Reviewerly/exHarmony huggingface dataset:
Comparison Between Collaborative Filtering and Content-Based Filtering

With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by describing the steps of these two algorithms gradually and analyzing their processes step by step, we can accurately analyze and summarize their advantages and disadvantages respectively. And on this basis, the respective areas which they are good at are mentioned. Moreover, this paper points out the shortcomings and limitations that still exist at present, and the direction for further improvement in the future. Finally, at the end of the paper, there are some overall comparation and summation about the two algorithms. And the hot research points of them in the future are discussed.
Risks and Solutions in Islamic Decentralised Finance

Decentralised Finance(DeFi) is a relatively new concept of banking and finance that relies on peer-to-peer payments enabled by the blockchain technology. Blockchain technology allows DeFi to provide financial services bypassing the traditional financial intermediaries, like banks or brokers. It is an innovative technology that is making a significant influence on the modern society due to its unique characteristics of security, decentralised nature and transparency. Although DeFi has rapidly evolved and has the potential to revolutionise the banking and finance industry, it is a fairly a new concept as compared to the traditional centralised systems and therefore participants should be aware of considerable risks and challenges linked with it. Thus, a systematic literature review was conducted to examine the different types of risks and challenges associated with DeFi, along with the suggested solutions to mitigate them or lessen them. Our analysis of the literature led us to classify the challenges into some categories that aim to give a fair sense of the challenges from a broader perspective without digging too deeply into any one particular issue. Although, DeFi is indeed a revolutionising technology, the knowledge of these risks in DeFi can help us to utilise the resources more efficiently to achieve our goals in this sector.
Use of ICT Tools in Higher Education in Peru during the COVID-19 Pandemic

This paper analyzes the impact of the COVID-19 pandemic and the consequent migration to digital learning environments on the habits of use of digital teaching tools among university professors in Peru. The existence of gender gaps in this impact has also been studied. For this purpose, quantitative analysis of the answers of 116 professors from different Peruvian universities to a questionnaire designed for this purpose was carried out. As a result, it has been obtained that the pandemic has led to an increase in the use of digital tools in all areas of teaching activity studied, mainly in the evaluation and sharing of teaching materials. In addition, although female professors reported a lower use of digital tools before the pandemic, the increase in their use has been greater than in males in all teaching areas.
Identifying Overly Restrictive Matching Patterns in SMT-based Program Verifiers (Extended Version)

Universal quantifiers occur frequently in proof obligations produced by program verifiers, for instance, to axiomatize uninterpreted functions and to statically express properties of arrays. SMT-based verifiers typically reason about them via E-matching, an SMT algorithm that requires syntactic matching patterns to guide the quantifier instantiations. Devising good matching patterns is challenging. In particular, overly restrictive patterns may lead to spurious verification errors if the quantifiers needed for proof are not instantiated; they may also conceal unsoundness caused by inconsistent axiomatizations. In this article, we present the first technique that identifies and helps the users and the developers of program verifiers remedy the effects of overly restrictive matching patterns. We designed a novel algorithm to synthesize missing triggering terms required to complete unsatisfiability proofs via E-matching. Tool developers can use this information to refine their matching patterns and prevent similar verification errors, or to fix a detected unsoundness.
lrSVD: An efficient imputation algorithm for incomplete high‐throughput compositional data

Abstract Compositional methods have been successfully integrated into the chemometric toolkit to analyse and model different types of data generated by modern high‐throughput technologies. Within this compositional framework, the focus is put on the relative information conveyed in the data by using log‐ratio coordinate representations. However, log‐ratios cannot be computed when the data matrix is not complete. A new computationally efficient data imputation algorithm based on compositional principles and aimed at high‐throughput continuous‐valued compositions is introduced that relies on a constrained low‐rank matrix approximation of the data. Simulation and real metabolomics data are used to demonstrate its performance and ability to deal with different forms of incomplete data: zeros, nondetects, missing values or a combination of them. The computer routines lrSVD and lrSVDplus are implemented in the R package zCompositions to facilitate its use by practitioners.
Teacher skills and knowledge for technology integration

