Reviewerly/exHarmony huggingface dataset:
Development of student’s submicroscopic representation ability on molecular geometry material using Augmented Reality (AR) media

Abstract The purpose of this research is to apply a learning using Augmented Reality media to develop submicroscopic representation ability. The method used is a classroom research on the fourth semester students of Chemistry Education department, UIN Sunan Gunung Djati Bandung. Information obtained by measuring students’ activities during learning, completion of worksheet, and submicroscopic representation ability after learning. The results obtained from the worksheet completion are included in the very good category (with an average of 86), with the highest ability shown in the determination of bonding domains and non-bonding domains based on Lewis structure (with an average of 94). Moreover, the results of submicroscopic representation ability tests after learning are included in the very good category (with an average of 81.5), with the highest ability shown in the determination of bonding domains and non-bonding domains based on Lewis structure (with an average of 92). This is because students discuss and collaborate very well and are supported by Augmented Reality (AR) media. Thus, Augmented Reality (AR) media can be used as one of media in developing students’ submicroscopic representation ability on molecular geometry material. Development of Students’ Submicroscopic Representation Ability on Molecular Geometry Material Using Augmented Reality (AR) Media.
The QUATRE structure: An efficient approach to tackling the structure bias in Differential Evolution

Differential Evolution (DE) is a very simple but powerful population-based stochastic algorithm for complex optimization, and the evolution of a certain individual in DE actually implements the search of the solution space. Usually, the movement of a certain individual from a former location to a new one during evolution is conducted by crossover operation, and there are two commonly used crossover schemes in different DE variants implementing such movement: one is exponential crossover and the other is binomial crossover. Though the binomial crossover conquers the representational bias existing in exponential crossover by separating all the parameters and treating them independently, the binomial crossover still has a bias, called the exploration bias, from a higher dimensional perspective of view. Therefore, in this paper, we first present the reason why such an exploration bias is still existing in the binomial crossover, and then present a novel QUATRE structure to tackle this bias. The new QUATRE structure is different from the former proposed QUATRE algorithm as some novel standards and adaptation schemes are involved into the new structure. This structure is validated under Congress on Evolutionary Computation (CEC) benchmarks for real-parameter single objective optimization, and the experiment results show its superiority.
Medical Images Denoising Method Based on Total Variation Regularization and Anscombe Transform

The following topics are dealt with: learning (artificial intelligence); error statistics; feature extraction; Internet of Things; convolutional neural nets; radio receivers; integrated circuit design; text analysis; software defined networking; radar signal processing.
Security and Privacy in the Age of Big Data and Machine Learning

New technologies have the potential to influence society in positive and negative ways. This article explores the possible impact of fast-growing, information-related technologies, such as big data analysis and machine learning, on our privacy and the future of our society and discusses methods to protect against malicious actors. The article ends with a call for action for regulations that need to be made and tools that need to be developed to help to impose them.
Children’s Story Classification in Indian Languages Using Linguistic and Keyword-based Features

The primary objective of this work is to classify Hindi and Telugu stories into three genres: fable, folk-tale, and legend . In this work, we are proposing a framework for story classification (SC) using keyword and part-of-speech (POS) features. For improving the performance of SC system, feature reduction techniques and combinations of various POS tags are explored. Further, we investigated the performance of SC by dividing the story into parts depending on its semantic structure. In this work, stories are (i) manually divided into parts based on their semantics as introduction, main, and climax ; and (ii) automatically divided into equal parts based on number of sentences in a story as initial, middle, and end . We have also examined sentence increment model, which aims at determining an optimum number of sentences required to identify story genre by incremental selection of sentences in a story. Experiments are conducted on Hindi and Telugu story corpora consisting of 300 and 150 short stories, respectively. The performance of SC system is evaluated using different combinations of keyword and POS-based features, with three well-established machine learning classifiers: (i) Naive Bayes (NB), (ii) k-Nearest Neighbour (KNN), and (iii) Support Vector Machine (SVM). Performance of the classifier is evaluated using 10-fold cross-validation and effectiveness of classifier is measured using precision, recall, and F-measure. From the classification results, it is observed that adding linguistic information boosts the performance of story classification. In view of the structure of the story, main, and initial parts of the story have shown comparatively better performance. The results from the sentence incremental model have indicated that the first nine and seven sentences in Hindi and Telugu stories, respectively, are sufficient for better classification of stories. In most of the studies, SVM models outperformed the other models in classification accuracy.
On Constant-Time QC-MDPC Decoders with Negligible Failure Rate

