Reviewerly/exHarmony huggingface dataset:
An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student's t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student's t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing one to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.
Item-Based Collaborative Memory Networks for Recommendation

Item-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user's preference from his or her historically interacted items, recommending new items that are the most similar to the user's preference. Recently, several works propose advanced neural network architectures to learn item similarities from rating data, by modeling items as latent vectors and learning model parameters by optimizing a recommendation-aware objective function. While much literature attempts to use classical neural networks such as multi-layer perception (MLP), to learn item similarities, there has been relatively less work employing memory networks for ICF, which can more accurately record the detailed information about entities than classical neural networks. Therefore, in this paper, we propose a powerful Item-based Collaborative Memory Network (ICMN) for ICF, which bases on the architecture of Memory Networks. Besides, a neural attention mechanism is adopted to focus on the most important historically interacted items. The core of our ICMN is the cooperation of external and internal memory and the contribution of the neural attention mechanism. Compare to the state-of-the-art ICF methods, our ICMN possesses the merit of powerful representation capability. Extensive experiments on two datasets demonstrate the effectiveness of ICMN. To the best of our knowledge, this is the first attempt that applies memory networks for ICF.
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task

We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating the ability of Transformers to benefit from increased training data size under conditions of fixed computational cost. We show that compositional generalization remains a challenge at all training sizes, and we show that increasing the scope of natural language leads to consistently higher error rates, which are only partially offset by increased training data. We further show that while additional training data from a related domain improves the accuracy in data-starved situations, this improvement is limited and diminishes as the distance from the related domain to the target domain increases.
Face Recognition and Summarization for Surveillance Video Sequences

Face recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies’ silhouette then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the temporal difference map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training.
Arte, creatividad y resiliencia: recursos frente a la pandemia

Art in its various forms, theater, narrative, music, circus, puppets, painting, ceramics, dance, movement&nbsp;and others, play a fundamental role in the processes of personal and social transformation. The Arts allows&nbsp;for expression, the power of humanization of the human beings has been proven since ancient time, giving&nbsp;emphasis within the area of human development.&nbsp;In this field, we see an evolution from the identification of art with an artistic product and with artists&nbsp;belonging to certain elites. Currently the concepts have evolved giving Art and Creativity a broader vision&nbsp;that generates educational, social and political processes.&nbsp;Art and Creativity play a crucial role in Resilience, concept currently widely spread because is a powerful&nbsp;valuable resource due to its impact on conflict resolution, in reinforcing internal strength and enhancing&nbsp;the necessary mechanisms to face crisis, uncertainty and trauma.&nbsp;The author has lived experiences with Theater of the Oppressed in India, Biodanza in Peru and Europe,&nbsp;Meditations in Movement, Narrative in Africa and Social Circus in Palestine. In 2019, she was invited&nbsp;by the World Alliance for Art Education to the world meeting held in Frankfurt, Germany to present her&nbsp;experiences in this field.&nbsp
Effects of thinking aloud on cognitive effort in translation

This study investigates the effects of thinking aloud on cognitive effort in translation as a function of source-text difficulty level. It does so by considering time on task, duration of different translation phases (i.e., orientation, draft, revision), cognitive effort of processing the source and target texts, and translation quality. Twenty participants took part in an English–Chinese translation experiment, which comprised two matched sessions – translating while thinking aloud and translating silently. Their translation processes were recorded by means of an eye tracker and a key logger. An adapted NASA Task Load Index was employed to elicit their subjective assessments of translation difficulty levels. The quality of their translations was evaluated. The results of the study reveal a number of important effects of thinking aloud on cognitive effort during translation: on translation duration, cognitive effort, the perceived level of difficulty of a translation as measured by NASA-TLX, and on translating easier texts.
Propagation-based incremental triangulation for multiple views 3D reconstruction

