Reviewerly/exHarmony huggingface dataset:
Embedding Brain Tissue for Routine Histopathology: A Processing Step Worthy of Consideration in the Digital Pathology Era

The importance of technical quality for histopathologic examination has only increased in recent years with the expanding use of digital pathology. The University of Kentucky Alzheimer's Disease Center (UK-ADC) Neuropathology Core has decades of experience with brain histopathology and has emphasized the importance of quantitative assessments of histopathologic hallmarks. Technical artifacts and nonuniform samples are challenging for high-throughput digital analyses after the slides have been scanned, so that methodological optimization may be helpful. We do not know of published literature that systematically reviews how different procedures at the various stages of tissue processing can impact the quality of the histopathologic preparations in human brain samples. We wanted to pass along our experience in the hope that it will help others to improve their results. Here we describe the UK-ADC method of embedding for neuropathologic evaluation and provide specific examples (with a comparison to another processing workflow) that help support the idea that the methods and tools used in the embedding process can alter the quality of the formalin-fixed paraffin-embedded histopathologic results. The process used at the UK-ADC has been successful for us, but results may vary in relation to each embedding machine and with other factors.
Pengaruh Faktor Meteorologi Terhadap Konsentrasi NO2 di Udara Ambien (Studi Kasus Bundaran Hotel Indonesia DKI Jakarta)

A combustion process in power plants, industry and transportation can cause air pollution problems, one of which is the pollutant of Nitrogen Dioxide (NO2) gas. One of the stages of air quality management is monitoring. In monitoring air quality, there are several factors that need to be considered because they can affect the measurement, there are meteorological factors. This study analyzes how temperature, humidity, rainfall and wind speed affect the measured NO2 concentration. NO2 and meteorological concentration data were taken at the same time at the location of the DKI-1 air quality monitoring station (Bundaran Hotel Indonesia, DKI Jakarta). Analysis of the relationship between measured NO2 concentrations with temperature, humidity, rainfall and wind speed was performed using multiple linear regression analysis. The analysis showed that there was a strong relationship between NO2 concentration and four meteorological factors. The R2 value indicates the effect of the four meteorological factors on NO2 concentrations is 38.3% and the remaining 61.7% is influenced by other factors not included in the measurements in this study.
An Enhanced Data Sparsity Reduction Method for Effective Collaborative Filtering Recommendations

Collaborative filtering recommender system suffers from data sparsity problem due to its reliance on numerical ratings to provide recommendations to users.This problem makes it difficult for the system to compute accurate similar neighbours for the items and provide good quality recommendations.Existing methods fail to pre-process the missing ratings of the new items and to predict cold items to the active users which lead to poor quality recommendations.In this work, a sparsity reduction method is presented to improve the quality of recommendations.The method utilises Bi-Separated clustering algorithm to cluster the ratings matrix simultaneously into users and items bi-clusters based on ratings classification.It also employs Bi-Mean Imputation algorithm to fill the missing ratings in the bi-clusters using the estimated means.The method then performs the traditional collaborative filtering process on the new rating matrix for cold items prediction.The experimental results demonstrated that compared to the existing method, the proposed BiSCBiMI improves density of the rating matrix by 5.75%, 10.73% and 7.35% as well as Mean Absolute Error (MAE) of the new items prediction for all of the considered datasets.The results indicated that, the proposed approaches are effective in reducing the data sparsity problem as well as items prediction, which in turn returns good quality recommendations.
Dynamic Web View Materialization

Dynamic Web applications, such as e-commerce Web application, extensively use databases as backend servers. These Web applications are highly dependent on the efficiency with which the Web databases can be accessed. A dynamic Web page consists of many small fragments of scripting languages, called Web views, which may require access to data in disparate databases. These Web views when materialized can enhance the efficiency of database access. An e-commerce Web application has large number of users involved in various personalized interactive activities. These generate a stream of Web page accesses and modification requests that necessitate taking dynamic decisions for selecting Web views for materialization, which minimize the average access time of Web views. In this paper, a Web view selection algorithm (WVSA) that selects Web views for materialization is proposed. Experimental results suggest that materializing Web views using WVSA increases the efficiency of Web view accesses.
In-vehicle object detection in the wild for driverless vehicles

