Reviewerly/exHarmony huggingface dataset:
Predicting Trends of Bitcoin Prices Based on Machine Learning Methods

Due to its characteristics of decentralization, no counterfeit currency, and anonymity, Bitcoin has developed incredibly rapidly, gradually realizing free exchange with real currency, and stepping into the purchase of real goods and services. This paper applied historical daily frequency data of Bitcoin and constructed traditional technical indicator factors such as CCI, AROON, MA, PSY, etc. Then logistic regression and XGBoost were leveraged to predict the rise and fall of the price of Bitcoin. The results showed that XGBoost classifier obtained a higher score than two logistic regressions, therefore the XGBoost method performed better until this stage: 163% of the return, 32% of the maximum retracement. This paper helps to buy and sell Bitcoin better, get a higher positive return, and may provide ideas for the stock market research.
A Comprehensive Rubric for Evaluating EduVR

Aim/Purpose: This article presents a comprehensive rubric for evaluating educational virtual reality experiences for mobile devices. The aim of this article is to systematically analyze research to address the quality of virtual reality experiences on mobile applications in order to extend the work of Lee and Cherner (2015) and their instructional application rubric. Background: Ratings in proprietary mobile application stores – The App Store and Google Play, etc. – are generic and do not provide meaningful evaluations of the virtual reality. This article utilizes research in the areas of virtual reality and education to present a comprehensive rubric for evaluating educational virtual reality for mobile applications, which continues to advance previously published, research-based rubrics. Methodology: The methodology uses a systematic process that spans multiple stages. The first stage was to locate pre-existing rubrics for virtual reality, followed by a review of literature focused on it. The third stage was to develop and vet a research-supported rubric for evaluating educational virtual reality. Contribution: The main contribution from this article is that it fills a gap in the literature by presenting a criterion-referenced, research-supported rubric for evaluating the quality of educational virtual reality for mobile devices (e.g., smartphones, tablets, and app-connected goggles). Findings: This paper’s findings include the domains, dimensions, and criterion-referenced Likert scale indicators in the form of rubric dimensions for evaluating educational virtual reality. The evaluative domains consist of (1) Positioning of the EduVR, (2) Avatar Level, (3) Virtual Environment, and (4) Virtual Experience. Recommendations for Practitioners: This rubric is a tool for instructional coaches, teacher educators, and instructional technologists to use when recommending virtual reality experiences for instructional purposes. Recommendation for Researchers: Researchers can use this tool to monitor the quality of educational virtual reality being developed for classroom use. They can also use this rubric to examine educational virtual reality experiences they would use in their studies and evaluate how those educational virtual reality experiences impact student learning, engagement, and collaboration. Impact on Society: We foresee this rubric being an aid in the development, selection, and purchase of educational virtual reality by educational institutions, educators, researchers, edtech developers, and edu-philanthropists, thus advancing the quality and expectations for educational virtual reality experiences. Future Research: Future researchers can further enhance the validity of this rubric by collecting large amounts of data from a diverse set of end users and stakeholders. Also, subsequent rubrics for evaluating augmented reality and extended reality comprise additional research avenues.
A Deep Analysis of an Explainable Retrieval Model for Precision Medicine Literature Search

Professional search queries are often formulated in a structured manner, where multiple aspects are combined in a logical form. The information need is often fulfilled by an initial retrieval stage followed by a complex reranking algorithm. In this paper, we analyze a simple, explainable reranking model that follows the structured search criterion. Different aspects of the criterion are predicted by machine learning classifiers, which are then combined through the logical form to predict document relevance. On three years of data from the TREC Precision Medicine literature search track (2017–2019), we show that the simple model consistently performs as well as LambdaMART rerankers. Furthermore, many black-box rerankers developed by top-ranked TREC teams can be replaced by this simple model without statistically significant performance change. Finally, we find that the model can achieve remarkably high performance even when manually labeled documents are very limited. Together, these findings suggest that leveraging the structure in professional search queries is a promising direction towards building explainable, label-efficient, and high-performance retrieval models for professional search tasks.
FEATURES OF THE ORGANIZATION OF THE EDUCATIONAL PROCESS IN THE INFORMATION AND EDUCATIONAL ENVIRONMENT

