Reviewerly/exHarmony huggingface dataset:
Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

&lt;p&gt;Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.&lt;/p&gt;
Anisotropic Phase Stretch Transform-Based Algorithm for Segmentation of Activated Sludge Phase-Contrast Microscopic Image

The activated sludge (AS) process is a biological treatment process of wastewater used in sewage treatment plants, in which settling of AS is vitally important for wastewater treatment.In AS, however, bulking caused by filamentous bacteria will significantly reduce the settling capacity of activated sludge.Traditionally, the physicochemical method has been used to monitor the status or performance of filamentous bacteria in activated sludge, while it is a very compromising means in modern digital quality control for wastewater treatment when digital image processing and analysis technology is used to determine the status of filamentous bacteria in activated sludge.In order to avoid the disadvantages of deficient direction selectivity in the translation vector field for isotropic phase stretch transform (PST) and high noise susceptibility in determining the status of filamentous bacteria in AS and low accuracy, an anisotropic PST (APST) method for filamentous bacteria test in AS was proposed in this paper; specifically speaking, by analyzing and deriving the disadvantages of the traditional isotropic PST kernel function, we designed an APST kernel function, put forward a non-maximum suppression strategy for processing of images from different aspects and combined with the relative total variation theory to form APST-based algorithm for segmentation of activated sludge phase-contrast microscopic image.According to a large number of experimental results, in terms of the overall segmentation effect or the subjective and objective evaluation indicators, the resultant algorithm in this paper is superior to the latest traditional PST segmentation algorithm, Canny image segmentation algorithm and Sobel image segmentation algorithm.Also, the segmentation results for filamentous bacteria showed that probability rand index (PRI) and global consistency error (GCE) indicators were all improved by about 30%, supporting the effectiveness and superiority of the algorithm in this paper.
Introduction to logical entropy and its relationship to Shannon entropy

We live in the information age. Claude Shannon, as the father of the information age, gave us a theory of communications that quantified an “amount of information,” but, as he pointed out, “no concept of information itself was defined.” Logical entropy provides that definition. Logical entropy is the natural measure of the notion of information based on distinctions, differences, distinguishability, and diversity. It is the (normalized) quantitative measure of the distinctions of a partition on a set-just as the Boole–Laplace logical probability is the normalized quantitative measure of the elements of a subset of a set. And partitions and subsets are mathematically dual concepts – so the logic of partitions is dual in that sense to the usual Boolean logic of subsets, and hence the name “logical entropy.” The logical entropy of a partition has a simple interpretation as the probability that a distinction or dit (elements in different blocks) is obtained in two independent draws from the underlying set. The Shannon entropy is shown to also be based on this notion of information-as-distinctions; it is the average minimum number of binary partitions (bits) that need to be joined to make all the same distinctions of the given partition. Hence all the concepts of simple, joint, conditional, and mutual logical entropy can be transformed into the corresponding concepts of Shannon entropy by a uniform non-linear dit-bit transform. And finally logical entropy linearizes naturally to the corresponding quantum concept. The quantum logical entropy of an observable applied to a state is the probability that two different eigenvalues are obtained in two independent projective measurements of that observable on that state.
Format of Musical Theatre Director's Training in Socio-Cultural Realities of Independent Ukraine

