Reviewerly/exHarmony huggingface dataset:
A novel computational methodology to estimate solar energy on building rooftops

Abstract Precise estimation of solar radiation is essential for sustainable development and renewable energy consumption. Building rooftops are appropriate locations for solar energy devices. Traditional solar potential estimation approaches usually adopt technologies such as light detection and ranging, which are expensive and available only for existing sites. In this paper, a novel methodology is presented to estimate the solar radiation components for any building rooftop surrounded by obstacles, using the ray‐triangle‐intersection method and discretization of 3D sky vault into sky elements. The proposed methodology accepts a 3D model, including all significant obstacles that produce a triangulated surface mesh and a discrete map. This mesh and map are utilized with solar angles and radiation data to estimate direct and diffuse radiation on the surfaces of the 3D structures. To validate the proposed algorithm, we discuss two case studies. Finally, the 3D radiation maps are generated using solar irradiation data for Karachi, illustrating the effects of nearby obstacles. Several optimizations are applied to the developed algorithm for reducing computational cost. The significant advantage of the methodology is that the skymap is location‐independent. A skymap generated once can be used for the same 3D model located anywhere in the world, with the appropriate solar angles data. This work will benefit consultants, governments, and households in the efficient use of solar energy potential for both existing rooftops and future constructions.
Benchmarking Regression Methods: A comparison with CGAN

In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in image processing applications which involve large, continuous output spaces. There is almost no application of these powerful tools to problems having small dimensional output spaces. Regression problems involving the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$, with $m$ small (e.g., $m=1$ or just a few) is one good case in point. The standard approach to solve regression problems is to probabilistically model the output $y$ as the sum of a mean function $m(x)$ and a noise term $z$; it is also usual to take the noise to be a Gaussian. These are done for convenience sake so that the likelihood of observed data is expressible in closed form. In the real world, on the other hand, stochasticity of the output is usually caused by missing or noisy input variables. Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input. CGAN is naturally suited to design such implicit models. This paper makes the first step in this direction and compares the existing regression methods with CGAN. We notice however, that the existing methods like mixture density networks (MDN) and XGBoost do quite well compared to CGAN in terms of likelihood and mean absolute error, respectively. Both these methods are comparatively easier to train than CGANs. CGANs need more innovation to have a comparable modeling and ease-of-training with respect to the existing regression solvers. In summary, for modeling uncertainty MDNs are better while XGBoost is better for the cases where accurate prediction is more important.
Examining Cloze Tests as a Measure of Linguistic Complexity in L2 Writing

Given that cloze tests are strongly associated with learners' writing proficiency, the present study examines the relationship between cloze test scores and specific linguistic features in second language (L2) writing.We investigate whether cloze tests can adequately and reliably measure linguistic features of syntactic and lexical complexity in L2 writing.Furthermore, the present study investigates the effect of the scoring method and L2 proficiency level on the relationship between cloze test scores and linguistic complexity features.Analysis of 60 students' writing compositions found the syntactic and lexical complexity features in length-related measures, complex nominals, and lexical variation to be significantly correlated with their cloze test scores.There was no significant difference in the effects of the two scoring methods.Also, cloze tests were most informative for high-level learners and least informative for intermediate-level learners.Implications for the use of cloze tests as a diagnostic measure in L2 writing classrooms are provided.
Digital reading: genealogy of formation and prospects for its development in a digitalized society

The theme of digital reading is becoming ever more relevant with a growing understanding of the importance of reading for developing both an individual and society as a whole. The issue addressed in the article is connected with the recently evolved concept of “digital reading”. The article is aimed at understanding digital reading and its core modifications – static (reading from the screen of an electronic gadget), reading of hypertext, reading in polymedia culture, as well as understanding what makes digital reading different (ideologically, physiologically, technologically) from traditional forms of reading. The author examines its genealogy as it was formed in the context of the evolution of writing and the emergence of new media and information centered printed culture. As a result the author specifies her own definition of digital reading and comes to the conclusion that it is most actively employed in education, which is rapidly becoming digitalized. The author also emphasizes the special function of modern libraries (public libraries, children’ libraries, school and university libraries), which should play the role of the “competent adult” in helping the user to develop the digital reading competences.
Theoretical Interpretation of Learned Step Size in Deep-Unfolded Gradient Descent

