Reviewerly/exHarmony huggingface dataset:
A Study on the Use of Data Mining in the Planning of Investment Field

One of the most important strategic decisions taken by company managers at corporate level is in which areas to invest and how to manage and organize investments.Strategic management proposes portfolio analysis techniques in this regard.It is stated that portfolio analysis techniques, which have been criticized at many points, should be considered as initial techniques and should be supported by other techniques in practice.At this point, the question of whether or not data mining techniques will be used to identify new investment areas and allocate resources has constituted the main problematic of this study.In this study, it has been investigated whether there are association rules between investment areas thus, it is tried to reach the evaluations that may affect the strategic decisions in the process of determining investment areas, by using data belonging to various investor organizations.For this purpose, Association Rules Mining was conducted using data from 102 holding companies.As a result of the study, 35 rules were produced above the 50% confidence level.It is presented as a suggestion to the enterprises in which they can benefit from these rules in their investment planning.
Investigating intra-tumor heterogeneity in non-small cell lung cancer using multiplexed immunohistochemistry and deep learning

Non-Small Cell Lung Cancer(NSCLC) is the most common cause of cancer death in Canada and worldwide. As the immune component of the NSCLC tumor microenvironment(TME) is highly prognostic of patient outcome, further understanding of TME and the spatial organization of the immune cells within the TME is needed for better patient prognosis and treatment planning. Current immunohistochemistry techniques quantify immune cell counts and density, but generally cannot asses the spatial relationship between tumour and immune cells. We have developed a multiplexed Immunohistochemistry(mIHC) procedure combining multiple labels per round with several rounds, enabling analysis of immune cell populations on a slide through consecutive cycles of staining, destaining & hyperspectral imaging. By integrating serial imaging, sequential labeling & image registration, we are able to spatially map the TME. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. We have trained a deep learning segmentation method to accurately segment individual cell nuclei within overlapping clusters of nuclei. By combining a mIHC technique which enables the detection of multiple markers with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation, we can analyze the cell-cell interactions between immune and tumour cells, enhancing our ability to perform molecularly based single cell analysis of multiple cell types simultaneously within the tissue. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of NSCLC’s.
Topic Trends in Issue Tracking System of Extended Reality Frameworks

Extended Reality (XR) is an emerging technique with a lot of application domains. In this paper, we present an exploration study of two XR frameworks, in particular investigating the trends of topics in their issue tracking systems over time.
Background Concentrations of Trace Elements and Sediment Accumulation Rates for Tropical Pristine Environments

Trace elements are pollutants that can be released into the environment causing adverse effects on ecosystems and human health. Once released into the environment, they can be dispersed by the wind and deposited in soil and bodies of water and accumulate in marine sediments. After entering the food chain, trace elements can accumulate in the human body at harmful levels. Background concentrations of trace elements were defined for marine pristine environments of Panama. Trace elements such as As, Co, Cr, Cu, Ni, Pb, V and Zn were determined from sediment cores. Temporal records of trace element concentrations and its fluxes were dated for the period of 1906 to 2017 by using the 210 Pb technique. Higher flux rates of trace elements were observed since the 1950s and were attributed to an increase in human activities and climate variability. Since the enrichment factor in cores was always <2, there is no contamination, and the trace elements are mainly derived from lithogenic sources. Threshold Effect Level and Probable Effect Level (TEL and PEL) indicated that Cr, Cu, As, and Ni can pose a risk to the biota and therefore to humans, even at natural levels. The periodic monitoring of marine environments is recommended in order to minimize human health risks, damages to the biota, and to control some human activities with a possible contamination potential.
Customer Experience Management (CEM) Supports the Quality of School Based on NFC

Schools as providers of educational services are a gateway to the progress of a nation. Many prospective students look for schools with good quality in order to get something that they aspire to. While the reference for assessing school services for prospective students can only be known from students who have graduated or through poster media. Meanwhile, the assessment of student experience only refers to personal opinions and only one factor such as teaching services or administration. Each student has a different argument about school services. This study will explain the creation of a framework for determining service levels in NFC-based schools. Evaluation of services in schools using information technology through smartphone applications connected to the computer provided at the information service desk. The information generated in the form of evaluating educational services in schools aims to improve the quality of learning and academic administrative services. Furthermore, the information generated will be made a report by the data processor addressed to the leadership. With this service, it is expected that there will be an increase in the quality of education and learning services that are able to produce quality graduates and can become information and references for prospective students in choosing schools as a reference for educational services
Self-Supervised Marine Video Analysis via Siamese Network

