3000 WP PV system to supply the managerial building, and this PV system is considered a research device to develop the possibility of substituting the conventional energy with renewable energy. This paper discussed the performance comparison of the fixed panel and solar-tracking solar panel application for the PV system in PT. Pertamina (Persero) RU-III Plaju. The performance analysis is including final yield, reference yield and performance ratio. Experiment results show that the fixed panel has more energy production with the average percentage of final yield is 3.3% and capacity factor is 13.6%, while the PV panels with solar-tracker have the average final yield of 2.3% and capacity factor of 9.6%. Therefore, this study shows that the application of solar-tracker is not necessary for PV panels in Palembang, due to the average maximal power achieved from 11 AM to 02 PM every day.
Learners’ Perceptions of the Design Principles of Blackboard

Recognising the usability challenges that are linked to tools like the Blackboard learning management system are significant. This paper presents the outcome of an assessment of adherence to design principles on Blackboard from the learner-user perspective at Botho University, Botswana. As the university is shifting from a conventional teaching mode to a more contemporary blended mode, slow progress in using Blackboard is observed among learners. It is critical to increase the usability of Blackboard by addressing imbalances, such as those observed in this study. Addressing these issues will increase the effective usage of Blackboard and they can be seen as the main agenda for the learning, teaching, and assessment strategy of Botho University. It is critical to gather the opinions of learners who study specific modules in programmes that could be offered by any of the faculties. The study was conducted using evaluation questionnaires completed by learners, who are the users of Blackboard. It is found that users are generally satisfied with the expectations concerning the tool's usage. The study identified issues of low internet bandwidth and the need for more training. These issues have been raised to increase and enhance the accessibility and usability of Blackboard.
Visual Storytelling by Novelette

Storytelling is an effective way of communicating information and knowledge, and it is widely adopted in heterogeneous contexts, from education by improving critical thinking and enhancing learning practice, to journalism by encouraging coherent stories of news supported by graphical representations. However, storytelling platforms seem to be targeted to a specific audience without showing how they can be adapted to heterogeneous needs, from class support in education to mechanisms to overcome the syndrome of the white page. In this article, we propose Novelette, a digital storytelling environment, and we show how it can be applied in heterogeneous contexts and by the different target audience. We present Novelette operating mechanisms, its architecture, and we overview different use cases, from tales creation Rodari style to data- and media-stories. By use-cases, we desire to make evident that the same platform can generate stories engaging for any target audience.
Trajectory Generation for Mobile Robots in a Dynamic Environment using Nonlinear Model Predictive Control

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long-range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close to the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.
Online Presence Implementation System in the New Normal Era

Abstract The current covid 19 outbreak has a very wide impact on various sectors in the world including in Indonesia, one of which is the education sector in Indonesia. So that to deal with the covid 19 virus, Indonesia applies the new normal era policy that is currently being implemented. The application of online learning in the education sector is one of the government policies in the new normal era. All education sectors including tertiary institutions implement an online learning system and online presence. With online presence, it is necessary to review the success factors of implementing the online presence information system. So it is necessary to conduct an evaluation related to the online presence information system with the aim of knowing the factors that support the success of implementing online presence. To evaluate the online presence using the DeLone &amp; McLean method to determine the factors that influence the success of the information system success model. The results obtained state that the system quality factors, information quality, service quality and user satisfaction affect the successful implementation of online presence in the new normal era.
Digital Tools for virtual courses for university teachers affected by COVID-19

Due to the 2020 scenario, the pandemic caused by the COVID-19 virus prompted a substantial change in the methods used by teachers when teaching and implementing their classes.As a support mechanism for teachers in the face of the radical change from physical presence to virtual presence, two courses were designed by the School of Pedagogy of the Universidad Panamericana: the first on Instructional Design and the second on didactic tools that enhance interaction and revitalization of classes.The summer of 2020 was the ideal time to train and prepare teachers in the instructional design of online classes because, although the educational model of the university is face-to-face, the circumstances and health limitations forced a strategic change to carry on with the quality that characterizes the institution.The preliminary results that we analyze with the SPSS software indicate that for 90.5% of the teachers the content of the course contributed to their professional development and that 87.2% have sufficient elements to redesign their subject for the following semester, which is executed in virtual mode.
Genetic Algorithm and Particle Swarm Optimization for Parameter Optimization of Least-Square Support Vector Regression Model in Electricity Load Demand Forecasting

Abstract Accuracy is very important in time series forecasting where the model obtained depends on historical, linear, or nonlinear data patterns. This study aims to forecast short term electricity load in East Java, Indonesia, whereas electricity load is one of major and vital need in our daily life. The linear approach is carried out using the ARIMA method, while the nonlinear approach used in this study consist of the SVR and LSSVR models. The parameter selection greatly affects the results of accuracy, so an optimization method is needed. The usual grid search optimization method does not guarantee an optimum solution and more importantly, it is not efficient in some practical applications. Therefore metaheuristic optimization methods are needed, including GA and PSO which can find the entire possible space for the search for solutions. PSO is easier to apply but is prone to have premature convergence due to trapped in the minimum locale. To overcome this, modified PSO is developed to seek the optimal position according to the specified criteria. The result of this research is that the accuracy of the nonlinear approach is much better than the linear approach. The addition of an optimization method to the SVR provides a significant change in accuracy compared to LSSVR. Meanwhile, MPSO is the best optimization method because it produces a lowest RMSE value.
Centrality and Scalability Analysis on Distributed Graph of Large-Scale E-mail Dataset for Digital Forensics