To balance the accuracy and efficiency in multiple-view triangulation with sequential images, a high-efficiency propagation-based incremental triangulation (INT) method, carving three-dimensional (3D) scene points by updating the incoming feature track one by one without iterations, is proposed. Based on the INT method, a more accurate iteration-limited INT method is also established with few iterations to bound the propagated errors, ensuring the accuracy of subsequent 3D reconstruction. Finally, experimental results demonstrate that the proposed methods can balance the efficiency and accuracy in different multiple-view INT situations.
A Single Image 3D Reconstruction Method Based on a Novel Monocular Vision System

Three-dimensional (3D) reconstruction and measurement are popular techniques in precision manufacturing processes. In this manuscript, a single image 3D reconstruction method is proposed based on a novel monocular vision system, which includes a three-level charge coupled device (3-CCD) camera and a ring structured multi-color light emitting diode (LED) illumination. Firstly, a procedure for the calibration of the illumination’s parameters, including LEDs’ mounted angles, distribution density and incident angles, is proposed. Secondly, the incident light information, the color distribution information and gray level information are extracted from the acquired image, and the 3D reconstruction model is built based on the camera imaging model. Thirdly, the surface height information of the detected object within the field of view is computed based on the built model. The proposed method aims at solving the uncertainty and the slow convergence issues arising in 3D surface topography reconstruction using current shape-from-shading (SFS) methods. Three-dimensional reconstruction experimental tests are carried out on convex, concave, angular surfaces and on a mobile subscriber identification module (SIM) card slot, showing relative errors less than 3.6%, respectively. Advantages of the proposed method include a reduced time for 3D surface reconstruction compared to other methods, demonstrating good suitability of the proposed method in reconstructing surface 3D morphology.
Implementation of the YII Framework-Based Job Training Assessment System

The current significant progress has been experienced by the publication of Information media, one of which is the scoring system. In measuring the ability of students or students in learning outcomes during the learning process assessment system is very important in an educational institution both high school and college. Universitas Raharja is one of the educational institutions that have implemented the assessment system (PEN +) online, which provides services to the lecturers of the University of Raharja in the input of the value that can be accessed anywhere and Anytime. However, nowadays for the process of the input of the value of work lecture (CTF) that has been run by students is still done manually or not computerized because lecturers still have to come to the campus to fill out a list of student value lists Submitted to the RPU. This is very inefficient, because in the assessment system PEN + lecturers have not been able to input the value of work lecture (KKP) in real time. Therefore, there is a need for development in the Coursework Assessment System (CTF) where the value of the input form is Grade. In this study there were 10 (ten) literature studies on the assessment. Thus there are 2 (two) advantages that the lecturer does not need to come to the campus to fill the form that is then handed over to the RPU and notifications for a list of values that students receive will be faster.
Order-sorted Homeomorphic Embedding Modulo Combinations of Associativity and/or Commutativity Axioms*

The Homeomorphic Embedding relation has been amply used for defining termination criteria of symbolic methods for program analysis, transformation, and verification. However, homeomorphic embedding has never been investigated in the context of order-sorted rewrite theories that support symbolic execution methods modulo equational axioms. This paper generalizes the symbolic homeomorphic embedding relation to order–sorted rewrite theories that may contain various combinations of associativity and/or commutativity axioms for different binary operators. We systematically measure the performance of different, increasingly efficient formulations of the homeomorphic embedding relation modulo axioms that we implement in Maude. Our experimental results show that the most efficient version indeed pays off in practice.
Three-Dimensional Reconstruction of Indoor and Outdoor Environments Using a Stereo Catadioptric System

In this work, we present a panoramic 3D stereo reconstruction system composed of two catadioptric cameras. Each one consists of a CCD camera and a parabolic convex mirror that allows the acquisition of catadioptric images. We describe the calibration approach and propose the improvement of existing deep feature matching methods with epipolar constraints. We show that the improved matching algorithm covers more of the scene than classic feature detectors, yielding broader and denser reconstructions for outdoor environments. Our system can also generate accurate measurements in the wild without large amounts of data used in deep learning-based systems. We demonstrate the system’s feasibility and effectiveness as a practical stereo sensor with real experiments in indoor and outdoor environments.
An Improved Time-Based One Time Password Authentication Framework for Electronic Payments

