Reviewerly/exHarmony huggingface dataset:
The Relationship between the Consistency of Users' Ratings and Recommendation Calibration

Fairness in recommender systems has recently received attention from researchers. Unfair recommendations have negative impact on the effectiveness of recommender systems as it may degrade users' satisfaction, loyalty, and at worst, it can lead to or perpetuate undesirable social dynamics. One of the factors that may impact fairness is calibration, the degree to which users' preferences on various item categories are reflected in the recommendations they receive. The ability of a recommendation algorithm for generating effective recommendations may depend on the meaningfulness of the input data and the amount of information available in users' profile. In this paper, we aim to explore the relationship between the consistency of users' ratings behavior and the degree of calibrated recommendations they receive. We conduct our analysis on different groups of users based on the consistency of their ratings. Our experimental results on a movie dataset and several recommendation algorithms show that there is a positive correlation between the consistency of users' ratings behavior and the degree of calibration in their recommendations, meaning that user groups with higher inconsistency in their ratings receive less calibrated recommendations.
Views and Controls

In the last chapter, we looked at what makes SwiftUI tick, the features that make the magic happen. However, that was a bit dry, mostly consisting of theory. The big thing in capitals that makes up and is part of SwiftUI is UI – that which is visual. So, in this chapter, we shall look at the various UI elements and their native UIKit counterparts. This could also function as a ready reckoner to help choose the appropriate element as required.
Lightweight Functional Logic Meta-Programming

Meta-interpreters in Prolog are a powerful and elegant way to implement language extensions and non-standard semantics. But how can we bring the benefits of Prolog-style meta-interpreters to systems that combine functional and logic programming? In Prolog, a program can access its own structure via reflection, and meta-interpreters are simple to implement because the “pure” core language is small. Can we achieve similar elegance and power for larger systems that combine different paradigms? In this paper, we present a particular kind of functional logic meta-programming, based on embedding a small first-order logic system in an expressive host language. Embedded logic engines are not new, as exemplified by various systems including miniKanren in Scheme and LogicT in Haskell. However, previous embedded systems generally lack meta-programming capabilities in the sense of meta-interpretation. Indeed, shallow embeddings usually do not support reflection. Instead of relying on reflection for meta-programming, we show how to adapt popular multi-stage programming techniques to a logic programming setting and use the embedded logic to generate reified first-order structures, which are again simple to interpret. Our system has an appealing power-to-weight ratio, based on the simple and general notion of dynamically scoped mutable variables. We also show how, in many cases, non-standard semantics can be realized without explicit reification and interpretation, but instead by customizing program execution through the host language. As a key example, we extend our system with a tabling/memoization facility. The need to interact with mutable variables renders this a highly nontrivial challenge, and the crucial insight is to extract symbolic representations of their side effects from memoized rules. We demonstrate that multiple independent semantic modifications can be combined successfully in our system, for example tabling and tracing.
Anonymity in Complex Transactions for e-Business

Anonymity of the customer and each merchant is a key property that must be considered in a fair exchange e-commerce protocol. The customer may not want to reveal sensitive data of his identity (as credit card number, information about customer’s bank, customer’s account number) so that this information can not be used by merchant in commercial purpose to build spending habits of the customer. Also, a merchant may want to remain anonymous. For example, a merchant who has business in many areas may want that his customers can not link transactions where the merchant is involved in all these areas. In our previous work [4], a fair exchange e-commerce protocol for complex transactions in that a customer wants to buy several different physical products from different merchants is proposed. This solution provides customer anonymity, non-repudiation, integrity and confidentiality of data exchanged between the parties. In this paper, we extend the solution from [4] obtaining both anonymity of customer and each merchant, while preserving all properties obtained in [4].
Vulnerability of Face Recognition to Deep Morphing

It is increasingly easy to automatically swap faces in images and video or morph two faces into one using generative adversarial networks (GANs). The high quality of the resulted deep-morph raises the question of how vulnerable the current face recognition systems are to such fake images and videos. It also calls for automated ways to detect these GAN-generated faces. In this paper, we present the publicly available dataset of the Deepfake videos with faces morphed with a GAN-based algorithm. To generate these videos, we used open source software based on GANs, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. We show that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to the deep morph videos, with 85.62 and 95.00 false acceptance rates, respectively, which means methods for detecting these videos are necessary. We consider several baseline approaches for detecting deep morphs and find that the method based on visual quality metrics (often used in presentation attack detection domain) leads to the best performance with 8.97 equal error rate. Our experiments demonstrate that GAN-generated deep morph videos are challenging for both face recognition systems and existing detection methods, and the further development of deep morphing technologies will make it even more so.
ELIXIR's ELIXIR’s Long-term sustainability plan (2019)