Deep unfolding is a promising deep-learning technique in which an iterative algorithm is unrolled to a deep network architecture with trainable parameters. In the case of gradient descent algorithms, as a result of the training process, one often observes the acceleration of the convergence speed with learned non-constant step size parameters whose behavior is not intuitive nor interpretable from conventional theory. In this paper, we provide a theoretical interpretation of the learned step size of deep-unfolded gradient descent (DUGD). We first prove that the training process of DUGD reduces not only the mean squared error loss but also the spectral radius related to the convergence rate. Next, we show that minimizing the upper bound of the spectral radius naturally leads to the Chebyshev step which is a sequence of the step size based on Chebyshev polynomials. The numerical experiments confirm that the Chebyshev steps qualitatively reproduce the learned step size parameters in DUGD, which provides a plausible interpretation of the learned parameters. Additionally, we show that the Chebyshev steps achieve the lower bound of the convergence rate for the first-order method in a specific limit without learning parameters or momentum terms.
NUSANTARA MASK HERITAGE MALAYSIA: INFOGRAPHIC APPLICATION DEVELOPMENT OF MASKS OF MALAYSIAN INDIGENOUS TRIBES AT THE MUSEUM OF ASIAN ART MALAYSIA BASED ON AUGMENTED REALITY AS MEDIA OF TOURISM EDUCATION

The museum is a fun learning tool for the community. The Museum of Asian Art is one of the museums in Malaysia which was founded by Malaya University. The museum has three showroom floors and represents three civilizations; India, China and Islam. Every day the museum is very crowded by tourists to find information about artifact objects. Lots of artifacts stored in this museum include textiles, musical instruments, ceramics, masks, paintings, weapons and others. The museum itself is the right place to store and preserve ancient objects so they can still be seen and used as a source of learning and cultural preservation for the nation's next generation. This research takes the artifacts that are masks because the results of observations made by researchers, information about masks at the Museum of Asian Art Malaysia is very minimal compared to other artifacts, there are only name tags but there is no deeper information about the mask. So that it still cannot be used as a learning medium to the maximum. From this problem, researchers developed Nusantara Mask Heritage Malaysia (NUSMARI MALAYSIA) products based on Augmented Reality. The research method used is the development model into 4 steps: (1). Research and Information Collecting, (2). Planning, (3). Develop Preliminary Form Of Product, (4). Final Product Revision. The result of this development is a learning media application that can help tourists of all ages to more easily learn the mask of the Orang Asli Malaysia in the museum.
BlockPGP: A Blockchain-based Framework for PGP Key Servers

Pretty Good Privacy (PGP) is one of the most prominent cryptographic standards offering end-to-end encryption for email messages and other sensitive information exchange.PGP allows to verify the identity of the correspondent in information exchange as well as the information integrity.PGP implements asymmetric encryption with certificates shared through a network of PGP key servers.In this paper, we propose a new PGP management framework with the key servers infrastructure implemented using blockchain technology.Our approach offers fast propagation of certificate revocation among PGP key servers and elimination of man-in-the-middle risks.It also grants users the required access control to update their own PGP certificates, which is not the case with the current PGP key servers.A prototype has been implemented using Ethereum blockchain and an open source key server, named Hockeypuck.Finally, we evaluated the prototype with extensive experiments.Our results show that our solution is practical and it could be integrated with the existing public PGP key servers infrastructure.
User Keystroke Authentication and Recognition of Emotions Based on Convolutional Neural Network

The article is devoted to the problem of improving Biometric identification systems based on Keystroke Dynamics for recognizing emotions and authenticating users of information systems through the implementation of modern neural network solutions based on Convolutional Neural Network (CNN). It is established that the difficulties of such implementation are associated with coding the keystroke parameters to a form suitable for CNN processing. A coding procedure based on the presentation of fixed-size keystroke parameters in the form of a color square image is proposed. Each encoded text symbol corresponds to a separate point of the image and is characterized using the corresponding ASCII code and keystroke parameters such as the key hold time and the time between keystrokes. Experimental studies showed that the proposed coding procedure made it possible to use CNN for analyzing Keystroke Dynamics and achieve recognition error of emotions and personality at the level of the best modern recognition systems.
Computer Network Forensics Assistance Methodology Focused on Denial of Service Attacks

