Reviewerly/exHarmony huggingface dataset:
Digital Supply Chain Management Using AI, ML and Blockchain

Supply Chain Management (SCM) is a kind of network in which each phase of product development is tracked right from gathering raw materials till the delivery of the product. Developing a product, distributing it to suppliers, delivering to customers, collecting feedback, etc., all are tracked and maintained using supply chain management. A secured supply chain should be held to ensure that fake products do not enter into the market. For example; if counterfeit drugs enter the market, then it will have various side effects on patients as well as it will affect the pharmaceutical companies. Hence, a secure supply chain is a must. The significance of SCM has increased as it plays a vital role in the decision-making process. Hence, a proper supply chain should be maintained along with a technology which will also help in the decision-making process. One such technology is Artificial Intelligence (AI). AI is the technology in which machines are programmed to think like humans and perform an action accordingly. Machine Learning can be used in detecting fake products that enter the market. Also, there are various algorithms in AI which can be used in the decision-making process. As the digitalization of SCM is significant, similarly, its security is also worthwhile. SCM can be implemented as a Drug Supply chain Management system, which Blockchain can be integrated to counter the supply of counterfeit drugs into the market by tracking. This chapter discusses Digitalized and AI-based Drug Supply Chain Management. With Supply chain analytics tracking each step of the inventory process: from the loading dock to the supply cabinet, to the patient’s hospital room, Hospitals can expect massive Supply chain analytics are used to measure, monitor, and improve individual business processes as well as the overall performance and health of the supply chain. Drug supply chain analytics will enable demand visibility, inventory visibility and Freight analytics.
Text Classification Using Convolutional Neural Networks

Most of the information (more than 80%) is stored as text, and text mining is a very important process as it is an initial step in the process of text classification, and this is especially the case in the Arabic language. The Aim of The Study is to classify Arabic texts according to specific categories using advanced performance indicators We used Data Templates as a platform for managing and organizing Apache Spark to solve big data challenges. Apache Spark offers several integrated language APIs. nlp lib was used for text processing. The data is pre-processed through several steps, namely separating the words into one text on the basis of the space between words, cleaning the text of unwanted words, restoring the words to their roots, as well as the feature selection process is a critical step. in text classification. It is a preprocessing technology. In this paper, one way to determine which TF attributes are used how often each feature appears in the document is that they consider the first level of the feature selection process. Then we use TF-IDF to determine the significance of the feature in the document, and this is the last step in the preprocessing Outcomes Text classification . Results were evaluated using advanced performance indicators such as accuracy, Precision and recall. A high accuracy of 96.94% was achieved.The main objective of this paper is to classify basic texts quickly and accurately, according to the results as long as the feature size is suitable, the most advanced technology is superior to other pass rate methods due to the reasonable reliability and perfect pruning level.
Landing AI on Networks: An Equipment Vendor Viewpoint on Autonomous Driving Networks

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks.
Clinical Characteristics of Patients with Pulmonary Thromboembolism Based on Computer Statistical Analysis

In order to explore the reliable clinical characteristics of patients with pulmonary thromboembolism, this paper applies computer statistical analysis methods to the analysis of clinical characteristics of patients with pulmonary thromboembolism. Moreover, starting from the actual situation, this paper combines experiments to study the case, uses sample screening and sample processing to group samples freely, and conducts grouping reliability research through data statistics methods. After verifying the reliability of the grouping, this paper combines the samples to test the effectiveness of the computer statistical analysis method applied to the clinical characteristic analysis of patients with pulmonary thromboembolism and conducts numerical analysis in combination with the comparative analysis method. The results of the research show that the computer statistical method proposed in this paper has a good effect on the clinical characteristics of patients with pulmonary thromboembolism and meets the actual needs of clinical analysis.
Named Entity Recognition based Resume Parser and Summarizer

Resumes are unstructured documents and the language used in the resume will be ambiguous and variable. Screening documents in large amounts is a difficult operation, and recruiters and hiring managers waste a lot of time going through each and every CV of the candidates. The extraction of valuable information from a resume is difficult due to this variability. It necessitates an urgent need to comprehend the context in which words are used. Using spaCy, a powerful natural language processing (NLP) package, this study suggests a machine learning method to phrase matching in resumes, focused on the extraction of specific entities. Like a human recruiter, it can examine and extract comprehensive information from resumes. While parsing, it maintains track of the terms in order to classify people. Specific Important entities are extracted from the resume document and then it is stored for later classification. Document ranking is processed and the final rankings are depicted in descending order with respect to the score of the resume in order to make it easy for the recruiters to shortlist the resumes quickly and efficiently. Recruiters may pick the needed applicants based on the scores rather than sifting through stacks of resumes from unqualified prospects.
Complex-Valued Migrativity of Complex Fuzzy Operations

