Reviewerly/exHarmony huggingface dataset:
Motion-Driven Tracking via End-to-End Coarse-to-Fine Verifying

Target appearance and motion variations are the primary challenges in visual tracking. To tackle these challenges, top-performing trackers commonly rely on constructing complex appearance or motion models. However, the efficacy of these models in enhancing track performance can be limited by the lack of effective and seamless integration. The utilization of simplistic handcrafted fusion methods may even exacerbate the issue, resulting in a decline in tracking performance. To address this issue, we propose an end-to-end coarse-to-fine verifying approach in our motion-driven tracker. At the coarse level, we developed a motion prediction module (MPM) that efficiently extracts and utilizes motion information by leveraging the differences between adjacent frames. The MPM constructs not only a position prior for the decoder but also hybrid features that combine both motion and appearance. At the fine level, we employ a deformable transformer-based appearance model to accurately verify a local region centered on the predicted locations from the MPM. To further enhance the generalization capability of our tracker, we propose the use of an instance domain discriminator (IDD) during the training phase. This discriminator is based on domain adaptation theory and aims to sharpen the distinction between the target and other instances, thereby improving the robustness of tracking. Experimental results on five popular benchmarks, including GOT10k, LaSOT, TrackingNet, OTB, and VOT, validate the effectiveness of our proposed tracker.
An Empirical Study on the Problems and Improvement Directions of Coding and Software Education for Non-majors in the Era of the 4th Industrial Revolution

In this study, an empirical case study was conducted on core competencies and KPIs of coding and SW education targeting non-major undergraduates at H University to suggest problems in the current education and directions for improvement accordingly.&#x0D; The problems of non-majors’ coding and SW education are as follows. First, as a result of the analysis of KPIs for core competencies, coding, and SW education, satisfaction was lower than importance in all items, requiring improvement. Second, among core competencies, adaptability to change requires intensive management. Third, intensive management is required for the improvement of work processing ability, the degree of help in developing individual competency, and the composition of interesting educational contents. In order to improve these problems, first, the cultivation of adaptability to change should be set as an important educational goal and the curriculum should be operated. Second, it is required to improve work processing ability, operate programs that can help develop individual capabilities, and improve the composition of interesting educational contents.&#x0D; The results of this study are expected to provide basic information needed to conduct coding and SW education and to improve educational competitiveness for non-major students who are not computer-related majors at H University.
Adaptive Graph-Based Support Vector Data Description for Weakly-Supervised Anomaly Detection

We propose a novel method for weakly-supervised anomaly detection, where a limited number of labeled normal samples and a sufficient number of unlabeled samples are available for modeling. In particular, we seamlessly integrate label propagation with manifold graph learning into a support vector data description model. Consequently, the estimated manifold graph as well as its parameters will be adaptive to label propagation and benefit the anomaly detection performance. It is superior to most graph-based models that perform manifold graph learning separately by an independent step before label propagation. Theoretically, we derive a stability analysis based on the Rademacher complexity. Further, the effectiveness of the proposed method is demonstrated through several benchmark data sets and a real example of fault detection for high-speed train wheels. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article provides a weakly-supervised anomaly detection method and addresses the challenge of insufficient normal samples for training. The proposed method integrates the adaptive embedded label propagation with adaptive manifold graph learning into a support vector data description model to additionally exploit the intrinsic data distribution information of the unlabeled data in the model formulation. It ensures that the results are jointly optimal for manifold representation and anomaly detection such that the detection accuracy is improved.
Jointly Imputing Multi-View Data with Optimal Transport

The multi-view data with incomplete information hinder the effective data analysis. Existing multi-view imputation methods that learn the mapping between complete view and completely missing view are not able to deal with the common multi-view data with missing feature information. In this paper, we propose a generative imputation model named Git with optimal transport theory to jointly impute the missing features/values, conditional on all observed values from the multi-view data. Git consists of two modules, i.e., a multi-view joint generator (MJG) and a masking energy discriminator (MED). The generator MJG incorporates a joint autoencoder with the multiple imputation rule to learn the data distribution from all observed multi-view data. The discriminator MED leverages a new masking energy divergence function to make Git differentiable for imputation enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, Git yields over 35% accuracy gain, compared to the state-of-the-art approaches.
MulGT: Multi-Task Graph-Transformer with Task-Aware Knowledge Injection and Domain Knowledge-Driven Pooling for Whole Slide Image Analysis

Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields. However, most previous works only discuss the SINGLE task setting which is not aligned with real clinical setting, where pathologists often conduct multiple diagnosis tasks simultaneously. Also, it is commonly recognized that the multi-task learning paradigm can improve learning efficiency by exploiting commonalities and differences across multiple tasks. To this end, we present a novel multi-task framework (i.e., MulGT) for WSI analysis by the specially designed Graph-Transformer equipped with Task-aware Knowledge Injection and Domain Knowledge-driven Graph Pooling modules. Basically, with the Graph Neural Network and Transformer as the building commons, our framework is able to learn task-agnostic low-level local information as well as task-specific high-level global representation. Considering that different tasks in WSI analysis depend on different features and properties, we also design a novel Task-aware Knowledge Injection module to transfer the task-shared graph embedding into task-specific feature spaces to learn more accurate representation for different tasks. Further, we elaborately design a novel Domain Knowledge-driven Graph Pooling module for each task to improve both the accuracy and robustness of different tasks by leveraging different diagnosis patterns of multiple tasks. We evaluated our method on two public WSI datasets from TCGA projects, i.e., esophageal carcinoma and kidney carcinoma. Experimental results show that our method outperforms single-task counterparts and the state-of-theart methods on both tumor typing and staging tasks.
The Critical Analysis of E-Commerce Web Application Vulnerabilities

Large-scale deployments of web applications occur continuously. The failure to validate or sanitize form inputs, improperly configured web servers, and application design flaws are the main causes of security vulnerabilities that continue to infect web applications, allowing hackers to access sensitive data and using legitimate websites as a breeding ground for malware. These vulnerabilities can be used to compromise the security of the application. The largest problem that enterprises face is how to create a web application that satisfies their needs for safe processes, E-Commerce, and the transmission of sensitive data. OWASP updates and releases a list of the top 10 web application vulnerabilities every few years. Along with the OWASP Top 10 Threats, this chapter also discusses each vulnerability's possible effects and how to avoid them. According to the OWSP (Open Online Application Security Project) Top Ten, this document analyses the most serious web vulnerabilities, their causes, and their impacts.
Employing Blockchain and Machine Learning for Monitoring the Accumulation and Dispensation of Covid-19 Vaccine

Fair vaccine distribution is a humanitarian priority. Despite this scientific breakthrough, inoculation of the global population itself is an enormous challenge. Even though the vaccination process for SARS-CoV-2 (COVID-19) started globally just after a year of outbreak, it still continues to present significant challenges. One such challenge was gaining and maintaining public trust about the viability of the antibody and the effectiveness of inoculation itself. However, vaccines’ adequacy is dictated by the accessibility and receptiveness of the dissemination framework, which is divided among the collaborators. Most disseminated records, such as the blockchain, provide an open, public, and permanent framework for a variety of uses. In this work, we propose a framework that uses the Blockchain mechanism for vaccines’ safety, security, transparency, and traceability. The Blockchain technology employed in this proposed endeavor ensures and manages vaccine registration, storage, and delivery. Furthermore, Machine Learning (ML) based prediction is also used for predicting the future trends of the COVID-19 vaccination. A feedback system is also provided to keep track of the number of vials in case a patient has any negative effects. In many aspects, such as good efficiency and scalability, the proposed work shows promising results.
Overcoming an Evasion Attack on a CNN Model in the MIMO-OFDM Wireless Communication Channel

Implementation of deep learning (DL)-based algorithms for wireless communication channels can drastically enhance the discrepancy between features and interference. However, the performance of DL-based algorithms can be degraded by multiple different attackers, whereby a DL-based model is subjected to adversarial input to purposefully and maliciously fool or defeat the DL-based model. To overcome an evasion attack, this work contributes toward implementing a channel coding technique, specifically convolutional channel coding, for improving a CNN model’s performance in a multi-input multi-output-orthogonal frequency-division multiplexing (MIMO-OFDM) wireless channel. In the preliminary stage, our CNN architecture has been trained by the mini-batch gradient descent algorithm, and we have achieved approximately 98% accuracy. In the presence of an evasion attack, we have deployed convolution channel coding for a CNN model in an MIMO-OFDM wireless channel. The effectiveness and performance of the model has been evaluated using the metrics BER, classification accuracy, physical layer security, and reliability, in the presence of an evasion attack. In the proposed model, a significant reduction in BERs has been obtained, and also a significant enhancement in wireless security in the presence of an evasion attack, which is accompanied by a robust and accurate wireless communication channel.
A Multi-feature Analysis of Accented Multisyllabic Malayalam Words—a Low-Resourced Language