The QC-MDPC code-based KEM Bit Flipping Key Encapsulation (BIKE) is one of the Round-2 candidates of the NIST PQC standardization project. It has a variant that is proved to be IND-CCA secure. The proof models the KEM with some black-box (“ideal”) primitives. Specifically, the decapsulation invokes an ideal primitive called “decoder”, required to deliver its output with a negligible Decoding Failure Rate (DFR). The concrete instantiation of BIKE substitutes this ideal primitive with a new decoding algorithm called “Backflip”, that is shown to have the required negligible DFR. However, it runs in a variable number of steps and this number depends on the input and on the key. This paper proposes a decoder that has a negligible DFR and also runs in a fixed (and small) number of steps. We propose that the instantiation of BIKE uses this decoder with our recommended parameters. We study the decoder’s DFR as a function of the scheme’s parameters to obtain a favorable balance between the communication bandwidth and the number of steps that the decoder runs. In addition, we build a constant-time software implementation of the proposed instantiation, and show that its performance characteristics are quite close to the IND-CPA variant. Finally, we discuss a subtle gap that needs to be resolved for every IND-CCA secure KEM (BIKE included) where the decapsulation has nonzero failure probability: the difference between average DFR and “worst-case” failure probability per key and ciphertext.
Asymmetric uncertainties in measurements: SOAD a Python package based on Monte Carlo Simulations

Handling uncertainties has a great importance in order to avoid biased results. The nature of these uncertainties is mostly convenient for specific assumptions, making calculations easier. However, when the uncertainties are not small, symmetric and Normally distributed, one needs more sophisticated methods. In this case, using Monte Carlo Simulations is one of the most reliable options among others, with least assumptions. In this work, we present our newly developed Python package, SOAD (Statistics Of Asymmetric Distributions) that handles calculations using measurements with asymmetric uncertainties by Monte Carlo Simulations, which is easy to use and capable of performing multiple mathematical operations consecutively. The theoretical background of the algorithm and the selected Probability Distribution Function for representing the asymmetric uncertainties are obtained from the literature. The codes were successfully applied to High Energy Astrophysics data and compared with some other methods to see in which circumstances they differ from each other.
Mother Tree Optimization

This paper introduces a new swarm intelligence algorithm called Mother Tree Optimization (MTO) for solving continuous optimization problems. MTO uses a set of cooperating agents that evolve based on the communication between Douglas fir trees mediated by the mycorrhizal fungi network that transfers nutrients between plants of the same or different species. In order to assess the performance of the MTO algorithm, we conducted extensive experiments on its variants, with and without climate change. In this regard, we run several statistical and graphical analyses on the resulting solutions when solving well-known test functions. In the statistical analysis, the average, standard deviation, and minimum number of function evaluations are calculated for various levels of solution quality. In the graphical analysis, qualified run-length distributions are used to show the probability of solving a suite of well-known test functions at different levels of solution quality. The results demonstrate that MTO with climate change is able to reach the global solution for all the problems considered. In addition, this MTO variant generally requires fewer function evaluations than Particle Swarm Optimization and Bacterial Foraging to reach a solution of a given quality.
Designing Food Ordering Application Based on Android

Abstract The purpose of this study is to measure the effectiveness of ordering food and drinks using an android-based application and manually. The method used in this study was a descriptive approach, through observation and design of an Android-based app to order food and drinks. In measuring the results of effectiveness, consumers will be given the option to use the system manually and use the app. The results of this study indicate that many consumers prefer to use app compared to manual orders. Using the app provides convenience, for customers to see and order food and drinks only from the desk, without the need to come in person. Apart from providing comfort to consumers, it also provides convenience to restaurant owners to manage each menu that is served.
3D texture-based face recognition system using fine-tuned deep residual networks

As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
Boolean Matrix Decomposition for Label Space Dimension Reduction: Method, Framework and Applications

