Reviewerly/exHarmony huggingface dataset:
Application of artificial intelligence in secure decentralized computation enabled by TOTEM

TOTEM is a framework that allows users to execute their own code on access restricted datasets with controlled computation. It provides data security by restricting the exchange of data across the network, instead, the data will be executed within the data owner's environment itself. This ensures the data owners have full control over their data. The framework has been patented under US Patent No: US11,121,874 B2 in 2021. In this study, we are focusing on two areas, (i) how to improve the core features of the TOTEM architecture to an advanced level by introducing AI and (ii) the application of the TOTEM architecture in various sectors. Blockchain-enabled federated learning is getting more attention these days and its unique advantages while used within the TOTEM architecture are also discussed in this article.
HUBUNGAN BIAYA HIDUP DAN PENGELUARAN KONSUMSI MAHASISWA PADA PRODI PENDIDIKAN EKONOMI JURUSAN PIIS FKIPUNIVERSITAS TANJUNGPURA PONTIANAK

Abstractthe title of the research report is the Relationship of Cost of Living and Expenditure of Student Consumption in the Economics study program of Social science department FKIP of Tanjungpura University.The basis of the research is referring to the income theory that the cost of living provided by parents to students is a fee given periodically can be every month or every year, and is sourced from the income of parents transferred for limited education costs and the small amount is very dependent. The ability of the student's parents, while the expenditure of student consumption is strongly influenced by the demands of the student's needs in accordance with the consumption pattern that is influenced by the desire to consume or called marginal propencity to consume or MPC. So referring to the income theory, there is a relationship between the cost of living for students provided by parents and consumption expenditures made for education costs, even the effect not only on consumption but also on student savings, because income will affect consumption and savings that are the needs of students, then student consumption expenditures can be categorized as follows:Student consumption expenditures are less than the cost of living received by studentsStudent consumption expenses are equal to the cost of living received by studentsStudent consumption expenditures are greater than the cost of living received by studentsFurthermore, the problem of this study the author wants to describe the condition of students of economic education programs from the possibility of phenomena above, by survaiing the cost of living and student consumption expenditures in pursuing given by the parents of students and the use of fees carried out by students as a pattern of student consumption expenditure in pursuing education. The problem of this study is "How to relationship the Cost of Living and Expenditure of Student Consumption in FKIP Untan Economic Education Program"With the following sub-issues:How diversity of living costs received by students in FKIP Untan economic education study programHow diversity of student consumption expenditure on students of FKIP Untan economic education programHow is there an opportunity to save from the release of its consumption?How the cost of living relationship with student consumption patterns in students of FKIP Untan economic education programHow to relate the cost of living provided by parents with the pattern of spending on savings for educational activities in students of FKIP Untan economic education programHow is the relationship between student consumption patterns and the amount of savings for the purposes of upcoming educationalactivities. And the results can be seen from the description and understanding of the hypothesis. . Keyword : Relationship of Cost of Living and Expenditure of Student Consumption
Research on the Current Situation and Improvement of Three-Dimensional Learning of Compulsory Education in China in the Post-epidemic Era

In the post-epidemic era, compulsory education is carried out simultaneously at three dimensions of school education, family education and online education. Based on the methodology of literature survey, questionnaire survey and interview, and the current status, this paper proposes targeted and practical countermeasures to the compulsory education in China in the post-epidemic era. For the post-epidemic era, this study proposes to form a new three-dimensional education system to enhance the quality of compulsory education in China. To achieve this goal, this paper recommends methods such as optimizing and integrating education resources, constructing a brand-new learner-center education system, and also leveraging modern information technologies. These strategies are necessary to ensure the education system back to normal status under the prevent and control measures during the pandemic and are also in line with the proactive procedures for education reform and development adopted by internationally and domestically.
INFLUENCE OF THE STUDENT SELF-GOVERNMENT ON THE DEVELOPMENT OF LEADERSHIP QUALITIES OF HIGHER EDUCATION SEEKERS: ATTITUDE OF SCIENTIFIC AND TEACHING EMPLOYEES