Accented Automatic Speech Recognition (AASR) is an active area of research in all languages. Even though the research has advanced far in the area of Automatic Speech Recognition (ASR) in languages like English, Arabic, and Chinese; the research is still in its infancy in the area of AASR. Malayalam is a language that is rich with many varieties of accents spoken in different parts of Kerala. Here in this work, the authors discuss the various experiments conducted on the accented features and propose a novel approach to better represent the accented speech data for the Malayalam language. There is no benchmark data available for the accented Malayalam speech and hence authors have collected data across five districts to represent five different accents from people of all age groups. The data collected includes multisyllabic isolated utterances recorded under a natural recording environment using crowdsourcing methods. The data collected have been properly annotated and then underwent a series of experiments to find the best feature extraction method that would best represent the accented speech signals. In every experiment, we used different features to build machine learning models using different machine learning approaches. And finally, we combined all the feature extraction methods and constructed a deep learning model using Long Short-Term Memory—Recurrent Neural Networks (LSTM-RNN).
Evaluation of Federated Learning Strategies on Industrial Time Series Fault Classification

Federated Learning (FL) can enable industrial devices to collaboratively learn a shared Machine Learning (ML) model while keeping all the training data on the device itself. The FL inherently solves the data privacy issues where as it also reduces the cloud data hosting cost and communication overhead. However, existing FL algorithms and frameworks are generically designed and not evaluated against the industrial applications. In case of industrial applications, the data generated from assets and machines are mostly non-independent and Identically Distributed (non-IID) which demands for an investigation and evaluation on the existing FL algorithms against such non-IID industrial data. This paper presents a survey of FL frameworks, algorithms, and optimizers, and evaluates their performance against the industrial data. The paper shares the results of extensive numerical experiments conducted with the edge device, Raspberry pi. The data set used in this study is benchmarking fault classification industrial data, called PRONTO data set, for both IID and non-IID cases. With the experimental results, the paper demonstrates that the adaptive optimization-based FL algorithm, $$\texttt {FedAdam}$$ outperforms other algorithms on an industrial time series fault classification application.
Human-Robot Collaboration – Managing the Integration of Service Robots in the Frontline

Service robots is a trending topic. As more and more robots enter the frontline workplace, the question arises how human employees will adapt to robot co-workers. The notion of human-robot work relationships is still novel. In this chapter we explore how service organizations can prepare for the transition of a human-robot workforce. We present a framework for the digital transformation of the service frontline and the integration of human-robot teams.
A GMDH Approach for Forecast Monthly Rainfall in Southeast of Para

Modeling for rainfall forecasting is widely used in climate forecasting. A high volume of precipitation or scarcity brings serious problems for society. Thus, a methodology for monthly rainfall forecast is presented using Group Method of Data Handling (GMDH) and sea surface temperature (SST). The intelligent model gets the mean monthly SST, in predefined and temporally lagged areas, after a variable selection step. For model training, precipitation data from the Climate Prediction Center were used. The methodology was applied in a certain area of the municipality of Maraba, located in the southeastern region of the Para state. The results obtained with GMDH overcame those got by using a conventional multilayer Artificial Neural those got by using a conventional multilayer Artificial Neural Network (RNA), reaching values for regression coefficient (R) and root mean square error (RMSE) equal to 0.96363 and 30.8100 in the test stage, while the RNA-based model got 0.81712 and 68.2607, respectively. Results show the GMHDs effectiveness for the monthly rainfall prediction, constituting an important tool for the planning and assistance to decision makers.
Tetrahedron genuine entanglement measure of four-qubit systems

