Reviewerly/exHarmony huggingface dataset:
VRBagged-Net: Ensemble Based Deep Learning Model for Disaster Event Classification

A flood is an overflow of water that swamps dry land. The gravest effects of flooding are the loss of human life and economic losses. An early warning of these events can be very effective in minimizing the losses. Social media websites such as Twitter and Facebook are quite effective in the efficient dissemination of information pertinent to any emergency. Users on these social networking sites share both textual and rich content images and videos. The Multimedia Evaluation Benchmark (MediaEval) offers challenges in the form of shared tasks to develop and evaluate new algorithms, approaches and technologies for explorations and exploitations of multimedia in decision making for real time problems. Since 2015, the MediaEval has been running a shared task of predicting several aspects of flooding and through these shared tasks, many improvements have been observed. In this paper, the classification framework VRBagged-Net is proposed and implemented for flood classification. The framework utilizes the deep learning models Visual Geometry Group (VGG) and Residual Network (ResNet), along with the technique of Bootstrap aggregating (Bagging). Various disaster-based datasets were selected for the validation of the VRBagged-Net framework. All the datasets belong to the MediaEval Benchmark Workshop, this includes Disaster Image Retrieval from Social Media (DIRSM), Flood Classification for Social Multimedia (FCSM) and Image based News Topic Disambiguation (INTD). VRBagged-Net performed encouraging well in all these datasets with slightly different but relevant tasks. It produces Mean Average Precision at different levels of 98.12, and Average Precision at 480 of 93.64 on DIRSM. On the FCSM dataset, it produces an F1 score of 90.58. Moreover, the framework has been applied on the dataset of Image-Based News Topic Disambiguation (INTD), and exceeds the previous best result by producing an F1 evaluation of 93.76. The VRBagged-Net with a slight modification also ranked first in the flood-related Multimedia Task at the MediaEval Workshop 2020.
Revisiting Simple Neural Probabilistic Language Models

Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements.In this paper, we revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003), which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word.When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks.Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies.Inspired by this result, we modify the Transformer by replacing its first selfattention layer with the NPLM's local concatenation layer, which results in small but consistent perplexity decreases across three wordlevel language modeling datasets.
Library’s Augmented Reality (AR) Projects: Foreign Experience

The article analyzes the positive foreign experience of implementing augmented reality (AR) library projects. It has been found out that the most common uses of augmented reality in libraries today are providing access to information products with augmented reality and their promotion, navigation of library space and resources, promoting user education through the creation of appropriate applications with learning materials and resources and simplification for the user assimilation of information due to the diversity and interactivity of its visual presentation, informing and providing access to cultural heritage sites, organizing tours and exhibitions. Given that the use of AR in libraries not only expands the range of services and resources but also positively affects its reputation as an interactive creative institution, open to users and innovation, AR technology is effective not only in the library service system but also in the marketing activities of the institution, helping to attract new users. Current approaches to the implementation of library projects using augmented reality technology are highlighted. Their assistance in strengthening corporate culture, inter-institutional ties, and civil society is substantiated. It has been proved that the AR applications created as a result of project implementation operate on the principle of integrated information products, which allow the user to access distributed information from one access point via mobile phone, integrating not only information of the virtual space of the Internet environment but also real, physical, space. The advantages of AR implementation in the library have been determined, among which: budget realization, easy integration with available technologies, and promotion of interactivity of library service. The need for professional development of librarians and the organization of training courses on the use of immersive technologies in library activities is emphasized.
Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers

The preservation of relevant mutual information under compression is the fundamental challenge of the information bottleneck method. It has many applications in machine learning and in communications. The recent literature describes successful applications of this concept in quantized detection and channel decoding schemes. The focal idea is to build receiver algorithms intended to preserve the maximum possible amount of relevant information, despite very coarse quantization. The existent literature shows that the resulting quantized receiver algorithms can achieve performance very close to that of conventional high-precision systems. Moreover, all demanding signal processing operations get replaced with lookup operations in the considered system design. In this paper, we develop the idea of maximizing the preserved relevant information in communication receivers further by considering parametrized systems. Such systems can help overcome the need of lookup tables in cases where their huge sizes make them impractical. We propose to apply genetic algorithms which are inspired from the natural evolution of the species for the problem of parameter optimization. We exemplarily investigate receiver-sided channel output quantization and demodulation to illustrate the notable performance and the flexibility of the proposed concept.
PENINGKATAN HASIL BELAJAR IPS MELALUI METODE CARD SORT SISWA KELAS VIIB DI SMP NEGERI 3 KAMPAK