The article is devoted to the analysis of the results of the survey conducted with scientific and pedagogical workers of 23 higher education institutions which was aimed at revealing the attitude of scientific and pedagogical workers to student self-government bodies’ activity and its influence on the development of higher education students’ leadership qualities. Emphasis is placed on the relevance of the chosen research subject, determined by the significant impact of student self-government on various aspects of student life in higher education institutions. The extent to which scientific and pedagogical workers of the higher education institutions are informed about the activities of student self-government bodies and their opinion about the leadership qualities of higher education seekers on the basis of a survey of 104 respondents is revealed. The author’s questionnaire consisted of three blocks of questions. The first block made it possible to collect information about the respondents. With the help of the second block, the scientific and pedagogical workers’ awareness of the activities of student self-government bodies was revealed, and in the third, the scientific and pedagogical workers’ assessment of the influence of student self-government bodies on the development of leadership qualities of higher education students was defined. The α-Cronbach reliability coefficient was calculated in order to check the internal consistency of the questionnaire. Based on the statements of the respondents, it was proven that the representatives of the teaching community are familiar with the activities of the student self-government bodies in the higher education institutions according to their place of work and the methods of student self-government. More than half of the surveyed scientific and pedagogical workers consider the influence of student self-government bodies on the organization of the educational process in higher education institutions and the development of leadership qualities of higher education students to be significant. Constructive judgments of the respondents regarding the improvement of student self-governance activities for the successful development of leadership qualities of higher education students are given.
Petrology, geochemistry and Ar-Ar geochronology of eclogites in Jinshajiang orogenic belt, Gonjo area, eastern Tibet and restriction on Paleo-Tethyan evolution

As one of the important Paleo-Tethys suture zones in eastern Tibet, the Jinshajiang orogenic belt is of great significance to study the tectonic evolution of the main suture zone of Paleo-Tethys. In this paper, eclogites developed in the Jinshajiang suture zone in Gonjo area, eastern Tibet, are selected as specific research objects, and petrological, geochemical and Ar-Ar geochronological analyses are carried out. The major element data of the whole rock reveals that the eclogite samples have the characteristics of picritic basalt-basalt and belong to the oceanic low potassium tholeiites. The results of rare earth elements and trace elements of the samples show that the protoliths of eclogites have characteristics similar to oceanic island basalt (OIB) or normal mid ocean ridge basalt (N-MORB). Muscovite (phengite) from two eclogite samples yield the Ar-Ar plateau ages of 247 ± 2 Ma and 248 ± 2 Ma respectively, representing the peak metamorphic age of eclogite facies and the timing of complete closure of the Jinshajiang Paleo-Tethys Ocean. Muscovite and biotite selected from the hosting rocks of eclogite yield the Ar-Ar plateau ages are 238 ± 2 Ma and 225 ± 2 Ma respectively, reflecting the exhumation age of eclogites and their hosting rocks. Combined with the zircon U-Pb dating data (244 Ma) of eclogites obtained in previous work, it can be concluded that the Jinshajiang Paleo-Tethys ocean was completely closed and arc-continent collision was initiated at about 248–244 Ma (T<sub>2</sub><sup>1</sup>). Subsequently, due to the large-scale arc (continent)-collision orogeney between Deqin-Weixi continental margin arc and Zhongza block (T<sub>3</sub><sup>1</sup>–T<sub>3</sub><sup>2</sup>), the eclogites were rapidly uplifted to the shallow crust.
e-Healthcare record management using blockchain technology

Electronic health records (EHR) provide an end-to-end ecosystem that unites patients, healthcare providers, and pharmacists. For the sustainable and enhanced performance of the healthcare industry of any province, EHR has become a key requirement. If a secure storage and transmission mechanism are incorporated into the infrastructure, it has the capability to support architecture based on Internet-of-Things (IoT). Such distributed settings may be equipped with blockchain technology to guard sensitive information regarding a person&apos;s medical status. Blockchain has been emerged as the most trustworthy technology for mitigating the risk of cyber-attacks and frauds in banking, cryptocurrency, insurance, supply chain, and healthcare as well. This chapter shows how blockchain technology can be used to monitor and manage patient data, prescriptions, and medicine traceability with minimum or no falsification of real data. A five-layer architecture has been presented in this study using Blockchain-based IoT (BIoT) for decentralized traceability in the medicine supply chain. The viability and effectiveness of BIoT have been validated by utilizing the real data from the Ethereum Blockchain ledger. The pharmaceutical industry can also utilize the proposed framework to implement blockchain design for robust supervision of the drug-supply chain. The amalgamation of transparency in information-sharing among collaborators will also help in regulating the quality of medicine.
A hybrid artificial bee colony algorithmic approach for classification using neural networks

Artificial neural networks are an integral component of most corporate and research functions across different platforms. However, depending upon the nature of the problem and quality of initialisation values, the usage of standard stochastic gradient descent always risks the possibility of getting trapped in local minima and saddle points for smaller neural networks in particular. One way to overcome this is by using algorithms with proven global search capabilities to train the network. This allows the neural net to reach the optimum values for weights regardless of the initialisation parameters used during training. Two algorithms are proposed based on modifications to the original artificial bee colony algorithm and their performances are analysed extensively on three benchmark datasets of increasing complexity. The first (NMABC), employs neural network appropriate initialisation and linear search space expansion. This is integrated into the second (LHABC), and incorporates stochastic gradient descent into the employed phase of the bees for faster convergence. It is found that the proposed algorithms consistently outperform standard approaches in all cases.
Rule-based activity recognition