The problem addressed in this paper is the difficulty in criminalizing denial of service attacks in Brazil.With the advent of Law 12.737 of 2012 in Brazil, known as the Computer Crimes Law, these attacks could be considered crimes.However, no procedures were found to support it.This paper proposes a methodology based on the 2012 Computer Crime Law to assist computer networks forensic analysis, focused on charging offenders who commit denial of service attacks, as well as to present a computational architecture to automate its steps.For this purpose, it was promoted a review of related works and also dedicated sections for the clarification of terms needed to contextualize the research.At the end of the article, the methodology and its steps are presented, and also the proposed architecture and the results of experiments performed to validate the proposal.It is concluded that the availability of the information obtained by the aid of the proposed methodology demonstrates that the investigation authority can proceed with the duly substantiated liability of the offending agents.
Eye Movement Event Detection with Deep Neural Networks

This paper presents a comparison of event detection task in eye movement with the exact events recorded from eye tracking device. The primary goal of this research work is to build a general approach for eye-movement based event detection, which will work with all eye tracking data collected using different eye tracking devices. It utilizes an end to end method based on deep learning, which can efficiently utilize eye tracking raw particulars that is further grouped into Saccades, post-saccadic oscillations and Fixations. The drawback of deep learning method is that it requires a lot of preprocessing data. At first, we have to build up a strategy to enlarge handcoded information, with the goal that we can unequivocally augment the informational index utilized for preparing, limiting the run through time on coding by a human. Utilizing this all-encompassing hand-coded information, we instruct neural networks model to process eye-development fixation grouping from eye-movement information in the absence of any previously defined extraction or post-preparing steps.
Deep Consistent Illumination in Augmented Reality

Visual coherence is a basic requirement for providing a realistic scene in Augmented Reality. As one of the most important factors, illumination consistency is usually achieved through two steps: real lighting estimation and virtual object rendering. In this paper, we propose an end-to-end method to adjust the luminance and shadow of the virtual object in the composite image using a deep neural network. It learns the varying appearances of the virtual object under different illumination and viewpoints from a large amount of synthesized images, and transforms the illumination of the composite image into coherence according to the appearance of the real world. The effectiveness of our method is demonstrated through extensive experiments.
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data

Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.
Tabled Typeclass Resolution

Typeclasses provide an elegant and effective way of managing ad-hoc polymorphism in both programming languages and interactive proof assistants. However, the increasingly sophisticated uses of typeclasses within proof assistants, especially within Lean's burgeoning mathematics library, mathlib, have elevated once-theoretical limitations of existing typeclass resolution procedures into major impediments to ongoing progress. The two most devastating limitations of existing procedures are exponential running times in the presence of diamonds and divergence in the presence of cycles. We present a new procedure, tabled typeclass resolution, that solves both problems by tabling, which is a generalization of memoizing originally introduced to address similar limitations of early logic programming systems. We have implemented our procedure for the upcoming version (v4) of Lean, and have confirmed empirically that our implementation is exponentially faster than existing systems in the presence of diamonds. Although tabling is notoriously difficult to implement, our procedure is notably lightweight and could easily be implemented in other systems. We hope our new procedure facilitates even more sophisticated uses of typeclasses in both software development and interactive theorem proving.
Analysis of Secure Bit Rate for Quantum Key Distribution based on EDU-QCRY1

Most cryptographic mechanisms, such as symmetric and asymmetric cryptography, often involve the use of cryptographic keys. However, all cryptographic techniques will be ineffective if the key distribution mechanism is weak. The security of most modern cryptographic systems of key distribution mechanism is based on computational complexity and the extraordinary time needed to break the code. Quantum Key Distribution (QKD) or Quantum Cryptography is attracting much attention as a solution of the problem of key distribution; QKD offers unconditionally secure communication based on quantum mechanics. In this article we analyze of secure bit rate with the most popular QKD protocols. Also, we give a short state of the art of Quantum Cryptography.
Development of physics-based learning media for android integrated with earthquake disaster education