In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles. A challenge is the difficulty to detect objects in moving under the wild conditions, while illumination and image quality could drastically vary. In this work, to address this challenge, we exploit Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to handle with the wild conditions. In our work, a GAN was trained with low-quality images to handle with the challenges arising from the wild conditions in smart cities, while a cascaded SSD is employed as the object detector to perform with the GAN. We used tested our approach under wild conditions using taxi driver videos on London street in both daylight and night times, and the tests from in-vehicle videos demonstrate that this strategy can drastically achieve a better detection rate under the wild conditions.
Kesiapan Visualisasi Data Kemiskinan sebagai Rujukan dalam Perencanaan Pengembangan Kebijakan

This study aims to find out what factors influence the readiness of data visualization that is used as a reference for policy development planning. The important role associated with a data visualization requires responses from several users, considering that in the process of procurement and construction of data visualization sometimes an institution does not pay attention to readiness studies, moreover it is intended to visualize poverty data. The method in this study there are three stages, which consist of literature review, operationalization, and writing of research reports. The research model used is the Technology Readiness Index model from Parasuraman with motivators, optimism, innovation, inhibitors, discomfort, insecurity and output readiness variables. The results obtained stated that insecurity factors had an effect on readiness. Given the respondents from various elements with different educational backgrounds and also many gaps arise in its application, it is necessary to choose respondents wisely to get more valid results
The influence of blockchain technology on exchange safety and costs

The goal of this paper is to show the impact of blockchain technology on transaction costs, as well as to show the factors which increase exchange safety when using the blockchain and to point out the potential problems with this technology.A blockchain is an open source, decentralized database, using the Internet and peer-to-peer architecture to register transactions.The potential of this technology leads to the hypothesis that it may become the equivalent of the invisible hand of traditional market exchange, meaning primarily the autonomy of transactions that use it.Blockchain is a mechanism which allows to enforce the execution of agreed contracts through encryption.The qualities of blockchain technology bring the transactions made with its use closer to the conditions of a perfectly competitive market, especially when it comes to transparency and supply and demand dispersion.This results in the diminished ability to influence the price on both sides of the market.
KPWr n82 NER model (on Polish RoBERTa base)

The named entity recognition model for fine-grained categories of entities (82 types) was trained on the KPWr corpus using Polish RoBERTa base language model. Details can be found on the following page: https://github.com/mczuk/xlm-roberta-ner
Blind quality assessment for multiply distorted stereoscopic images towards IoT-based 3D capture systems

Empowered by 5G mobile communication networks, multimedia processing has been considered as a very promising application of Internet-of-Things (IoT). Stereoscopic image quality assessment (SIQA), as an important part of 3D capture system, can be embedded in the cloud or fog servers to automatically monitor the perceptual quality of the collected stereoscopic images. In this paper, a novel blind image quality assessment method towards IoT-based 3D capture systems is developed for multiply-distorted stereoscopic images (MDSIs), in which five complementary channels, including left view, right view, cyclopean map, summation map and difference map, are jointly considered in dictionary learning for characterizing the monocular receptive field (MRF) and binocular receptive field (BRF) properties of the visual cortex in response to MDSIs. Additionally, the high order statistics scheme is adopted by utilizing the statistical differences between the codebook and images to ensure the stable and robust quality prediction performance for MDSIs. The proposed method shows competitive prediction performances on four benchmark databases compared with the existing SIQA metrics.
Application of Remote Sensing Technology in Macro-Ecological Environment Monitoring

&lt;p&gt;At present, all countries in the world attach great importance to the development and application of remote sensing technology, which is widely used in many fields. By means of detection methods, this technology combines physics knowledge and modern network technology to provide relevant information for human exploration of geology, atmosphere, ocean and weather. In recent years, the global economy has developed rapidly. However, the environmental pollution has become increasingly serious at the same time. Industrial enterprises have discharged a large number of pollutants, resulting in air pollution, water pollution, soil pollution and so on, which seriously endanger human health and life safety. Environmental monitoring is the basis of effective control of environmental pollution. Remote sensing technology can be applied to carry out environmental monitoring and improve the monitoring effect of environmental monitoring.&lt;/p&gt;
Hybrid of Support Vector Machine Algorithm and K-Nearest Neighbor Algorithm to Optimize the Diagnosis of Eye Disease

In order to optimize the diagnosis of eye disease, the authors want to propose the combination of Support Vector Machine and K-Nearest Neighbor Algorithm to Optimize Diagnosis of Eye Disease. This is a combination method of SVM to get a better classification to diagnose the eye disease. The problem is how to get a better classification to diagnose eye disease. In this research, the hybrid of SVM-KNN is more accurate to classify the eye disease than SVM itself. The accuracy using the combination SVM-KNN algorithm is 94.67%.
Library Management System using RFID

