Reviewerly/exHarmony huggingface dataset:
Fusion-Based Semantic Segmentation Using Deep Learning Architecture in Case of Very Small Training Dataset

Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.
An Eye Movement Experiment Study on Differences between English and Chinese Reading by English Majors

Eye movement technology has been widely applied in reading research with the advantages of sensitivity and validity. The study aims to explore the differences of bilingual reading ability, the processing characteristics among English majors through eye movement experiments. 30 English majors were involved in this study. The results are as followed. (1)The processing characteristics of English majors in Chinese reading and English reading are different. The number of fixation and regression is significantly higher in Chinese reading than that in English reading. (2)The comprehension rate of Chinese and English reading is almost the same, but the reading efficiency of Chinese reading is better. (3)This study is helpful to further improve the undergraduate English major reading and develop related bilingual courses.
A Case Study: Application of RCCA to Solve a Critical Electronics Manufacturing Problem

SUMMARY & CONCLUSIONSThis case study shows how a seemingly inconsequential process change implemented by a subcontractor on a production lot exposed a hidden physical weakness in the supplied assembly. The consequences were a late discovery of hidden failures, a costly root cause, and corrective action (RCCA) analysis, and the product recall to implement the corrective actions.The study shows that early slow progress at failure discovery was later accelerated by using a very capable failure analysis infrastructure. The study explains how an underspecified design was the root cause that led to the overstress condition that triggered the failures.Finally, the study provides results of tests and analysis to fill in missing stress-quantifying information needed to assess the effectiveness of the corrective action. The study concludes with a review of lessons, including the need for thorough design reviews to avoid issues that led to these failures.
Effect of Acceleration of Moving Object During Collision with Stationary Object

In an application where a robotic arm hits an object, the effect of impact is required to be assessed. A classical approach determines the effect of impact based on the velocities of the objects. Magnitudes of acceleration are ignored in the analysis. In the robotic application, velocity shall always be associated with acceleration as it is a start and stop type of motion. This paper demonstrates the need of accounting for the acceleration magnitudes also to determine the effect of impact.
Multi-Dimensional Scaling on Groups

Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in data science. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental "frequencies" (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.
A Dense Margin Network for Human Activity Recognition Based on Augmented Channel State Information

Human activity recognition based on channel state information (CSI) has received widespread attention in recent years due to its low cost and privacy protection. However, its accuracy can be significantly reduced when most of the current recognition approaches are applied to new users who have not participated in model training. To address this issue, a new passive human activity recognition method based on CSI is proposed in this work. A cycle-consistent generative adversarial network (CycleGAN) is used to map old user CSI activity data to new user CSI activity data. Multiple dense blocks from a dense convolutional network (DenseNet) and a large margin cosine (margin) module are then combined to automatically extract features with representative differences in different activities to identify multiple human activities. According to the results of the model on SignFi data and self-collected data on the eight daily activities of ten volunteers, the activity recognition accuracy of new users is found to reach 95.63% and 96.67%, respectively. The experimental results demonstrate that the proposed method can achieve better performance than the benchmark approaches.
PROBLEMS AND LESSONS OF MASS REMOTE EDUCATION IN HIGH SCHOOL

The article focuses on the problem of adapting teachers and students to remote learning technologies and assessing the prospects for applying a combined format of learning after the pandemic
Sentiment Analysis of Twitter Images Through Novel Sequential Attention-Based Deep Metric Network