This article describes the features of the organization of the educational process in the information and educational environment. The paper substantiates the necessity for targeted work on the implementation of educational programs using e-learning, distance learning technologies, taking into account the functioning of the electronic information and educational environment. Various interpretations of the information and educational environment in the scientific literature are analyzed. The advantages of training with the use of information and communication technologies are presented. In particular, students ' responsibility for their learning process increases, which in turn has a positive effect on the effectiveness of self-education. Various formats of online teaching and the possibility of using distance learning technologies to motivate students to take an active part in the learning process are described. The article summarizes the experience of organizing online training, identifies the difficulties with organizing online training of students using webinar platforms, and describes possible solutions. Teachers primarily relate such difficulties to the limited technical capability of the Internet channel for mass use, the complexity of control by teachers, the loss of attention of students, etc. Modern technical features of many webinar platforms are described. The role of knowledge of psychological and didactic peculiarities of online teaching and the teacher’s competences in the information and educational environment are emphasized. The article describes the factors that determine the effectiveness of the educational process in the information and educational environment.
Strong-Separation Logic

Abstract Most automated verifiers for separation logic are based on the symbolic-heap fragment, which disallows both the magic-wand operator and the application of classical Boolean operators to spatial formulas. This is not surprising, as support for the magic wand quickly leads to undecidability, especially when combined with inductive predicates for reasoning about data structures. To circumvent these undecidability results, we propose assigning a more restrictive semantics to the separating conjunction. We argue that the resulting logic, strong-separation logic, can be used for symbolic execution and abductive reasoning just like “standard” separation logic, while remaining decidable even in the presence of both the magic wand and the list-segment predicate—a combination of features that leads to undecidability for the standard semantics.
Learning Disentangled User Representation Based on Controllable VAE for Recommendation

User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretability of the recommender system. However, existing collaborative filtering methods learning disentangled representation face problems of balancing the trade-off between reconstruction quality and disentanglement. In this paper, we propose a controllable variational autoencoder framework for collaborative filtering. Specifically, we adopt a modified Proportional-Integral-Derivative (PID) control to the $$\beta $$ -VAE objective to automatically tune the hyperparameter $$\beta $$ using the output of Kullback-Leibler divergence as feedback. We further introduce item embeddings to guide the system to learn representation related to the real-world concepts using a factorized Gaussian distribution. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines. We further evaluate our model’s effectiveness to control the trade-off between reconstruction error and disentanglement quality in the recommendation.
The Single-Noun Prior for Image Clustering.

Self-supervised clustering methods have achieved increasing accuracy in recent years but do not yet perform as well as supervised classification methods. This contrasts with the situation for feature learning, where self-supervised features have recently surpassed the performance of supervised features on several important tasks. We hypothesize that the performance gap is due to the difficulty of specifying, without supervision, which features correspond to class differences that are semantic to humans. To reduce the performance gap, we introduce the single-noun prior - which states that semantic clusters tend to correspond to concepts that humans label by a single-noun. By utilizing a pre-trained network that maps images and sentences into a common space, we impose this prior obtaining a constrained optimization task. We show that our formulation is a special case of the facility location problem, and introduce a simple-yet-effective approach for solving this optimization task at scale. We test our approach on several commonly reported image clustering datasets and obtain significant accuracy gains over the best existing approaches.
Platform for Situated Intelligence

We introduce Platform for Situated Intelligence, an open-source framework created to support the rapid development and study of multimodal, integrative-AI systems. The framework provides infrastructure for sensing, fusing, and making inferences from temporal streams of data across different modalities, a set of tools that enable visualization and debugging, and an ecosystem of components that encapsulate a variety of perception and processing technologies. These assets jointly provide the means for rapidly constructing and refining multimodal, integrative-AI systems, while retaining the efficiency and performance characteristics required for deployment in open-world settings.
The Effect of Contextual Physics Teaching Materials Assisted by an Android-Based Virtual Lab to Improve Students’ Conceptual Understanding During the COVID-19 Pandemic