The relevance of the study lies in the need to find a solution to the problem of training music directors in Ukraine under the influence of a paradigm shift in theatrical art, which requires the harmonization of Ukrainian and European educational systems. The scientific novelty of the article is in determining the ways of professional education modernization in accordance to the rector O. Tymoshenko’s strategic vision of the ‘musical directing’ specialization development at the Tchaikovsky National Academy of Music of Ukraine. The purpose of the study is to determine the strategic directions of training a musical theatre director in the socio-cultural realities of independent Ukraine, to predict the prospects for their further transformations in the context of European integration of art and educational directing schools. Applied research methods were: culturological - to analyze the impact of socio-cultural challenges of today on changing the paradigm of theatrical art; system analysis - to determine the strategic directions of training a music director in the context of O. Tymoshenko's conceptual vision; structural and functional - to determine the model of education of a music director in the context of integrative links between specializations in the implementation of joint projects. Evolutionary changes in music directing education which are connected to European integration processes in culture and art were considered. It was found that the paradigm of theatrical art is influenced by the socio-cultural challenges of today, which have both positive and negative consequences and require modernization of professional training of music directors. It was found out that: positive results consist in exchange of achievements in the field of musical theater, growth of art skills, joint international projects, innovative searches in the context of competition-festival activity, negative - in decrease of national repertoire in comparison with foreign, partial loss of national art identity and change of value orientations from permissive-cognitive to entertaining-hedonistic, creation of art performances on show-market of consumer products. Prospects for further research related to the analysis of world directing and educational systems, creative and pedagogical work of iconic figures of musical theatre and their extrapolation to Ukrainian realities were predicted.
Responsible Garbage Disposal Attitudes and Readiness for Usage of Biodegradable Plastic Bags

Concerns on irresponsible waste disposal have been on the agenda of many world governments, including Malaysia. The buildup of wastes in Malaysia has resulted in significant environmental problems and health issues. One alternative to rectify the problem is by using biodegradable plastic bags. Therefore, this paper aims at showcasing the public’s mindset on responsible garbage disposal and the readiness to use biodegradable plastics. The study employed a qualitative approach; hence, these are initial findings derived from the analyses of 118 respondents with a 77.1% returned rate. The implications of the findings will be discussed in the paper. Keywords: Responsible waste disposal; biodegradable plastic usage; green economy; environmental democracy eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i19.3211
A smallest computable entanglement monotone

The Rains relative entropy of a bipartite quantum state is the tightest known upper bound on its distillable entanglement – which has a crisp physical interpretation of entanglement as a resource – and it is efficiently computable by convex programming. It has not been known to be a selective entanglement monotone in its own right. In this work, we strengthen the interpretation of the Rains relative entropy by showing that it is monotone under the action of selective operations that completely preserve the positivity of the partial transpose, reasonably quantifying entanglement. That is, we prove that Rains relative entropy of an ensemble generated by such an operation does not exceed the Rains relative entropy of the initial state in expectation, giving rise to the smallest, most conservative known computable selective entanglement monotone. Additionally, we show that this is true not only for the original Rains relative entropy, but also for Rains relative entropies derived from various Rényi relative entropies. As an application of these findings, we prove, in both the non-asymptotic and asymptotic settings, that the probabilistic approximate distillable entanglement of a state is bounded from above by various Rains relative entropies.Full version available at https://arxiv.org/abs/2201.00835
A Text Detection and Recognition Algorithm for English Teaching Based on Deep Learning

Traditional English teaching cannot make effective use of various resources, and the scheduling ability is poor. People cannot accurately obtain the information in the English textbook text in the learning process, resulting in some people who cannot better learn and master the English language. For this problem, this study adopts deep learning algorithm and establishes an English teaching text algorithm based on association semantic rules to mine the features between sentences and phrases in the text provided by English teachers. The proposed algorithm completes the feature extraction of the English teaching text and also analyzes the association analysis between semantics in English teaching text. In fact, its essence is to get English teaching association rules on the basis of information theory. By combining with semantic similarity information, English teaching text can be accurately detected and identified. The simulation results show that the proposed algorithm can accurately extract English teaching text information, and the accuracy and convergence speed during extraction are higher than other competing algorithms.
A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets

Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industry. However, medical data characteristics such as relatively small datasets for rare diseases or imbalance in class distribution for rare conditions significantly restrains their adoption and reuse. Imbalanced datasets lead to difficulties in learning and obtaining accurate predictive models. This paper follows the FAIR paradigm and proposes a technique for the alignment of class distribution, which enables improving image classification performance in imbalanced data and ensuring data reuse. The experiments on the acne disease dataset support that the proposed framework outperforms the baselines and enable to achieve up to 5% improvement in image classification.
Prediction of Distant Recurrence in Breast Cancer using a Deep Neural Network