At present, people are using various social media platforms (Google plus, Facebook, Twitter, etc.) and have started uploading many images. People have initiated to express their individual emotions through posting of images in social media. Thus, visual sentiment analysis (VSA) has the ability to envisage the sentiment related to an image via examination of image contents. It allows to obtain the overall opinion of public on a specific topic. Analyzing sentiments and seeing the sentiment behind individual images posted by social media users enable to organize and perform planning for future accordingly. Various research is concentrating only on the sentiment analysis of textual contents. Moreover, only little investigation is carried out to propose models that can predict VSA. Hence, this study proposes a novel sequential attention-based deep metric network (SA-DMNet) to analyze the sentiment of visual contents in Twitter dataset. The proposed novel SA-DMNet undergoes a series of process to yield the sentimental output. Through the proposed model, sentiments or any kind of emotions in the images can be easily analyzed as positive or negative. The proposed system is compared with other traditional models. The comparative analysis explored effective results, and it has outperformed many other state-of-the-art methods. Performance analysis is also carried out to determine the efficiency of the proposed model. It is evaluated in terms of various metrics such as precision, recall, accuracy, and F1 score. The results revealed that the proposed novel SA-DMNet is effective in terms of the specified metrics thereby confirms its efficacy.
Photovoltaic power prediction model based on parallel dendritic neural model

Dendritic neural model (DNM) has characteristics of a simple structure and a fast convergence speed. However, when a single DNM is applied to a scene with a large data set, the number of branch layers often needs to be increased, which makes the structure of DNM larger and leads to a poor prediction accuracy. From this perspective, this paper proposes a parallel-structure based DNM with multiple sub-networks, which uses a fuzzy C-means clustering (FCM) algorithm to divide the data set. The FCM algorithm can effectively reduce the amount of data required for the training of each sub-network. Consequently, actual photovoltaic data simulation results verify that the accuracy of the photovoltaic power prediction model can be further improved, and the proposed model is effective and efficiency.
Zero Watermarking Algorithm in Wavelet Domain Based on Chaotic Scrambling

Zero-watermark is the data information constructed by extracting a certain amount of eigenvalues from the original carrier image. In order to achieve effective copyright authentication, feature value and copyright information are calculated together to generate zero-watermarking. It is very important to extract the feature information of carrier image. Considering the efficiency and stability of the security and concealment, we redesign the zero-watermark extraction algorithm. The security and concealment of zero watermarking can be improved by using chaotic encryption for both image and water copyright image. Extracting feature information from low frequency region of wavelet can make zero-watermark have good stability. Singular value decomposition (SVD) is used to extract the maximum singular value of the sub block in the low frequency region of the wavelet, and the feature information is extracted to construct the zero watermarking. Experimental results show that the proposed algorithm is effective in extracting zero-watermark information.
Combination of ELM and BEMD for Target Recognition of SAR Images

For the problem of target recognition of synthetic aperture radar (SAR) images, a method based on the combination of bidimensional empirical mode decomposition (BEMD) and extreme learning machine (ELM) is proposed. BEMD performs feature extraction for SAR images, producing multi-layer bidimensional intrinsic mode functions (BIMF). These BIMFs covey the discrimination of the original target while effectively eliminating the noises. ELM conducts the classification of each BIMF with high efficiency and robustness. Finally, the decisions from different BIMFs are fused using a linear weighting strategy to reach a reliable decision on the target label. The proposed method compensates the relatively low adaptivity of ELM to noise corruption by BEMD feature extraction. Moreover, the multi-layer BIMFs provide more discriminative information for correct decision. Hence, the overall recognition performance can be improved. As an efficient recognition algorithm, the proposed method can be used in an embedded system for wide applications. Experiments are designed and implemented on the moving and stationary target acquisition and recognition (MSTAR) dataset. The proposed method is tested under both the standard operating condition (SOC) and extended operating conditions (EOCs). The results reflect its effectiveness and robustness via quantitative comparisons.
Augmented global attention network for image super‐resolution

Convolutional networks dominate many machine vision fields. Nevertheless, a significant drawback of the convolution operation is that it only operates in the local region, so it lacks global information. Self-attention has become the latest technology for capturing long-range interactions, but it is mainly used for generative modeling and sequence modeling tasks. Using self-attention to tackle super-resolution as a substitute for convolution is considered. Therefore, augmented global attention convolution (AGAC) is proposed as an alternative to convolution to use self-attention for super-resolution. The proposed augmented global attention convolution can capture global context to produce more realistic super-resolution results. Due to the most existing works that have not exploited position information, a two-dimensional relative self-attention mechanism is proposed to enhance self-attention. To deal with the super-resolution task, the authors come up with an augmented global attention convolutional network (AGAN) to enhance the convolution operator with the self-attention mechanism through concatenating the convolution pattern map with the generated set of feature maps. Many experiments and analyses are conducted to demonstrate that the proposed model surpasses the advanced models with comparable parameters and performance.
Automatic Detection Method of Concrete Cracks in Construction Engineering Based on Improved Watershed Algorithm

