Reviewerly/exHarmony huggingface dataset:
Improving Sentiment Classification Performance through Coaching Architectures

Intelligent systems have been developed for years to solve specific tasks automatically. An important issue emerges when the information used by these systems exhibits a dynamic nature and evolves. This fact adds a level of complexity that makes these systems prone to a noticeable worsening of their performance. Thus, their capabilities have to be upgraded to address these new requirements. Furthermore, this problem is even more challenging when the information comes from human individuals and their interactions through language. This issue happens more easily and forcefully in the specific domain of Sentiment Analysis, where feelings and opinions of humans are in constant evolution. In this context, systems are trained with an enormous corpus of textual content, or they include an extensive set of words and their related sentiment values. These solutions are usually static and generic, making their manual upgrading almost unworkable. In this paper, an automatic and interactive coaching architecture is proposed. It includes a ML framework and a dictionary-based system both trained for a specific domain. These systems converse about the outcomes obtained during their respective learning stages by simulating human interactive coaching sessions. This leads to an Active Learning process where the dictionary-based system acquires new information and improves its performance. More than 800, 000 tweets have been gathered and processed for experiments. Outstanding results were obtained when the proposed architecture was used. Also, the lexicon was updated with the prior and new words related to the corpus used which is important to reach a better sentiment analysis classification.
Clustering of Fine Art-Images as Digital Learning Content using Data Mining-Image Analysis Techniques

Digital learning was currently packaged exclusively by adding images to attract the students' interest. The use of images as learning content is rarely well-presented as there was no attention given to how such images were produced. About this, Fine Art-Drawing Images are seen as a breakthrough; they have many types, and to use them as learning content, they must be clustered. Thus, this study aimed to determine and predict fine art-drawing images based on the characteristics of drawing techniques. The study used thirty images as input data in which each category (i.e., charcoal, chalk, pastel, pencil, pen and ink, and book illustration) consisted of five sample images. This study used the image analysis technique to process data with software orange data mining. This study revealed that there was a difference between the proportion of actual and proportion of predicted. Actual data were grouped by image technique only, while the prediction result considered image technique, texture, and image coloring. The similarity of drawing technique gives the result of predictive data, which is different from actual data. In a nutshell, this image analysis technique can be used to determine the cluster and prediction of images that have different characteristics and attributes.
Table Enrichment System for Machine Learning

Data scientists are constantly facing the problem of how to improve prediction accuracy with insufficient tabular data. We propose a table enrichment system that enriches a query table by adding external attributes (columns) from data lakes and improves the accuracy of machine learning predictive models. Our system has four stages, join row search, task-related table selection, row and column alignment, and feature selection and evaluation, to efficiently create an enriched table for a given query table and a specified machine learning task. We demonstrate our system with a web UI to show the use cases of table enrichment.
Comparative Analysis of K-Means and K-Nearest Neighbor Algorithm for Telecom Fraud Detection

This study discusses problems that often occurs, namely telecom fraud. One of them is Telkomsel as the provider and is responsible for telecommunications facilities in Indonesia. Telecom Fraud is a fraudulent activity in the service with the aim of using the service illegally by avoiding the charges used by the user and causing losses to the operator. In this study, the classification of Telkomsel's quota data was carried out using the K-Nearest Neighbor algorithm and the K-Means algorithm. And, testing the confusion matrix as a comparison of the prediction results of the algorithm. By testing the K-Means algorithm using k = 2 and obtained an accuracy value of 0.8. While the K-Nearest Neighbor has a value with a higher level of accuracy with a value of 0.99 as an accurate classification method
Construction of College Students' Course Management Information System Based on Data Center and Parallel Model

Construction of the college students' course management information system based on data center and parallel model is discussed in this research. First, this research work continues to refine and generate more detailed classes according to the relationship between the internal components of each subsystem. For example, the bus class can be divided into: internal, local, system, external and other bus classes according to its layout range attributes. Then, this research work considers the parallel model, wherein the site management mainly includes basic functions such as site addition, deletion, replacement and site attribute management. The addition of the site is completed by the system administrator. When generating, just select the corresponding template as needed, and then set the site parameters, such as site name, folder name, etc., the system can automatically generate the course website according to the template. Furthermore, the data center optimization model is designed to make the model efficient.
Design of Intelligent Development Platform for broadcasting talents from the perspective of Internet Multimedia Data Dynamic Transmission