The aim of this document is to set out previous, current and future actions in order to support the sustainability and long-term funding of ELIXIR’s activities, particularly ELIXIR Nodes and the services they run. The plan is aimed at both internal and external stakeholders: internal stakeholders being the ELIXIR partners who run the Nodes and services that require sustaining, and the external stakeholders being the funding agencies (national, European and International) and policy-makers that have the means to do so.
Interactive Teaching Methods at the University as a Mean of Legal Competence Formation

The article is devoted to the analysis of the actual problem associated with the disclosure of the characteristics of interactive teaching methods at the University and their pedagogical capabilities in the formation of legal competence of students. The purpose of the study was to identify pedagogical opportunities of interactive teaching methods in the formation of legal competence of students – future lawyers on the basis of theoretical analysis of scientific literature. Research methods: theoretical (analysis of scientific and pedagogical literature on the problem, comparison, generalization, transfer, etc.), empirical (questioning, observation). The paper shows that legal competence is an integrative concept that includes a set of competencies in the field of law, the ability to perform socially significant functions in society, as well as the readiness of the individual to conscious active work to prevent and eliminate illegal behavior of citizens. The structural components are identified and the classification of the functions of legal competence is given, which includes two groups: invariant (reflecting the essence of the phenomenon described by this concept) and variable (reflecting the specific changes occurring with the phenomenon under study at this stage of its development). The main features of interactive teaching methods in higher education influencing the formation of legal competence of students are revealed. It is shown that interactive teaching methods have, along with the content of training, an independent potential for the development of legal competence of future lawyers. This potential is associated with the activation of students’ thinking, increasing the density of emotionally rich communication of participants, ensuring that individual characteristics are taken into account in the learning process.
Where do labels come from?

The use of machine learning in remote sensing often depends on the use of labels to guide an algorithm as to what matters in the data. This supervised learning can effectively transfer human understanding to algorithms such as those used in Deep Learning to analyze large complex data sets. However, this approach can be brittle and lead to poor performance and confusion in decision making. Here we provide an analysis of labels to reveal their limitations in learning algorithms. We frame the supervised learning problem as a simplification of an interaction between unsupervised and reinforcement learning. From this perspective, we develop a formalism of specific value to remote sensing that uses information-theoretic techniques to determine the forward model that preserves the information content between two supervised learning problems using different data but sharing the same labels.
Method of Neural Network Training with Integer Weights

This paper presents the method of training the feedforward neuronal network with integer weights and integer input and output signals. Neural networks of this type are better suited for embedded systems and hardware implementation than neural networks with floating weights. Also, the new method allows training neural networks on embedded devices with limited resources. This algorithm has been developed taking into account that the resulting integer weights require less memory for storage, and arithmetic operations over them are easier for hardware implementation. A simple activation function is proposed that works with integer data and limits weights and bias in a certain range during neural network training. The simulation results show that the new training method gives a similar result while training with floating-point arithmetic for XOR operation. To validate the robustness of the new algorithm to train neural networks with integer arithmetic, a prototype was created based on the CYAT817 microcontroller with the Cortex-M0+ core.
Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection

Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric. Given that AUC is not differentiable, common practice is to train the CNN using a loss functions based on other performance metrics such as cross entropy and monitoring AUC to select the best model. In this work, we propose to fine-tune a trained CNN for prostate cancer detection using a Genetic Algorithm to achieve a higher AUC. Our dataset contained 6-channel Diffusion-Weighted MRI slices of prostate. On a cohort of 2,955 training, 1,417 validation, and 1,334 test slices, we reached test AUC of 0.773; a 9.3% improvement compared to the base CNN model.
Efficient Pruning Methods for Obtaining Compact Associative Classifiers with Enhanced Classification Accuracy Rate

The integrated approach involving supervised classification and association rule mining for development of classification model is becoming a promising strategy for building compact and accurate classifiers. As discussed in literature, in large databases the association rule mining techniques may produce large rule sets, this paper attempts to propose and implement several pruning methods to overcome the problems underlying the Associative Classification approach. The aim is to efficiently utilize the rules produced for classification in compact form and represent a rule set to be the part of classifier model with maximum data coverage and enhanced accuracy. This paper also presents experimental evaluation of the pruning methods on various datasets taken from UCI machine learning repository with the consideration of earlier approaches.
Virtual Reality in Early Phases of Architectural Studies Experiments with first year students in immersive rear projection based virtual environments