This is a quasi-experiment research with a quantitative approach.This research aimed to examine the effect of contextual physics teaching materials assisted by android-based laboratories in improving students' conceptual understanding.The sampling in this research were students in class XI IPA 4 (as a modelling class) and XI IPA 6 (as an implementation class) at SMAN 1 Muntilan.The sampling technique used is purposive sampling.The conclusion of this study is the use of contextual physics teaching materials with the help of an android-based virtual lab can improve students' conceptual understanding.These results prove that contextual physics teaching materials assisted by android-based virtual labs are effectively used to improve students' conceptual understanding.This finding is an effective alternative to learning in distance conditions and can also be used face-to-face.
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness

Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from different model families or training processes. To better protect ML systems against adversarial attacks, several questions are raised: what are the sufficient conditions for adversarial transferability and how to bound it? Is there a way to reduce the adversarial transferability in order to improve the robustness of an ensemble ML model? To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness. Our theoretical analysis shows that only promoting the orthogonality between gradients of base models is not enough to ensure low transferability; in the meantime, the model smoothness is an important factor to control the transferability. We also provide the lower and upper bounds of adversarial transferability under certain conditions. Inspired by our theoretical analysis, we propose an effective Transferability Reduced Smooth(TRS) ensemble training strategy to train a robust ensemble with low transferability by enforcing both gradient orthogonality and model smoothness between base models. We conduct extensive experiments on TRS and compare with 6 state-of-the-art ensemble baselines against 8 whitebox attacks on different datasets, demonstrating that the proposed TRS outperforms all baselines significantly.
Experimental Validation of Energy-Optimal Turning Radii for Skid-Steer Rovers

For skid-steer rovers, a point turn consumes a lot of power compared to a straight-line motion. Hence, turning radius, in path planning for this kind of rover, is a factor that should be considered explicitly. In this article, the energy-optimal paths for skid-steer rovers are presented, based on a method using Pontryagin’s minimum principle. The finite number of extremals are compared and an optimal map is generated that shows which path type requires minimum energy depending on start/end pose for a particular example. Moreover, the turns in optimal paths (aside from a small number of special cases where start and end points are extremely close) are to be circular arcs of a particular turning radius, $$R',$$ equal to half of a skid-steer rover’s slip track. $$R'$$ is the turning radius at which the inner wheels of a skid-steer rover are not commanded to turn. Experiments with a Husky UGV rover validate the energy-optimality of using $$R'$$ turns on hard ground. Moreover, the experimental results confirm that circular arc - line - circular arc (CLC) paths save energy compared to point turn - line - point turn (PLP) paths, on both hard ground and sand. It should be noted that PLP is the most commonly used maneuver to go from a starting to an end pose for skid-steer rovers.
Developing Amharic Sign Language Recognition Model for Amharic Characters Using Deep Learning Approach

Abstract Hearing-impaired people use Sign Language to communicate with each other as well as with other communities. Usually, they are unable to communicate with normal people. Most of the people without hearing disability do not understand the Sign Language and unable to understand hearing-impaired people. So, they need recognition of Sign Language to text. In this research, a model is optimized for the recognition of Amharic Sign Language to Amharic characters. A convolutional neural network model is trained on datasets gathered from a teacher of Amharic Sign Language. Frame extraction from Amharic Sign Language video, labeling and annotation, XML creation, generate TFrecord, and training models are major general steps followed for developing models to recognize Amharic Sign Language to characters. After training of the neural network is completed, the model is saved for recognition of Sign Language from a video system or from the frame of video. The accuracy of the model is the summation of confidence of individual alphabets correctly recognized divided by the number of alphabets presented for evaluation for Faster R-CNN and SSD. Hence, the mean average accuracy of the Faster R-CNN and Single-Shot Detector is found to be 98. 25% and 96 % respectively. The model is trained and evaluated for the character of the Amharic language. The research will continue to include the remaining words and sentence used in Amharic Sign Language to have a full- edged Sign Language recognition model to a complete system.
Finite-Memory Strategies in POMDPs with Long-Run Average Objectives

Partially observable Markov decision processes (POMDPs) are standard models for dynamic systems with probabilistic and nondeterministic behaviour in uncertain environments. We prove that in POMDPs with long-run average objective, the decision maker has approximately optimal strategies with finite memory. This implies notably that approximating the long-run value is recursively enumerable, as well as a weak continuity property of the value with respect to the transition function.
Spectral Clustering of Events in Social Media Flood Images based on Multimodal Analysis