Design of intelligent development platform for broadcasting talents from the perspective of Internet multimedia data dynamic transmission is studied in the paper. In the algorithm designed, the threshold τ needs to distinguish the known outliers from the new ones in the anomalous region. Most of the outliers of the new ones are much larger than those of the known ones. According to this characteristic, the calculation method of the threshold τ is designed. The algorithm research can then improve the operation performance of the algorithm to a certain extent, continuously then optimize the internal mechanism, simplify the operation process, improve the reliability of the algorithm data operation and furthermore, the intelligent development platform for broadcasting talents is designed considering the multimedia optimization. Through the testing under different scenarios, the proposed model is validated.
Imbalanced Histopathology Image Classification Using Deep Feature Graph Attention Network

Recent deep learning studies have shown great progress in metastasis detection over histopathology whole slide images (WSIs). As WSIs are extremely large, most of existing studies adopt patch level analysis, leveraging spatial context to enhance patch-wise classification. However, class imbalance in patch distribution may result in an exceedingly large number of false-negatives, thereby worsening the WSI level classification performance. In this paper, we propose a novel framework for classification in class imbalanced datasets, which adopts a graph attention network to capture feature dependent interactions and a minority preferred inference mechanism for patch-level classification. Our experiments on CAMELYON16 show that the proposed method substantially improves detection of the minority class (tumour) under a highly imbalanced class distribution.
Understanding Visual Investigation Patterns Through Digital “Field” Observations

An extensive body of work in visual analytics has examined how users conduct analyses in scientific and academic settings, identifying and categorizing user goals and the actions they undertake to achieve them. However, most of this work has studied the analysis process in simulated or isolated environments, leading to a gap in connecting these findings to large-scale business (enterprise) contexts, where visual analysis is most needed to make sense of the large amounts of data being generated. In this work, we conducted digital ”field” observations to understand how users conduct visual analyses in an enterprise setting, where they operate within a large ecosystem of systems and people. From these observations, we identified four common objectives, six recurring visual investigation patterns, and five emergent themes. We also performed a quantitative analysis of logs over 2530 user sessions from a second visual analysis product to validate that our patterns were not product-specific.
Anomaly Detection in EM Images - A Zero-Shot Learning Approach

Reconstructing dense neural wiring diagrams in the brain require 2D registration and 3D alignment ([1]) of thousands of serial sections imaged using a serial section electron microscopy (ssEM). Each serial section imaged comprise of thousands of high resolution images that include both the tissue, film, and resin regions. The stitching accuracy is challenged by the presence of anomalies (film and resin), which causes misalignments between the images that represent them. Detection of such anomalies is required to achieve a high quality stitching and alignment. Automated pixel based segmentation of such anomalies requires training machine learning models on dataset-specific ground truth, which is tedious to acquire. In this work, we propose a zero-shot learning approach to perform pixel based segmentation of resin and film regions on unknown datasets without ground truth. Our pipeline consists of a segmentation network based on U-Net architecture coupled with an unsupervised image-to-image translation method that uses Cycle GAN to translate the input image to match the style of images used for training the U-Net. Performance results on various EM datasets show that the segmentation class accuracy of resin and film improve significantly. By eliminating the need for ground-truth annotations, we facilitate the process of stitching and alignment by automation of detecting anomalies. Our zero-shot learning approach can potentially be utilized in scenarios where ground truth annotations are hard to acquire.
Efficient bi-traits identification using CEDRNN classifier for forensic applications

