Reviewerly/exHarmony huggingface dataset:
A Learning-Based Framework for Depth Perception using Dense Light Fields

The rapid development of optical sensors technology has accompanied a growing demand for visual measurement systems in emerging areas that need to interpret the real three-dimensional physical world, such as self-driving cars, mobile robotics, Advanced Driver Assistance Systems (ADAS), and medical diagnostic in 3D imaging. In these systems, for modeling the physical world, it is necessary to unify visual information with depth measurements. Light Field cameras have the potential to be used in such systems as a versatile hypersensor. Since Light Fields represent the scene’s visual information from multiple viewpoints, it is possible to calculate the depth information through trigonometric operations. This paper proposes a learning-based framework that allows unifying scene depth with visual information obtained from Light Fields. The structure of the proposed framework is composed of four main modules.
Full Reference Stereoscopic Objective Quality Assessment using Lightweight Machine Learning

Decades of research on Image Quality Assessment (IQA) have promoted the creation of a variety of objective quality metrics that strongly correlate to subjective image quality. However, challenges remain when considering quality assessment of 3D/stereo images. Multiple objective quality metrics for 3D images were designed by extending the well-known 2D metrics. As a result, these solutions tend to present weaknesses under 3D-specific artifacts. Recent works demonstrate the effectiveness of machine-learning techniques in the design of 3D quality metrics. Although effective, some machine learning-based solutions may lead to high computational effort and restrict its adoption in low-latency lightweight systems/applications. This paper presents a study on full-reference stereoscopic objective quality assessment considering lightweight machine learning. We evaluated four different decision tree-based algorithms considering eight distinct sets of image features. The classifiers were trained using data from the Waterloo IVC 3D Image Quality Database to determine the subjective quality score measured using Mean Opinion Score (MOS). The results show that RandomForest generally obtains the best accuracy. Our study demonstrates the feasibility of decision tree-based solutions as an accurate and lightweight approach for 3D image quality assessment.
Novel Light Field Encoding Framework Based on Optical Flow and Phase Correlation

This work proposes a novel Light Field encoding framework based on optical flow and phase correlation. This encoder works by combining one reference sub-aperture image (SAI) with optical flow and phase correlation information of a few SAIs to synthesize all SAIs of the Light Field. In order to improve the quality of the synthesized Light Field, an HEVC encoder is modified to encode synthesis error and add a quality enhancement layer. The proposed encoder outperforms JPEG Pleno 4D Transform Mode encoder and the HEVC anchor in lower bitrates. Also, it can be easily adapted to the existing multimedia infrastructure.
Using Machine Learning on Testing IoT Applications: a systematic mapping

Internet of Things (IoT) devices are increasingly present in people’s daily lives. Thus has increased research interest in investigating strategies that can ensure that these applications work as expected considering specific and vital characteristics of IoT, for example, security, performance and interoperability. In a testing point of view, there is a need to optimize and define an efficient strategy, from its planning to its execution. Considering all the steps that can be taken to test an IoT application, this process, if performed manually, can demand great effort and time. Machine learning (ML) algorithms have been applied in several areas of computing in order to optimize and automate processes that involve large volumes of data. In this paper, we present a systematic mapping resulting in 40 studies that highlights techniques or approaches that use machine learning algorithms for the most diverse goals within the IoT application testing process, such as the use of neural networks for predicting the cost of time in the preparation and execution of tests; identification of security attacks; and automatic generation of test cases from textual language. We also identified that the vast majority of testing techniques are focused on a specific IoT characteristic (e.g., security, performance), specially security, and apply the machine learning algorithm in two ways: directly in the algorithm, called predictive maintenance, or during the execution of planned tests, both of them bring difficulties related to extracting and defining data to train ML algorithms.
A Cascade Approach for Gender Prediction from Texts in Portuguese Language

Author Profiling is a prominent research area in which computational approaches have been proposed to predict authors’ characteristics from their texts. Gender, age, personality traits, and occupation are examples of commonly analyzed characteristics. It is a task of growing importance, with applications in different areas such as forensics, marketing, and e-commerce. Although a lot of research has been conducted on this task for some widely used languages (e.g., English), there is still a lot of room for improvement in studies involving the Portuguese language. Thus, this work contributes by proposing and evaluating a cascading approach, which combines a weighted lexical approach, a heuristic, and a classifier, for the gender prediction problem using only textual content written in the Portuguese language. The proposed approach considers both specificities of the Portuguese language and domain characteristics of the texts. The results obtained from the proposed approach showed that exploring the specificities of the Portuguese language and domain characteristics of the texts can positively contribute to the performance of the gender prediction task.
An Auto-ML Approach Applied to Text Classification