The purpose of this study was to determine the level of student learning effectiveness through the Card Sort method in Social Science (IPS) subjects. Social studies subjects are one of the compulsory subjects for junior high school (SMP) students. Social studies is a subject that must be mastered by students because the material content is very important. However, it is unfortunate that many students do not have a high interest in social studies subjects. Apart from the material that requires a lot of memorization, conventional teacher learning methods also make students even more bored. By using Classroom Action Research (CAR) and through the data validation stage, the results of this study showed the value of learning outcomes and the level of student learning completeness were much higher than using conventional methods.
Support Vector Machine optimization with fractional gradient descent for data classification

Data classification has several problems one of which is a large amount of data that will reduce computing time. SVM is a reliable linear classifier for linear or non-linear data, for large-scale data, there are computational time constraints. The Fractional gradient descent method is an unconstrained optimization algorithm to train classifiers with support vector machines that have convex problems. Compared to the classic integer-order model, a model built with fractional calculus has a significant advantage to accelerate computing time. In this research, it is to conduct investigate the current state of this new optimization method fractional derivatives that can be implemented in the classifier algorithm. The results of the SVM Classifier with fractional gradient descent optimization, it reaches a convergence point of approximately 50 iterations smaller than SVM-SGD. The process of updating or fixing the model is smaller in fractional because the multiplier value is less than 1 or in the form of fractions. The SVM-Fractional SGD algorithm is proven to be an effective method for rainfall forecast decisions.
That Looks Hard: Characterizing Linguistic Complexity in Humans and Language Models

This paper investigates the relationship between two complementary perspectives in the human assessment of sentence complexity and how they are modeled in a neural language model (NLM).The first perspective takes into account multiple online behavioral metrics obtained from eye-tracking recordings.The second one concerns the offline perception of complexity measured by explicit human judgments.Using a broad spectrum of linguistic features modeling lexical, morpho-syntactic, and syntactic properties of sentences, we perform a comprehensive analysis of linguistic phenomena associated with the two complexity viewpoints and report similarities and differences.We then show the effectiveness of linguistic features when explicitly leveraged by a regression model for predicting sentence complexity and compare its results with the ones obtained by a fine-tuned neural language model.We finally probe the NLM's linguistic competence before and after fine-tuning, highlighting how linguistic information encoded in representations changes when the model learns to predict complexity.
User Interface Pattern for AR in Industrial Applications

The background of the paper is that there are currently no specifications or guidelines for the design of a user interface for an augmented reality system in an industrial context. In this area, special requirements apply for the perception and recognition of content, which are given by the framework conditions of the industrial environment, the human–technology interaction, and the work task. This paper addresses the software-technical design of augmented reality surfaces in the industrial environment. The aim is to give first design examples for software tasks by means of sample solutions. For a user-oriented implementation, the methods of personas and an empirical investigation were used. Personas are a stereotypical representation of end users that reflect their characteristics and requirements. For the subsequent development of the pattern catalog, different prototypes with layout and interaction variants were tested in an empirical study with 50 participants. By observing the current realizations, these can be examined more closely in terms of their specific use in an industrial environment. The result is a pattern catalog with 26 solutions for layout and interaction variants. For the layout variants, no direct favorite of the testers could be ascertained; the existing solutions already offer a wide spectrum, which are chosen according to personal preferences. For interaction, on the other hand, it is important to enable fast input. During the study, gesture control revealed weaknesses in this regard. This can be supportive in the development of an industrial augmented reality system regarding a user-oriented representation of the interface.
The effect of addition of hydroxyapatite from skipjack tuna (katsuwonus pelamis) fish bone flour to the transverse, impact, and tensile strength of heat cured acrylic resin