Breast cancer is the most common cancer diagnosed in women, and it is ranked as the second highest cancer with high mortality rate. Breast-cancer recurrence is the cancerous tumor that returned after treatment. Cancer treatments such as radiotherapy are performed mainly to kill cancer cells; however, some cells may have survived and multiply themselves at the same area as the original cancer (local recurrence) or to any other part (distant recurrence). Distant recurrence occurs when cancer cells spread to other parts of the body, most commonly to bone, breast, liver, and lungs. This study employed an Artificial Neural Network of the deep learning approach to predict distant recurrence of breast cancer. Factors that contribute to the risk of recurrence are age, type of surgery performed, tumor size, breast subtype, estrogen receptor, progesterone receptor, undergoing chemotherapy or not, and lymph node involvement. The actual value of distant recurrence is also considered to be a variable. Principal Component Analysis using five and three principal components was conducted. The outcome indicates that the model has accuracy of up to 0.80 using three principal components.
Serious Games and Mixed Reality Applications for Healthcare

Serious games are games in which the main goal is not entertainment, but a serious purpose ranging from the acquisition of knowledge to interactive training, to name just a few [...]
Porting a benchmark with a classic workload to blockchain

Many cross-organization cooperation applications of blockchain-based distributed ledger technologies (DLT) do not aim at innovation at the cooperation pattern level: essentially the same "business" is conducted by the parties, but this time without a central party to be trusted with bookkeeping. The migration to DLT is expected to have a negative performance impact, but some DLTs, such as Hyperledger Fabric, are accepted to be much better suited performance-wise to such use cases than others. However, with the somewhat surprising, but ongoing absence of application-level performance benchmarks for DLTs, cross-DLT comparison for "classic" workloads and the evaluation of the performance impact of "blockchainification" is still ill-supported. We present the design and Hyperledger Caliper-based open implementation of a full port of the classic TPC-C benchmark to Hyperledger Fabric, complete with a structured approach for transforming the original database schema to a smart contract data model. Initial measurements about the workload characteristics that will affect the design of large-scale performance evaluations are also included.
A Study on College English Mobile Learning Teaching Strategies Based on Smart-phone Applications

With the development of information technology, Micro-course, Wisdom Class, Massive Open Online Courses, and so on have emerged. Smart-phones, as one of the modern mobile learning devices, come to learners’ rescue to get access to those brand-new learning styles. Students can more easily achieve autonomous learning anytime, anywhere with smart-phone apps. In the teaching practice and experience, there still exists some problems in the traditional process of English teaching in college, such as single English teaching model, rigid English learning methods and “dumb” English. This paper proposes some teaching methods that combine smart-phone apps with mobile English learning. These strategies can enhance students’ self-control and self-learning ability and improve the efficiency of English teaching.
Forensic data model for artificial intelligence based media forensics - Illustrated on the example of DeepFake detection

The recent development of AI systems and their frequent use for classification problems poses a challenge from a forensic perspective.In many application fields like DeepFake detection, black box approaches such as neural networks are commonly used.As a result, the underlying classification models usually lack explainability and interpretability.In order to increase traceability of AI decisions and move a crucial step further towards precise & reproducible analysis descriptions and certifiable investigation procedures, in this paper a domain adapted forensic data model is introduced for media forensic investigations focusing on media forensic object manipulation detection, such as DeepFake detection.
GC Insights: Rainbow colour maps remain widely used in the geosciences

Abstract. Rainbow colour maps are known to be problematic yet remain widely used in scientific communication. This study extends work by Stoelzle and Stein (2021) to investigate the extent of their use in geoscience publications. It is found that over half (55 %) of all papers surveyed from six geoscience journals from the years 2005, 2010, 2015 and 2020 (n=2638) contained at least one visualisation that uses rainbow or red–green colour schemes and are therefore potentially misleading and colour-inaccessible. Recent changes to the submission guidelines for all European Geosciences Union (EGU) journals would seem to place greater responsibility in the future with editors and reviewers to identify and correct colour issues as part of the review process.
Extreme Zero-Shot Learning for Extreme Text Classification