Automated Machine Learning (AutoML) is a research area that aims to help humans solve Machine Learning (ML) problems by automatically discovering good model pipelines (algorithms and their hyperparameters for every stage of a machine learning process) for a given dataset. Since we have a combinatorial optimization problem for which it is impossible to evaluate all possible pipelines, most AutoML systems use Evolutionary Algorithm (EA) or Bayesian Optimization (BO) to find a good solution. As these systems usually evaluate the pipelines’ performance using the k-fold cross-validation method, the chance of finding an overfitted solution increases with the number of pipelines evaluated. Therefore, to avoid the aforementioned issue, we propose an Auto-ML system, named Auto-ML System for Text Classification (ASTeC), that uses the Bootstrap Bias Corrected CV (BBC-CV) to evaluate the pipelines’ performance. More specifically, the proposed system combines EA, BO, and BBC-CV to find a good model pipeline for the text classification task. We evaluate our proposal by comparing it against two state-of-the-art systems, the Tree-based Pipeline Optimization Tool (TPOT) and Google Cloud AutoML service. To do so, we use seven public datasets composed of written Brazilian Portuguese texts from the sentiment analysis domain. Statistical tests show that our system is equivalent to or better than both of them in all evaluated datasets.
A Movement Analysis Application using Human Pose Estimation and Action Correction

Human pose estimation (HPE) is an important field of computer vision that aims to predict poses of individuals from videos and images. It has been used in many different areas including human-computer interaction, motion analysis, surveillance, action prediction, action correction, augmented reality, virtual reality, and healthcare. Executing movements correctly is crucial in all kinds of physical activities, both to increase performance and reduce risk of injury. HPE is poised to help athletes better analyse the quality of their movements. This work proposes a model for movement analysis, repetition count, and movement correction in physical exercises using HPE. For this purpose, a study is carried out in the field of HPE applied to sports and another study is focused on HPE for correction and postural analysis. From this, it is verified what is the state of the art in HPE for physical exercises and what is the best method for analyzing movements. This work implements an application with improvements in respect to other related work, focusing mainly on the feedback presented to the user when performing a certain movement. To validate the proposed model, a quantitative research was carried out using the Unified Theory of Acceptance and Use (UTAUT). For both people who exercise and professionals in the field of physical education, the results demonstrate that the application is able to analyze the biomechanics of movement, responding with speed and precision to execution errors. Among other results are: user satisfaction, interest in using the application in the future, and agreement in relation to good performance in helping and analyzing physical exercises.
Real Time Detection of Mobile Graphical User Interface Elements Using Convolutional Neural Networks

In this work, we model the Graphical User Interface (GUI) detection challenge as an object detection problem from Computer Vision (CV) domain. Based on our literature review, we identified some works with similar proposals but suffering from reproducibility and comparability problems. Thus, we propose to mitigate these problems by creating a standardized dataset that can be used for training and evaluating CV algorithms in Mobile GUI. For this purpose, we use Rico's Android application screen collection and semantic annotation of GUI elements and labelled them using the standard Microsoft COCO format for object detection. Finally, we split the dataset into three main challenges: 1) clickable and non-clickable elements; 2) interface components detection; and 3) icons detection. We trained a baseline algorithm considered state-of-the-art on real-time object detection from the YOLO family. Finally, we present quantitative results for the three proposed challenges.
Introducing Contextual Information in an Ensemble Recommendation System for Fashion Domains

In online marketing environments, we have seen strong growth in the fashion domain, allowing consumers to access a worldwide network of brands. Despite the significant advances of the so-called Recommender Systems in more traditional scenarios, they still fail to offer a personalized and reliable fashion shopping experience that allows customers to discover products that suit their style and products that complement their choices or challenge them with new ideas. In this work, we propose a new ensemble recommendation system that combines different context information (customer-product interaction, item characteristics and user behaviour) with the predictions (recommendations) of different state-of-the-art traditional Recommender Systems to recognize new patterns in user-item interaction and to ensure a desirable level of personalization for fashion domains. Specifically, in the present work, we present a first instantiation that combines a collaborative filtering neural network method, a non-customized classical method and domain context information. In our experimental evaluation, considering two Amazon data collections, the instantiation of our proposal presented significant gains of up to 80% of MRR, 70% of NDCG and 108% of Hits compared with the methods considered state-of-the-art for the fashion recommendation scenario.
KOLABORASI UGM, UNIVERSITAS PETRA SURABAYA, UNCEN DAN UOGP SERTA PARTISIPASI MAHASISWA KKN DALAM PEMBUATAN MCK RAMAH LINGKUNGAN AKIBAT BENCANA ALAM KABUPATEN JAYAPURA