Abstract Recently, the large number of labels challenges the traditional multi-label classification methods in various application scenes. We introduce a framework of multi-label classification based on Boolean matrix decomposition for giving various multi-label classifiers the ability to predict labels in a high dimension label space. The BMD module in this framework satisfies two conditions of ‘the column use condition’ and exacts BMD, which can reduce the burden on training and predicting task of multi-label classifier to some extent, and the predicted result of multi-label classifier can be restored to original label space by simply Boolean multiplying with matrix. Experimental results on yeast datasets demonstrate that our framework can work particularly well on datasets with a large number of labels and obtain a better predicting accuracy. In summary, the methods discussed in this paper constitute important basic for utilizing more multi-label classifiers in high label dimension space, which is the main contribution of this paper.
FRAMER

Security mechanisms for systems programming languages, such as fine-grained memory protection for C/C++, authorize operations at runtime using access rights associated with objects and pointers. The cost of such fine-grained capability-based security models is dominated by metadata updates and lookups, making efficient metadata management the key for minimizing performance impact. Existing approaches reduce metadata management overheads by sacrificing precision, breaking binary compatibility by changing object memory layout, or wasting space with excessive alignment or large shadow memory spaces.
Blockchain implimentation on E-voting System

Day to day revolutionizing technology is coming up with their positive impacts on our social life. And this all-time globally connected network enables us to access variety of resources easily. One such revolution is Blockchain. With its special characteristics of immutability and decentralized architecture, many services are shifting towards it. One potential application of blockchain can be found in e-voting schemes. It has been a challenge since a long time for building an e-voting system which satisfies all legal requirements of legislators. Distributed ledger technologies can offer infinite range of applications. This paper discusses various e-voting system frameworks conceptualized by different teams. Blockchain will bring its benefits on e-voting systems including immutability of votes, security of system, real-time validation and updation of count of votes in global ledger with not depending upon number of nodes in the network.
Cryptocurrency Price Prediction using Time Series and Social Sentiment Data

With data accumulated at a rapid phase through multiple channels, algorithmic trading becomes critical in stock markets and crypto markets. In algorithmic trading, an innovative approach to integrating machine learning can provide data-driven solutions to help people invest with minimal risk and maximum returns. This study explores various machine learning techniques to model the nonlinear relationship between bitcoin prices and social sentiment data and predict the price values with some lead time. Also, the cryptocurrency market is very volatile and lacking strict governing bodies and regulators across regions making it more complex and challenging to predict the prices. Through the analysis, it is found that the sentiment data model is superior in capturing the nonlinear relationship compared to the conventional methods of technical indicators and decision trees, while the neural network models are robust and offer better accuracy in predicting bitcoin price.
Principles of Physical and Information Model Integration for Cybersecurity Provision to a Nuclear Power Plant

For complex technical objects the research of cybersecurity problems should take into account both physical and information properties of the object. The paper considers a hybrid model that unifies information and physical models and may be used as a tool for countering cyber threats and for cybersecurity risk assessment at the design and operational stage of an object's lifecycle.
SWVP - A Requirements Prioritization Technique for Global Software Development

Requirements prioritization plays a vital role in software release planning, and the process requires good interaction between stakeholders. Requirement prioritization process is challenging in global software development (GSD) context, where communication and coordination are core bottlenecks. Based on the issues found in the literature and industry survey, this paper proposes a requirements prioritization technique for GSD settings based on stakeholders' weight, vote, and priority (SWVP). The technique has two phases: (1) a calculation of stakeholders' profile weights and setting up communication parameters and (2) a voting process supporting the actual prioritization, based on the weights from phase (1). The proposed technique was applied to three industrial projects. The feedback of the practitioners after using SWVP was encouraging, and the feedback results indicate that SWVP could be a practical solution for requirements prioritization in GSD contexts.
Implementing Voronoi-based Guided Hybrid A* in Global Path Planning for Autonomous Vehicles

Planning a global path to navigate autonomous vehicles from a generic perspective defines the overall maneuvers and performance of autonomous vehicles. Inefficient and time-consuming approaches limit the performance of autonomous vehicles in planning a path to reach the desired target position. This paper presents a low-cost and computationally efficient approach of fusing the well-known Hybrid A* search algorithm with Voronoi diagram path planning to find the shortest possible non-holonomic route in a hybrid (continuous-discrete) environment for autonomous vehicles in valet parking applications. The primary novelty of our method stems from two points: at first, Voronoi diagram is exerted to introduce an improved and application-aware waypoint creator to produce the correct waypoints for the Hybrid A* algorithm and then the derived shortest optimum path regarding the non-holonomic constraints of the urban vehicles is planned using Hybrid A* search algorithm. The method has been extensively tested and validated and proven to up to 45% (30% on average) faster than the basic Hybrid A* algorithm.
GitHub's milestone tool: A mixed‐methods analysis on its use