This paper will discuss about the usage of RFID or Radio Frequency Identification in libraries.This will highlight the advantages of using RFID in Library management and discuss the various purposes of deploying such technology to use.The RFID technology is a form of wireless identification technology that uses radio waves to identify, track with the help of a receiver and a transmitter that is attached to the object.The RFID tags will be attached on books and the student or the staff will be issued an RFID card similar to that of their ID card.These cards and tags will be scanned using an RFID scanner/reader.The books will be then, issued when the user tag and the book tag is read and tagged.The books can be tracked and the other essential data like the existing and issued books can be kept in records.
Post Covid-19 Era: Higher Education Transformation from Offline to Online

During the past few months, with somewhere to coronavirus everywhere, the invisible virus has taught us lesson. Post COVID-19 pandemic has disrupted the entire scenario of higher education and the educational Institutes in India has undergone massive reformed transformation. We have started to look into new openings to educate ourselves to find the solutions to paradigm shift in learning from traditional teaching method of chalk, talk and board to virtual teaching by means of innovations in technology. However, structural imbalances exist even in this digital era. The major issues with the rural learning is the problem of internet access, electricity and ICT tools like laptops and smart phones. The Government of India should take steps, consider the ground reality, work to bring poor, lower stratum of society, underprivileged students in the forefront of main stream quality education. Our country’s policies should ensure a strong foundation in this difficult time of crisis to innovate, ensure quality education to all for few critical months and future as well with sustainable inclusive development.
EBSMOTE: Evaluation-Based Synthetic Minority Oversampling TEchnique for Imbalanced Dataset Learning

Imbalanced data pose a tremendous challenge to standard machine learning classifiers which assume balanced training data. These methods are inclined to accurately classify the majority samples, whereas the minority ones are often misclassified. Oversampling techniques are commonly used in the literature to address this problem. In a complex data environment, most of the oversampling techniques may fail to identify useful original minority instances for oversampling. The consequence is that needless synthetic samples may be generated to obstruct the class boundary and aggravate the leaning task. To handle this issue, a novel method, called Evaluation-Based Synthetic Minority Oversampling TEchnique (EBSMOTE), is offered to handle the imbalanced data problem by following five key steps: 1) synthetic instances are created based on SMOTE; 2) synthetic instances are sorted as good or bad based on the estimated rate of variance between the synthetic and the original minority and majority samples; 3) the best synthetic instances are selected based on the good ones; 4) a criterion is defined to reject the synthetic instances that are more likely to be noisy; 5) new synthetic instances are created based on the good ones using SMOTE algorithm and the best set of synthetic instances obtained are added to constitute the balanced training data. The approach is extensively evaluated on several real-world datasets at different imbalance ratio. The C4.5 and Logistic Regression (LR) classifiers are employed for experimental validation. The empirical results show that the proposed method is superior to the compared ones.
Gaussian based Image Segmentation Algorithm

This study adopted the Gaussian equation to eliminate noisy pixels in a segmented image.The equation was converted into a convolution kernel and the values were normalized to attain a perfect result.In order to preserve every information in the image, the border pixels were padded with zeros before the image was convolved with the designed kernel.A variance was computed from the result obtained from the convolution operation and the pixel that obtained the minimum variance was selected as the threshold.In order to obtain the optimum threshold, the computed threshold was multiplied by the optimization constant which ranges from 0.1 to 1, and the resultant was considered as the final threshold for the segmentation process.The Gaussian algorithm was evaluated with global, Otsu and adaptive algorithms.The performance metrics used were signal to noise ratio (defines the sensitivity of algorithm), mean square error and running time (specifies the execution time of an algorithm).In the first experiment which was made up of 20 noise free images, the adaptive attained the highest rating of 7.117dB.This was followed by the Gaussian algorithm at 5.231dB.The poorest performance was seen in the global at 1.988dB.The second experiment was done with ten noisy images.In the experiment, the Gaussian recorded the highest rating of 3.124dB, while the adaptive scored the poorest at 1.847dB.From the running time, the fastest algorithm was seen in the global at 2.814s.This was followed by the Otsu at 9.814s.There was only a difference of 2.107s between the average execution time of the Otsu and the Gaussian This was due to the noise suppressing mechanism in the Gaussian.The slowest algorithm was the adaptive which recorded a running time of 110.594sThe Gaussian algorithm performed better when there was presence of noise in the image.This explains why it recorded sensitivity rate of 3.124dB in the second experiment.However, for noise free images, the adaptive algorithm is the best despite the poor performance in the running time.
Math approach of implementing ISO 27001

