Reviewerly/exHarmony huggingface dataset:
A Fast Neural Network Learning Algorithm with Approximate Singular Value Decomposition

Abstract The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O ( mn 2). We consider algorithms with a complexity of O ( mnl ),where l&lt;n and l is often significantly smaller than n . Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
VIMO: Simultaneous Visual Inertial Model-Based Odometry and Force Estimation

In recent years, many approaches to Visual Inertial Odometry (VIO) have become available.However, they neither exploit the robot's dynamics and known actuation inputs, nor differentiate between desired motion due to actuation and unwanted perturbation due to external force.For many robotic applications, it is often essential to sense the external force acting on the system due to, for example, interactions, contacts, and disturbances.Adding a motion constraint to an estimator leads to a discrepancy between the model-predicted motion and the actual motion.Our approach exploits this discrepancy and resolves it by simultaneously estimating the motion and the external force.We propose a relative motion constraint combining the robot's dynamics and the external force in a preintegrated residual, resulting in a tightly-coupled, sliding-window estimator exploiting all correlations among all variables.We implement our Visual Inertial Model-based Odometry (VIMO) system into a state-ofthe-art VIO approach and evaluate it against the original pipeline without motion constraints on both simulated and real-world data.The results show that our approach increases the accuracy of the estimator up to 29% compared to the original VIO, and provides external force estimates at no extra computational cost.To the best of our knowledge, this is the first approach exploiting model dynamics by jointly estimating motion and external force.Our implementation will be made available open-source.
School Clustering Using Fuzzy C Means Method

Subang State Polytechnic is one of tertiary institution which was established in 2014. As a new tertiary institution this institution certainly competes with other tertiary institutions in obtaining prospective students. Currently, Subang State Polytechnic determines some schools to be visited for socialization activities for New Student Admissions based on the large number of students in the schools at Subang district. However, it does not prove that it has influenced the students to enroll in Subang State Polytechnic. This research highlights the issues involved in each school including the number of graduates, graduates who continue to colleges, graduates who continue to Subang State Polytechnic, average score of school’s national examination, average report card grades, number of counseling guidance teachers, number of college socialization, and the distance from the school to Subang State Polytechnic. As a result, there are 40 schools that are selected to determine as the potential schools do a socialization. The grouping schools use the Fuzzy C Means method and determe the number of groups or clusters use Modified Partition Coeffcient (MPC). The results of the MPC calculation revealed that there are 3 clusters, each cluster has a cluster center and members. In cluster I, there are 9 schools, cluster II has 16 schools, and cluster III consists of 15 schools. The results of the clustering assisted the New Student Admissions committees in determining the potential schools to do a socialization activities and it was a major step towards the success of the New Student Admissions at Subang State Polytechnic.
Drug Demand Prediction Model Using Adaptive Neuro Fuzzy Inference System (ANFIS)

Drug planning is the process of activities in the selection of types, quantities, and prices in accordance with the needs and budget for the coming procurement period in order to avoid the occurrence of excess or emptiness of drug supplies when needed by patients. Management of planning that is not optimal drug needs will have a negative impact on hospitals, both medically and economically, because 50-60% of the total budget used for treatment and medical equipment, uncertainty of drug needs due to disease population and the number of patients can change according to conditions the volume of patient diagnostic data, thus requiring an automatic way to select drug needs according to disease progression. This study aims to create a prediction model for drug needs with the ANFIS method, the data analysis framework used is sourced from drug usage / sales data at the Royal Prima Hospital 2016-2017 by building a software that implements the ANFIS method. Stages of application testing are carried out by applying the previous year's data to predict the current year, namely the 2016 data for 2017 predictions, while the 2017 data for 2018 predictions. The data source will be used to analyze the ANFIS membership function that generates parameters for the ANFIS method in training and testing data. The results of the analysis of the ANFIS parameters will be updated to produce a small error value (close to 0), based on the value of Root Mean Square Error (RMSE), then an evaluation is carried out with a quantitative and qualitative analysis of the predicted results with the existing system.
Interdisciplinary Survey of Fault Localization Techniques to Aid Software Engineering