• Several operations are handled for bi-traits-centered offender identification. • Image quality is highly increased by the proposed method of offender identification. • The system solves the computation issues of tree structure part model. • A new way is used for weight value selection in DNN. • To prove the effectiveness of the proposed algorithms. The evidence, traits, in addition to clues that are amassed as of the Crime Scenes (CS) are analyzed by a process termed Forensic application. Nevertheless, the Forensic Analysis (FA) faces the challenges of irrelevant Feature Extraction (FE), which is because of the over impression of Finger-Print (FP), facial expressions, pose-invariant, together with poor lightings that bring about the mis-identification of the offender. To trounce such challenges, this paper proposes a bi-traits-based offender identification using Cross Entropy-based Deep Remainder Neural Network (CEDRNN) for forensic application. Pre-processing, FE, Feature Selection (FS), together with identification are ‘4′ phases that the proposed model encompasses. In the pre-processing phase, the input data is processed via modifying the image size, augmenting the Image Contrast (IC), and neglecting the unwanted data. Then, as of the pre-processed data, the more apt and helpful data are extracted in FE. Then, the Gaussian Sailfish Optimization (GSO) technique selects the important data in the FS phase. Finally, the CEDRNN identifies the offender centered on the selected features. In this research, publically available datasets are utilized for comparing the outcomes of the CEDRNN with the previous top-notch algorithms. The experimental outcomes exhibited that the CEDRNN attains the highest accuracy and also effectively identifies the offender without mis-prediction.
IoT-Based Monitoring of Temperature and Humidity Using Infrared Thermography for Cryptocurrency Mining Room

Nowadays, as modern cryptocurrency machines operate faster and hotter, the fans are insufficient to cool constant CPU and GPU usage. The researchers built specialized Mining Rig Cases for these machines to withstand excessive heat. Aside from temperature, another environmental factor that affects the performance of Cryptocurrency machines is relative humidity. The researchers created an IoT-based Monitoring system to regulate and monitor the Cryptocurrency machines as excessive heat and humidity can decrease the lifespan of these machines. The Infrared camera used color mapping and Otsu&#x2019;s Segmentation Algorithm for image processing. Otsu Segmentation Algorithm separates the background and foreground of the image while Color Mapping Algorithm converts colors of the image into temperature. The researchers also tested the power consumption and data sizes to determine the efficiency of the monitoring system. Using Convolutional Neural Network, the researchers trained 300 images to assess the state of the Cryptocurrency Mining rig. Additionally, a two-tailed T-Test will determine any significant difference between the two algorithms. Upon training the images, the results obtained show that the two-tail P-value of temperature and humidity is 0.35 and 0.2566, respectively, which affirms no significant difference. However, the power consumption and data size had a substantial difference with P-values of 0.0004 and 3E-06, respectively. Moreover, it shows that the application of Otsu reduces the data size and consumes more power due to deep learning.
Continuous authentication for IoT smart home environments

The diverse range of Internet of Things (IoT) devices in smart homes results in users having to deal with a variety of technologies with different and incompatible user authentication methods. Continuous authentication offers an intelligent solution to this problem, although its application within IoT is currently in its infancy, and the limitations of sensors, power and processing capabilities present challenges when compared to traditional user devices.
Automated Path Planning for unmanned aerial vehicle in Urban Dynamic Area

Abstract This paper addresses the path planning and autonomous obstacle avoidance problem of UAVs in urban dynamic area. A flight path planning strategy for UAVs in complex urban environments is proposed.First, the A* algorithm is used to construct a desired global path in a 3D static environment, which is used as the static reference path for dynamic obstacle avoidance below.The environmental and the key points of algorithm are also elaborated. In this paper, the dynamic obstacles are divided into three categories, then, in order to avoid the collision between dynamic obstacles and static optimal paths, two strategies to achieve local online path adjustment are proposed. Finally, the seven order minimum snap trajectories generation based on piecewise polynomials is utilized to smooth the flight path.We can obtain a smooth trajectory based on UAV dynamics and safety. The simulation results verify the effectiveness of the proposed UAV path planning strategy in the dynamic and complex urban area.
A Navigation Algorithm for UUVs to Avoid Shape Changeable Obstacles

In order to guide an undersea unmanned vehicle (UUV) avoiding obstacles in an underwater environment, an navigation algorithm is proposed in this paper. It can be applied to planar environment, the shape of obstacles could be changeable. This method combines local path planning and global path planning together, switching between two modes so as to safely navigate the UUV’ s way. In order to show the performance of the proposed algorithm, the simulation results are presented by Matlab. The reason why we promoting this algorithm is that it is a computational efficient algorithm, comparing with the prior ones, the cost could be saved while achieving the expected results successfully.
Category Separation For Weakly Supervised Multi-Class Cell Counting