The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant labels for an input text instance from a large label set.However, the XMC setup faces two challenges: (1) it is not generalizable to predict unseen labels in dynamic environments, and (2) it requires a large amount of supervised (instance, label) pairs, which can be difficult to obtain for emerging domains.In this paper, we consider a more practical scenario called Extreme Zero-Shot XMC (EZ-XMC), in which no supervision is needed and merely raw text of instances and labels are accessible.Few-Shot XMC (FS-XMC), an extension to EZ-XMC with limited supervision is also investigated.To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.Specifically, we develop a pre-training method MACLR, which thoroughly leverages the raw text with techniques including Multi-scale Adaptive Clustering, Label Regularization, and self-training with pseudo positive pairs.Experimental results on four public EZ-XMC datasets demonstrate that MACLR achieves superior performance compared to all other leading baseline methods, in particular with approximately 5-10% improvement in precision and recall on average.Moreover, we show that our pre-trained encoder can be further improved on FS-XMC when there are a limited number of ground-truth positive pairs in training.Our code is available at
Data Privacy, Regulations and Legal Issues on COVID-19 Tracking Apps: A Scoping Review

It cannot be deniable that smartphone apps have grown exponentially and are playing a crucial role in the response to the COVID-19 pandemic in many countries. This paper aims to investigate data privacy, regulations and legal issues on COVID-19 tracking apps. A literature search will be followed the PRISMA guidelines extension for a scoping review. The search will be conducted on PubMed and Google Scholar. A total of 38 articles from 7,626 articles were reviewed. Mostly articles report on data privacy. Not many articles report on regulations and legal issues. However, there are many challenges on COVID-19 applications such as security risks, privacy issues, political, ethical, and legal risks, and standardization issues.
Real-time twitter data analytics of mental illness in COVID-19: sentiment analysis using deep neural network

The World Health Organization (WHO) &lt;span&gt;states that the COVID-19 epidemic is being treated as a pandemic, with thousands of individuals infected and dead worldwide. School and college students are suffering from their online classes without any physical activities. Working men and women are also suffering from their working situations, as lots of people have lost their jobs and unemployment rates have become high due to the pandemic, and people are also losing physical contact with other family members, friends, and colleagues. The main objective of the proposed model is to monitor and analyse the real-time Twitter data-related tweets, such as coronavirus mental illness that are commonly used while referencing the pandemic. We have compared three deep learning approaches to sentiment analysis and found them to be useful. The first deep learning technique is to use a basic recurrent neural network (RNN), and the second is to use a deep learning RRN with long short-term memory (LSTM), followed by a gated recurrent unit (GRU). The experiment results indicate that the recurrent neural network built using GRU has the maximum accuracy of 99.47% for positive, negative, and neutral words and statements in Twitter data.&lt;/span&gt;
Evacuating Vulnerable People During a Tsunami Disaster in Japan: A Simulation Using Wheelchairs

Safe, rapid evacuation during natural disasters is particularly challenging for people who are vulnerable and/or need assistance. This study evaluated the feasibility of an evacuation route from a residential area to a designated destination for wheelchair-bound people and their caregivers. Thirteen individuals participated in an evacuation simulation in which they pushed wheelchairs carrying individuals from a start point to a designated destination along a specified route. The simulation was implemented on September 9, 2018, between 9:00 AM and noon. The weather was recorded hourly. Individuals in the wheelchairs were weighted to equal the average weight for Japanese men aged ≥65 years. The route comprised three sections: two were within the predicted tsunami inundation zone and one was on higher ground. The gradients, distances, and altitudes of the three sections were determined using official documentation. Participants were videotaped as they traversed the route, and their speeds and times recorded as the wheelchairs crossed from one section to the next. Immediately upon reaching the destination, participants completed a questionnaire assessing the physical effects of the activity. The route was 280.6 m long with a total vertical increase of 13.9 m. Mean total transit time was 3 min 25 s, and the mean time taken to evacuate the predicted tsunami inundation zone (at 210.2 m) was 2 min 15 s. All participants’ speeds were slowest in the third section, which had the steepest gradient (11.14%). Ten participants reported moderate difficulty, difficulty pushing, and sweating. Nine participants reported shortness of breath. The results support use of the evacuation destination during a predicted earthquake tsunami ≥8 m high at landfall.