The current method of concrete crack detection is divided into manual method, which is more complex, and it requires higher detection environment and lower detection efficiency and accuracy. Therefore, the automatic detection method of concrete crack in construction engineering is proposed based on the improved watershed algorithm. The high-resolution CCD sensor camera is used to collect the image and to process the image. After the image is segmented by the improved watershed algorithm, the crack features are extracted. The fracture characteristics are classified as decision tree attributes, and decision tree is established to realize automatic crack detection. The simulation results show that the detection error rate of the proposed method is less than 0.3%, and the detection efficiency is higher and the effect is better.
Smart grid oriented Internet of Things technology and application analysis

Abstract In the development of modern society, the Internet itself with the characteristics of diversification and virtualization, has played a good application value. The Internet of Things can realize the interaction and construction of related network resources through the effective integration of physical and virtual bodies. It makes the overall information network communication and power industry structure to achieve the corresponding transformation and industrial upgrading, and ultimately promotes the transformation of social production structure. This provides good convenience for people and promotes the development of the whole society.
General Trans-Compiler based Mobile Applications Converter

Deployment on different platforms has been a great issue for mobile companies that aim to maximize the return on investments by making their mobile applications available on different mobile platforms. Consequently, the app may be developed several times to match different platforms. Therefore, there is a need to have solutions that enable the developers to develop the app once, and run it everywhere to reduce the cost of development and reach out to maximum users across several platforms. In this paper a tool is provided with the most popular languages /frameworks (native, cross platform) based on their market share to select the desired framework among them to code with, get the developed application written in the target language that meets their business requirements, and give the ability to convert native application to cross platform one. A trans-compiler based solution is introduced to convert the UI and UI-related backend code of application from a set of languages to any other language in the set and ability of adding more languages and frameworks in the future to achieve the concept of generalization of code conversion.
Applying Variational Circuits in Deep Learning Architectures for Improving Discriminative Power of Speaker Embeddings

Blankson, Raphael Pavlovskiy, EvgeniyRecently, the advancement in quantum technologies has had massive impact on the development of quantum algorithms on near-term quantum devices. Variational circuits, a combination of both quantum and classical algorithms, have been very useful in this advancement on near-term quantum devices. Despite these advances, most quantum applications in machine learning (deep learning) especially in transfer learning have been proof-of-concept in the qubit system and very little in the continuous-variable space but no or little application to audio data. This study applies variational circuits to practical real-life speaker classification data for the first time in the continuous-variable system. In separate experiments, the quantum model was combined with a simple convolutional neural network and ResNet18 model, respectively, and the results were compared to classical ResNet18 model applied on the same speaker dataset. The simple convolutional model outperformed the ResNet18 quantum model significantly after one epoch. Further, investigation is needed to model-specific problems that classical models cannot solve.
Optimal Convergence Rate in the Quantum Zeno Effect for Open Quantum Systems in Infinite Dimensions

Abstract In open quantum systems, the quantum Zeno effect consists in frequent applications of a given quantum operation, e.g., a measurement, used to restrict the time evolution (due, for example, to decoherence) to states that are invariant under the quantum operation. In an abstract setting, the Zeno sequence is an alternating concatenation of a contraction operator (quantum operation) and a $$C_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>C</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> -contraction semigroup (time evolution) on a Banach space. In this paper, we prove the optimal convergence rate $$\mathcal {O}(\tfrac{1}{n})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mstyle> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mi>n</mml:mi> </mml:mfrac> </mml:mstyle> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> of the Zeno sequence by proving explicit error bounds. For that, we derive a new Chernoff-type $$\sqrt{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mi>n</mml:mi> </mml:msqrt> </mml:math> -Lemma, which we believe to be of independent interest. Moreover, we generalize the convergence result for the Zeno effect in two directions: We weaken the assumptions on the generator, inducing the Zeno dynamics generated by an unbounded generator, and we improve the convergence to the uniform topology. Finally, we provide a large class of examples arising from our assumptions.
Implementation of a secure multi-cloud storage framework with next-generation cryptosystems and split-protocol