At present, advanced equipment has provided strong data support for marine scientific research. However, it is unrealistic to rely on manpower analysis to analyze the huge amount of data. Therefore, it is an effective and good method to use computer vision method to automatically identify and analyze marine video. In this paper, a self-supervised learning method based on siamese network is designed to learn the effective visual representation in marine unlabeled video, and the model is transferred to three downstream tasks: marine organism action recognition, marine organism detection, and marine scene recognition. We are on the latest dataset to experiment to evaluation the effectiveness of our method. Experimental results show that our method has certain competitiveness and effectiveness in the three downstream tasks.
Droplet Deposition Characteristics Detection Method Based on Deep Learning

Accurate acquisition of spraying quality parameters of plant protection Unmanned Aerial Vehicle (UAV) is helpful to analyze the deposition and distribution of pesticides in field crops. It is of great significance for pest control. UAV spray quality detection system was used to collect droplets, the droplet deposition parameters can be detected by processing the droplet images. However, the spots and the adhesion of the droplets led to large data errors. In order to solve this problem, this paper proposed a recognition method of adhesive droplets based on Deeplab V3+ deep learning network, and a concave point matching segmentation algorithm based on convolutional neural network was designed to segment adhesion droplets. Used water instead of pesticides to spray with plant protection UAV to verify the detection effect of the above method. The test results showed that this method was accurate in the identification and extraction of droplets in the detection of droplet coverage. Compared with the droplet coverage measured by Deposit scan, the average relative error is 3.81%. Compared with the manual counting method, the detection error of the droplet deposition density was 3.17%. It was better for the segmentation of adhesion droplets. This method improves the accuracy of UAV spray quality detection, so as to ensure the correctness of plant protection UAV spraying decisions.
Domino: Twin interfaces to unveil the influence of personality traits on usability

This pictorial describes the design and design process of the Domino interfaces: a pair of light interfaces consisting of a tangible user interface and its touchscreen equivalent. This pair of user interfaces is designed to be used in an exploratory comparative study concerning the influence of personality traits on perceived usability. We present the design process of this pair of user interfaces, focussing on the unique challenges of designing a functionally equivalent set of physical and graphical user interfaces. The exploratory comparative study, which utilizes these Domino interfaces, is presented next. We continue with the results from the study after which we conclude with a concise discussion and outlook on future work.
OPAL: On-the-go Physical Attack Lab to Evaluate Power Side-channel Vulnerabilities on FPGAs

Power side-channel attacks on intellectual property blocks (IP) are prevalent in a wide spectrum of computing platforms ranging from smartphones to large-scale servers. However, the existing frameworks to evaluate such attacks require physical access to the designs. In addition, they are limited by their cost and inability to scale well. They rely on external devices to monitor and modify physical operating conditions of a target system. Furthermore, their operation and deployment require deep electrical engineering expertise such as making board-level modifications. This work introduces On-the-go Physical Attack Lab (OPAL), which is a hardware/software framework to evaluate power side-channel information leakage of a hardware design on FPGAs. OPAL leverages on-chip components available in most commercial FPGAs. It does not require any additional specialized equipment, and thus, OPAL is a low-cost, flexible, and portable platform that can be controlled remotely. We demonstrate the feasibility, portability, and performance of OPAL by implementing it on Intel Arria 10 and Stratix 10 FPGA boards. We use OPAL to evaluate an open-source AES implementation for side-channel vulnerabilities.
Performance of list mode Hotelling observer and comparison to a neural network observer