Fault localization (narrowing down the cause of a failure to a small number of suspicious components of the system) is an important concern in many different engineering fields and there have been a large number of algorithmic solutions proposed to aid this activity.In this work, we performed a systematic analysis of related literature, not limiting the search to any specific engineering field, with the aim to find solutions in nonsoftware areas that could be successfully adapted to software fault localization.We found out that few areas have significant literature, in this topic, that are good candidates for adaptation (computer networks, for instance), and that although some classes of methods are less suitable, there are useful ideas in almost all fields that could potentially be reused for software fault localization.
Hacks for Cost-Justifying Usability

This paper is designed to guide user-centered design professionals in running negotiations of usability cost justification with product owners. The paper introduces hacks that can be used by usability professionals to convince entrepreneurs or other decision makers to invest in usability improvements. The traditional cost-justifying usability approach focuses on emphasizing the potential profits that can be expected. This "goal-setting" approach, commonly represents a strong influence on the investment decision of young, nascent entrepreneurs. The new approach, which we are suggesting, is about avoiding losses and costs stemming from not taking any action. This is a "fear-setting" approach, which tends to influence the investment decisions of older and more experienced entrepreneurs. Usability professionals can thus identify and classify their audience regarding to age and years of experience in order to choose the most efficient mode of presenting justification of usability cost.
Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

Market Timing is the capacity of deciding when to buy or sell a given asset on a financial market.Market Timing strategies are usually composed of components that process market context and return a recommendation whether to buy or sell.The main issues with composing market timing strategies are twofold: (i) selecting the signal generating components; and (ii) tuning their parameters.In previous work, researchers usually attempt to either tune the parameters of a set of components or select amongst a number of components with predetermined parameter values.In this paper, we approach market timing as one integrated problem and propose to solve it with two variants of Particle Swarm Optimization (PSO).We compare the performance of PSO against a Genetic Algorithm (GA), the most widely used metaheuristic in the domain of market timing.We also propose the use of trend representative testing to circumvent the issue of overfitting commonly associated with step-forward testing.Results show PSO to be competitive with GA, and that trend representative testing is an effective method of exposing strategies to various market conditions during training and testing.
How Are Situation Picture, Situation Awareness, and Situation Understanding Discussed in Recent Scholarly Literature?

There are several different definitions of situation awareness.However, all of them have in common is knowing and understanding of what is happening, an understanding of future changes or problems, and the prediction of the future situation and the decisions to be made on its basis.Situation picture and Situation Awareness are narrow.Situation understanding of the situation is the understanding of the decision-makers and their assistants about what has happened, the circumstances that have affected them, the goals of the different parties and the possible development options of the events needed to make decisions on a particular issue or subject.The results of this study indicate that the recent discussion in scholarly literature focus on situation awareness.A further result is that the context of the many of the recent literature are focused on issues related to cyber security or on intelligent systems, thus on IT systems, which are very relevant to modern situation awareness and understanding in these modern times where more and more systems become digitalized and interconnected..
Engineering of a training program for future teachers based on information and communication technologies

The initial training of future teachers relies heavily on the pedagogical period to reinforce their professional integration.This is why it is part of the curricula, benefiting from a fairly important time slot.On the other hand, the classical design of the pedagogical course does not favor the maximum attainment of the predefined objectives.The break between academic knowledge and professional knowledge is the main cause; disciplinary acquisitions at the training center constituting the basic training of future teachers are little or absolutely not related to the reality of the profession.This state of affairs has forced those responsible for education policy in training to look for new opportunities for action to redress training and promote their professional qualifications.A new conception of the pedagogical course has been prescribed by the competent authorities in a form known as integrative alternation accompanied by a new spirit of professional socialization.The pluvalue of the new design is explained by a new way to go about improving the training / employment adequacy.It is a question of adopting the reflexive posture to maximize the relations between the theoretical bases and the teaching practices by exploiting all the tools offered by the information and communication technology (TICS) A questionnaire was sent to the trainers to identify the type of engineering used and the use of information and communication technology was optimized.
A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data

