Reviewerly/exHarmony huggingface dataset:
Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher's community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal processing (DSP).We then summarize recent developments in developing basic GSP tools, including methods for graph filtering or graph learning, graph signal, graph Fourier transform (GFT), spectrum, graph frequency, etc. Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness, we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application, we thoroughly demonstrated that graph filtration is efficient for big data analytics.
Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas.Better management of pedestrians leads to improved use of public places.Behavior of pedestrian's is a major factor of crowd management in public places.There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment.In this paper, we have proposed a new method for pedestrian's behavior detection.Kalman filter has been used to detect pedestrian's using movement based approach.Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans.Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes.Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier.Performance has been evaluated using pedestrian's data from avenue and UBI-Fight datasets, where both have different environment.The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively.These results are more accurate as compared to other existing methods.
Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality.We contributed to meeting the challenges of big data visualization using the embedded method based "Select from model (SFM)" method by using "Random forest Importance algorithm (RFI)" and comparing it with the filter method by using "Select percentile (SP)" method based chi square "Chi2" tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm.Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently, the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons, the study suggests (SFMLR) using SFM based on RFI algorithm for feature selection, with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.
Quantum Oblivious Transfer with Reusable Bell State

In cryptography, oblivious transfer (OT) is an important multiparty cryptographic primitive and protocol, that is suitable for many upperlayer applications, such as secure computation, remote coin-flipping, electrical contract signing and exchanging secrets simultaneously.However, some nogo theorems have been established, indicating that one-out-of-two quantum oblivious transfer (QOT) protocols with unconditional security are impossible.Fortunately, some one-out-of-two QOT protocols using the concept of Crépeau's reduction have been demonstrated not to conform to Lo's no-go theorem, but these protocols require more quantum resources to generate classical keys using all-or-nothing QOT to construct one-out-of-two QOT.This paper proposes a novel and efficient one-out-of-two QOT which uses quantum resources directly instead of wasting unnecessary resources to generate classical keys.The proposed protocol is not covered by Lo's no-go theorem, and it is able to check the sender's loyalty and avoid the attack from the receiver.Moreover, the entangled state of the proposed protocol is reusable, so it can provide more services for the participants when necessary.Compared with other QOT protocols, the proposed protocol is more secure, efficient, and flexible, which not only can prevent external and internal attacks, but also reduce the required resources and resource distribution time.
Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era.Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems.This may result in compromised security of the systems, scams, and other such cyberattacks.These attacks hijack huge quantities of the available data, incurring heavy financial loss.At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them.With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model.The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs.To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique.In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs.Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model.The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository.The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.
A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance.In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase.The two-phase paradigm enables our model to significantly reduce the data distribution gap, including the gap between negative entities and other entities, as well as the gap between negative relations and other relations.In addition, we make the first attempt at combining entity type and entity distance as global features, which has proven effective, especially for the relation extraction.Experimental results on several datasets demonstrate that the span-based joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-ofthe-art span-based models for the joint extraction task, establishing a new standard benchmark.Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.
Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT).Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors' profiling for an immediate response.However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method's effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art.The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.
MFVC: Urban Traffic Scene Video Caption Based on Multimodal Fusion

With the development of electronic technology, intelligent cars can gradually realize more complex artificial intelligence algorithms. The video caption algorithm is one of them. However, current video caption algorithms only consider single-visual information when applied to urban traffic scenes, which leads to the failure to generate accurate captions of complex sets. The multimodal fusion algorithm based on Transformer is one of the solutions to this problem. However, the existing algorithms have the difficulties of a low fusion performance and high computational complexity. We propose a new video caption Transformer-based model, the MFVC (Multimodal Fusion for Video Caption), to solve these issues. We introduce audio modal data and the attention bottleneck module to increase the available information to describe the generative model and improve the model effect with less operation costs through the attention bottleneck module. Finally, the experiment is conducted on the available datasets, MSR-VTT and MSVD. Meanwhile, to verify the effect of the model on the urban traffic scene, the experiment is carried out on the self-built traffic caption dataset BUUISE, and the evaluation index confirms the model. This model can achieve good results on both available datasets and urban traffic datasets and has excellent application prospects in the intelligent driving industry.
Review on online operation insulator identification and fault diagnosis based on UAV patrol images and deep learning algorithms

