Reviewerly/exHarmony huggingface dataset:
A Novel Model for Explainable Hostel Recommender System Using Hybrid Filtering

Recommender systems help humans in filtering and finding the right information from the enormous amount of data. Hostels are more famous than hotels for solo travelers, but no prior research related to recommender systems has been conducted in this domain. Hostels allow users to provide multi-criteria ratings and traditional recommender systems are not able to provide effective recommendations in case of multi-dimensionality i.e. contextual information and multi-criteria ratings. So, we have proposed a novel hybrid recommender system (SAFCHERS) that chooses the hostel’s features for computation dynamically and provides explainable and better recommendations than the traditional recommender systems.
Formalization of the prediction and ranking of SDLC models

The study of Software Engineering professional practices includes the use of the formal methodology in a software development. Identifying the appropriate methodology will not only reduce the failure of software but will also help to deliver the software in accordance with the predetermined budget and schedule. In literature, few works have been developed a tool for prediction of the most appropriate methodology for the specific software project. In this paper, a method for selecting an appropriate SDLC model based on a ranking manner from the highest to the lowest scoring. The selection and ranking of appropriate SDLC elaborate the related SDLC’s critical factors, these factors are given different weights according to the SDLC, then these weights are used by the proposed mathematical method. The proposed approach has been extensively experimented on a dataset by software practitioners who are working in the software industry. Experimental results show that, the proposed method represents an applicable tool in predicting and ranking suitable SDLC models on various types of projects, such as: life-critical systems, commercial uses systems, and entertainment applications.
Parallel and Sharing Attention Mechanism for Cross-Media Annotation and Retrieval

There are still many problems in cross-media annotation and retrieval. The semantic gap between the features of multiple media and the low-level visual features do not carry semantics, and the presence of noise has a great impact. This paper proposes a cross-media semantic learning model that combines Parallel and sharing attention mechanism, which sets semantic graph network topology and node information in the graph neural network. Testing on experimental data show that the network structure proposed in this paper can achieve better accuracy than traditional methods.
Concealed Communication in Online Social Networks

Online social networks are used frequently: staying in contact with friends and sharing experiences with them is very important. However, users are increasingly concerned that their data will end up in the hands of strangers or that personal data may even be misused. Secure OSNs can help. These often use encryption to keep the communication between the participants incomprehensible to outsiders. However, participants in such social networks cannot be sure that their data is secure. Various approaches show that even harmless-looking metadata, such as the number of contacts of a user, can be evaluated to draw conclusions about a user and the communication. These attack methods are analyzed and existing secure OSNs are examined, whether these attack methods can be utilized to violate the user’s privacy. To prevent these privacy attacks, protocols for a secure centralized OSN are developed. Metadata is obscured in the presented OSN and end-to-end encryption is used for secure communication. Additionally, communication channels are concealed like in mix networks such that adversaries cannot determine which user is accessing which data or which user is communicating with whom even with full access to the server.
Design of a Font Size Independent OCR System for Urdu Nastaleeq Based Printed Text

Urdu Nastaleeq text font is considered as a standard composing font for Urdu as well as Arabic script. It is widely used in Sub-continent and Middle East region in the form of printed media, books and old historical scrolls. However, the electronic availability of useful knowledge written in Urdu is not fully available due to the lack of techniques available for digitization of old handwritten or printed scripts. We develop a font size independent optical character recognition (OCR) system to recognize Nastaleeq based Urdu written script. In the first step, preprocessing is performed using binarization, median filtering and thinning of the scanned image. In second, step line and ligature segmentation are performed. The classification or recognition of Urdu font is achieved using Back Propagation Multilayer Perceptron Neural Network (BP-MLP-NN). Where multiple features such as raw, central and scale-invariant movement along with area, centroid and orientation are used to train and recognize the character. The designed prototype successfully detected individual Urdu characters with 98% accuracy on a self-generated database and 96% accuracy on scanned textbook data.
ANALISIS KEAKTIFAN SISWA DENGAN MENGGUNAKAN STRATEGI LEARNING STARTS WITH A QUESTIONS DI KELAS V SDN 68 BANDA ACEH