Objective:This study aims to determine the effect that occurs when heat cured acrylic resin is added hydroxyapatite which is synthesized from skipjack fish bones.Material and Methods: This study using 27 samples of heat cured acrylic resin for each mechanical strength divided into 3 groups, namely groups with the addition of 0%, 2%, and 5% hydroxyapatite from the weight of the polymer.Results: The analysis showed a significant decrease in transverse strength with 5% addition of hydroxyapatite group (One-way AnovaÂ test, p = 0.001), a significant decrease in impact strength occurred in both groups of 2% and 5% addition of hydroxyapatite (One-way Anova test, p = 0.000), and also the addition of 2% &amp; 5% hydroxyapatite caused a significant decrease in tensile strength (One-way Anova test, p = 0.018).Conclusion: Hydroxyapatite synthesized from skipjack fish bone affects the mechanical strength of acrylic resins, namely transverse, impact and tensile.
A Comparative Analysis of Machine Learning Models developed from Homomorphic Encryption based RSA and Paillier algorithm

Nowadays, the use of Auto ML for development of machine learning (ML) models is increasing day by day. In which users need to upload their dataset to develop machine learning models. Where security of user's data is a very big problem. Which can be solved using Homomorphic encryption. In this work, first the spiderman correlation of the machine learning models developed using encrypted dataset is compared with each other. In which the algorithms used to encrypt dataset are Homomorphic Encryption (HE) based Rivest Shamir Adleman (RSA) and Paillier algorithm. Where the homomorphic encryption based Rivest Shamir Adleman (RSA) is a multiplicative operation and the HE-based Pailler supports additive operations. These algorithms are used to encrypt data and store it on cloud servers. An in-house key generator is developed to generate keys for HE-based RSA algorithm and used this algorithm to encrypt data. Then a comparison between the time taken by these algorithms to encrypt the data and develop the machine learning model using Azure auto Machine Learning is done where the HE-based RSA algorithm is winner.
Abductive Spaces: Modeling Concept Framework Revision with Category Theory

A formal model of abductive inference is provided in which abduction is conceived as expansive and contractive movements through a topological space of theoretical and practical commitments. A pair of presheaves over the (Heyting algebra) space of commitments corresponds to communities sharing commitments on the one hand and possible obstructions to commitments on the other. In this framework, abductive inference is modeled by the dynamics of redistributed communities of commitment made in response to obstructive encounters. This semantic-pragmatic model shows how elementary category theory tools can be used to formalize abductive inference while hewing close to ordinary intuitions about collective agency and reasoning.
Private Text Classification with Convolutional Neural Networks

Text classifiers are regularly applied to personal texts, leaving users of these classifiers vulnerable to privacy breaches.We propose a solution for privacy-preserving text classification that is based on Convolutional Neural Networks (CNNs) and Secure Multiparty Computation (MPC).Our method enables the inference of a class label for a personal text in such a way that (1) the owner of the personal text does not have to disclose their text to anyone in an unencrypted manner, and (2) the owner of the text classifier does not have to reveal the trained model parameters to the text owner or to anyone else.To demonstrate the feasibility of our protocol for practical private text classification, we implemented it in the PyTorchbased MPC framework CrypTen, using a wellknown additive secret sharing scheme in the honest-but-curious setting.We test the runtime of our privacy-preserving text classifier, which is fast enough to be used in practice.
Guaranteed distributed machine learning: Privacy-preserving empirical risk minimization

<abstract> Distributed learning over data from sensor-based networks has been adopted to collaboratively train models on these sensitive data without privacy leakages. We present a distributed learning framework that involves the integration of secure multi-party computation and differential privacy. In our differential privacy method, we explore the potential of output perturbation and gradient perturbation and also progress with the cutting-edge methods of both techniques in the distributed learning domain. In our proposed multi-scheme output perturbation algorithm (MS-OP), data owners combine their local classifiers within a secure multi-party computation and later inject an appreciable amount of statistical noise into the model before they are revealed. In our Adaptive Iterative gradient perturbation (MS-GP) method, data providers collaboratively train a global model. During each iteration, the data owners aggregate their locally trained models within the secure multi-party domain. Since the conversion of differentially private algorithms are often naive, we improve on the method by a meticulous calibration of the privacy budget for each iteration. As the parameters of the model approach the optimal values, gradients are decreased and therefore require accurate measurement. We, therefore, add a fundamental line-search capability to enable our MS-GP algorithm to decide exactly when a more accurate measurement of the gradient is indispensable. Validation of our models on three (3) real-world datasets shows that our algorithm possesses a sustainable competitive advantage over the existing cutting-edge privacy-preserving requirements in the distributed setting. </abstract>
BALAdIN: truthfulness in collaborative access networks with distributed ledgers