Virtual Reality is quite commonly used in architectural education, however mostly in higher semesters and within elective courses.This paper reports about various teaching scenarios using Virtual Reality in projection based immersive environments at very early phases of architectural studies, within the first two semesters.Various student questionnaires were carried out and clearly show benefits for students to gain spatial awareness in their design projects, and for discussing their design intentions.Experiences with cyber sickness and issues like accessibility to the immersive environments are discussed as well within the given context.
Learning from demonstration with model-based Gaussian process

In learning from demonstrations, it is often desirable to adapt the behavior of the robot as a function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task. In this paper, we propose a novel multi-output Gaussian process (MOGP) based on Gaussian mixture regression (GMR). The proposed approach encapsulates the variability retrieved from the demonstrations in the covariance of the MOGP. Leveraging the generative nature of GP models, our approach can efficiently modulate trajectories towards new start-, via- or end-points defined by the task. Our framework allows the robot to precisely track via-points while being compliant in regions of high variability. We illustrate the proposed approach in simulated examples and validate it in a real-robot experiment.
State of the Art of Statistical Learning Theory

As we have previously seen, the general purpose of machine learning is to automate the process of acquiring knowledge based on observed data. Despite this general objective, the way in which this process is carried out may vary considerably depending on the nature of considered data and the final goal of learning. Thus, it can be characterized in many completely different ways. What remains unchanged, however, is that the nature of the learning paradigms. Despite their apparent variety, they can be roughly called “statistical”, which means that they are all related to revealing the statistical nature of the underlying phenomenon. To this end, the main goal of statistical learning theory is to analyze and formalize the process of knowledge acquisition and to provide a theoretical basis and an appropriate context for its analysis within a statistical framework.
Poster

We present GRANDPA, a finality gadget, that is a protocol that can be used to provide provable finality for a blockchain. It works in addition to a block production mechanism and a chain selection rule, that on their own would only provide eventual consensus. The design of GRANDPA aims at separating these two protocols as cleanly as possible and obtain formal guarantees for the finality gadget. GRANDPA attempts to finalise the prefix of the chain that $2/3$ of voters agree on, whether that is one or thousands blocks. It has been implemented by Parity Technologies and deployed on large testnets for the Polkadot protocol. We also present properties GRANDPA achieves and review GRANDPA's advantages in flexibility over comparable protocols.
The Effect of E-Learning, Student Facilitator and Explaining Model Learning and Self-Regulated Learning on 11th Grade Students Learning Outcomes of Economic Subject in Senior High School 1 Perbaungan School Year 2019/2020

This study aims to determine students taught by E-Learning and SFAE models obtain higher economic learning outcomes compared to SFAE models without E-Learning. The study is conducted in odd semester of Academic Year 2019/2020 in class XI of SMA Negeri 1 Perbaungan. The sampling technique of this study uses a cluster random sampling technique to establish an experimental group and a control group. The research approach is quantitative research with a quasi-experimental research design with non-equivalent (Pre Test and Post Test Design). The research design is pretest and posttest control design and the research design can be presented with a 2 x 2 factorial design with the Two Path Analysis of Variance (ANAVA) technique. The results shows that there is a difference between student learning outcomes in the E-Learning class + SFAE Learning Model, and student learning outcomes applied by the SFAE Learning Model model and students who gained the greatest influence from E-Learning + SFAE Learning Model on student economic learning outcomes, namely 0 , 0929. It means that E-Learning + SFAE Learning Model can explain 9.29% the effect of variations in the score of student learning outcomes.
A comparison of nonlinear modal synthesis using a time varying linear approximation and direct computation

Modal synthesis is a common technique to model linear musical systems efficiently using a parallel bank of second-order digital filters to represent the modes of a resonating instrument. However, many instruments exhibit nonlinear behavior when driven at high amplitudes, often resulting in amplitude dependent mode center frequencies. This behavior can be modeled in a simplified manner using a time varying linear modal structure or could be directly modeled using a nonlinear mode approximation such as that of a Duffing oscillator. The time-varying linear approach is compared to the direct approach to gauge if the approximation is valid within perceptual limits. Particular emphasis is put on weakly nonlinear modes which may be found in a string instrument with thin vibrating plates.
Implementation of Nutrition Education to Knowledge, Behaviour, and Performance of Junior High School children