Abstract The aim of this research is to produce physics based learning media for Android integrated with earthquake disaster education. This study uses a 4D development model. Data collection instruments in the form of expert validation sheets which include media experts and material experts. The research data was collected through observation sheets, questionnaire sheets, and analyzed descriptively quantitatively. The result shows that the development of physics learning media based on android was carried out by the development procedures. Then, the result of expert validation shows that the physics learning media has been developed well. Moreover, the physics learning media is suitable for learning process.
Data-dependence of plateau phenomenon in learning with neural network—statistical mechanical analysis <sup>*</sup>

Abstract The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers. The phenomenon was actively inspected in the 1990s and found to be due to the fundamental hierarchical structure of neural network models. Then, the phenomenon has been thought of as inevitable. However, the phenomenon seldom occurs in the context of recent deep learning. There is a gap between theory and reality. In this paper, using statistical mechanical formulation, we clarified the relationship between the plateau phenomenon and the statistical property of the data learned. It is shown that the data whose covariance has small and dispersed eigenvalues tend to make the plateau phenomenon inconspicuous.
EVALUASI PROGRAM GERAKAN LITERASI SEKOLAH DI SEKOLAH DASAR WILAYAH KOTA DAN KABUPATEN TANGERANG

This study aims to obtain data empirically about the implementation of the School Literacy Movement (SLM) program in elementary schools in Tangerang City and Regency, Banten Indonesia. It discusses how far the SLM program has been implemented and what obstacles have been experienced in implementing it in the level of elementary schools. This type of research is a qualitative descriptive research located at SDN Jurumudi 2, SDN Gempol Sari, SDN Sudimara Timur 5, SDN Ciakar, dan SDS Mutiara Hati Tangerang. Subjects were principals, teachers and students. The research instruments used are interview, observation and documentation. The results of the study indicate that the implementation of the SLM program in elementary schools in Kota and Kabupaten Tangerang is merely at the habituation stage. Therefore, the implementation of the SLM program in primary schools needs to be improved through comprehensive and sustainable socialization and assistance in order to achieve the objectives of the SLM program.
Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

Stemming in each language has a different process and is determined according to the structure of the language.Stemming is mostly used as a complete step in the processing of words and phrases.There are many stemming algorithms available, and some used as a process for word processing.One function of stemming is to detect word errors in Indonesian.In this study, researchers created the Indonesian words error detection system using Nazief and Adriani algorithm.In the trials conducted, the system will accept text input obtained from the user.Then the system will preprocess the text.In this study, there are three stages of preprocessing, namely tokenization, case folding, and filtering.After the stages in preprocessing are finished, the system will call each word for the process of stemming.The results of the stemming will be compared with the base words available in the database.If it does not match, then the word is highlighted and is considered an error word.The first finding is the Nazief Adriani's algorithm can be able to detect words error until 100%.The second finding is the Nazief Adriani's algorithm also detect non-words error, the accuracy of detecting is 97.464%.
Automatic Search for the Linear (Hull) Characteristics of ARX Ciphers: Applied to SPECK, SPARX, Chaskey, and CHAM-64

Linear cryptanalysis is an important evaluation method for cryptographic primitives against key recovery attack. In this paper, we revisit the Walsh transformation for linear correlation calculation of modular addition, and an efficient algorithm is proposed to construct the input-output mask space of specified correlation weight. By filtering out the impossible large correlation weights in the first round, the search space of the first round can be substantially reduced. We introduce a concept of combinational linear approximation table (cLAT) for modular addition with two inputs. When one input mask is fixed, another input mask and the output mask can be obtained by the Splitting-Lookup-Recombination approach. We first split the n -bit fixed input mask into several subvectors and then find the corresponding bits of other masks, and in the recombination phase, pruning conditions can be used. By this approach, a large number of search branches in the middle rounds can be pruned. With the combination of the optimization strategies and the branch-and-bound search algorithm, we can improve the search efficiency for linear characteristics on ARX ciphers. The linear hulls for SPECK32/48/64 with a higher average linear potential (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mtext>ALP</mml:mtext></mml:mrow></mml:math>) than existing results have been obtained. For SPARX variants, an 11-round linear trail and a 10-round linear hull have been found for SPARX-64 and a 10-round linear trail and a 9-round linear hull are obtained for SPARX-128. For Chaskey, a 5-round linear trail with a correlation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>61</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> has been obtained. For CHAM-64, 34/35-round optimal linear characteristics with a correlation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>31</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>/</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>33</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:mrow></mml:math> are found.