In Data Science, Clustering is one of the most popular techniques. It has wide potential application in data analysis, market research, pattern recognition, image processing, etc. Spectral Clustering is a clustering algorithm shows improved performance than the conventional clustering algorithms. To handle Social media multimodal data in Event Clustering and for better understanding of the context of the post, Spectral Clustering of Events in Flood Images Based on Multimodal Analysis (SCEFIMA) approach is proposed. This approach makes use of Post's Image and textual content (Multimodal data) in Clustering the events. Also considering Spatial and Temporal Information in understanding the post. The existing approaches lacks on multimodal analysis in social media data. Use of Multimodal data gives the better understanding of the context of the post in event clustering. In addition to this, Using Multimodal data in Event Clustering significantly boost performance than the existing methods. Detecting and Clustering events from multimodal data in social networks helpful in monitoring events by public organizations and authorities. Also helpful in early planning for preventive measures.
Efficient conformer-based speech recognition with linear attention

Recently, conformer-based end-to-end automatic speech recognition, which outperforms recurrent neural network based ones, has received much attention. Although the parallel computing of conformer is more efficient than recurrent neural networks, the computational complexity of its dot-product self-attention is quadratic with respect to the length of the input feature. To reduce the computational complexity of the self-attention layer, we propose multi-head linear self-attention for the self-attention layer, which reduces its computational complexity to linear order. In addition, we propose to factorize the feed forward module of the conformer by low-rank matrix factorization, which successfully reduces the number of the parameters by approximate 50% with little performance loss. The proposed model, named linear attention based conformer (LAC), can be trained and inferenced jointly with the connectionist temporal classification objective, which further improves the performance of LAC. To evaluate the effectiveness of LAC, we conduct experiments on the AISHELL-1 and LibriSpeech corpora. Results show that the proposed LAC achieves better performance than 7 recently proposed speech recognition models, and is competitive with the state-of-the-art conformer. Meanwhile, the proposed LAC has a number of parameters of only 50% over the conformer with faster training speed than the latter.
Real-Time Recognition and Location of Indoor Objects

Object recognition and location has always been one of the research hotspots in machine vision. It is of great value and significance to the development and application of current service robots, industrial automation, unmann... | Find, read and cite all the research you need on Tech Science Press
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation

News recommendation is critical for personalized news access. Existing news recommendation methods usually infer users' personal interest based on their historical clicked news, and train the news recommendation models by predicting future news clicks. A core assumption behind these methods is that news click behaviors can indicate user interest. However, in practical scenarios, beyond the relevance between user interest and news content, the news click behaviors may also be affected by other factors, such as the bias of news presentation in the online platform. For example, news with higher positions and larger sizes are usually more likely to be clicked. The bias of clicked news may bring noises to user interest modeling and model training, which may hurt the performance of the news recommendation model. In this paper, we propose a bias-aware personalized news recommendation method named DebiasRec, which can handle the bias information for more accurate user interest inference and model training. The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module. The bias representation module is used to model different kinds of news bias and their interactions to capture their joint effect on click behaviors. The bias-aware user modeling module aims to infer users' debiased interest from the clicked news articles by using their bias information to calibrate the interest model. The bias-aware click prediction module is used to train a debiased news recommendation model from the biased click behaviors, where the click score is decomposed into a preference score indicating user's interest in the news content and a news bias score inferred from its different bias features. Experiments on two real-world datasets show that our method can effectively improve the performance of news recommendation.
Reducing Representation Drift in Online Continual Learning.

In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Previous work in this setting often tries to reduce catastrophic forgetting by limiting changes in the space of model parameters. In this work we instead focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. Starting from a popular approach, experience replay, we consider metric learning based loss functions which, when adjusted to appropriately select negative samples, can effectively nudge the learned representations to be more robust to new future classes. We show that this selection of negatives is in fact critical for reducing representation drift of previously observed data. Motivated by this we further introduce a simple adjustment to the standard cross entropy loss used in prior experience replay that achieves similar effect. Our approach directly improves the performance of experience replay for this setting, obtaining state-of-the-art results on several existing benchmarks in online continual learning, while remaining efficient in both memory and compute. We release an implementation of our experiments at this https URL