Reviewerly/exHarmony huggingface dataset:
Current Trends on Sentimental Analysis on Youtube Videos

YouTube, one of the foremost watched video sharing websites with millions of different types of contents and their creators with innovative ideas was originated in USA. You Tube has been recorded as the second largest program watched, searched and time spending platform by the millions and billions of views after Google. The videos uploaded by the creators on You Tube majorly depend on the content, knowledge and skilled dynamics and social interaction between the channels (content creators) and user of it. Social networks like videos spreads and sensitivity of YouTube meta-level features provide an important impact on the popularity of videos. Emotions are indeed for social interactions. To build automatic speech recognization system, emotions play a vital role. poor data quality, public dataset, manual labelling, CPU bottlenecks, model over fitting, exploding gradient problem and Data storage are major challenges in the sentimental analysis from the videos. This paper describes the various approaches for sentiment analysis of videos and about the algorithms which provide quite accurate results and to improve the performance rate, parallel processing can be done in Graphical Processing Unit (GPU) using NVIDIA Compute Unified Device Architecture (CUDA) language.
Performance evaluation of a novel adaptive variable structure state estimator

An improved nonlinear adaptive state estimator called Adaptive Smooth Variable Structure Filter ASVSF has been proposed and its algorithm described. ASVSF extends the functionality and performance of a previously reported robust smooth variable structure filter (SVSF) with optimal boundary layer (SVSF-OBL). Improvement in performance includes the provision for accommodating unknown and time varying process noise covariance, which generally characterizes modelling uncertainty. The novelty of this proposed ASVSF estimator, which inherits the features of the SVSF, is that it adaptively provides an estimate of the unknown time varying process noise covariance (and hence called adaptive SVSF or ASVSF) which is required for determining the optimal boundary layer width of SVSF-OBL thus obviating the need of the prior knowledge of the process noise covariance. This makes the proposed estimator performance to be insensitive to (and therefore robust with respect to) unknown time varying process noise covariance while retaining the optimality of SVSF-OBL. The performance of the proposed ASVSF estimator is evaluated using Monte Carlo simulation and is compared with previously reported state estimators using a case of maneuvering civilian aircraft where a simplified and grossly approximate process model is used in the estimator/filter for tracking and thereby generating a time varying and unknown process noise covariance situation. Three different measures of Root Mean Square (RMS) error over the trajectory have been used for comparison which demonstrates the strengths of the proposed ASVSF estimator.
A customized method of web crawler for the automatic collection of embedded device firmware

Web crawlers can be divided into two categories, such as general framework crawlers, and customized web crawlers. The former has better versatility, but lower efficiency. The latter is designed for specific purposes, with strong pertinence and high efficiency. This article has designed and implemented customized web crawlers for multiple mainstream network equipment manufacturers, which are used to automatically and quickly obtain network device firmware. Compared with the general crawler framework, the customized crawler has better update ability, that is, it can quickly locate and capture the new version of the network device firmware. This paper uses Python programming language to implement the crawler and its framework. A total of 167GB of firmware files, approximately 17,048 firmware were obtained.
Walk a Mile in Their Shoes

Covid has changed how people work in many ways, but many of the outcomes have been paradoxical in nature. What works for one person may not work for the next (or even the same person the next day), and we have yet to figure out how to predict exactly what will work for everyone. As you saw in the composite personas described here, some people struggle with isolation and loneliness, have a hard time connecting socially with their teams, or find the time pressures of hybrid work with remote teams to be overwhelming. Others relish this newfound way of working, enjoying more time with family, greater flexibility to exercise during the day, a better work/life balance, and a stronger desire to contribute to the world. How can we build work environments, tools, and processes to support the full range of experiences people are going through?
Multi-Criteria Decision-Making-Based Model Selection Proposal in Artificial Learning Process

In this study, selection of the best classification model in the artificial learning process is considered as a Multi-Criteria Decision-Making problem. In this direction, machine learning- based 10 classification models have been obtained and seven of them have been eliminated according to the different parameters. The classification model with the best performance among three remaining alternatives has been determined by ELECTRE I. According to the model performances obtained within the scope of the study, the best model among the three alternatives would be determined depending on the initiative of the researcher. However, with the proposed model, this process has been moved to a scientific basis and the best of the three models based on Extreme Learning Machine (ELM), Naïve Bayes, and Support Vector Machine has been clearly determined as ELM. The proposed model, unlike its counterparts in the literature, is far from a complex structure, is understandable and can support users of all levels.
Context Multi-scale Fusion Network for Magnetic Resonance Image