Today's digital forensics software tools mostly do not offer automatic analysis methods to reveal evidences among huge amounts of digital files within hard disk images. It is important that finding evidence in digital and cyber forensics investigations as soon as possible by examining hard disk images. E-mails constitute a rich source of information in hard disk images, and they are the most possible data source to obtain an evidence. The analyzers search e-mail files by manually or using traditional methods in order to find an evidence. However, this operation could take a long time due to the size of the e-mail data which can contain a huge number of files and a huge volume of data. This study introduces an end-to-end distributed graph analysis framework for large-scale digital forensic datasets, and evaluates the accuracy of the centrality algorithms and the scalability of the proposed framework in terms of running time performance. The framework is comprised of specific processes to perform pre-processing, graph building, and algorithm activities. An architecture is introduced based on distributed big data techniques. Three different centrality algorithms are implemented to analyze the accuracy of our framework. Further, three implementations are provided to demonstrate the running time performance of our framework. Experiments are performed on Enron e-mail dataset to analyze the centrality algorithms, to evaluate the performance of the framework, and to compare the running times between the traditional approach and our approach. Moreover, the running time performance of the framework is evaluated under various parallelization level. The accuracy of the results is also evaluated and compared between the centrality algorithms. The comparison shows that some certain algorithms provide more accurate results and it is possible to improve the running time by orders of magnitude utilizing our end-to-end distributed graph analysis approach.
Natural Deduction Bottom Up

Abstract The paper introduces a new type of rules into Natural Deduction, elimination rules by composition. Elimination rules by composition replace usual elimination rules in the style of disjunction elimination and give a more direct treatment of additive disjunction, multiplicative conjunction, existence quantifier and possibility modality. Elimination rules by composition have an enormous impact on proof-structures of deductions: they do not produce segments, deduction trees remain binary branching, there is no vacuous discharge, there is only few need of permutations. This new type of rules fits especially to substructural issues, so it is shown for Lambek Calculus, i.e. intuitionistic non-commutative linear logic and to its extensions by structural rules like permutation, weakening and contraction. Natural deduction formulated with elimination rules by composition from a complexity perspective is superior to other calculi.
Smart Home Danger Prediction System to ensure People with Alzheimer's Disease Safety

At the origin of the Smart Home concept, the primary needs to provide an intelligent use of energy and create a better living environment for humans. Over the years, Smart Home technology has been adapted to many different areas and demands such as the comfort and safety of its residents. In particular, with the increase in the number of dependant people such as the elderly and people with special needs, the architecture and operation of Smart Homes became strongly related to these residents. Nevertheless, one category of inhabitants has too often been put aside, namely people with cognitive impairment. In this paper, we propose a danger prediction system to ensure the safety of people with Alzheimer's disease in a Smart Home. The proposed approach combines a prediction algorithm to a multiagent architecture in order to detect potential dangers inside the house. We then carry out a performance evaluation through simulation to demonstrate the reliability and effectiveness of the proposed approach.
A New Facial Authentication Pitfall and Remedy in Web Services

Facial authentication has become more and more popular on personal devices. Due to the ease of use, it has great potential to be widely deployed for web-service authentication in the near future whereby people can easily log on to online accounts from different devices without memorizing lengthy passwords. However, the growing number of attacks on machine learning especially the Deep Neural Networks (DNN) which is commonly used for facial recognition, imposes big challenges on the successful roll-out of such web-service face authentication. Although there have been studies on defending some machine learning attacks, we are not aware of any specific effort devoted to the web-service facial authentication setting. In this article, we first demonstrate a new data poisoning attack that does not require to have any knowledge of the server-side and just needs a handful of malicious photo injections to enable an attacker to easily impersonate the victim in the existing facial authentication systems. We then propose a novel defensive approach called DEFEAT that leverages deep learning techniques to automatically detect such attacks. We have conducted extensive experiments on real datasets and our experimental results show that our defensive approach achieves more than 90 percent detection accuracy.
Review credibility as a safeguard against fakery: the case of Amazon

Online reviews remain a reliable source for customers when making purchase decisions. Yet, the pervasiveness of fake reviews jeopardises this reliability and questions the quality of this content. In this paper, we provide empirical evidence from a major online retailer that mitigation against fakery can be successful. To that end, we proposed, tested, and validated an approach, based on existing safeguards, to quantify the credibility of reviews and thus reliably reduce product uncertainty. We also showed that reviews with sufficient credibility signals were effective at influencing product sales, and this influence was prevalent for both niche and new products on the market. As such, this study offers a novel approach to mitigate the impact of fakery in reviews posted to online infomediaries. Our work focuses primarily on Amazon as a major retailer but also provides further support by drawing on Yelp, another major review platform.
Non-convex Optimization in Digital Pre-distortion of the Signal