Student learning activities can be improved through the Learning Starts With A Questions (LSQ) strategy. The purpose of this study was to determine whether the learning activeness of students at SD Negeri 68 Banda Aceh could reach successful levels and activate the learning process after the Learning Starts With A Questions (LSQ) Strategy was applied. Data on student learning outcomes are obtained by observing student activities in the form of interview sheets which are determined through expert validators. This research has carried out the steps of learning in accordance with Learning Starts With A Questions (LSQ), both at the beginning of the learner and the end of the learner. With the results of observations and interviews, it can be concluded that the Learning Starts With A Questions (LSQ) Strategy can increase the activities of fifth grade students at SD Negeri 68 Banda Aceh
Improving Students’ Speaking Skills Through Videos at SMP Swasta Sinar Husni

This research was aimed to find out whether there is a significant effect of using video content making on students' speaking skill. The research method of this research was CAR (Classroom Action Research) in qualitative approach. The sample of this research was eight grade students of SMP SWASTA SINAR HUSNI which consisted of 33 students divided into one class. The data was obtained using pre-test, cycle 1, cycle 2 and post-test. Pre-test was before treatment and post-test was given after treatment. Cycle 1 was in an average of 64,54 and the percentage of successfulness was 15,7%. The Criterion of Minimum Completeness (KKM) was 51%. Cycle 2 was in average 68,48 and the percentage of successfulness was 22,8%. The Criterion of Minimum Completeness (KKM) was 69%. In post test the researcher took 33 students as the sample but the final result there were 25 students with the Criterion of Minimum Completeness (KKM) was 75%.  Then, the instrument used to collect data was written test in the form of a multiple choice. The pre test score is 55,75 and the post test is 78,48.The class percentage that has been implemented approach the Criteria of Minimum Competence (KKM) using the instrument such as video in this research. It can be concluded that the watching through video significantly influences students' speaking skill.
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views

Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objective, full-reference quality assessment framework which studies quality measurement for ODIs generated by GAN-based and CNN-based SISR methods. The quality assessment framework offers to utilize tangential views to cope with the spherical nature of a given ODIs. The generated tangential views are distortion-free and can be efficiently scaled to high-resolution spherical data for SISR quality measurement. We extensively evaluate two state-of-the-art SISR methods using widely used full-reference SISR quality metrics adapted to our designed framework. In addition, our study reveals that most objective metric show high performance over CNN based SISR, while subjective tests favors GAN-based architectures.
UX Aspects of AI Principles: The Recommender System of VoD Platforms

This paper aims to investigate the user experience with recommender systems of Video on Demand (VoD) platforms based in Machine Learning (ML), focusing on the Artificial Intelligence (AI) principles. We start from the hypothesis that the inclusion of AI algorithms has the potential to improve the user experience in digital systems, but they are still developed with a greater focus on technology, however, they should also consider more aspects regarding human factors. Nine principles on AI related to UX were selected from a compilation of seven lists of government and industry entities to understand the bases that every AI system should respect to ensure a good user experience. In sequence, we discuss their effects on the user experience of VoD platforms. To finish, the experience with these platforms were explored in a directed storytelling method involving thirty-one participants. Some behaviors and patterns found were analyzed and discussed to suggest guidelines to be applied to ML algorithms of VoD Platforms.
Speakers Use More Informative Referring Expressions to Describe Surprising Events

Author(s): Stegemann-Philipps, Christian; Butz, Martin V.; Winkler, Susanne; Achimova, Asya | Abstract: Production of referring expressions (the dog, it, Snoopy) is a complex process regulated by a combination of linguistic and cognitive constraints. In this paper, we explore the impact of world knowledge on the types of references speakers produce, focusing on predictability of event progressions. We argue that speakers are more likely to use a full noun phrase rather than drop the subject or use a pronoun when they describe an event progression they find surprising. In order to avoid the influence of distributional properties of event descriptions, we created an artificial world and trained subjects to recognize typical collision-event progressions within. Speakers then described novel scenes, which either conformed to their expectations or violated them, in a free production experiment. The results reveal that unpredictable event progressions lead to a more frequent production of full noun phrases, in contrast to reduced linguistic expressions (pronouns and null subjects). We conclude that speakers choose more informative descriptions to talk about surprising events.
Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