One-time Password is important in present day scenario in the purposes of improving the security of electronic payments.Security sensitive environment or perhaps organization avoid the resources from unauthorized access by allowing different access control mechanism as user authentication.There are several safety issues in one Password based authentication.However, studies show that OTP sent over SMS are causing different causes and issues, which lead to precious time, delay in transaction.User authentication can be raised with more levels within the procedure of multi-factor authentication scheme.Time-based One-time Password and biometrics are one of the widely accepted mechanisms that incorporate multi-factor authentication.In this paper, we approach the Time-based OTP authentication algorithm with biometric fingerprints to secure an electronic payment.This algorithm uses a secret key exchanged between the client and the server and uses a certain password through the algorithm.The shuffle of the TOTP approach better wear by screening the key as being a QR code, as revealed in the majority movable applications are able to read.It offers confidentiality at the application level within the system to protect user credential within equal entities (the user and the server) for preventing brute force and dictionary attacks.Thus, the proposed system design is possible for users because of the lack of the concern of holding its own hardware token or additional charges from the short message service.Our suggested approach has been found to improve safety performance substantially compared to existing methods with regard to authentication and authorization.This research hopes to boost research effort on further advancement of cryptosystems surrounding multi-factor authentication.
Visualising the Knowledge Domain of Artificial Intelligence in Marketing: A Bibliometric Analysis

As the number of research outputs in the field of AI in Marketing increased greatly in the past 20 years, a systematic review of the literature and its developmental process is essential to provide a consolidated view of this area. This study conducted a bibliometric analysis for the knowledge domain of AI in Marketing by using 617 research outputs from the Web of Science database from 1992 to 2020. Knowledge maps of AI in marketing research were visualised by employing CiteSpace software.
Exploring the Students Feelings and Emotion Towards Online Teaching: Sentimental Analysis Approach

Data mining is a method to refine raw data to useful information. In education, data mining is a significant research part used to progress the value of education by observing students' performance and understanding their learning patterns. Real-time student feedback would empower faculty and students to comprehend the teaching and learning problems in the most user-friendly way for the students. This paper uses a Lexicon based sentimental analysis technique to analyze students' feelings and emotions through their feedback by correlating learning analytics to grounded theory. The sentiment analysis technique is a computational process to identify and classify subjective information such as positive, negative, and neutral from the source material. It can extract feelings and emotions from a piece of a sentence. Hence this paper aims to recognize the students' positive or negative feelings and distinguished emotions, towards online teaching. The methodology undertakes four processes. The first process is data extraction from the feedback collected from the students through open-ended questions (Text) and is used as source material and imported to R studio. The second process is data cleaning /data preprocessing, removal of annoying data, and separation of data. The third process is sentimental analysis, which divides the data into positive, negative, and neutral categories/groups. This lexicon-based method of sentimental analysis is used to classify the sentiments. The results were estimated using sentiment scores and emotional variance. The sentiment scores result found that students have positive sentiments/emotions towards online teaching and emotions vary concerning the online class timing.
Two-Round Password-Based Authenticated Key Exchange from Lattices

Password-based authenticated key exchange (PAKE) allows participants sharing low-entropy passwords to agree on cryptographically strong session keys over insecure networks. In this paper, we present two PAKE protocols from lattices in the two-party and three-party settings, respectively, which can resist quantum attacks and achieve mutual authentication. The protocols in this paper achieve two rounds of communication by carefully utilizing the splittable properties of the underlying primitive, a CCA (Chosen-Ciphertext Attack)-secure public key encryption (PKE) scheme with associated nonadaptive approximate smooth projection hash (NA-ASPH) system. Compared with other related protocols, the proposed two-round PAKE protocols have relatively less communication and computation overhead. In particular, the two-round 3PAKE is more practical in large-scale communication systems.
The correlations among critical thinking skills, critical reading skills and reading comprehension