The Hotelling observer (HO) is a commonly used linear observer for detection or classification tasks. The conventional implementation operating on binned data normally involves inversion of covariance matrices and estimation of the difference in means of two vectors. However, the conventional calculation can’t be directly applied to list mode data. The situation is salvageable by using the attribute list to construct a Poisson point process in attribute space,1 which makes the computation of HO quite different. In this work, we present an example of computing the HO test statistic on list mode data. The observer performance is measured on a signalknown- exactly and background-known-statistically task. The receiver operating characteristic (ROC) curve of the HO on list mode data is compared to the corresponding approximation by use of supervised learning methods proposed in the paper2 on binned data, where a single-layer neural network (SLNN) is used to approximate the HO test statistic. The comparison shows that the HO on list mode data outperforms the binned data. The result demonstrates the fact again that list mode data contains more information comparing to its binned version.
Machine learning for Autopsy reports Forensic using Text Classification Techniques

A forensic autopsy is a surgical process in which experts collect a deceased body&#x0027;s internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It&#x0027;s only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure.
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data

Every dataset is flawed, often in surprising ways that data scientists might not anticipate. However, popular machine learning methods are mostly black-boxes. Due to their lack of interpretability, they might learn defective knowledge from these datasets, which can be difficult to detect. In this work, we show how interpretable machine learning methods such as EBMs can help users detect problems that are lurking in their data. Specifically, we provide a number of case studies, where EBM discovers various types of common dataset flaws, including missing values, confounding and treatment effects, data drift, bias and fairness, and outliers. In each case study, we analyze the flaws using visualization of EBM shape functions combined with domain knowledge. We also demonstrate that in some cases interpretable learning methods such as EBMs provide simple tools for correcting problems when correcting the data is difficult.
Face Expression Recognition in Video Using Hybrid Feature Extractor and CNN-LSTM

Face expression recognition in videos is a challenging task as it involves extracting and combining high level temporal and spatial features from the video sequences to automatically identify human expression categories. In this paper, we propose a Hybrid feature extractor, analyze various deep learning classifiers and their performance. Addressing illumination invariance and scale invariance, this method initially pre-processes the input video to remove noise and uses Viola jones algorithm to detect face region; then the high significant features, such as temporal features, spatial domain features, and the frequency domain features are extracted. Further, dimensionality issues are handled effectively using the Principle Component Analysis (PCA). Finally, the fused features are fed to train CNN-LSTM, a hybrid deep learning classifier for identifying face expressions. Experiment was conducted on SAVEE, RAVDESS and AFEW Datasets. The proposed method obtained an overall accuracy, precision, and recall of 90.5%, 92.39%, 88.18%, respectively.
Research on Learning Method of Attribute Network Representation for Edge Node Discovery

The main task of network representation learning is to learn low-dimensional dense vector representations for nodes in the network, and to provide efficient data support for network analysis tasks such as node classification, link prediction, and node clustering. The existing network representation learning methods mainly include structure representation learning and attribute representation learning. However, researchers usually only consider the characteristics of regular nodes when modeling the structural and attribute characteristics of the network, ignoring the importance of edge nodes in the network. Although the number of edge nodes in the network is often lower than that of conventional nodes, they play a vital role in solving network analysis problems. In response to the above problems, this paper proposes an attribute network representation learning method ANE_BN for edge node discovery. This method applies structural representation learning and attribute representation learning methods to the representation learning problem of edge nodes for the first time, not only considering edge nodes attribute modeling also considers how to integrate attribute information and structural information into the node representation learning model. Specifically, first, the similarity of nodes in network structure is calculated by fusing high-level network structure information, and the similarity of nodes in attributes is calculated by cosine similarity, which provides data support for structure representation and attribute representation learning. Then, the non-negative matrix factorization method is used to model the representation learning model of the node in both the network structure and the attribute, and the relationship between the structural feature and the attribute feature is modeled by introducing the incidence matrix, so as to achieve structural stability and real-time attributes complementary advantages between sexes. In the experiment, the effectiveness of the algorithm ANE_BN proposed in this paper is verified through a real standard data set and compared with the existing classic algorithm. The experimental results show that the ANE_BN method can be better under the premise of ensuring the effectiveness of conventional node representation learning. The improved edge node indicates the effectiveness in various network analysis applications.
Comparison of Effect of learning rate of Neural Network Performance in Deep learning neural networks using the stochastic gradient descent algorithm