Purpose Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD). Design/methodology/approach In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks. Findings Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms. Originality/value This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.
MATHEMATICAL STORIES WRITTEN BY PRE-SERVICE PRESCHOOL AND PRIMARY SCHOOL TEACHERS: EVALUATION FOR LITERARY AND MATHEMATICAL QUALITY

P. Demény 1, I. Zsoldos-Marchis 2 1University Babes-Bolyai Cluj-Napoca (ROMANIA) 2Babes-Bolyai University (ROMANIA)
A Robust Way of Dimensionality Reduction

We present a novel group of information driven linear transformations, planned for discovering low-dimensional embeddings of multivariate information, in a way that ideally saves the structure of the information. The all-around examined PCA and Fisher's LDA are demonstrated to be unique individuals in this group of changes, and we exhibit how to sum up these two methods, for example, to upgrade their execution. Moreover, our strategy is the one and only one, as far as we could possibly know, that reflects in the subsequent installing both the information organizes and pairwise connections between the information components. Much more along these lines, when data on the clustering (labeling) decomposition of the information is known, this data can likewise be coordinated in the linear transformation, coming about in embeddings that plainly show the partition between the groups, just as their interior structure. The entirety of this makes our method truly adaptable and amazing, and lets us adapt to sorts of information that different methods neglect to depict appropriately.
IT-TB_PML_analytical-tectogrammatical

The Index Thomisticus Treebank (IT-TB) is the syntactically annotated portion of the Index Thomisticus corpus (Busa, 1974-1980), which collects the opera omnia of Thomas Aquinas. The IT-TB includes the “analytical” (i.e. surface syntactic) annotation of the entire “Summa contra Gentiles” (4 books), as well as of the concordances of lemma “forma” from “Scriptum super libros sententiarum magistri Petri Lombardi” (entire) and from “Summa Theologiae” (part). In total, the IT-TB consists of more than 450,000 nodes in around 25,000 sentences. The annotation guidelines, inspired by those of the analytical layer of the Prague Dependency Treebank, are available here: http://static.perseus.tufts.edu/docs/guidelines.pdf. The IT-TB features also more than 2.000 sentences from “Summa contra Gentiles” annotated at the tectogrammatical (i.e. underlying syntactic) layer. The annotation guidelines are those of the Prague Dependency Treebank: https://ufal.mff.cuni.cz/pdt2.0/doc/manuals/en/t-layer/pdf/t-man-en.pdf.
Comparative Analysis of Speech Recognition Open API Error Rate

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of numbers and secondly, the recognition rate of Ga Na Da Hangul are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.
Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems

Recommender systems are often designed based on a collaborative filtering approach, where user preferences are predicted by modelling interactions between users and items. Many common approaches to solve the collaborative filtering task are based on learning representations of users and items, including simple matrix factorization, Gaussian process latent variable models, and neural-network based embeddings. While matrix factorization approaches fail to model nonlinear relations, neural networks can potentially capture such complex relations with unprecedented predictive power and are highly scalable. However, neither of them is able to model predictive uncertainties. In contrast, Gaussian Process based models can generate a predictive distribution, but cannot scale to large amounts of data. In this manuscript, we propose a novel approach combining the representation learning paradigm of collaborative filtering with multi-output Gaussian processes in a joint framework to generate uncertainty-aware recommendations. We introduce an efficient strategy for model training and inference, resulting in a model that scales to very large and sparse datasets and achieves competitive performance in terms of classical metrics quantifying the reconstruction error. In addition to accurately predicting user preferences, our model also provides meaningful uncertainty estimates about that prediction.
Hyper-parameter Tuning using Genetic Algorithms for Software Effort Estimation