Artificial intelligence has great potential for use in smart grids. Power system image recognition based on artificial intelligence is an important research direction. The insulator is essential equipment for the power grid and is related to operational safety. Online operating insulator location identification and fault diagnosis technologies based on unmanned aerial vehicle (UAV) patrol the images, and deep learning algorithms have been continuously suggested and developed. These technologies have achieved good results in practical application. By compiling the recent literature on insulator detection technology, three common application scenarios and research difficulties are uncovered: The need for increased detection accuracy and real-time speed; faulty image recognition of complex backgrounds and target occlusion; and multiscale object and small object detection improvements. At the same time, the improved algorithms in the literature are comprehensively summarized, and the performance evaluation indices of various algorithms are compared.
Security threats in cyberspace

The article is an attempt to highlight the main types of security threats in cyberspace. As literature provides a multitude of different approaches, standards, methodologies, and proposals for the classification of threats, the article focuses on threats to privacy and national ICT security. Cyberspace is subject to increasingly sophisticated and targeted threats, while our growing reliance on cyberspace exposes our privacy to risks, giving rise to new and significant security gaps. Due to its specific characteristics, it generates serious threats to individuals as well as to national and international security. Depending on the research perspective we adopt, these threats are variable, multidimensional, and multifaceted in nature. Therefore, they require systematic analysis and response.
Analysis of Signs and Effects of Surgical Breast Cancer Patients Based on Big Data Technology

Big data in health care has gained popularity in recent years for disease prediction. Breast cancer infections are the most common cancer in urban Indian women, as well as women internationally, and are impacted by many events across countries and regions. Breast malignant growth is a notable disease among Indian women. According to the WHO, it represents 14% of all malignant growth tumors in women. A couple of studies have been directed utilizing big data to foresee breast malignant growth. Big data is causing a transformation in healthcare, with better and more ideal results. Monstrous volumes of patient-level data are created by using EHR (Electronic Health Record) systems data because of fast mechanical upgrades. Big data applications in the healthcare business will assist with improving results. Conventional forecast models, then again, are less productive in terms of accuracy and error rate because the exact pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and kinds of datasets utilized (trait-based or picture based). This audit article looks at complex information mining, AI, and profound learning models utilized for recognizing breast malignant growth. Since "early identification is the way to avoidance in any malignant growth," the motivation behind this audit article is to support the choice of fitting breast disease expectation calculations, explicitly in the big information climate, to convey powerful and productive results. This survey article analyzes the precision paces of perplexing information mining, AI, and profound learning models utilized for distinguishing breast disease on the grounds that the exactness pace of a specific calculation relies upon different factors such as execution structure, datasets (little or enormous), and dataset types (quality based or picture based). The reason for this audit article is to aid the determination of suitable breast disease expectation calculations, explicitly in the big information climate, to convey successful and productive outcomes. Thus, "Early discovery is the way to counteraction in the event of any malignant growth."
Comparative Analysis of the Interlinks between Globalization, Governance and Development in African Economic Communities

This paper examines the complex causal relationship between globalization, governance and development in African countries using a comparative approach. The sample comprises 51 countries in five groups over the period 1996–2018. The cointegration analysis shows that there is a long-run relationship between globalization, governance and human development in Africa as a whole sample. However, at regional level, such a relationship does not exist in the Economic Community of Central African States (ECCAS) bloc. In the long run, globalization and governance affect positively human development in all samples. The findings indicate also that there was unidirectional causality from globalization and governance to development at the continental level. At the regional level, there is unidirectional causality from globalization to development in East African Community (EAC) and Southern African Development Community (SADC). In these regions, there is also a bidirectional causality between governance and development. In the ECCAS, the causality runs from governance to development and from development to globalization in the Economic Community of West African States (ECOWAS). The results support no evidence of causality between the variables in North Africa (Arab Maghreb Union, AMU). Finally, the overall globalization index does not Granger-cause governance. The main implication of our study is that improving governance and development is a very challenging issue, and the impact of globalization should not be neglected.
Bayesian inference