The occurrence of flash floods in Sentani claimed many victims, some suffered minor and serious injuries that could not be counted, as well as many people's houses that were washed away and damaged so that the community was placed in refugee camps prepared by the government. To deal with the people affected by the flash floods that occurred in Sentani, the government could not reach out to the entirety to evacuate the evacuation sites that had been prepared. Some of the refugee campsprepared by the government do not have facilities for the community's necessities of life, namely MCK. Through this service, we are moving to conduct a field survey to see which locations can be helped to cope with life in refugee camps. The purpose of this service is to help small asey communities living in refugee camps who do not have MCK, so we encourage final semester Planology students to do KKN in that place to build environmentally friendly MCK. The materials used to build MCK houses are light steel, the process is easy to do. simple and practical. In this service, we provide training to students and the community how to cut canal C or mild steel using a hand grinder. Likewise in the assembly of bolts to the frame using a hand drill for both installation and removal if there is an error. This training is easily accepted and carried out by KKN students and the community, so that the making of MCK can be completed properly and used by the community of Kampung Asei Kecil Telaga Maya. In this service, together with KKN students, it really helps the government to overcome the lives of the people in refugee campus. Keywords: Mild steel; environmentally friendly MCK; Sentani
Constructing Interval Type-2 Fuzzy Systems (IT2FS) with Memetic Algorithm: Elucidating Performance with Noisy Data

Fuzzy modeling is a challenging task and becomes more complex when designing T2FS, which requires identification of more parameters as compared to T1FS. The problem of fuzzy modeling can be expressed as a high-dimensional search and optimization process, and EAs have the ability to search for optimal solutions in high-dimensional search space, so researchers used various EAs for fuzzy modeling. GAs are widely used for finding solutions in large search spaces, and MAs have characteristics of both global and local optimizations. This paper describes how to use MAs and GAs to identify IT2FS, including how to build MFs for both input and output, as well as how to generate a rule base from a data collection. The efficiency of T1FS and IT2FS for noisy data is also compared with GAs and MAs in the paper. For comparison, we consider four different problems: a rapid Ni–Cd battery charger, data from Box and Jenkins’s gas furnace, and the iris and wine classification datasets. In the presence of noise, the results imply that IT2FS is more efficient than T1FS, and MAs are more efficient than GAs.
A Personalized Recommendation Algorithm for Semantic Classification of New Book Recommendation Services for University Libraries

With the rapid development of information technology and the Internet, it is difficult for university readers to find books of real interest or value from a large number of books by relying only on traditional retrieval-based services. This paper applies data mining technology and personalized recommendation algorithm based on semantic classification for new book recommendation service in university libraries. The personalized recommendation algorithm based on semantic classification establishes a book feature model and a reader preference model based on title keywords. The different recommendation strategies in the system framework are detailed. For the borrowing data of different colleges and departments, the improved association rule algorithm is used to mine the book association rules, and the reader’s borrowing history is matched with the association rules to generate a book recommendation list; according to the reader’s borrowing preference characteristics, the reader preference model is used as the basis. Class subdivision and then combined with the book feature model and reader preference model, the collaborative filtering recommendation algorithm and the content-based recommendation algorithm are applied to generate a book recommendation list. The active service method not only improves the service level of the university library, makes the development of the university library more comprehensive and humanized but also explores the potential information needs of readers, improves the borrowing rate of books in the collection, and maximizes the utilization rate of book resources. In the experiment of this paper, the personalized recommendation algorithm division of semantic classification is adopted. According to the division of its algorithm, the corpus is divided into 9603 training documents and 3299 test documents, with certain accuracy.
Contribute to Open Science with Research-based Learning: Explorative Research Action Study applied to Peer-to-Peer Energy Trading

Open science become a mandatory in most funding program. However, literature review on technical domain where algorithms and programs are the core of the contribution show it remains not shared. This article aims to present an instructional module that aims to both sensitize students to research and allow them to participate to research community and open science through replication. This module was implemented two years in a row with computer engineering students. The implementation of the module has been done according to the research action approach, meaning the authors was also involved in the course as a teacher. The course was oriented towards exploring the different peer-to-peer energy trading algorithms. This article describes the pedagogical methods used in class and discuss challenges for adoption of this method more broadly.
A Type-Directed Algorithm to Generate Random Well-Formed Parsing Expression Grammars