Abstract Social coding site GitHub provides developers with many management tools to facilitate project maintenance and developer collaboration. Milestone tool, in particular, plays an important role in organizing and tracking progress on groups of issues or pull requests in a project. However, few research has analyzed the milestone tool, even though it has been used in practice for a long time. In this paper, we want to address this literature gap and present an ongoing work aimed at investigating the use of the milestone tool in GitHub open‐source projects. We conduct a mixed‐methods analysis in a large‐scale dataset of GitHub projects, to help developers gain some insights into the milestone tool, including its usage, benefits, and limitations. We quantitatively investigate the basic adoption of milestone tool and its correlation with project properties. We also survey developers to understand the reasons for using milestone tool or not and their perceptions of the milestone tool. We find that certain types of projects use milestone tool more than others. Adopting the milestone tool is associated with more commits, more releases, and more project popularity, but the current milestone tool also has some limitations. These observations can then be forwarded to the GitHub community for follow‐up and can result in them potentially making a better milestone tool.
A General Approach to the Extrinsic Calibration of Intelligent Vehicles Using ROS

Intelligent vehicles are complex systems which often accommodate several sensors of different modalities. This paper proposes a general approach to the problem of extrinsic calibration of multiple sensors of varied modalities. Our approach is seamlessly integrated with the Robot Operating System (ROS) framework, and allows for the interactive positioning of sensors and labelling of data, facilitating the calibration process. The calibration problem is formulated as a simultaneous optimization for all sensors, in which the objective function accounts for the various sensor modalities. Results show that the proposed procedure produces accurate calibrations.
Evacuation Supporting System Based on IoT Components †

The response to natural disasters usually requires evacuation procedures that should be followed in a quick and orderly fashion. While census data provide information on the population distribution during sleeping hours, the evacuation procedure may arrive at any moment of the day, even when people are not at home; e.g., in their daily activities. This particular situation causes preparedness plans based on census information to become minimally effective; therefore, more dynamic and context-aware strategies are required to properly address these evacuation processes. In this paper, an evacuation supporting system, based on IoT-networked devices, is proposed to guide people to safe places or shelters once the alert of an extreme event has been issued. The system is interactive and support crowd-sensing; this allows people to upload information on the state of the routes and the shelters, thus keep an updated status of the evacuation routes. The contribution of the paper is twofold. On one side, an evolutionary algorithm is proposed to provide coverage in a breadcrumb deployment of information posts called witness units. On the other, an application based on geographical information system is presented as an intermediary between the witness units and the users; this application guides people towards safe places or shelters.
Consistent Subtyping for All

Consistent subtyping is employed in some gradual type systems to validate type conversions. The original definition by Siek and Taha serves as a guideline for designing gradual type systems with subtyping. Polymorphic types à la System F also induce a subtyping relation that relates polymorphic types to their instantiations. However, Siek and Taha’s definition is not adequate for polymorphic subtyping. The first goal of this article is to propose a generalization of consistent subtyping that is adequate for polymorphic subtyping and subsumes the original definition by Siek and Taha. The new definition of consistent subtyping provides novel insights with respect to previous polymorphic gradual type systems, which did not employ consistent subtyping. The second goal of this article is to present a gradually typed calculus for implicit (higher-rank) polymorphism that uses our new notion of consistent subtyping. We develop both declarative and (bidirectional) algorithmic versions for the type system. The algorithmic version employs techniques developed by Dunfield and Krishnaswami for higher-rank polymorphism to deal with instantiation. We prove that the new calculus satisfies all static aspects of the refined criteria for gradual typing. We also study an extension of the type system with static and gradual type parameters, in an attempt to support a variant of the dynamic criterion for gradual typing. Assuming a coherence conjecture for the extended calculus, we show that the dynamic gradual guarantee of our source language can be reduced to that of λ B, which, at the time of writing, is still an open question. Most of the metatheory of this article, except some manual proofs for the algorithmic type system and extensions, has been mechanically formalized using the Coq proof assistant.