Reviewerly/exHarmony huggingface dataset:
Decoding Technology for Compressed Videodata in Infocommunication Systems

The article substantiates the basic principles for constructing a method for recovering compressed dynamic images of a stationary background. Basic approaches for improving technologies for syntactical coding of transformants with the formation of the space of identifiers of discrete positions of a two-dimensional structural-spectral description are substantiated. An effective syntactic representation of the transformed P-type frame segments is developed on the basis of one-dimensional two-base object-position coding under conditions of flexible pair-element inequality. It is proposed to perform reconstruction of compressed dynamic images of a stationary background on the basis of: reconstruction of a binary mask matrix, reconstruction of a dynamic component, reconstruction of a matrix of signs. A technology has been built to restore the current frame in the original dynamic space without losing information based on the recovery of the current differential-presented frame and information about the base frame.
Did ML Chips Heat Up the Chip Design Arena?

Presents the introductory editorial for this issue of the publication.
Subjective Quality Assessment of Ground-based Camera Images

Image quality assessment is critical to control and maintain the perceived quality of visual content. Both subjective and objective evaluations can be utilised, however, subjective image quality assessment is currently considered the most reliable approach. Databases containing distorted images and mean opinion scores are needed in the field of atmospheric research with a view to improve the current state-of-the-art methodologies. In this paper, we focus on using ground-based sky camera images to understand the atmospheric events. We present a new image quality assessment dataset containing original and distorted nighttime images of sky/cloud from SWINSEG database. Subjective quality assessment was carried out in controlled conditions, as recommended by the ITU. Statistical analyses of the subjective scores showed the impact of noise type and distortion level on the perceived quality.
Possibilities for Correcting SQL Exercises Automatically

In this study, we will present a method for evaluating DDL exercises in an exam situation. In relational database systems, we have not just our data in the database but the metadata about our objects as well in so-called data dictionary tables and views. If we give DDL exercises in an exam and students have to run their statements in a system, not just write them, then we can check the objects they made.
Modeling of Magnetic Pickups for Electric Musical Instruments

In this paper, the modeling of the magnetic pickup of electric musical instruments, such as an electric guitar, an electric bass and an electromechanical keyboard instrument, is investigated. Focus is placed on the Rhodes electric piano, which gained popularity in 1960s and is still highly valued among jazz, soul and popular musicians. In most existing studies, the induced electromotive force (EMF) in the pickup of musical instrument is calculated using simplified methods in which the vibrator is modeled as a point without volume. In this study, detailed finite element analyses are performed to calculate the EMF in the pickup system as well as the estimation of the effect of the eddy current in the system. A solution procedure to obtain the EMF for a class of problems in which movement of the vibrator of the musical instrument is described by finite degrees of freedom is proposed. Sound generated based on the pickup model is evaluated as well. Simplified calculation methods of the existing studies are also reviewed and a modification is proposed to improve the accuracy.
Computation of Strain in Deformed Pearlitic Steel Using Digital Image Correlation Technique

In the present paper, digital image correlation (DIC) technique has been used to investigate the deformation process in metal and alloys. For this purpose, of a sample of pearlitic steel, comprising a fine distribution of alternate layers of ferrite and cementite, was deformed, and the same region of the sample was investigated before and after deformation. The analysis of the deformation of the sample was carried out using an open-source software Ncorr. Using DIC technique displacements along preselected x and y-axes were estimated, which were, in turn, used in computing strains along x and y directions representing normal strains along with shear strains. The results obtained have elucidated variation and localization of the strain at different points of the sample.
Use of Smartphones for Self-Regulated Foreign Language Learning Activities

Smartphones can significantly affect the development of foreign languages in two distinct ways. Firstly, online informal learning of languages may result in naturalistic foreign language acquisition while mobile assisted language learning implies the use of smartphones following a conscious decision to engage in language learning activities that would result in the improvement of one's language competence. Based on quantitative and qualitative methodology applied on a sample of undergraduate students in Slovenia, the main objective of this chapter is to explore the use of smartphones for self-regulated English language learning activities beyond the language classroom.
Intelligent CALL

