Reviewerly/exHarmony huggingface dataset:
Research on frosting detection method of air source Heat pump based on vision Technology

Abstract The outdoor pipe coil frosting of air source heat pump seriously affects the working efficiency of the equipment. Therefore, a new frost detection algorithm is proposed to optimize the defrosting process. First of all, the image difference algorithm is introduced to remove the interference factors in the image. Then, the brightness correction and denoising of the image are carried out. Finally, combined with image processing algorithms such as contour detection and Otsu multi-threshold segmentation to achieve regional frosting detection, the efficiency and accuracy of frosting detection are further improved. The experimental results show that, compared with the existing methods, the proposed algorithm not only improves the accuracy of frost detection, but also has better treatment effect on light changes and regional frost. Finally, the detection algorithm is added to the defrosting process. Experimental results show that the effect of intelligent defrosting is better than that of current defrosting strategies.
Online Surveillance and Education for Digital Competence

Digital competence has become increasingly important in modern societies and is today central to the possibility of participating on equal terms as a citizen in a contemporary democracy. Thus, it is now stressed as a crucial learning objective, nationally as well as internationally. One pervasive consequence of the digitalization of society is the facilitation of intrusive online surveillance: when we are online, we leave traces that provide useful information to companies, organizations, and individuals, who can collect, process, use, and share this information. The purpose of this article is to reveal the need for an increased awareness of the surveillance aspect of digitalization in teacher education and schools. The argument is partly based on a questionnaire survey with 560 current and former Swedish student teachers, about online behavior and privacy. The results indicate that Swedish teachers in general need to further their digital competence in order to be able to appropriately aid their pupils in developing digital literacy. Given that Swedish student teachers can be expected to possess a comparatively very high level of digital competence, we think it is safe to generalize this point to comprise teachers in many other countries as well. We argue that an awareness of the surveillance aspect of digitalization is crucial to being a cognizant citizen in a democratic society, and that it should therefore constitute a natural part of education for digital competence. Keywords: education for digital competence, digital competence, education for digital literacy, digital literacy, digitalization, online surveillance, soft surveillance, surveillance, surveillance culture, privacy
Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method

It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and to understand the characteristics of postoperative nursing safety events in otolaryngology surgery patients. This paper carried out preoperative safety protection for 385 inpatients, and the results showed that there were 52 cases of postoperative safety nursing events. This experiment found that the coinfected lesions (P = 0.001, HR = 14.283, 95.0% C1: 9.365~21.038), the treatment period (P = 0.001, HR = 13.716, 95.0% CI: 7.147~20.275), during hospital treatment (P = 0.002, HR = 15.208, 95.0% CI: 8.918-24.237), antibiotic use (P = 0.001, HR = 14.054, 95.0% CI: 8.163-21.739), and hypertension (P = 0.001, HR = 13.976, 95.0% CI: 7.926-22.385) are the important factors affecting postoperative care; using the association rule method to control and analyze the main risk factors of postoperative infection and bleeding in ENT patients can significantly improve the prognosis.
Methods and Algorithms for Correlation Analysis in R

Correlations tests are arguably one of the most commonly used statistical procedures, and are used as a basis in many applications such as exploratory data analysis, structural modelling, data engineering etc. In this context, we present correlation, a toolbox for the R language and part of the easystats collection, focused on correlation analysis. Its goal is to be lightweight, easy to use, and allows for the computation of many different kinds of correlations.
The Truth in Fake News: How Disinformation Laws Are Reframing the Concepts of Truth and Accuracy on Digital Platforms

Abstract The European Union’s (EU) strategy to address the spread of disinformation, and most notably the Code of Practice on Disinformation and the forthcoming Digital Services Act, tasks digital platforms with a range of actions to minimise the distribution of issue-based and political adverts that are verifiably false or misleading. This article discusses the implications of the EU’s approach with a focus on its categorical approach, specifically what it means to conceptualise disinformation as a form of advertisement and by what standards digital platforms are expected to assess the truthful or misleading nature of the content that they distribute because of this categorisation. The analysis will show how the emerging EU anti-disinformation framework marks a departure from the European Court of Human Rights’ consolidated standards of review for public interest and commercial speech and the tests utilised to assess their accuracy.
Neural Assemblies as Precursors for Brain Function