With the pervasive use of digital technology in all aspects of our lives, and the rapid advances and deployment of new technologies in education, the use and teaching of technology in education is a necessary professional repertoire for teachers to ensure students' well-being. Influential education policy frameworks posit that teachers need competence in the integration of technology for agile delivery of teaching in blended/fully online modes and for fostering students' digital literacy. Future-oriented professional development programs need to go beyond individual learning and engage leadership in creating the necessary conditions for technology-enhanced learning innovations at institutional and system levels.
Ecopedagogy and ecopedagogical literacy

Ecopedagogy is an environmental pedagogy that emerged from Paulo Freire’s work on popular education models within Latin America. Ecopedagogues focus on teaching students to critically read the inseparable connections between environmental violence and social injustice, why environmentally violent acts occur, and the human world as part of Earth. Increasingly essential in our globalizing world, ecopedagogical literacy also includes reading the local-to-global politics of how these aspects are systematically hidden in (in/non)formal education that justifies socio-environmental oppressions and planetary unsustainability. Themes problematized include globalizations contested terrains, (non-)critical environmental pedagogies, othering (e.g., coloniality, racism, patriarchy, (non)citizenship), anthropocentricism, epistemological (de)legitimization, (eco)linguistics, and postdigitalism.
Immersive Virtual Reality in Nursing Education. What About 360° Videos?

Nursing simulation is a teaching method that supports and integrates student learning, along with clinical training and formal teaching without overlapping or replacing them. Virtual reality (VR) is one of the most recent technologies applied to nursing simulation. The added value of VR consists precisely in the immersion and in the sense of presence (the feeling of being there) that it can generate, allowing people to learn from the experience they are living as if it were happening in real life. The 360° video is a type of VR used in many sectors and increasingly in the healthcare one. The peculiarity of this experience is that the environments are not virtually reconstructed by a computer but they are real images and sounds previously recorded by a video camera. Therefore, the 360° video has great potential especially in nursing training and simulation. The aim of this study was to conduct a scoping review on the 360° video applied to nursing education and how this experience was evaluated. A comprehensive electronic search of five electronic databases (PubMed, Embase, Web of Science, CINAHL, and Scopus) was conducted from their inception to December 2021. Five studies met the inclusion criteria and were considered in this review. These studies encourage the use of this VR technology, but further research is necessary to explore the advantages of the 360° video as learning method in nursing education and to better understand how to evaluate this immersive experience in order to guarantee the effectiveness in terms of learning outcomes.
Intelligent Wheelchairs Rolling in Pairs Using Reinforcement Learning

Intelligent wheelchairs aim to improve mobility limitations by providing ingenious mechanisms to control and move the chair. This paper aims to enhance the autonomy level of intelligent wheelchair navigation by applying reinforcement learning algorithms to move the chair to the desired location. Also, as a second objective, add one more chair and move both chairs in pairs to promote group social activities. The experimental setup is based on a simulated environment using gazebo and ROS where a leader chair moves towards a goal, and the follower chair should navigate near the leader chair. The collected metrics (time to complete the task and the trajectories of the chairs) demonstrated that Deep Q-Network (DQN) achieved better results than the Q-Learning algorithm by being the unique algorithm to accomplish the pair navigation behaviour between two chairs.
Automatic item generation

Automatic item generation (AIG) is a research area where cognitive and psychometric modeling practices guide the production of tests that include items generated with the aid of computer technology. AIG can be used to produce large numbers of high-quality test items that are required to support the current transition from paper to computer-based educational testing. Two general methods are available for producing items using automated processes. The first method relies on the use of templates to structure the testing content. This method is described as template-based AIG. The second method relies on the use of techniques such as natural language processing and machine learning to structure the content found in a corpus or knowledge base. As a result, it does not require templates. This method is described as non-template-based AIG. We describe, illustrate, and evaluate these two technology-enhanced methods for creating educational testing items.
The efficacy of learning strategies from the ICAP perspective