This chapter shows readers the importance and application of pattern matching in learning languages; specifically, the application of natural language processing to address specific problems of Japanese learners of English at a public university. The chapter introduces the concepts of patterns, detection, and detection methods. The author turns to the pedagogic application of pattern matching, first discussing the relevant theory, then describing hacks developed by language teachers and learners. The final section describes and evaluates iCALL tools developed at the University of Aizu, including a mobile app and the Pronunciation Scaffolder, a real-time presentation script annotator.
Spatial Frequency Domain Entropy Dissimilarity Measure

An entropy-based spatial frequency domain 2D image dissimilarity measure is proposed and demonstrated. The metric is an extension to pixel-pixel based entropy dissimilarity measures (EDM) proposed by Tsai and Wu. The new spatial frequency domain EDM maintained all desired features of EDM while overcoming limitations such as requiring the two digital images to be compared to have the same size in pixels. The research in inspired by the need of analyzing the Advanced LIGO time series data sets in developing a real-time and blind gravitational wave event detection algorithm. Compared to other dissimilarity measures, frequency domain entropy dissimilarity measure (FD-EDM) is spatial information sensitive and agnostic to image sizes and resolutions. It also further improves the computational efficiency of normal pixel-pixel EDM.
Entropy of PRNGs and the Accuracy of Monte-Carlo Simulations in a Publicly Distributed Computing Environment

Recently, cloud computing has become of great interest to many computational fields in the physical sciences. Cloud computing offers scalable and economical computational resources to researchers. However, this new paradigm comes at a cost of new security vulnerabilities. While there are methods for ensuring computational attestation for various computations, these methods can be limited, as they may require extensive computation costs, a prior knowledge of boundary conditions or a solution to the computation, or may be limited to deterministic calculations, yet at the very heart of modern physics research lies the requirement for non-deterministic models. Pseudo-random number generators (PRNG) are used for many such experiments to simulate the randomness. It is vital for the PRNGs to have a high degree of entropy. A cloud is an excellent choice for running large scale simulations. However, existing research posit that that PRNGs within virtual machines running in a cloud may possess a lower entropy than that of a local machine. In this paper, we examine this problem. Our results hint that computations in the cloud are equal to that of a local machine and that snap shot vulnerabilities may not apply to these simulations.
Vehicle Disambiguation from Multiple Observations

We introduce disambiguation as a generalization of re-identification. The field of re-identification has been used in identifying objects like people and vehicles passing by surveillance cameras. However, all of these problems have one thing in common: they all begin with a query image and seek to find matching images in galleries from other cameras. In our problem, a database is provided of objects captured from multiple cameras, but a query image is not provided. The challenge then is to disambiguate the suspect by searching through the detections across cameras and identifying the common object. In this paper we introduce the problem of disambiguation and we show our multi-stage approach using deep convolutional neural networks and a pair of reasoning engines which identify a common target even without a known query image to reference. For the purposes of this paper, we select vehicles as the target dataset, but we believe that our approach translates directly to other object classes (e.g. people).
Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter

This paper presents a low computational complexity solution to the problem of joint multi-target tracking and parameter estimation. The multi-target filtering approach is based on the linear-complexity factorial cumulant filter presented in this conference last year, and the parameter estimation approach is based on the single-cluster point process method for joint multi-object estimation and parameter estimation. The joint multitarget and measurement process is approximated at each step with a Panjer point process, which permits the low cost solution. It is shown in simulated studies the paper that the approach is more robust than using a Poisson point process prior since second-order information is retained and propagated.
Particle Gaussian Mixture Filters: Application and Performance Evaluation

The particle Gaussian mixture filters are a new class of Bayesian estimation techniques that have been proposed for the general multimodal nonlinear filtering problem. In this paper, we evaluate the estimation performance of the particle Gaussian mixture filters on a collection of benchmarking problems that have been selected from recent literature. The problems are chosen to facilitate comparisons with the estimation results of other recently proposed general purpose nonlinear filters. We investigate the effect of coupling, the dimensionality of the problem and the number of particles on the estimation performance. Our results indicate that the performance of the particle Gaussian mixture filters are at par with feedback particle filters and log homotopy based Daum Huang particle flow filters on the test problems.
Poster: Development and Implementation of Electronic Applications Based on Arduino Platform for a First Basic Course