This paper describes some neural representations that may be helpful for realising intelligence in the human brain. The ideas come from the author's own cognitive model, where a number of algorithms have been developed over time. Through developing and trying to implement the architecture, ideas like separating the data from the function have become architecturally appropriate and there have been several opportunities to make the system more orthogonal. Similarly for the human brain, neural structures may work in-sync with the neural functions, or may be slightly separate from them. Each section discusses one of the neural assemblies with a potential functional result, that cover ideas such as timing or scheduling, inherent intelligence and neural binding. Another aspect of self-representation or expression is interesting and may help the brain to realise higher-level functionality based on these lower-level processes.
Constructivist pedagogy in E-learning: Solving problems of interaction with a student

The study focuses on improving e-learning effectiveness and its implementation during the COVID-19-induced lockdown. The study involved 328 students aged 20 to 24 years with an equal number of males and females. Research shows that e-learning is mostly used together with embedded pedagogical tools and technologies rather than single training programs to improve the quality of teaching based on a unified method. Constructivism is an effective approach for this purpose. The research results can be used in programs with embedded learning elements based on constructivist pedagogy in online and blended learning at higher educational institutions.&#x0D; Keywords: e-learning; constructivism; student-teacher interaction; problem-based learning; student-centered learning; individual learning path
Dimensionality reduction in images for appearance-based camera localization

Appearance-based Localization (AL) focuses on estimating the pose of a camera from the information encoded in an image, treated holistically. However, the high-dimensionality of images makes this estimation intractable and some technique of dimensionality Reduction (DR) must be applied. The resulting reduced image representation, though, must keep underlying information about the structure of the scene to be able to infer the camera pose. This work explores the problem of DR in the context of AL, and evaluates four popular methods in two simple cases on a synthetic environment: two linear (PCA and MDS) and two non-linear, also known as Manifold Learning methods (LLE and Isomap). The evaluation is carried out in terms of their capability to generate lower-dimensional embeddings that maintain underlying information that is isometric to the camera poses.
Teleportation of hybrid entangled states with continuous-variable entanglement

Hybrid entanglement between discrete-variable (DV) and continuous-variable (CV) quantum systems is an essential resource for heterogeneous quantum networks. Our previous work showed that in lossy channels the teleportation of DV qubits, via CV-entangled states, can be significantly improved by a new protocol defined by a modified Bell state measurement at the sender. This work explores whether a new, similarly modified, CV-based teleportation protocol can lead to improvement in the transfer of hybrid entangled states. To set the scene, we first determine the performance of such a modified protocol in teleporting CV-only qubits, showing that significant improvement over traditional CV-based teleportation is obtained. We then explore similar modifications in the teleportation of a specific hybrid entangled state showing that significant improvement over traditional CV-based teleportation is again found. For a given channel loss, we find teleporting the DV qubit of the hybrid entangled state can always achieve higher fidelity than teleporting the CV qubit. We then explore the use of various non-Gaussian operations in our modified teleportation protocol, finding that, at a cost of lower success probability, quantum scissors provides the most improvement in the loss tolerance. Our new results emphasize that in lossy conditions, the quantum measurements undertaken at the sender can have a surprising and dramatic impact on CV-based teleportation.
Using of airborne gamma-ray spectrometric data to the exposure of potassic alteration -recognition of alteration relates to gold mineralization