The prevalence of obeslity increases from year to year. One of the factors that causes obesity was food habits. Precautions need to be taken to undergo this phenomenoms. One effort that can be done was to provide nutrition education to children. This research studied the role of nutrition education given to school age children (8th grade junior high school) towards knowledge, behavior, and overall performance. The study was conducted by giving pre-test and post-test to students who were divided into 2 groups. The first group treated with nutrition education, while the second group did not. Each group consisted of 37 students. The results showed that nutrition education have a strong influence in to knowledge, behaviour, and also the performance of the student. Students who are involved in research are included in the middle to lower class of economics so there are not many opportunities to choose food. Research needs to be done in other locations with middle to upper economic levels.
ANALISIS PENGENDALIAN INTERN DALAM MENUNJANG EFEKTIVITAS PENGELOLAAN KAS (STUDI KASUS PADA KOPERASI JAYA MAKMUR SEJATI MANISREJO)

Cash management at Koperasi Jaya Makmur Sejati Manisrejo is to support effectiveness in carrying out each of its operational activities. This study aims to analyze whether the internal control system at the Koperasi Jaya Makmur Sejati Manisrejo is in accordance with the theory.The results of this study indicate that there is an ineffective internal control where there are multiple tasks and authorities carried out by Koperasi Jaya Makmur Sejati Manisrejo employees. Cash management carried out by Koperasi Jaya Makmur Sejati Manisrejo carrying out its operational activities is already good. It would be better if there was a separation of authority that was appropriate and clear to the duties of each employee. Monitoring that is less than the maximum can lead to fraud will be better if the supervisor is more active and intense in checking. The manual system conducted by the Koperasi Jaya Makmur Sejati Manisrejo will be more effective if it uses a computerized system in carrying out operational activities. Keywords: Internal Control, Effectiveness of Cash Management
Object Graph Networks for Spatial Language Grounding

Consider a domestic robot being asked to pick up cup nearest to the plate. Natural language is an intuitive way for humans to interact with robots. However, enabling robots to comprehend natural language, and correctly interpret spatial references, is challenging for two reasons. Firstly, phrases must be semantically represented in structures that can be processed computationally; secondly correspondences must be found to map these structures to models that represent objects, relationships and actions in the environment. Recently neural networks have demonstrated a strong potential to address both challenges, most notably in the context of Visual Question Answering (VQA) where they have performed well at answering natural language questions about images. However, the state-of-the-art networks for VQA tasks are not directly applicable to robotic applications. They do not support interfaces suitable for integration with a robotic system and most have a limited capacity to interpret spatial phrases. In this paper we present a neural network architecture trained on synthetic data and evaluated on synthetic and real data. It correctly interprets referring spatial relationships in phrases such as the one above and provides a modular interface that allows a robot to localise an object in the environment from such a phrase.
Tag Clouds for Software Documents Visualization

Legacy software documents are hard to understand and visualize. The tag cloud technique helps software developers to visualize the contents of software documents. A tag cloud is a well-known and simple visualization technique. This paper proposes a new method to visualize software documents, using a tag cloud. In this paper, tags visualize in the cloud based on their frequency in an alphabetical order. The most important tags are displayed with a larger font size. The originality of this method is that it visualizes the contents of JavaDoc as a tag cloud. To validate the JavaDocCloud method, it was applied to NanoXML case study, the results of these experiments display the most common and uncommon tags used in the software documents.
Over-Segmentation of VHR Satellite Images Using Nonparametric Bayesian Iterative Clustering

Over-segmentation has often been employed to simplify representation of images and speed the process of image analysis, where each over-segment is also called a superpixel. However, the superpixels generated by the existing algorithms rely heavily on some preset parameters, for example, number of superpixels. In this paper, we present a novel superpixel algorithm under the framework of nonparametric Bayesian image clustering, which is called Nonparametric Bayesian Iterative Clustering (NBIC). Unlike traditional approaches for image clustering, Bayesian nonparametric models provide a principled way to infer the number of clusters from observed data. Therefore, unlike traditional approach to image over-segmentation, the NBIC is free of preset number of superpixels. The NBIC to superpixel segmentation is based on histogram clustering, and local histograms are extracted as observation. For each observation, compute the probability of each class and sample a class label accordingly. The cluster probabilities depend on the number of classes in the pixel neighborhood and the distance of cluster center. This method groups pixels into meaningful regions without setting any parameters related to superpixels in the nonparametric Bayesian theory. The adaptive amount of superpixels can eventually be converged, and the size of superpixels can automatically be controlled by iterative calculation