Distributed ledger technology (DLT), also known as “Blockchain” is one of the trendiest digital innovations in our era. While firstly applied to crypto-currencies, the trustworthiness inherent to DLT paved the way to many new usages notably in sectors such as land registration or banking where confidence in transactions is crucial. In the telecommunications sector, DLTs have enabled the development of future network architectures, such as new decentralised and multi-actor access networks. Having identified this as a great opportunity, we designed BALAdIN, a novel Blockchain-powered decentralised application that improves network coverage thanks to partnerships. Indeed, it allows a crowd of local actors such as shop tenants to deploy mobile cells with the help of a consortium of telcos. The traffic conveyed by each actor is traced thanks to a decentralised and trustworthy network monitoring mechanism we have designed. This mechanism both solves the centralisation dilemma caused by Blockchain Oracles and allows each actor to be rewarded depending on usage. In this paper, we focus on the description of this network usage monitoring mechanism. We then study the feasibility of its implementation onto regular Blockchain by studying its performance regarding the throughput and the propagation of the resulting Blockchain transactions. Finally, we derive a scalable deployment scheme for our novel Blockchain-powered decentralised network metering application BALAdIN.
Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization

&#x0D; &#x0D; &#x0D; &#x0D; A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.&#x0D; &#x0D; &#x0D; &#x0D;
Skewness-Based Projection Pursuit as an Eigenvector Problem in Scale Mixtures of Skew-Normal Distributions

This paper addresses the projection pursuit problem assuming that the distribution of the input vector belongs to the flexible and wide family of multivariate scale mixtures of skew normal distributions. Under this assumption, skewness-based projection pursuit is set out as an eigenvector problem, described in terms of the third order cumulant matrix, as well as an eigenvector problem that involves the simultaneous diagonalization of the scatter matrices of the model. Both approaches lead to dominant eigenvectors proportional to the shape parametric vector, which accounts for the multivariate asymmetry of the model; they also shed light on the parametric interpretability of the invariant coordinate selection method and point out some alternatives for estimating the projection pursuit direction. The theoretical findings are further investigated through a simulation study whose results provide insights about the usefulness of skewness model-based projection pursuit in the statistical practice.
Investigating Clustering Technique on Students’ Writing Skill in Narrative Text

Abstract— The purpose of this study was to explain the effect of clustering technique toward students’ writing skill of narrative text. This study used a quasi-experimental design with post-test only control group design. The population of this study was all students of tenth graders at SMA Tulus Bhakti Bekasi, with the amount of the students were 132 students. The samples of this research were taken by cluster random sampling consisting of 60 students. The students were divided into two classes, 30 experimental class (X IPS 1) and 30 other students in the control class (X IPS 2). The instrument used to collect data was learning achievement test in form of subjective test. The statistic method used to analyze the data was One-Way ANOVA using SPSS through the testing of Null Hypothesis. The result of ANOVA showed that value of sig (significance) is lower than 0.05 (0.000&lt;0.05) or Fobserved with df (1/58) is higher than Ftable (28.185&gt;4.01). Accordingly, the Null Hypothesis (Ho), stating that there is no effect of clustering technique on grade ten students’ writing skill of narrative text at SMA Tulus Bhakti, was rejected and the Alternative Hypothesis (Ha), stating that there is significant effect of clustering technique on grade ten students’ writing skill of narrative text at SMA Tulus Bhakti, was accepted. Based on the analysis of the data, the research has empirically proved that there is an effect of clustering technique has an effect on grade ten students’ writing skill of narrative text at SMA Tulus Bhakti Bekasi. Keywords— clustering techniques; quasi-experimental method; writing skill