Cloud storage is a popular method of storing and sharing data. Numerous cloud service providers offer storage as a service, while others focus on sharing as a service. Many service providers focus their services on collaboration and integration with multiple tools to help with inline commenting and workflow. In recent days, working from home and new normal has attracted secure sharing services for strict access control. Data is new gold there for having the correct type of access control that satisfies the organization&#x2019;s existing policies is a crucial feature. Also, the demand for extensive data sharing is increasing day by day. December 2020&#x2019;s cyber-attack on SolarWinds &#x0026; FireEye is the primary security service provider in the U.S. and worldwide. Organizations are more concerned about their data at rest or in motion. To meet current industry needs, we have demonstrated a secure file sharing framework with a three-prong security strategy by employing next-generation cryptography coupled with proxy re-encryption and splitting data at bit level on multiple heterogeneous clouds. This paper aims to present the idea of split-protocol as a protection technique for security, integrity, and availability of data stored on the public cloud. We have used three encryption techniques for this experiment, namely AES 128-, AES, 256, and RLP, and compared their overheads and timings with different file sizes and types. Implementation and results indicate the feasibility of the proposed architecture without client involvement.
The role risk-management plays in reducing insider threat’s in the federal Government

Today, organizations face a growing cybersecurity threats to their IT infrastructure. The ever expanding remote-workforce has made the risk of insider-threats even more dangerous. How can organizations be better prepared to handle the risk of the next insider-threat? The Federal Government is no different, as one of the largest employer’s in the world, the growing risk of an insider-threat is always present. In a systematic review, the evidence will identify how Federal agencies can utilize risk-management to be better prepared to combat an insider-threat. Recommendations will be made to help Federal agencies be on the lookout for the next viable insider-threat.
Design of Multiobjective Path Planning System for Intelligent Transportation Based on ZigBee Technology

In recent years, due to the growth in the number of vehicles and traffic facilities construction lag, traffic safety problems and congestion problems become increasingly prominent. Traffic intelligentization, alleviates the problem of congestion, travel more convenient become the focus of attention. In order to solve the above problems, a multi-objective path planning system based on ZigBee technology is designed. In the intercepted road network planning area, the similarity measure conditions between the path paths are determined, and then the congestion coefficient is calibrated by combining the multi-objective optimization idea. Based on this, an electronic map document is established, and a suitable database host model is selected by using the MapX development tool. The path planning system based on ZigBee is designed. Simulation experiments are set up to highlight the practical application value of multi-objective optimization path planning system by comparing with the shortest path and the shortest traditional time.
A pupil location algorithm based on cascaded Haar feature and OTSU dynamic threshold segmentation

With the development of visual human-computer interaction, non-invasive eye-tracking technology has been applied in medical diagnosis, psychological research, augmented reality and other fields. As a key step of video-based eye tracking technology, pupil detection requires strong robustness, real-time performance and high precision. Although many pupil detection algorithms have been developed, pupil detection is still a challenge when there are so many interference factors such as bright light, reflection, eyelid or eyelash occlusion, or low image resolution. Aim to address the above difficult problems of pupil detection, this paper uses cascaded Haar features and Otsu dynamic threshold segmentation to improve the performance of the existing pupil detection algorithm. The performance of the improved algorithm is verified with the public human eye image dataset from Swirski with high off-axis and severe eyelash occlusion. The results show that the accuracy of the improved algorithm is 86.3% and 89.7% in the error range of 5 pixels and 10 pixels, respectively. The detection algorithm in this paper can provide accurate pupil center coordinates for eye tracking, which lays a foundation for the high-precision calibration and measurement of the following eye tracker.