Automatic and accurate segmentation of multi-object organs from magnetic resonance images (MRI) is a critical step in clinical diagnosis and treatment. This is still an open problem due to many difficulties, such as the volume difference among multi-objects is large and the similarity of surrounding tissues is high. In this paper, we propose a new multi-object segmented pyramid context multi-scale fusion network based on a U-shaped structure (CMFNet). We first design Resnet and Squeeze-and-Excitation module to form a feature extraction module to capture more location information in the encoding stage. Moreover, we also modify the skip connections and use a global feature fusion module to extract semantic information, aiming at providing different levels of global context information for the decoder stage. Then, we further use a scale-aware fusion module to capture multi-scale contextual information in high-level features. These two modules can gradually obtain rich global and multi-scale context information. In addition, in order to assist multi-object segmentation, we also design an edge guidance model to extract edge information in the decoding stage and fuse it in the last decoding layer. We do extensive experiments on CHAOS datasets, and the results show that the proposed segmentation algorithm is superior to state-of-the-art methods on Dice Similarity coefficient of 89.91% and Volume Similarity of 95.61%, which is significantly higher than the algorithms such as UNET, UNET+GFM and UNET+SAPF. therefore, it also proves the effectiveness of our method.
An Approach to Networking a New Type of Artificial Orthogonal Glands within Orthogonal Endocrine Neural Networks

Currently, artificial intelligence and intelligent algorithms for the control of dynamic systems are the main focus for building Industry 4.0 services and developing novel, innovative industrial solutions. This paper proposes a novel intelligent control structure specifically tailored for treating environmental stimuli and disturbances in operational environments of dynamic systems. The structure is based on the Orthogonal Endocrine Neural Network (OENN) and Artificial Orthogonal Glands (AOGs). The operational mechanism of each AOG acquires and processes environmental stimuli and generates artificial hormone concentration values at the gland output. These values are introduced to the appropriate OENN layer to provoke the network with collected environmental insights. To verify the applicability of the proposed structure on a complex dynamical nonlinear system, it was tested in a laboratory environment on the laboratory magnetic levitation system (MLS). The main experimental goal was to test the tracking performance of a levitation object when the new control logic was applied. The results were compared with two additional intelligent algorithms and a default linear quadratic (LQ) control logic. OENN + AOG structure showed improved tracking performances compared with traditional LQ control and better adaptability to environmental conditions compared with similar existing solutions.
Image Super-Resolution using GAN - A study

Reconstructing low-resolution images to high-resolution images by building a neural network is quite challenging but can be used in many applications like medical imaging, public surveillance, or old photo recovery. Compared to previous methods, deep learning has a breakthrough in high-resolution accuracy and speed. By applying a deep network with Generative Adversarial Networks, this model aims to enhance low-resolution images to produce high-resolution images. The major focus is to reconstruct the image with high resolution by developing the image with low-resolution in order to preserve the main details in the reconstructed images.
A Hybrid Page Ranking Algorithm Based on User Behavior

Search engine, one of the most important applications in our lives, is dependent on page ranking algorithm. User search experience is directly affected by the quality of the page ranking algorithm, and poor ranking leads to users spending much more time to find their desired webpage from the search results. This study, on the basis of the Weighted PageRank algorithm, proposes a new algorithm, the Topic Weighted PageRank algorithm, that considers topic relevance between webpage content and users&#x0027; search queries, and ensures ranking results are strongly correlated to user needs. Subsequently, this study improved the Topic Weighted PageRank algorithm in many aspects after considering factors such as the popularity of webpages, user preference for websites, and user preference. Finally, indicators of page ranking algorithm effectiveness were defined, and experiments were conducted to verify the effectiveness of the page ranking method proposed in this study. The results show that the proposed page ranking method performs better in various indicators than many of page ranking algorithms declared in recent years.
Automatic Target Detection in Cluttered Infrared Imagery using Background Modeling Approach