Abstract Quantifying genuine entanglement is a key task in quantum information theory. We study the quantification of genuine multipartite entanglement for four-qubit systems. Based on the concurrence of nine different classes of four-qubit states, with each class being closed under stochastic local operation and classical communication, we construct a concurrence tetrahedron. Proper genuine four-qubit entanglement measure is presented by using the volume of the concurrence tetrahedron. For non genuine entangled pure states, the four-qubit entanglement measure classifies the bi-separable entanglement. We show that the concurrence tetrahedron based measure of genuine four-qubit entanglement is not equivalent to the genuine four-partite entanglement concurrence. We illustrate the advantages of the concurrence tetrahedron by detailed examples.
Hyperparameter Optimized DNN for OFDM Signal Detection in the Presence of CFO and PO

In any multi-carrier modulation (MCM) system, it is crucial to have accurate channel estimation. Typically, the choice of channel state information (CSI) based on the least-square (LS) and minimum-mean square error (MMSE) estimators has been the traditional approach. With the advancement of deep learning algorithms, it has become a state-of-the-art algorithm for signal detection. However, deep learning comes with various hyperparameters that should be tuned for maximum performance. In this paper, an optimized Deep Neural Network (DNN) based signal detection approach has been proposed for the orthogonal frequency division-multiplexing (OFDM) system in the carrier frequency offset (CFO) and phase offset (PO)impaired environment at the receiver end. Thorough experiments were carried out to determine the optimal architecture for accurate signal detection. The optimum architecture is of shape 64-12-78-22-64, where 12,78,22 are the number of neurons in the hidden layer.
Generation of Highlights From Research Papers Using Pointer-Generator Networks and SciBERT Embeddings

Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper.Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines.We aim to automatically construct research highlights given certain segments of the research paper.We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings.We test our model on a benchmark dataset, CSPubSum and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation.For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature.On the CSPubSum data set, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper.It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines.On the new MixSub data set, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78,9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25, outperforming other models.
Self-calibration algorithm for installation angle deviation of bionic polarization compound eyes

A self-calibration algorithm based on unsupervised optimization for polarizer installation angle deviation is proposed and used in a multi-aperture bionic polarization compound eye system. To simplify calibration operation, under the condition that the calibration-polarized light information is unknown, this algorithm fully exploits redundancy and random polarization information in the scene, and uses a non-convex multi-objective discrete parameter sorting optimization method to achieve angle self-calibration. Compared with ordinary calibration procedures, the algorithm requires less stringent conditions, achieves online calibration and is more accurate. It also can be applied to camera polarization arrays, division-of-focal-plane polarization cameras, and other polarization devices.
Surface Topography of Transparent Plate Using Fizeau Interferometer with Suppression of Intensity Modulation

The derivation of 19-fringe phase-modulated algorithm that can suppress the first order nonlinear intensity modulation during wavelength modulating is presented. We visualize the proposed algorithm in the frequency domain in connection with the Fourier representation to discuss the properties of the algorithm. The 19-fringe algorithm yields the smallest phase error compared to the conventional algorithms. Lastly, we assess the surface of the transparent fused silica plate using the wavelength-modulating Fizeau interferometer and the 19-fringe algorithm. From the experiment results, it is indicated that the accuracy of the surface assessment for the fused silica plate is 4.0 nm.
Implementasi Software Hec-Ras 4.1.0 Dan Epa Storm Water Management Model (Swmm) 5.1.0 Pada Efektivitas Analisis Saluran Drainase (Studi Kasus Desa Kelet Kecamatan Keling Kabupaten Jepara)

In the area of highway Kelet - Bangsri area, Kelet Village, drainage problems and flooding often occur along the highway during the rainy season. From the problems found that there are no drainage channels as a result of residential construction that does not pay attention to the drainage function and can result in damage to road construction. This final project analyzes drainage channels with the aim of knowing alternative flood management. The results of the hydrological planning calculations will be used as the basis for hydraulic analysis, rainfall calculations using the isohyet method. The hydraulics analysis was carried out using the HEC-RAS and EPA SWMM programs. Based on the results of the analysis of existing conditions, it was found that inundation occurs because the capacity of the channel is not able to drain the flood discharge, such as the dimensions of the channel that are not suitable so that new planning is needed. The results of the analysis of the EPA SWMM software are more effective to be used as an alternative for handling the right flood problem according to the condition of the Kelet - Bangsri Highway, Kelet Village, Keling District, Jepara Regency with the planning of the new drainage channel dimensions of B = 0.6m and H = 0.8m.