Research on software effort estimation has investigated the impact of hyper-parameter tuning approaches for machine learning. Many automated tuning approaches have been proposed in the literature to improve the performance of their base models. In this study we compare the Dodge, Grid, Harmony, Tabu, and Random Search algorithms against Standard Genetic, 1+1 Genetic, and Compact Genetic Algorithms in terms of their impact to model accuracy and stability. This evaluation was performed using the ISBSG R18 dataset and on the Support Vector Regression, Classification and Regression Trees, and Ridge Regression techniques. Results of the Scott-Knott analysis show that the genetic algorithms perform similar or better to the other tuners, achieving an improvement of standardized accuracy of up to 0,21 and final values of up to 0,53. Genetic algorithm-tuned Support Vector Regression achieves the highest accuracy while improving estimation stability relative to other tuners.
CAREER INFORMATION SEEKING BEHAVIOR AMONG GRADUATES IN PUBLIC UNIVERSITY

Career information-seeking behavior is an individual’s ability to prepare themselves for education and career as a process of their career development. Objectives of this study aimed to identify students’ perceptions of the career guidance services offered at the university and to determine student behavior in the process of seeking career information. This study uses survey design to identify of career information seeking behavior involving 6,550 graduates from a university as a sample in this study. Instruments used in this study are part of the Graduates Tracer Study to obtain information on graduate employment, further education, perceptions on the curriculum and facilities, counselling services. Data were analysed using descriptive statistics such as mean, standard deviation, percentage, frequency, and crosstab. Findings showed that students' perceptions of career guidance services were at a satisfactory level (M = 4.05, SP = .828), especially the provision of information on employment and career opportunities got the highest satisfaction (M = 4.19, SP = .86). While the behavior of seeking career information among graduates who have not worked showed that 57.2% of respondents had attended an interview and 42.8% of the respondents had never attended an interview. This study has implications for the services provided by counselling and career centres in public universities. And, the impact on university indicators to improve the graduate’s employability. Further studies should focus on longitudinally design studies by comparing study samples from several different years
Weakly Supervised Salient Object Detection Based on Image Semantics

To reduce the dependence of salient object detection on pixel-level labels, we propose a weakly supervised salient object detection method based on image semantics.Using the combined model of the fish network and the attention mechanism, on the basis of the image semantic heat map, the weak labels were trained and updated by cosine similarity.At the same time, we used a training induction strategy.In the initial stage of network training, a simple dataset was used to induce the entire network to make it have certain capabilities.Then, after continuously increasing the complexity of the dataset, the network's ability to extract features became stronger and stronger.Experiments were conducted on four saliency detection data sets, and compared with traditional supervision methods.The experimental results show that the F-MAX value of this method is increased by 0.03-0.08 on each data set on average, and the MAE is reduced by 0.02-0.05.Under the weak supervision label, this method can more accurately extract the salient features.
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

Author(s): Alanqary, Alwa; Lin, Gloria Z; Le, Joie; Zhi-Xuan, Tan; Mansinghka, Vikash; Tenenbaum, Josh | Abstract: When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teaching, offering assistance, and deciding between blame or forgiveness. However, Bayesian models of theory of mind have generally not accounted for these mistakes, instead modeling agents as mostly optimal in achieving their goals. As a result, they are unable to explain phenomena like locking oneself out of one's house, or losing a game of chess. Here, we extend the Bayesian Theory of Mind framework to model boundedly rational agents who may have mistaken goals, plans, and actions. We formalize this by modeling agents as probabilistic programs, where goals may be confused with semantically similar states, plans may be misguided due to resource-bounded planning, and actions may be unintended due to execution errors. We present experiments eliciting human goal inferences in two domains: (i) a gridworld puzzle with gems locked behind doors, and (ii) a block-stacking domain. Our model better explains human inferences than alternatives, while generalizing across domains. These findings indicate the importance of modeling others as bounded agents, in order to account for the full richness of human intuitive psychology.