PEGs are recognition formalism proposed by Brian Ford to describe top-down recursive parsers. Unfortunately, to determine whether an arbitrary PEG will terminate when parsing an input is undecidable. As termination is a desired property for parsing tools, Ford proposes a well-formedness test that guarantees every PEG that satisfies it, terminates on any input. Therefore, when experimenting with PEG-based tools using property-based testing, it is mandatory to randomly generate only well-formed PEGs, since a naive generation strategy may lead a PEG-based parser into an infinite loop. To overcome that, we propose an algorithm for generating well-formed PEGs, prove its correctness and implement it as a library in the Racket programming language.
Characterizing Data Scientists’ Mental Models of Local Feature Importance

Feature importance is an approach that helps to explain machine learning model predictions. It works through assigning importance scores to input features of a particular model. Different techniques exist to derive these scores, with widely varying underlying assumptions of what importance means. Little research has been done to verify whether these assumptions match the expectations of the target user, which is imperative to ensure that feature importance values are not misinterpreted. In this work, we explore data scientists’ mental models of (local) feature importance and compare these with the conceptual models of the techniques. We first identify several properties of local feature importance techniques that could potentially lead to misinterpretations. Subsequently, we explore the expectations data scientists have about local feature importance through an exploratory (qualitative and quantitative) survey of 34 data scientists in industry. We compare the identified expectations to the theory and assumptions behind the techniques and find that the two are not (always) in agreement.
Legal Protection of Private Electronic Life: Problems and Solutions

As the concept of individual’s private electronic life has broadened, it became a real challenge for humanity which is subject to infringement, violations, and penetration on one hand and difficult to technologically and legally control, being a human right, on the other. Thus, methods for controlling and protecting it have become more difficult. Consequently, the difficulty to control methods of committing the crime of penetrating private electronic life and to include that in strict legal provisions assist to achieve a successful protection for it. Undoubtedly, the absence of legislative provisions for some forms of crimes endangers the future of individual’s private electronic life, hence fails in to punish penetrators and parasites, due to the legislative provision which stipulates that no crime and no punishment can be inflected without a provision. The current study sheds light on private electronic life of individuals through presenting problems and challenges that it encounters, besides the preventive remedial possible solutions for this phenomenon which disturbs whoever deals with information technology.
Cryptocurrencies in Turkey; Facts, Figures and Trends

A decade has passed since the global financial system introduced a new phenomenon, cryptocurrencies and blockchain technology. That is evolving in Turkey where there is strong involvement with world finance capital. In this paper, we first investigate main dynamics of cryptocurrencies. Their characteristics, usage, functions and role in globe briefly. The remainder of the chapter devoted solely Turkish case. How the legal system fits into those new instruments. We also searched the drives of Turkish citizen’s demand for these new assets and their ability to cope with this investment opportunity. In the end, we do share our humble opinions for the future trends of cryptocurrency for Turkish economy.
Challenges and Opportunities of Blockchain Integration in the Egyptian Banks: A Qualitative Analysis

The purpose of the research is scanning the opportunities and threats of implementing blockchain technology in one of the major applications in the banking sector in Egypt: cross border payment and settlement. To reach this aim a qualitative analysis was conducted in which a semi-structured interviews approach was used. The main findings manifested the ability of this technology to prevent fraud, enhance transparency, and ensure financial stability and accountability. It enables a secure environment to store and transmit data between organizations and can reduce uncertainty. The process is faster, and this can promote customer experience. The results disclosed that integrating blockchain technology within the current systems in the Egyptian banks should undergo a transitional phase in which the technology can be outsourced to ensure security and efficiency. The originality of the research lies in the attempt to reconnoiter the readiness of the Egyptian banking sector to embrace innovative technologies.
How the Cryptocurrencies React to Covid-19 Pandemic? An Empirical Study Using DCC GARCH Model (2019–2021)

The paper aims to examine the dynamic conditional correlation between three major cryptocurrencies: Bitcoin, Litecoin, and Ethereum, during the Covid-19 pandemic. Our sample includes two panels: before/during Covid-19 covering the periods of 01/01/2019 to 12/31/2019 and from 01/01/2020 to 01/03/2021 using hourly data and the DCC GARCH model. The empirical results found that the Covid-19 pandemic positively impacts the cryptocurrencies prices starting from the second semester of 2020. Furthermore, these digital currencies became more vulnerable and showed a high level of volatility, especially Ethereum and Litecoin. Besides, we concluded that there was no significance on the short-run volatility effect during the Covid-19 period, which means the continuity of persistence in the long term.