Abstract The objective of this paper work is to create a mathematical approach that can quantify the need of implementing the ISO 27001 in a company and the actual benefits of doing so. The scope is to identify the factors that determine vulnerabilities, what damages are caused by these factors, the risk and impact level of the factors. The approach is from the point of view of costs caused by implementing the standard compared with costs of potential damage by not implementing.
Analysis and Classification of Requirements Specification for Web Application Development: A Case Study Approach

In this paper, we formulated, designed, implemented and evaluated a model used for classifying stakeholders' requirements that are specified for web application development. The study employed both qualitative and quantitative research approaches in a case study. Requirements were elicited from stakeholders using the interview approach. This involved speaking with the stakeholders directly via groupware and asking them questions about their specific needs that are relevant to the development of web application. In particular, 10 customers of Procrea8 Technology Solution Limited and 9 developers were used as respondents. An interactive genetic algorithm was used to formulate the model. The design was specified using the Unified Modeling Language (UML) tool, and implemented using specified web technology tools. The model was evaluated for completeness and consistency using recall and precision as parameters. The results showed that a list of ordered requirements was produced based on the stakeholders' priorities inputted into the model. The output indicated the order of priorities finally assigned to each of the requirements. The evaluation revealed that the model is effective, efficient, userfriendly, reliable (with 96.3% accuracy), scalable (prioritized over 500 requirements), less timeconsuming (prioritizing over 500 requirements) and able to update ranks whenever changes occur automatically. Also, the model evaluation indicates 97.1% precision (consistency), and 96.0% recall (completeness). The study shows that requirements engineers could use the model to collate stakeholders’ requirements from wide geographical locations.&#x0D; Keywords: Requirements analysis, requirement prioritization, requirements engineering, web application, requirement specification.
Augmented Reality Production Monitoring Control Room

The idea of visiting fully virtual worlds separate from our own has long captured the public’s imagination and taken precedence over the “augmenting” of our existing world. On the other hand, AR has long held many practical applications in enterprise environments, such as industrial manufacturing trough tools used for monitoring production, training for personnel and remote maintenance.
Blockchain and Industry Use Cases

This chapter introduces some interesting use cases in a myriad of different industries. For some of you, the reason might be obvious. When you start a discussion about the blockchain technology, many people like to say that they still haven’t seen a working implementation in an industry, except for cryptocurrencies. Well, here we leave the theoretical path and show cases that are actually in use today. While there is still work to be done on the blockchain technology, this hasn’t stopped industry leaders from implementing the technology and changing the way they work.
Audio Interview: Covid-19 and Contact Tracing

Covid-19 and Contact Tracing In this audio interview conducted on August 19, 2020, the editors discuss the use of contact tracing to limit the spread of SARS-CoV-2 and the challenges posed by certa...
Speckle projection profilometry with deep learning

Traditional single speckle pattern matching algorithms always suffer from the low measurement accuracy and cannot be used to measure complex surface objects. A speckle projection profilometry with deep learning was proposed to realize the pixel-by-pixel matching. The siamese convolutional neural network structure was applied and extended where the main speckle pattern and the auxiliary speckle pattern were fed into the neural network patch by patch. It was expected that the feature from the speckle pattern patches could be extracted by the convolution operation. In this way, the features were fused and the matching coefficient between the two patches was obtained, which could be further used to formulate the disparity data and then the three-dimensional (3D) object was reconstructed. The experiment results demonstrate that with the proposed method  3D measurement with an accuracy of about 290 μm could be achieved through a single speckle pattern.
SoS Architectural Requirements Modeling and Analysis

Abstract Analysing the architectural requirements of System-of-Systems is a daunting task, usually involving a complicated process of enterprise architecture modeling and verifying. So the paper proposes a method of capability-oriented requirements modeling for developing the architecture requirements of System-of-Systems. A multi-layer ontologies framework is suggested to model the architecture requirements. To check the consistency of performance requirements, a formal inference method is designed to verify the requirements models. Finally, a case study is provided to demonstrate the applicability of the method.