Deep neural network (DNN) models are widely applied in biomedical image studies since DNN models take advantage of massive data to provide improved performance over traditional machine learning models. However, like any other data-driven models, DNN models still face generalization limitations. For example, a model trained on clinical data from one hospital may not perform as well on data from another hospital. In this work, a novel approach is proposed to determine confident samples from unseen data on which a DNN model will have improved performance. Confident samples are defined as inliers identified by an outlier detector, which is based on projection of training data onto a standard feature space (e.g. ImageNet feature space). The hypothesis of the proposed method is that in a standard feature space, a DNN model will perform better on the inlier data samples and more poorly on the outliers. While projecting the unseen data to a standard feature space, if data points are detected as inliers, then the model will likely have consistent performance on those inliers as those patterns have already been “seen” from the training dataset. To validate our hypothesis, experiments were conducted using publicly available digit image datasets and chest X-ray images from three unseen datasets collected across U.S. and Canada hospitals. The experimental results showed consistently improved performance across various DNN models on all confident samples from unseen datasets.
Correction to: Learning nonlinear input–output maps with dissipative quantum systems

Unfortunately, some errors were detected after publication of the article referred to in the title. This errata corrects errors in the proofs of Lemma 5 on page 23 and Proposition 1 on page 26 of the article. The authors also noticed a weaker condition for Lemma 1 on page 19 to hold. The corrections and improvements do not impact the main results and conclusion of the article.
Engaging students in writing recount text through Google Maps

Writing is undoubtedly an essential productive skill for every student in the modern era. Thus, this study investigates how students utilize the popular application of Google Maps in writing recount text and finds out their responses toward the learning activity. This research used descriptive qualitative research since it was conducted during the learning process of English subject at one high school in Karawang, Indonesia. Data sources were obtained through data triangulation consisting of observation, interviews, and documentation. This research included three phases related to checking knowledge, introducing and utilizing Google Maps in writing experiences, and finally giving assignments to students regarding their trip experience in a particular location. Based on the results of the observation, the process of teaching and learning using online Google Maps application in ELT context can enhance students’ writing as well as promote their engagement. Data from interviews shows positive responses from students in learning English through the implementation of the Google Maps application to develop their writing skills. As for the documentation, learning outcome of writing recount text stated in the lesson plan was successfully achieved through the carefully planned activities using Google Maps, and this could be seen from the students’ works. Research also reveals students are more motivated in an interactive and more fun learning process due to realistic and appealing object visualization, and not to mention its mobile practicality feature.
Cross-Corpus Data Augmentation for Acoustic Addressee Detection

Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants.In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and introduce the first competitive baseline (unweighted average recall equals 0.891) for the Voice Assistant Conversation Corpus that models the first setting.We jointly solve both classification problems, using three models: a linear support vector machine dealing with acoustic functionals and two neural networks utilising raw waveforms alongside with acoustic low-level descriptors.We investigate how different corpora influence each other, applying the mixup approach to data augmentation.We also study the influence of various acoustic context lengths on AD.Two-second speech fragments turn out to be sufficient for reliable AD.Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.
A Relational Time-Symmetric Framework for Analyzing the Quantum Computational Speedup

The usual representation of quantum algorithms is limited to the process of solving the problem. We extend it to the process of setting the problem. Bob, the problem setter, selects a problem-setting by the initial measurement. Alice, the problem solver, unitarily computes the corresponding solution and reads it by the final measurement. This simple extension creates a new perspective from which to see the quantum algorithm. First, it highlights the relevance of time-symmetric quantum mechanics to quantum computation: the problem-setting and problem solution, in their quantum version, constitute pre- and post-selection, hence the process as a whole is bound to be affected by both boundary conditions. Second, it forces us to enter into relational quantum mechanics. There must be a representation of the quantum algorithm with respect to Bob, and another one with respect to Alice, from whom the outcome of the initial measurement, specifying the setting and thus the solution of the problem, must be concealed. Time-symmetrizing the quantum algorithm to take into account both boundary conditions leaves the representation to Bob unaltered. It shows that the representation to Alice is a sum over histories in each of which she remains shielded from the information coming to her from the initial measurement, not from that coming to her backwards in time from the final measurement. In retrospect, all is as if she knew in advance, before performing her problem-solving action, half of the information that specifies the solution of the problem she will read in the future and could use this information to reach the solution with fewer computation steps (oracle queries). This elucidates the quantum computational speedup in all the quantum algorithms examined.
Prose reading: The influence of text-reader factors