Unlike the learning-based approach, where one does not need to know the details of a rehabilitation exercise, the rule-based approach requires a detailed description of each exercise to be recognized and evaluated in terms of a set of rules. Exactly because of this, it is difficult for the learning-based approach to provide specific feedback to patients regarding exactly what was performed correctly and what requires improvements. The rule-based approach could provide much more specific feedback to the patients. Studies on rule-based activity recognition can be roughly divided into two categories: (1) ad hoc rules that are applicable to a particular rehabilitation exercise; and (2) general purpose rules that are designed to define a broader range of exercises or activities. Another perspective related to rehabilitation exercises is the tolerance on movements within which are considered correct.For both types studies, such uncertainty can be captured explicitly by a threshold in the measurement as part of the rule, or by using the fuzzy interference using some membership definition. Fuzzy inference has also been used to detect activities.
Comparison of the efficacy of Natural Language Processing Algorithms at classifying Cyberbullying Tweets

Machine Learning is frequently used to predict and classify data. Natural Language Processing (NLP) uses machine learning to classify strings of words. There are many different machine learning models that can be used for NLP, with three main categories being regression, decision tree, and neural net models. Each has their own advantages and drawbacks. After being trained and tested on a set of tweets concerning cyberbullying, Logistic Regression, XGboost, and Long Short-Term Memory (LSTM) were compared in terms of several metrics, including accuracy, recall, precision, and f1-score. Afterwards, the metrics were considered in combination with model runtime and complexity to determine which model was most appropriate for the given dataset and other similar datasets. Logistic Regression was found to lack sufficient complexity to properly classify the data. LSTM had worse metrics than XGboost and had significantly higher complexity and runtime. XGboost performed best, with the highest metrics and relatively short runtime.
Motivational Strategies Applied by the English Tutors to Teach Young Learners

Young learners’ characters are different from adolescents and adults so their teachers need to apply some strategies to motivate them for studying English well. Some children’s motivated behavior such as high attention and participation in learning appeared at an English course institution in Palangka Raya, GLC. Therefore, this study aimed to describe the motivational strategies applied by the English tutors of GLC to teach young learners. The study used a qualitative approach with a case study. In collecting the data, it used observation, interviews, and documentation. The research findings showed when it was related to the motivational strategies in teaching English based on Dornyei, two tutors of GLC applied four aspects of motivational strategies. They were creating the basic motivational conditions, generating initial motivation, maintaining and protecting motivation, and then encouraging positive retrospective self-evaluation. These strategies were applied in the learning process to bring out, preserve, and enhance their motivation.
COBALT MOBILITY AND REACTIVITY IN WADI EL-YATIM SOIL IN THE WEST DESERT –EGYPT

This research was aimed to shed light on the cobalt content, its mobility and reactivity in Wadi El-Yatim soil in the Western Desert -Egypt.To achieve this, six soil profiles were taken, with nineteen soil samples representing the top, middle, and bottom of the valley.The results showed that: -The texture of the soil in the valley soils was sandy in all soil samples that represent the valley soils.The soil reaction ranged from neutral to alkaline, and the electrical conductivity ranged from 5.25 to 50.20 dS/m at 25 ᴼC.The total content of calcium carbonate ranged from 2.93 to 67.76 gkg -1 .The soluble sodium cation was dominant, and the order of the content of the soluble cations was as follows: -sodium > calcium > magnesium > potassium.The results also showed that the total content of some microelements was as follows: Iron > manganese > lead > zinc > copper > cobalt.It was found from the results that the chemically extracted, available microelements content was: -Iron > manganese > zinc > copper > cobalt > lead.The results also showed that the chemical forms of the cobalt element were as follows: -the total form > the residual form > the chemically extracted form > the form bound with organic matter > the form bound with iron and manganese > the exchangeable form > the form bound with carbonates > the soluble form.The results were discussed statistically between the chemical forms of cobalt and some soil variables.
Computational method to artificially insert clusters of microcalcifications in digital breast tomosynthesis