Bayesian statistical methods have become more popular in different applications of scientific research over the past several decades. This is mainly due to the computing capacity of modern machines and the recent advances in Bayesian computational methodologies. In this chapter, we give a brief introduction to Bayesian analysis and discuss the difference between Bayesian and classical frequentist statistics. To illustrate Bayesian methodologies, a diagnostic COVID-19 test is used to present the basic principles of the Bayesian approach, prior distribution, likelihood function, and posterior distribution. As an application of the Bayesian methodologies, we introduce Bayesian linear regression and Gaussian process regression and their Bayesian inference framework.
Object Detection Algorithm Based on Improved YOLOv5 for Basketball Robot

At present, large high-precision target detection models are limited by the computing power and memory size of robot embedded devices, and there are problems with parameter redundancy and slow inference speed. This paper proposes an object detection algorithm applied to a basketball robot based on improved yolov5 to address these problems. The improved YOLOv5 algorithm uses GhostNet for lightweight improvement and introduces the Coordinate Attention (CA) mechanism to enhance the expression of learning features in mobile networks to achieve efficient object detection on basketball robots. According to the experimental results, the improved algorithm on YOLOv5s model framework reduces the parameters by 46.84% and the giga floating-point operations per second (GFLOPs) by 48.13%, which has a significant accuracy advantage compared with the algorithm based on other lightweight networks with lightweight improvements. The improved algorithm has high application value in deploying object detection on mobile robot embedded devices.
Enhancing Entity Linking with Contextualized Entity Embeddings

Entity linking (EL) in written language domains has been extensively studied, but EL of spoken language is still unexplored. We propose a conceptually simple and highly effective two-stage approach to tackle this issue. The first stage retrieves candidates with a dual encoder, which independently encodes mention context and entity descriptions. Each candidate is then reranked by a LUKE-based cross-encoder, which concatenates the mention and entity description. Different from previous cross-encoder which takes only words as input, our model adds entities into input. Experiments demonstrate that our model does not need large-scale training on Wikipedia corpus, and outperforms all previous models with or without Wikipedia training. Our approach ranks the $$1^\textrm{st}$$ in NLPCC 2022 Shared Task on Speech EL Track 2 (Entity Disambiguation-Only).
Overview of NLPCC 2022 Shared Task 7: Fine-Grained Dialogue Social Bias Measurement

This paper presents the overview of the shared task 7, Fine-Grained Dialogue Social Bias Measurement, in NLPCC 2022. In this paper, we introduce the task, explain the construction of the provided dataset, analyze the evaluation results and summarize the submitted approaches. This shared task aims to measure the social bias in dialogue scenarios in a fine-grained categorization which is challenging due to the complex and implicit bias expression. The context-sensitive bias responses in dialogue scenarios make this task even more complicated. We provide 25k data for training and 3k data for evaluation. The dataset is collected from a Chinese question-answering forum Zhihu ( www.zhihu.com ). Except for the above-mentioned bias attitude label, this dataset is also finely annotated with multiple auxiliary labels. There are 11 participating teams and 35 submissions in total. We adopt the macro F1 score to evaluate the submitted results, and the highest score is 0.5903. The submitted approaches focus on different aspects of this problem and use diverse techniques to boost the performance. All the relevant information can also be found at https://para-zhou.github.io/NLPCC-Task7-BiasEval/ .
Privacy-Preserving Record Linkage Using Local Sensitive Hash and Private Set Intersection

The amount of data stored in data repositories increases every year. This makes it challenging to link records between different datasets across companies and even internally, while adhering to privacy regulations. Address or name changes, and even different spelling used for entity data, can prevent companies from using private deduplication or record-linking solutions such as private set intersection (PSI). To this end, we propose a new and efficient privacy-preserving record linkage (PPRL) protocol that combines PSI and local sensitive hash (LSH) functions, and runs in linear time. We explain the privacy guarantees that our protocol provides and demonstrate its practicality by executing the protocol over two datasets with $2^{20}$ records each, in $11-45$ minutes, depending on network settings.
SuppliedTrust: A Trusted Blockchain Architecture for Supply Chains

The impact of COVID-19, shortage of chips and external factors has made a flurry demand, increased costs and significant delays in supply chains despite technological advancements in the supply chain management process. The blockchain technology is constantly being explored and attracts supply chains in adopting them to allow businesses to scale rapidly. In our work, we identify gaps between existing blockchain implementations and cybersecurity standards. We introduce a framework and show how we can implement secure and trusted blockchains onto the supply chains.