It is well known that many mineral deposits are related to hydrothermal processes, especially auriferous deposits. In this research paper, we show how to distinguish areas that have experienced hydrothermal activity, by construing airborne gamma-ray spectrometry data, especially potassium. An indirect application of aerial gamma-ray spectrometry technique is used to detect possible minerals deposits. In this region, an airborne gamma ray survey (K, eU and eTh) was carried out, based on irregular K, “F” parameter -, and Th-normalized of the K and eU anomalies. This work exhibits the mapping of hydrothermal regions possibly showing gold mineralization and the pointing out of new geological portions for this sequence, establishing prospective criteria for regional mineral exploration. We can suggest a new exploratory target, the area that is located next to Jabal Al-Ardiya (white colour on ternary map), which is characterized by high values of F-parameter, KD%, and K/eTh. Besides, enriched areas are found scattered in the north-eastern side of the study area. As result, the airborne gamma ray spectromertic data, might be used for estimation of prospective Gold Ore zones associated with the potassium alteration."
Analysis of ENF Signal Extraction From Videos Acquired by Rolling Shutters

&lt;p&gt;Submitted to IEEE Transactions on Information Forensics and Security (T-IFS). Under revision. &lt;/p&gt;
Time Evolution of Uniform Sequential Circuits

Simulating time evolution of generic quantum many-body systems using classical numerical approaches has an exponentially growing cost either with evolution time or with the system size. In this work, we present a polynomially scaling hybrid quantum-classical algorithm for time evolving a one-dimensional uniform system in the thermodynamic limit. This algorithm uses a layered uniform sequential quantum circuit as a variational ansatz to represent infinite translation-invariant quantum states. We show numerically that this ansatz requires a number of parameters polynomial in the simulation time for a given accuracy. Furthermore, this favourable scaling of the ansatz is maintained during our variational evolution algorithm. All steps of the hybrid optimization are designed with near-term digital quantum computers in mind. After benchmarking the evolution algorithm on a classical computer, we demonstrate the measurement of observables of this uniform state using a finite number of qubits on a cloud-based quantum processing unit. With more efficient tensor contraction schemes, this algorithm may also offer improvements as a classical numerical algorithm.
DFGR: Diversity and Fairness Awareness of Group Recommendation in an Event-based Social Network

Abstract An event-based social network (EBSN) is a new type of social network that combines online and offline networks, and one of its important problems is recommending suitable activities to users. However, the current research seldom considers balancing the accuracy, diversity and fairness of group activity recommendations. To solve this problem, we first compute activity similarity based on context and construct an activity similarity matrix and an activity similarity graph, and define a weighted coverage on the maximum similarity graph. Second, we propose a greedy algorithm that achieves an approximate ratio, and propose a new index to evaluate the fairness of a recommendation list S to group g . Finally, we conduct experiments on real datasets, and the experimental results show that the proposed method can better balance the accuracy, diversity and fairness of recommendations than the existing methods.
Detecting Label Errors in Token Classification Data

Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that contain label errors in token classification datasets. We study 11 different straightforward methods that score tokens/sentences based on the predicted class probabilities output by a (any) token classification model (trained via any procedure). In precision-recall evaluations based on real-world label errors in entity recognition data from CoNLL-2003, we identify a simple and effective method that consistently detects those sentences containing label errors when applied with different token classification models.
Standpoints of Parents and Teachers on the Inclusion of Answers Key in the Self-Learning Modules (SLM): A Qualitative Inquiry

This qualitative-phenomenological study determined the standpoints of participants on the inclusion of answers key in selflearning modules.It comprised of fourteen (14) participants selected using a purposive sample technique, with seven (7) parents in a virtual in-depth interviews and seven (7) public elementary teachers in a focus group discussion.The data analysis employed data coding and thematic analysis.For the parents, there were issues emerged from the problem namely: finding answers key incorrect, vague, and incomplete; promotion of laziness and dishonesty; and difficulties in guiding children.Meanwhile, for the teachers, issues emerged: duplication of answers from answer key; incompleteness of answers key; and lack of guidance from parents.To address the issues, parents mentioned ways: discouraging child's dependence to answers key; being present to guide child's learning; using other references; and skipping unclear and difficult activities.While teachers also pointed out ways: coordinating with learners and parents; separating answer sheets from the modules; and providing alternative mode of giving assessment to learners.They also cited insights they could share to others, for the parents: a call for removal of answers key; a call for accurate and clear presentation of answers key; a call for parental guidance in answering modules; and a call for provision of learning resources.While for the teachers: a call to attach the answers key as a guide to learners; a call to separate answers key; a call for parental guidance in answering modules; and a call for quality check of answers key.The results are deemed significant to public school teachers, parents, learners, DepEd officials, administrators, and future researchers to give awareness and to develop best practices utilizing the SLM with reliable answers key in order to attain quality education.
Posidaeja and Mami Wata: The online afterlives of two mermaid goddesses