Digital Breast Tomosynthesis (DBT) is a medical imaging modality that has been increasingly used for breast cancer screening. To improve the accuracy in the early detection of breast cancer, it is common to use tools based on image processing to improve the quality of DBT images and, consequently, the visibility of lesions of clinical interest. Microcalcification (MC) clusters are in the class of findings that may indicate the early stages of breast cancer. To evaluate the impact of image processing methods in the detection of breast lesions, human perception studies are usually performed, where detection accuracy is evaluated using positive and negative cases, i.e., images with and without lesions. However, only a small fraction of clinical cases have positive cases and the exact location of the lesion is not always known. Thus, in this work, we present a method to artificially insert MC cluster in normal exams of DBT. A set of MCs was segmented from clinical cases acquired in a prone stereotactic breast biopsy system. In the pipeline, we built a MC cluster and insert it in a 3D volume. Then, we project it on the detector and inserted the MC cluster into the projections of a clinical DBT exam. It is possible to define the size, contrast, and location of the MC cluster accordingly. We performed a two-alternative forced-choice (2-AFC) study with six experienced medical physicists and the average success rate was 53.83%, suggesting that readers, on average, could not distinguish between real and simulated MCs. Overall results showed that images with simulated MC are similar to the real clinical cases. Our source code is available at <a href="http://www.github.com/LAVI-USP/MCInsertionPackage-DBT">www.github.com/LAVI-USP/MCInsertionPackage-DBT</a>.
PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions

In histopathology, whole-mount sections (WMS) can be utilized to capture entire cross-sections of excised tissue, thereby reducing the number of slides per case, preserving tissue context, and minimizing cutting artefacts. Additionally, the use of WMS allows for easier correlation of histology and pre-operative imaging. However, in digital pathology, WMS are not frequently acquired due to the limited availability of whole-slide scanners that can scan double-width glass slides. Moreover, whole-mounts may still be too large to fit on a single double-width glass slide, and archival material is typically stored as tissue fragments. In this work, we present PythoStitcher, an improved version of the AutoStitcher algorithm for automatically stitching multiple tissue fragments and constructing a full-resolution WMS. PythoStitcher is based on a genetic algorithm that iteratively optimizes the affine transformation matrix for each tissue fragment using the Euclidean distance between corner points of neighbouring fragments. We demonstrate the efficiency and generalisability of PythoStitcher in a pilot validation of ten prostatectomy cases with four fragments and two oesophagectomy cases with two fragments. On average, PythoStitcher obtained the final stitching result in 60 seconds (range 51-73) for the prostatectomy cases and 68 seconds (range 43-94) for the oesophagectomy cases. The stitching accuracy was objectively evaluated by computing the average Euclidean distance between all corner points from the edges of neighbouring fragments. For the reconstructed WMS at full resolution, the average residual registration error was 1.6 mm (range 0.5-3.2) for the prostatectomy cases and 2.1 mm (range 0.5-3.8) for the oesophagectomy cases. The key strengths of PythoStitcher include its computational efficiency, the ability to reconstruct the WMS in full resolution, and an open-source implementation in Python.
Ki67 proliferation index quantification using silver standard masks

Deep learning (DL) systems obtain high accuracy on digital pathology datasets that are within the same distribution as the training set. When applied to unseen datasets, performance degradation occurs due to differences in acquisition hardware/software and staining protocols/vendors. This issue poses a barrier to translation since developed models cannot be readily deployed at new labs. To overcome this challenge, we present silver standard (SS) annotations as a method to improve the performance of deep learning architectures on unseen Ki67 pathology images. An unsupervised technique referred to as IHCCH was used to generate SS masks for Ki67<sup>+</sup> and Ki67<sup>−</sup> nuclei from the target lab. A previously validated architecture for Ki67, UV-Net, is trained with a combination of the gold standard (GS) and SS masks to enhance performance consistency. It was found that adding SS masks from the unseen center to the training pool improved performance over clinically relevant PI ranges.
On the benefits of region of interest detection for whole slide image classification

Whole slide image (WSI) classification methods typically use fixed-size patches that are processed separately and are aggregated for the final slide-level prediction. Image segmentation methods are designed to obtain a delineation of specific tissue types. These two tasks are usually studied independently. The aim of this work is to investigate the effect of region of interest (ROI) detection as a preliminary step for WSI classification. First, we process each WSI by using a pixel-level classifier that provides a binary segmentation mask for potentially important ROIs. We evaluate both single-resolution models that process each magnification independently and multi-resolution models that simultaneously incorporate contextual information and local details. Then, we compare the WSI classification performances of patch-based models when the patches used for both training and testing are extracted from the whole image and when they are sampled from only within the detected ROIs. The experiments using a binary classification setting for breast histopathology slides as benign vs. malignant show that the classifier that uses the patches sampled from the whole image achieves an F1 score of 0.68 whereas the classifiers that use patches sampled from the ROI detection results produced by the single- and multi-resolution models obtain scores between 0.75 and 0.83.