This paper shows a learning guide about the use of Arduino platform and the different utilities that can be implemented based on this platform for a first basic course. The paper can be useful as a guide for someone who wants to start in the world of microcontroller programming, with examples to consolidate the knowledge learned. Some students of the School of Industrial Engineering of the University of Malaga (Degree in Industrial Design) approach the study of an engineering career with little knowledge of electronics. This degree contains basic skills on learning electronics and the use of the Arduino platform and its development possibilities can offer students an interesting view of electronics, making better use of classes. The work is based on both theoretical (to make the components known) and practical (using real assemblies) development to consolidate the knowledge learned. Therefore, once the basic components necessary to carry out various practices have been explained, the theoretical performance and the programming of Arduino is explained and the various practices that will be set up in the laboratory are presented, as an application of Arduino for different uses. All applications are accompanied by information about the components used, an assembly guide illustrated with Fritzing schematics and a photo of the actual assembly, as well as an explanation of the instructions used to program the microcontroller illustrated with an image of the code used. The main idea of this work is to replace the traditional laboratory practices that require more advanced knowledge in electronics with a set of simple practices carried out in Arduino that allow students to have an approximate idea of basic electronics with little knowledge. After three years of carrying out this new methodology for this first basic electronic course, the surveys demonstrate a better adaptation of the students to the study of electronics. In addition, the marks obtained have improved considerably and the students have the sensation of learning electronics in a simple and fun way.
System design for business location searching using distance approach based on Android

Abstract Indonesia is large country with dense populations. Many entrepreneurs take Indonesia as world business target since people are commonly using a smart phone in their daily life. In Bengkalis, people are looking for business location conventionally by visit the shop and others. Social media like Facebook and Instagram also give the information about the shop and its products. But all the method was very inefficient since consumer spend a lot of energy, time, and money to buy something. Business and technology are two thing that can’t be separated where there are greatly needed to support the success of business. The distance location formula between two points P and Q with latitude and longitude (x1, y1) and (x2, y2) respectively named coordinate. This formula base on Haversine Formula which is very accurate to calculate the distance. In this paper, we create a system of business location searching using distance approach. The main purpose of this application is to give the visitors about the information of product and a map that show the nearest shop according to product they needed, distance estimation to reach the location.
Optimal Estimation of Solar Radiation on Flat Surfaces for the Design of Energy Systems using Artificial Neural Networks

Solar energy systems use solar radiation to obtain useful energy, so to design and implement these systems anywhere on the surface of the earth it is very important to know the value of the incident solar radiation in the selected place. This radiation is usually obtained from meters such as pyranometers, pyrheliometers or actinographs from meteorological stations located near the place. As these instruments are expensive and usually have high measurement errors (5 - 9%), it is necessary to estimate the radiation in an optimal way. In this work, two mathematical methods are used to estimate the value of incident solar radiation on horizontal and tilted surfaces. The methods are: Method based on astronomical equations and method based on Artificial Neural Networks. The case study was conducted for the geographical location of the National University of Engineering (Lima, Peru). A database of meteorological variables measured for ten years and averaged every month was used to compare their measurements with the estimated results of the proposed mathematical methods. The results revealed that the estimated values of global solar radiation when applying the astronomical method differs on average 9% with respect to that provided by the database and 6% when applying Artificial Neural Networks.
Anchor Word Extraction Enhancement using Coreference Resolution for Narrative texts

Text summarization is an important area in Natural Language Processing studies. It has been shown that good representative words extracted from a story, called anchor words, give an insight of the story. A human can link those words to get a high level idea of the story. However, in order to get the gist of a story, determining important words of a story is not enough. A mechanism capable of determining words which are more important than others is essential to connect the words to get a meaningful idea. In this paper, an algorithm has been proposed where Coreference Resolution is used to determine the anchor words. It has been shown through experiments that the proposed method improves the anchor Word detection task tremendously.
AI-Based 360-degree Video Generation from Monocular Video for Immersive Experience