Cell quantification in immunohistochemically stained images assists the prognostic evaluation of tumor progression. The development of lighter artificial intelligence-assisted diagnosis models can further improve the efficiency of whole slide image analysis and control training costs. However, recent count-level regression models are limited in the multi-category cell counting task. Therefore, we propose a cellular quantification framework under the count-number supervision by introducing a category-separation learning method, which instructs the model to discriminate differences between various cells. Moreover, a novel loss function is presented to control the representation learning of multi-category cells. In addition, we also validate the performance of Transformer-based architecture. By comparing with learning methods under dots-labeled supervision, the proposed method realize the simultaneous counting of multi-category cells and exhibit competitive results on the public dataset.
MVC Algorithm Design of Smart Mobile Marketing Micro-Classroom System based on Android SDK Technology

MVC algorithm design of smart mobile marketing micro-classroom system based on Android SDK technology is implemented in the paper. The parallel framework is an application framework developed based on the Android platform. It provides many components to help applications make calls, mainly including content providers, location managers, resource managers, window managers, and XMPP services. With this target, the SDK will be combined for the analysis. When these SDK platforms are invoked during the program development, the request information data is sent to the remote server for the service. Therefore, before conducting vulnerability detection, the operating rules and motivation of such SDKs must be first understood. Then, the MVC framework is then applied, and the smart mobile marketing micro-classroom system is then implemented. Through the testing on different models, the designed systen is validated.
Cyclic longest common subsequence

We present an efficient algorithm for finding the longest common subsequence of two sequences in cases where the starting points of the sequences are not known. In addition, we differentiate between cases when there can or cannot be assumptions regarding the clustering of the subsequences.
Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children’s Innovative Ability

Innovation ability is an important part of children's core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children's innovative ability.In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children's innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children's innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children's innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children's innovation ability.
Weakly Supervised Classification using Multi-Level Instance-Aware Optimization on Cervical Cytologic Image

The pathological images of liquid-based cytology are widely used in cervical cancer screening, and its large resolution has always limited the efficiency of diagnosis. Weakly supervised learning is an efficient method for computer-aided diagnosis. However, its performance may also be limited by the rough annotation. Therefore, we propose an optimized multi-instance classification framework to learn more reliable representation from multi-level instance awareness. We first introduce deep self-attention modules following various layers of the instance-level encoder, which promotes the model to learn the relationship between instances. Then we cluster the instance features in each bag to strengthen distinguishability. In addition, we propose an adaptive instance mask strategy to facilitate the learning of relevant features from suspicious samples with weak attention. Our method performs a significant improvement by comparing with competitors, and attention visualization also reveals its effectiveness.
Faster R-CNN for IPSC-Derived Mesenchymal Stromal Cells Senescent Detection from Bright-Field Microscopy

iPSC-derived mesenchymal stromal cells (iMSCs) play an important role in cell therapy and regenerative medicine, but the differentiation and proliferation ability of senescent iMSCs decline greatly, which will also bring heterogeneity and potential side effects. The whole senescent degree of iMSCs can only be obtained by vital stain. However, this process will cost a lot of manpower, money and time. To solve this problem, we apply deep learning for automated iMSCs senescent recognition, which can quickly and accurately get the senescent situation of single-cell without staining. The adopted Faster R-CNN uses ResNet as the backbone network with an FPN module. Experiments on the collected dataset show that our method has achieved a detection accuracy of 0.768 in the mixed test set of each generation of cells and the independent test set of each generation of cells.
Computational Analysis of Interleaving PN-Sequences with Different Polynomials

Binary PN-sequences generated by LFSRs exhibit good statistical properties; however, due to their intrinsic linearity, they are not suitable for cryptographic applications. In order to break such a linearity, several approaches can be implemented. For example, one can interleave several PN-sequences to increase the linear complexity. In this work, we present a deep randomness study of the resultant sequences of interleaving binary PN-sequences coming from different characteristic polynomials with the same degree. We analyze the period and the linear complexity, as well as many other important cryptographic properties of such sequences.