Target detection is the primary purpose of a real-time visual surveillance system. However, this is a challenging problem due to high variability in the appearance of targets and the computational cost of existing algorithms prevents real-time deployment. This study proposes a method for real-time automatic detection of moving ground targets such as, vehicles, humans, animals, etc., in image sequences captured by a stationary infrared imaging system. The proposed research work utilizes a background modeling approach to reduce the computation cost when compared to existing algorithms in dynamic background conditions (when the camera itself is moving). The proposed method uses statistical variations of pixels over the temporal domain to compute the background model. Experimental results demonstrate that the proposed algorithm can detect intruding targets from infrared imaging video with a detection sensitivity of 0.88 and a false alarm rate of 0.001.
Multi-View Subspace Clustering Based on Affinity Matrix and Low-rank Tensor Decomposition

Subspace clustering is one of the important methods to solve the problem of high-dimensional data clustering, and the performance of traditional methods based on spectral clustering largely depends on the affinity matrix learned directly from the original data or their computational representation. In this paper, we propose multi-view subspace clustering based on affinity matrix and low-rank tensor decomposition (ATMSC) algorithm, which not only constructs a more meaningful affinity matrix but also makes full use of multi-view complementary information and explores high-order correlation. The Alternating Direction Multiplier Method (ADMM) can efficiently find the optimal solution to the problem. Extensive experiments on real datasets show that our method outperforms other state-of-the-art methods.
Intelligent Sorting System

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through trajectory planning. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets, SIFT algorithm is used to extract image features, and K-D tree algorithm is used to match image features. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. trajectory planning, straight-line and arc interpolation algorithms are used. The experimental results show that the system meets the design requirements.
Competitive and Collaborative Learning Accelerates the Convergence of Deep Convolutional Neural Networks

In the training of convolutional neural networks (CNNs), the layer-by-layer learning based on the backpropagation (BP) algorithm causes that in each round of weights update, the learning of the latter layer determines the learning of the former layer, while the former layer cannot directly affect the latter layer. This means that the flow of error information is unidirectional, causing non-cooperative learning between layers, thereby reducing the convergence speed of the networks. In this work, we propose a network structure that evaluates the relative contribution of each layer in the CNNs to the final output error. During training, it indirectly realizes the bidirectional flow of information between layers, achieving the purpose of cross-layer collaborative learning. Our algorithm also fuses features at different scales on the detection networks, which we call the flexible feature fusion network(FFN). On public datasets, we have conducted rich experiments. With the help of FFN, the convergence speed of the object detection model is greatly improved. Without pre-training weight initialization, the convergence speed of the model is approximately doubled.
Are we prepared to learn digital in the digital economy and society?

Globalisation, digitalisation and the fourth industrial revolution are significant challenges for the knowledge economy and education in the 21st century. The integration of ICT tools into educational processes, the digital transition accelerated by the pandemic, all demand new learner and teacher competencies, the breaking down of classical hierarchies and the rethinking of output requirements to prepare learners for the level and tasks expected by employers and higher education. All citizens must be prepared for change and new expectations, especially in the field of education and information security. The results of the new DESI 2021 report present the frameworks that define digital competencies in all sectors, showing how the competencies defined in the European frameworks are embedded in different areas of society and the economy, where there is a gap and where progress is visible.
Enhancing the Quality of User Research Using Embedded IoT Sensors for Collecting Life Information

Abstract This study aims at developing a new user research method that uses IoT sensors embedded at users' homes to enable users to recall their memories. The proposed method was evaluated by experiments where four participants individually created user journey maps with quantity data that was collected for seven days. The results showed that IoT sensor data increased the quantity, clarity, and accuracy of recalled memories. This study argues that IoT sensors can be an effective approach to increasing user research quality by triggering users' memories without interfering with users' ordinary lives.
Computerized Data-Preprocessing To Improve Data Quality