The stochastic gradient descent optimization approach is used to train deep learning neural networks in this paper. Artificial neural networks are a subfield of deep learning that uses algorithms inspired by the structure and function of the brain. Deep learning systems are designed to learn feature hierarchies based on the composition of lower level characteristics at the top of the hierarchy. The various sorts of learning models are also discussed. We build a train and dataset with different samples (500, 2000, and 4000) and change the Epochs value in this research (100, 300 and 400).We also change the learning rate for different result. We Test the accuracy of learning rates. Classification accuracy on the training dataset is marked in blue, whereas accuracy on the test dataset is marked in orange. In This paper we find the best learning rate for good performance on the train and test sets. Keywords: Deep Learning, Epochs, Learning rate, Optimization
Online real-time pedestrian tracking from medium altitude aerial footage with camera motion cancellation

Using medium altitude footage in various image based pedestrian detection and tracking tasks have several advantages as opposed to low altitude footage. For instance, much larger covered area, or weaker perspective distortions and occlusions. Furthermore, it does not pose privacy issues. However, it also has a disadvantage that is the irregular camera motion. Such motions can be observed with some ground level footage as well. Some state-of-the-art multi-object tracking (MOT) algorithms, which excel at pedestrian tracking, handle these implicitly, however this is insufficient in the case of medium altitude footage. The problem is further enhanced in online real-time tracking where frames may need to be skipped. Although there are explicit frame registration methods developed for wide area motion imagery (WAMI), they operate on assumptions that are not true in medium altitude footage such as the background is mostly stationary. We show that by augmenting the pedestrian tracking algorithms with a simple visual object tracking (VOT) based frame registration method their accuracy on medium altitude footage can be substantially improved without hindering the execution speed. Moreover, we show that the algorithms’ execution speed can be significantly increased by better utilization of the resources. To evaluate these, we introduce helicopter video sequences with MOT annotation.
A Novel Projective Nonnegative Matrix Factorization Based on Fisher Discriminant Analysis

Nonnegative matrix factorization (NMF) is a popular tool for data analysis in the non-negative quadrant. However, NMF has undesirable performance in classification tasks due to using unlabeled data for training and its basis matrix cannot be directly utilized as a projection matrix. To overcome the limitations of NMF, this paper proposes a novel Fisher criterion-based projective nonnegative matrix factorization (FDPNMF) approach. Our FDPNMF model is established using the loss of decomposition and the loss of projection, which is incorporated with Fisher discriminant analysis. We solve the optimization problem of the FDPNMF model via gradient descent method and construct auxiliary functions to prove the convergence of the proposed algorithm. The proposed algorithm is evaluated on facial images for classification. Experimental results demonstrate the effectiveness and the surpassing performance of our FDPNMF method when compared with other representative methods.
Reflection of Own Achievements as a Condition of Successful Self-Presentation of the Future Specialist of Musical Art

In today's educational, social, political, and economic transformations, an important condition for overcoming crisis and problem situations is the development of reflection on one's own achievements in the conditions of professional development of future specialists. The effectiveness of the use of the Portfolio method in the training of future professionals lies in the dynamic development of all components of their reflective culture, which is formed in the process of continuous assessment while continuing education. This method has acquired the status of pedagogical technologies in educational practice and is especially important in the training of future music professionals, as it allows each student to express himself as a talented performer, artist, teacher and adjust their own educational route. professional achievements.For effective formation of reflection of own achievements in preparation of the future expert on disciplines of subject preparation in high school it is necessary to develop the approximate maintenance of a portfolio, to define its technological principles and to use them during development of a certain type of portfolio.Every musician must be able to present his self-presentation based on a portfolio of his own creative achievements. This will give you the opportunity to get a successful job and take the first step in your career.Successful employability of university graduates is an important indicator of both quality of education and practical skills. Like many other universities around the world, Chernivtsi National University (Ukraine) emphasizes the importance of embedding specific employability-based learning outcomes into curriculum. Recent studies review on graduates’ employability, reveals that a diverse range of skills and personal qualities are required in order to be successfully employed nowadays. Effective personal presentation skills are among them. They are especially important to advance in the Performing Arts career. The purpose of this study is to develop and implement elements of self-perception training into the university curriculum. Using a collaborative approach based on the principles of psychology and pedagogy, this hands-on toolkit aims to assist students in discovering their strengths and challenges. It also equips students with confidence and effective self-presentation skills to peruse careers as musicians. The Professional Portfolio Method has been chosen to achieve these goals.