Critical thinking skills, critical reading skills and reading comprehension were the most crucial life skills that should be mastered by the students, especially for university students. The objectives of this study were: (1) to find out whether or not there was a significant correlation among students critical thinking skills and reading comprehension, (2) to find out whether or not there was a significant correlation among critical reading skills and reading comprehension, and (3) to find out whether or not there was a significant correlation among the predictor variables (critical thinking skills and critical reading skills) and the criterion variable (reading comprehension) of English Education Study Program students of UIN Raden Fatah Palembang. In this study, 90 undergraduate English Education Study Program of UIN Raden Fatah Palembang in the academic year 2018/2019 in the fourth semester were chosen as a sample by means of purposive sampling. This study conducted correlational research design. The data were collected by using tests. Pearson Product Moment correlation and regression were used to analyzed the data. Based on the data collected and analyses applied in this study, the findings revealed that (1) there was a significant correlation among critical thinking skills and reading comprehension with r-value 0.810, (2) there was a significant correlation among critical reading skills and reading comprehension with r-value 0.844, (3) there was a significant correlation among predictor variables (critical thinking skills and critical reading skills) gave 71.3% contribution to criterion variable (reading comprehension).
Pandemic Pandemonium and Remote Working: An Investigation of Determinants and Their Contextual Behavior in Virtualization of Work-From-Home (WFH) Process

Disruption at the physical workplace, developed by threats like the coronavirus, triggers revisiting old assumptions and exploring opportunities for new ways of remote working. With the global epidemic spreading, businesses are gearing up with the managers and their respective teams to work from home (WFH). This research has offered a setting for advancing understanding of virtualization of WFH process by exploring the factors that enable or constrain the information and communication technology (ICT) enabled virtualization of processes in employee's WFH process through empirical support for the process virtualization theory (PVT). Setting pandemic outbreak as a context, outcome of this research is reliant on two independent studies conducted to examine the influencing factors. First study conducted just before the onset of pandemic outbreak, found that parts of the constructs proposed in the PVT had expected outcomes regarding the characteristics of process virtualization. Contrary to this, second study conducted after pandemic outbreak found that major constructs proposed in the PVT behaved otherwise regarding the characteristics of process virtualization. To fill the gaps in empirical knowledge, the enablers and inhibitors so found together may be motivations to anticipate business organizations and their workforces to experiment with this form of work process, predominantly improved flexibility for organizations and employees, improved productivity, quicker responsiveness to the needs and unexpected man-made and natural disasters, lower absenteeism, improved employee retention, greater cost control, along with more general social benefits.
Preserving Genomic Privacy via Selective Sharing

Although genomic data has significant impact and widespread usage in medical research, it puts individuals' privacy in danger, even if they anonymously or partially share their genomic data. To address this problem, we present a framework that is inspired from differential privacy for sharing individuals' genomic data while preserving their privacy. We assume an individual with some sensitive portion on her genome (e.g., mutations or single nucleotide polymorphisms - SNPs that reveal sensitive information about the individual) that she does not want to share. The goals of the individual are to (i) preserve the privacy of her sensitive data (considering the correlations between the sensitive and non-sensitive part), (ii) preserve the privacy of interdependent data (data that belongs to other individuals that is correlated with her data), and (iii) share as much non-sensitive data as possible to maximize utility of data sharing. As opposed to traditional differential privacy-based data sharing schemes, the proposed scheme does not intentionally add noise to data; it is based on selective sharing of data points. We observe that traditional differential privacy concept does not capture sharing data in such a setting, and hence we first introduce a privacy notation, ϵ-indirect privacy, that addresses data sharing in such settings. We show that the proposed framework does not provide sensitive information to the attacker while it provides a high data sharing utility. We also compare the proposed technique with the previous ones and show our advantage both in terms of privacy and data sharing utility.