This paper reviews application of modern optimization methods for functionals describing Digital Pre-distortion (DPD) of signals with orthogonal frequency division multiplexing (OFDM) modulation. The considered family of model functionals is determined by the class of cascade Wiener–Hammerstein models, which can be represented as a computational graph consisting of various nonlinear blocks. To assess optimization methods with the best convergence depth and rate as a properties of this models family, we multilaterally consider modern techniques used in optimizing neural networks and numerous numerical methods used to optimize non-convex multimodal functions.
Markerless 3D Virtual Glasses Try-On System

This paper presents the implementation of a markerless mobile augmented reality application called a virtual eye glasses try-on system. The system first detects and tracks human face and eyes. Then, the system overlays the 3D virtual glasses over the face in real time. This system helps the consumer to select any style of glasses available on the virtual space saving both time and effort when shopping online. A method based on local-invariant descriptors is implemented to extract image feature points for eyes detection and tracking. A new approach for camera pose estimation is proposed to augment real images with virtual graphics. Experiments are conducted using Haar cascade and speeded up robust features (SURF) cascade. The system is optimized and adapted for a mobile architecture.
An Experimental Survey on Methods for Integrating Scripts Into Adversarial Search for RTS Games

Real-time strategy games are challenging problems from an AI point of view. Specifically, they are particularly hard for tree search algorithms due to their combinatorial branching factors and the limited amount of time available to choose actions. As the community grows and many real-time strategy (RTS) game competitions are being held, much work has been done in the direction of integrating hand-authored scripted bots into tree search algorithms, with the goal of making search tractable. In this article, we survey a collection of representative algorithms which integrates scripts into search or planning algorithms. Then we compare them experimentally in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> RTS environment and examine the tradeoffs for designing such algorithms. We also discuss the potential future work in this direction of research and connections to other types of algorithms.
Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain—object tracking in assisted surgery in the domain of Robotic Osteotomies—that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility.
The Rare Word Issue in Natural Language Generation: A Character-Based Solution

In this paper, we analyze the problem of generating fluent English utterances from tabular data, focusing on the development of a sequence-to-sequence neural model which shows two major features: the ability to read and generate character-wise, and the ability to switch between generating and copying characters from the input: an essential feature when inputs contain rare words like proper names, telephone numbers, or foreign words. Working with characters instead of words is a challenge that can bring problems such as increasing the difficulty of the training phase and a bigger error probability during inference. Nevertheless, our work shows that these issues can be solved and efforts are repaid by the creation of a fully end-to-end system, whose inputs and outputs are not constrained to be part of a predefined vocabulary, like in word-based models. Furthermore, our copying technique is integrated with an innovative shift mechanism, which enhances the ability to produce outputs directly from inputs. We assess performance on the E2E dataset, the benchmark used for the E2E NLG challenge, and on a modified version of it, created to highlight the rare word copying capabilities of our model. The results demonstrate clear improvements over the baseline and promising performance compared to recent techniques in the literature.
English Multimedia Teaching Resources Integration System Based on Big Data Technology

In the era of big data, the integration and development of English teaching resources is faced with the challenge of large amount, diversity and hidden value of resources and data. Under the new situation, multimedia teaching has great practical significance. This requires contemporary English teachers to master the requirements and basic operations of multimedia teaching and integrate modern information technology with English teaching methods. Advanced network technologies such as cloud computing must be applied to multimedia teaching resources in colleges and universities to solve the problems of security and hardware resources encountered in the integration process. In the face of the arrival of the big data era, English teachers should change their teaching concepts in time, actively adapt to the requirements of big data era for teachers and students, reasonably use network data resources to develop English teaching resources, and achieve English teaching objectives. This paper analyzes the characteristics and application of big data, expounds the difficulties in the process of English multimedia teaching resources integration, and expounds the ways of school education resources integration and transformation.
Incremental Parameter Estimation of Stochastic State-Based Models

This paper presents an incremental learning approach for estimating the structural parameters in stochastic state-based models (SSMs). SSMs have proven to be useful for modelling biological and medical processes, as they can represent both time dependency and stochastic processes. A major challenge in modelling in bioinformatics is that learning processes usually rely on large publicly accessible databases. In this work, a new approach is presented, where models are trained incrementally locally at different data sources, e.g., hospitals, without having to pass on sensitive data. After learning, only the parameters of the model are passed on, in this case the arc weights of stochastic Petri nets. As a result, data protection and privacy of patients in hospitals are respected and it is no longer necessary to rely on the existence of a suitable accessible database. Simulations are used to evaluate the performance of the algorithm for a gene regulatory network.