The application of cognitive strategies for learning new information has been investigated in the literature for decades. ICAP, a theory about the four modes that students can engage to learn, can be used to rank and evaluate the effectiveness of strategies for enhancing learning with deeper understanding, based on the mode it belongs to, without carrying out an experimental study. Using the criteria for the ICAP modes, we categorized 50 different learning strategies according to the four modes of ICAP: Interactive, Constructive, Active, Passive, which predict learning levels in a descending order, from Interactive, to Constructive, to Active, to Passive.
Computational Alternative Photographic Process toward Sustainable Printing

We propose a computational alternative photographic process that integrates computer processing with the conventional printing method, particularly cyanotype. Cyanotype is a non-silver-halide process that has a lower environmental impact and is known for its availability to produce tri-color prints; however, the tri-color process is complex and time-consuming. To simplify this heavy printing process, our framework provides a user interface for image editing and optimization based on color simulation within a printable color gamut. We demonstrate image editing and tri-color cyanotype printing using our framework, indicating that it can reduce the number of user trials and errors.
Real-Time Omnidirectional Roaming in Large Scale Indoor Scenes

Neural radiance field (NeRF) has recently achieved impressive results in novel view synthesis. However, previous works on NeRF mainly focus on object-centric scenarios. They would suffer observable performance degradation in outward-facing and large-scale scenes due to limiting positional encoding capacity. To narrow the gap, we explore radiance fields in a geometry-aware fashion. We estimate explicit geometry from the omnidirectional neural radiance field that was learned from multiple 360° images. Relying on the recovered geometry, we use an adaptive divide-and-conquer strategy to slim and fine-tune the radiance fields and further improve render speed and quality. Quantitative and qualitative comparisons among baselines illustrated our predominant performance in large-scale indoor scenes and our system supports real-time VR roaming.
Technological Change and Market Conditions: Evidence from Bitcoin Fork

This article examines the impact of technological changes to cryptocurrency—known as “forking” that triggers blockchain splits—on market conditions. Despite the explicit distinction in log return distributions between the two splitting blockchains, adopting new technology does not result in a disparity in market conditions: no significant difference exists in market efficiency and long-term market equilibrium between the two splitting blockchains. Technological changes accompanying market separation do not impede the underlying uniformity in market conditions. The findings suggest that mutual information flows linked to market liquidity explain the results between the new and old forks.
Few-Shot Directed Meta-Learning for Image Classification

Learning from only few samples is a challenging problem and meta-learning is an effective approach to solve it. Meta-learning model aims to learn by training a large number of other samples. When encountering target task, the model can quickly adapt and obtain better performance with only few labeled samples. However, general meta-learning only provides a universal model that has certain generalization ability for all unknown tasks, which causes limited effects on specific target tasks. In this paper, we propose a Few-shot Directed Meta-learning (FSDML) model to specialize and solve the target task by using few labeled samples of the target task to direct the meta-learning process. FSDML divides model parameters into shared parameters and target adaptation parameters to store prior knowledge and determine the update direction. These two parts of the parameters are updated in different stages of training. We conduct experiments of image classification task on miniImageNet and Omniglot and the results show that FSDML has better performance.
A Balanced Triplet Loss for Person Re-Identification

Compared to cross-entropy in deep learning, triplet loss is less affected by the biased label information and widely used in fine-grained visual tasks. Especially in person re-identification (re-ID), triplet loss is improved with batch-hard sampling which only selects the hardest samples during the training process to reduce invalid triplets involved in the loss computation. The hardest samples’ loss computation can provide a more intense gradient descent than raw samples. However, the batch-hard triplet loss discards multiple samples with important information, which can negatively impact feature learning. Besides, the hardest samples cause loss stuck problems frequently in training. In this work, we propose a balanced triplet loss for comprehensive feature learning and stable model convergence. The balanced triplet loss only mines the hardest negative samples of each category within a mini-batch. Compared with batch-hard triplet loss, it preserves the features of all the negative categories rather than one negative category with the hardest negative sample. It achieves a balance between triplet selection and information loss. The experiments show that our method can produce competitive results in re-ID tasks. In addition, we analyze the correlation between the intensity of data mining and the granularity of feature learning and further adapt the balanced triplet loss to general fine-grained image classification. The experiments prove the adapted balanced triplet loss also outperforms cross-entropy in multiple datasets of different scales.
Interfering with the black-box-tradeoff model: Gephisto, a one-click Gephi for critical technical practice