Machine Learning (ML) has seen a sudden exponential rise in past decades. Numerous resources and documentation allow people to become ML practitioners. Companies make huge profits out of the analysis and predictions they make. ML Engineers are highly paid for their knowledge in this domain. It has become prevalent and much more comprehensible. One best out of the important stages in ML is Data preprocessing, and feature extraction. In Data Preprocessing itself, there are various tasks one needs to perform accurately to make the data provided. From handling missing values to encoding and normalization, each step has its importance and hence a professional must be adept with each of these steps. Data Preprocessing steps depend upon the type of data provided i.e. categorical data, continuous data, an array of images' pixels or even images themselves. With the requirement to deal with all the cleaning steps, it becomes quite strenuous to learn and become an expert. Moreover, it is time-consuming and does not guarantee expected results. Hence, there is a need to handle this issue. We aim to automate this complete process to ease the work of Machine Learning Engineers and make it more productive. Any user will only have to provide the dataset and does not have to manually select the processing techniques as provided by the latest Data Mining tools. The application will observe the dataset and apply the suitable techniques on its own. Since all the steps will be automated and the user will only have to provide the dataset, even the people who are not familiar with concepts of Machine Learning can pre-process the dataset. This allows the opening of opportunities for people from various domains who desire to perform Machine Learning operations.
Semantic based logistics data exchange model

In recent years, with the rapid development of various logistics industries, all kinds of logistics information systems and platforms have developed synchronously and rapidly. Due to its industry characteristics, there are natural rigid requirements of multi-source, heterogeneous constraints and business collaboration and sharing in the data exchange between logistics information systems. The data exchange based on semantic guidance will become the core foundation of the intelligent logistics information system platform. Therefore, this paper studies the knowledge sharing in the field of logistics, and studies the logistics data exchange model based on semantic guidance. Firstly, it analyzes and studies the standards in the field of logistics at home and abroad. Then, through the expansion of logistics semantics, the technology of ontology, metadata and Knowledge Graphs is used for annotation. According to the system theory, a three-tier standard system model is proposed and constructed to guide the construction of Knowledge Graphs for data exchange of multi-source heterogeneous logistics information system. The model integrates the metadata and ontology standards related to the field at home and abroad, reveals the important idea of taking the metadata standard as the core in the construction of Knowledge Graphs, makes the Knowledge Graphs constructed in the field realize semantic standardization, lays the foundation for knowledge sharing and interoperability, and provides semantic standards for the data exchange of multi-source and heterogeneous logistics information systems.
Research on Metallogenic System and Deposit Genesis: With Zhacun Goldmine in Weishan County of Yunnan as an Example

This paper mainly takes Zhacun goldmine as the research object and carries out research on the metallogenic system and genesis of the deposit in the area. The research methods are as follows: according to the geological characteristics of the metallogenic mining area in the Zhacun goldmine, combined with the comparative analysis of the metallogenic age, alteration characteristics and lithology between the Zhacun goldmine and the Lianhuashan alkali rich rock mass, and the analysis of the isotopic data in the Zhacun goldmine, this paper demonstrates that the magmatic activity of the Lianhuashan quartz monzonite porphyry plays an important role in the mineralization of this area, which is both an important ore source and fluid source. It is also the main heat source driving the circulation of ore-forming fluid. On this basis, through the analysis of the metallogenic elements in the area, the metallogenic system model of Zhacun goldmine is constructed. It is considered that under the action of long-term horizontal compressive stress, the atmospheric precipitation infiltrated into the formation continued to penetrate along the fractures and fissures to deep crust and finally mixed with the magmatic water sealed in the thick formation. During this period, a series of shallow and ultrashallow porphyry mass intruded, providing heat source and part of the ore-forming materials and resulting in incomplete homogenization under high temperature and high pressure, which formed highly mineralized mixed water. Under the combined action of long-term compressive stress and various deep thermal dynamics, these mineral fluids migrated along the buried deep faults near the core of the duplex anticline to the upper part of the crust. When the metal elements in these ore fluids migrated over long distances to the nappe fault zone of the gold-bearing fracture zone, the gold began to precipitate, and the native gold and the coarse-grained pyrite and quartz formed at the same time or earlier started to fill the fracture zone along the fissures and accumulate into minerals. Then, through the analysis of metallogenic conditions, gold mineralization process, and genetic mechanism of the deposit, it is proposed that the genetic type of the ore deposit in this area is magmatic mesothermal-epithermal gold deposit. The establishment of metallogenic system model in Zhacun goldmine will provide theoretical and practical guidance for deep and peripheral prospecting prediction in the area, carry out targeted and reasonable prediction, and improve prospecting effectiveness.