We propose an artificial intelligence (AI)-based framework for generating 360-degree videos from videos recorded by monocular cameras. We also show immersive virtual reality content generation using AI through an analysis of user experience that compares manually designed and AI-generated 360-degree videos based on the proposed framework. The production of 360-degree videos conventionally requires special equipment, such as omni-directional cameras. Our framework is applicable to a massive amount of existing camera and video, hence it increases the availability of 360-degree videos. We implemented our framework in two steps. First, we generate a three-dimensional point cloud from the input video. Then, we apply AI-based methods to interpolate the sparse point cloud based on geometric and semantic information. Our framework will be applicable to several usages such as assisting surveying past traffic accident videos and education on showing the historical townscape of 360 degrees, etc.
Dual Path Binary Neural Network

Binary neural networks can effectively reduce the number of required parameters but might decrease the classification accuracy. To solve the problem, we propose a dual-path binary neural network (DPBNN) in this paper. Experimental results show that our DPBNN can outperform other traditional binary neural network in CIFAR-10 and SVHN dataset. The proposed network is simple, so it is suitable to be implemented on embedded systems or SoC designs.
A Language Model-Based Design of Reduced Phoneme Set for Acoustic Model

A language model-based design of reduced phoneme set for acoustic model is proposed. In the case where the amount of training data is too small to train each phoneme model, the reduction of the phoneme set can lead to a reduced discriminative model of phonemes, which can increase homophones that yield degradation of speech recognition. The proposed approach enables us to reduce phonemes preventing the degradation, regarding pronunciation/word sequence confusion rate calculated from n-grams in a language model. In an experiment, the phoneme set designed with proposed approach was applied to Japanese large vocabulary speech recognition system. The word error rate with full 39 phonemes set was 9.5%, while the error rate with the 10 phonemes set designed with the proposed approach was 11.1%. The degradation was able to be prevented within 2%.
Introducing the permutations R package

Here I introduce the R package permutations, for manipulating permutations of a finite set. I show how the package has been used to investigate the megaminx puzzle and exhibit an 82-turn superflip that represents a new lower bound for this operation.
Using the SDK Affdex for a Complex Recognition System Based on a Webcam

The paper deals with the issues of the user's gender and age detection and classification of user emotional states assessed on the basis of the webcam input. The goal is the implementation of the Affdex SDK into a program which can recognize faces in front of the scanner. The program is to be able not only to classify the emotional state but also age and gender of one or more users. The first part of this paper (section Related work) focuses on ways of detection and classification of faces in the past. At the same this part describes also methods and classification of emotion, age and gender as well as methods used to detect faces and a more extensive description of the SDK and Affectiva company. The practical part of this paper looks into programming the pilot application and the final version written in C programming language and its methods' description.
Resource-rationality and dynamic coupling of brains and social environments

Leider and Griffiths clarify the basis for unification between mechanism-driven and solution-driven disciplines and methodologies in cognitive science. But, two outstanding issues arise for their model of resource-rationality: human brains co-process information with their environments, rather than merely adapt to them; and this is expressed in methodological differences between disciplines that complicate Leider and Griffiths' proposed structural unification.
Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning Approach

Visual Question Answering (VQA) concerns providing answers to Natural Language questions about images. Several deep neural network approaches have been proposed to model the task in an end-to-end fashion. Whereas the task is grounded in visual processing, if the question focuses on events described by verbs, the language understanding component becomes crucial. Our hypothesis is that models should be aware of verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. Our first contribution is a new VQA dataset (imSituVQA) that we built by taking advantage of the imSitu annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet. Second, we propose a multitask CNN-LSTM VQA model that learns to classify the answers as well as the semantic frame elements. Our experiments show that semantic frame element classification helps the VQA system avoid inconsistent responses and improves performance.
Designing Web-based Database Applications in CV Bahagia

Abstract The purpose of this paper are to analyze, and design web-based database system for operational acitivities, that use to assets storage or company document as well as booking online from client to CV Bahagia. This paper consists of data collection methods which include observation, interview and research. The other method that used for this database design is Database System Development Life Cycle (DSDLC). The results achieved are speed up operational activity CV Bahagia and minimize the chance of fraud in operational activities perpetrated by staff who work in CV Bahagia. To improve the company’s service, it is advisable to develop a live chat feature and custom design for stage and tent from client. So can more easily relate to client and can also fill orders in accordance with the wishes of the client.