This study highlights the importance of text-reader factors in prose reading. The study was carried out to identify the text-reader factors involved in enhancing reading comprehension. A qualitative research methodology was employed involving individual in-depth interview sessions with six average ability Form Two students from a secondary school. The interviews were conducted to gauge their views on the influence of text-reader factors in text comprehension. The interview questions centered on the literature textbook currently used in school and also the short story “Cheat” by Allan Baillie included in the book. Thematic analysis was carried out on the data collected. The findings show that text-reader factors affect text comprehension. The factors include pictures, font, the author, glossary, text organization, cover of the book and the length of the literary prose forms. The reader factors are related to the students’ life background, interest, and motivation in reading. The study implies that these text-reader factors must be met to ensure improved reading comprehension ability among students.
M-folding method–based elliptic curve cryptosystem for industrial cyber-physical system

Recently, cyber-physical system is widely used for smart system control in various fields. Various functions of the cyber-physical system must overcome the limited hardware resources constraint of an embedded system. In addition, the data required from the industrial cyber-physical system are critical; therefore, a highly secure encryption technique is required. However, security and computational throughput are incompatible with each other in the cryptographic technique; therefore, the industrial cyber-physical system needs to adopt a highly efficient and secure encryption technique considering the limited available resources. This study applies the m-folding method to the highly secure elliptic curve algorithm to improve efficiency and proposes the cryptosystem optimized for the resource-constrained industrial cyber-physical system. The proposed m-folding method–based elliptic curve encryption showed 50% faster encryption than the existing methods.
Sistem Informasi Penunjang Proses Pemesanan dan Desain Kue Pada Toko Kue Artisan Online Berbasis Web

Along with the development of times, artisan cakes are increasingly in demand by the public to celebrate special days or as a gift. Online artisan cake is a cake shop that uses social media like Instagram and Facebook to sell their cakes. The current cake ordering process is still done manually which is done through two stages. First, customer sees and chooses the type and design of cakes through Instagram or Facebook. Second, he or she places an order on the cake selected before via Line or WhatsApp. The need of two different media makes the ordering system less efficient. Apart from that, customer who want to make special or custom requests can only deliver their request through messages via Line or WhatsApp, so there are often misunderstanding and inaccuracies caused by either customer or cake maker different picture of request. Therefore, an information system is needed to support the order process and cake design at online artisan cake shops using Rapid Application Development (RAD) system development using prototyping. System modeling is developed by using UML 2.5, namely use case diagrams, activity diagrams, class diagrams and entity relationship diagrams. The final result of this final project is a website that supports ordering process and cake design at online artisan cake shops that can simplify the ordering process and order customization on the design of the ordered cakes. The website created has gone through the test phase and is ready to use.
Cloud Computing: A Study of the Cloud Computing Services

Cloud computing now easily tops any list of topics in computer science because of its far reaching involvements in many areas in computing, especially Big Data. Moreover, It is a one of the most emerging technology due to the provision of various computing infrastructure and services. Cloud computing has evolved through a number of phases that include mainframe computing, cluster computing, and grid computing, parallel computing, distributed computing and utility computing. Cloud computing is often considered the successor of grid, cluster and, mainframe computing. Cloud computing is the delivery of many services through the Internet. These resources include tools and applications like databases, infrastructure, platforms, data storage, servers, networking, and software. Many people and businesses use cloud for a number of reasons including cost savings, increased productivity, speed, efficiency, high performance, scalability, high computing power and security. This paper presents the definition of cloud computing, its characteristics, different cloud services and various platforms that are used in cloud computing.