This paper presents and justifies Gephisto, an experimental tool visualizing networks in one click. Gephisto’s design exemplifies how we can interfere with a user’s utilitarian goals, by giving them what they wish (an easy way to get a network map) but in disobedient ways (the produced map is different every time the tool is used) that encourage them to engage further with the tool’s methodological tenets. As an apparatus, Gephisto aims to incentivize untrained users to become more critical of their network mapping practices. As an intervention into the field of digital methods, it aims to show that tools that support critical thinking do not have to be hard to use and hostile to beginners. We criticize the idea that tools range from easy-to-use black boxes for unreflexive lazy-thinkers, to complex and demanding instruments for hard-thinking experts. We argue that learners need ease of use and critical thinking at the same time, and that it is possible to design tools that support both needs at once. We offer an alternative model where we acknowledge the active role of the user in deciding the tradeoff between learning to master the tool, and progressing toward their utilitarian goals. We argue that the design of the tool should not oppose the beginner’s need for assistance in decision making, but find other ways to incentivize critical thinking.
Evaluation of word embedding models to extract and predict surgical data in breast cancer

Abstract Background Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. Results We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. Conclusions The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.
Living with soft dragons

column Share on Living with soft dragons: between science fiction and human-computer interaction Authors: Jaz Hee-jeong Choi RMIT University RMIT UniversityView Profile , Ted Chiang View Profile Authors Info & Claims InteractionsVolume 29Issue 6November - December 2022pp 18–20https://doi.org/10.1145/3564017Published:03 November 2022Publication History 0citation168DownloadsMetricsTotal Citations0Total Downloads168Last 12 Months168Last 6 weeks15 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Sensor Placement Sensitivity and Robust Reconstruction of Wave Dynamics from Multiple Sensors

This paper presents a rigorous analysis of circumstances under which wave dynamics can be fully reconstructed from sensor networks. We prove that, in many cases, wave dynamics can be fully reconstructed from a single sensor, but that such reconstructions can be sensitive to small perturbations in sensor placement. In general, multiple sensors are necessary, and we outline a procedure for reconstructing the dynamics from them. We also show that if a network of sensors suffers from Gaussian noise, then the reconstruction error decreases as . We illustrate and verify these results numerically by simulating wave dynamics and then reconstructing from sensor data the initial state of the system and time series of the system at randomly chosen locations.
VMAF based quantization parameter prediction model for low resolution video coding

Perceptual video quality assessment (VQA) is now essential part of various video applications by controlling the quality for the videos delivered to the users. Recently, Video Multimethod Assessment Fusion (VMAF) is developed by Netflix, known as a full refence VQA model that combines spatial and temporal features to predict perceptual quality. This metric has stronger correlation with human visual system than the conventional metrics such as Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index (SSIM). However, the perceptual video quality is normally degraded due to the content and bitrate variation. This leads to uncomfortable experience for the viewers. In this context, this paper proposes an adaptive quantization parameter (QP) prediction model based on VMAF for video coding to achieve better perceptual quality at a reasonable bitrate. In particular, the Rate Distortion Optimization (RDO) progress of x.264 video codec is improved by using the VMAF scores to find optimal QP map. The results showed that the compression performance of x.264 codec with the proposed method achieved up to 5.95% bitrate saving when compared to the conventional x.264 encoder.
Ideal properties of rollup escape hatches

A rollup is a type of popular "layer two" scaling solution for slow-but-secure blockchains like Ethereum. A rollup perfoms computation of blockchain state updates off-chain but posts the inputs and the data to the underlying blockchain in order to benefit from its security. However, if rollup operators go offline, further state updates are no longer possible through the rollup; instead, state updates to the layer two state must be forced on the underlying blockchain. Such a mechanism is called an escape hatch as it allows state, and in particular digital assets, to escape from an inoperative rollup. We review the approaches from rollups developed by the community and highlight potential issues. We also establish a wish-list of properties that an escape hatch mechanism should have to be considered trustworthy and compatible with decentralization.