This article examines two examples of fanfiction on oceanic mythologies: ‘Another day, another offering’ by sweetbydesign (2021), about Posideaja, and ‘Mami Wata’ by lucien_cramp (2021). Through both stories, age-old archetypes of mermaid goddesses are harnessed for the environmental agenda. In their fannish rewritings, the authors inverse many Romantic mermaid tropes to empower the mermaid and to confirm the connection between the mermaid and oceanic awareness. Moreover, in their latest remediations, Posideaja’s and Mami Wata’s bodies go against widespread standards for physical beauty and for fitness (i.e. functionality, such as being fit for labour). As such, these two updated mermaids open up a wide range of possibilities for identification and inspiration for their creators as well as their audiences. With their new representations of Posideaja and Mami Wata, the authors thus address two interconnected problems: the oppression of women (in terms of beauty and fitness norms) and the environmental damage done to the oceans (in terms of the acidification of oceans, increases in ocean temperatures and rising sea-levels).
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models

Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of any given (or no) examples. ASDOT consists of two steps, data disambiguation and sentence fusion, both of which are amenable to be solved with off-the-shelf pretrained language models (LMs) with optional finetuning. In the data disambiguation stage, we employ the prompted GPT-3 model to understand possibly ambiguous triples from the input data and convert each into a short sentence with reduced ambiguity. The sentence fusion stage then uses an LM like T5 to fuse all the resulting sentences into a coherent paragraph as the final description. We evaluate extensively on various datasets in different scenarios, including the zero-/few-/full-shot settings, and generalization to unseen predicates and out-of-domain data. Experimental results show that ASDOT consistently achieves significant improvement over baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot setting.
Checks and Strategies for Enabling Code-Switched Machine Translation

Code-switching is a common phenomenon among multilingual speakers, where alternation between two or more languages occurs within the context of a single conversation. While multilingual humans can seamlessly switch back and forth between languages, multilingual neural machine translation (NMT) models are not robust to such sudden changes in input. This work explores multilingual NMT models' ability to handle code-switched text. First, we propose checks to measure switching capability. Second, we investigate simple and effective data augmentation methods that can enhance an NMT model's ability to support code-switching. Finally, by using a glass-box analysis of attention modules, we demonstrate the effectiveness of these methods in improving robustness.
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning

Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accuracy while maintaining low inference latency. To this end, we deploy a lightweight model on the edge and a heavyweight model on the cloud. A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model. Moreover, a supervised learning (SL)-based offloading strategy is applied to determine whether the sample should be processed on the edge or on the cloud. Our method is implemented on real hardware and tested on multiple datasets. The experimental results show that (1) The sizes of the lightweight models obtained by RL-based DNN compression are up to 87.6% smaller than those obtained by the baseline method; (2) SL-based offloading strategy makes correct offloading decisions in most cases; (3) Our method reduces up to 78.8% inference latency and achieves higher accuracy compared with the cloud-only strategy.
Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems

Do all instances need inference through the big models for a correct prediction? Perhaps not; some instances are easy and can be answered correctly by even small capacity models. This provides opportunities for improving the computational efficiency of systems. In this work, we present an explorative study on 'model cascading', a simple technique that utilizes a collection of models of varying capacities to accurately yet efficiently output predictions. Through comprehensive experiments in multiple task settings that differ in the number of models available for cascading (K value), we show that cascading improves both the computational efficiency and the prediction accuracy. For instance, in K=3 setting, cascading saves up to 88.93% computation cost and consistently achieves superior prediction accuracy with an improvement of up to 2.18%. We also study the impact of introducing additional models in the cascade and show that it further increases the efficiency improvements. Finally, we hope that our work will facilitate development of efficient NLP systems making their widespread adoption in real-world applications possible.