Complex fuzzy sets (CFSs), as an important extension of fuzzy sets, have been investigated in the literature. Operators of CFSs are of high importance. In addition, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>α</mi> <mo>−</mo> </math> migrativity for various fuzzy operations on [0, 1] has been well discussed, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>α</mi> </math> is a real number and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>α</mi> <mo>∈</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mn>0,1</mn> </mrow> </mfenced> </math> . Thus, this paper studies <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>α</mi> <mo>−</mo> </math> migrativity for binary functions on the unit circle of the complex plane <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi mathvariant="bold">O</mi> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>α</mi> </math> is a complex number and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>α</mi> <mo>∈</mo> <mi mathvariant="bold">O</mi> </math> . In particular, we show that a binary function is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>α</mi> <mo>−</mo> </math> migrativity for all <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>α</mi> <mo>∈</mo> <mi mathvariant="bold">O</mi> </math> if and only if it is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>α</mi> <mo>−</mo> </math> migrativity for all <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi>α</mi> <mo>∈</mo> <mfenced open="[" close="]" separators="|"> <mrow> <mn>0,1</mn> </mrow> </mfenced> <mo>∪</mo> <mover accent="true"> <mi mathvariant="bold">O</mi> <mo stretchy="true">¯</mo> </mover> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mover accent="true"> <mi mathvariant="bold">O</mi> <mo stretchy="true">¯</mo> </mover> </math> is the boundary point subset of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <mi mathvariant="bold">O</mi> </math> . Finally, we discuss the relationship between migrativity and rotational invariance of binary operators on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M14"> <mi mathvariant="bold">O</mi> </math> .
Learning Nonlinear Feature Mapping via Constrained Non-Convex Optimization for Unsupervised Salient Object Detection

Mimicking the biological visual attention mechanism to detect salient objects in images has been widely studied in recent years. Most of the existing computational models rely on external learning for saliency prediction, which however lack robustness in diversified scenes. In this paper, an unsupervised learning model is proposed to detect salient objects by fully exploiting the internal information of the scene. Specifically, we formulate saliency detection as a mathematical programming problem with which to learn a nonlinear feature mapping from multi-view features to saliency scores. The optimization objective is to maximize the between-class variance of the attended and background regions in the resulting saliency maps, which is statistically optimal. Moreover, to solve the non-convex constrained mathematical programming problem, a hybrid external point method based particle swarm optimization algorithm is developed to find the optimal solution in a rapid manner. Finally, extensive experiments are conducted on four classical saliency benchmark datasets to test the effectiveness of the proposed method and it shows superior qualitative and quantitative performance than the other 16 state-of-the-art unsupervised saliency models.
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate such knowledge and detect tax frauds. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades.
Two classes of power mappings with boomerang uniformity 2

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ q $\end{document}</tex-math></inline-formula> be an odd prime power. Let <inline-formula><tex-math id="M2">\begin{document}$ F_1(x) = x^{d_1} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M3">\begin{document}$ F_2(x) = x^{d_2} $\end{document}</tex-math></inline-formula> be power mappings over <inline-formula><tex-math id="M4">\begin{document}$ \mathrm{GF}(q^2) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M5">\begin{document}$ d_1 = q-1 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ d_2 = d_1+\frac{q^2-1}{2} = \frac{(q-1)(q+3)}{2} $\end{document}</tex-math></inline-formula>. In this paper, we study the boomerang uniformity of <inline-formula><tex-math id="M7">\begin{document}$ F_1 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M8">\begin{document}$ F_2 $\end{document}</tex-math></inline-formula> via their differential properties. It is shown that the boomerang uniformity of <inline-formula><tex-math id="M9">\begin{document}$ F_i $\end{document}</tex-math></inline-formula> (<inline-formula><tex-math id="M10">\begin{document}$ i = 1,2 $\end{document}</tex-math></inline-formula>) is 2 with some conditions on <inline-formula><tex-math id="M11">\begin{document}$ q $\end{document}</tex-math></inline-formula>.
English Speech Feature Recognition-Based Fuzzy Algorithm and Artificial Intelligent

It is necessary to study the application of digital technology in English speech feature recognition. This paper combines the actual needs of English speech feature recognition to improve the digital algorithm. Moreover, this paper combines fuzzy algorithm to analyze English speech features, analyzes the shortcomings of traditional algorithms, proposes the fuzzy digitized English speech recognition algorithm, and builds an English speech feature recognition model on this basis. In addition, this paper conducts time-frequency analysis on chaotic signals and speech signals, eliminates noise in English speech features, improves the recognition effect of English speech features, and builds an English speech feature recognition system based on digital means. Finally, this paper conducts grouping experiments by inputting students’ English pronunciation forms and counts the results of the experiments to test the performance of the system. The research results show that the method proposed in this paper has a certain effect.
Vehicle Intelligent Classification Based on Big Multimodal Data Analysis and Sparrow Search Optimization

Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.
A strawman with machine learning for a brain: A response to Biedermann (2022) the strange persistence of (source) “identification” claims in forensic literature

We agree wholeheartedly with Biedermann (2022) FSI Synergy article 100222 in its criticism of research publications that treat forensic inference in source attribution as an "identification" or "individualization" task. We disagree, however, with its criticism of the use of machine learning for forensic inference. The argument it makes is a strawman argument. There is a growing body of literature on the calculation of well-calibrated likelihood ratios using machine-learning methods and relevant data, and on the validation under casework conditions of such machine-learning-based systems.
Automated Registration for Dual-View X-Ray Mammography Using Convolutional Neural Networks

Automated registration algorithms for a pair of 2D X-ray mammographic images taken from two standard imaging angles, namely, the craniocaudal (CC) and the mediolateral oblique (MLO) views, are developed.A fully convolutional neural network, a type of convolutional neural network (CNN), is employed to generate a pixel-level deformation field, which provides a mapping between masses in the two views. Novel distance-based regularization is employed, which contributes significantly to the performance.The developed techniques are tested using real 2D mammographic images, slices from real 3D mammographic images, and synthetic mammographic images. Architectural variations of the neural network are investigated and the performance is characterized from various aspects including image resolution, breast density, lesion size, lesion subtlety, and lesion Breast Imaging-Reporting and Data System (BI-RADS) category. Our network outperformed the state-of-the-art CNN-based and non-CNN-based registration techniques, and showed robust performance across various tissue/lesion characteristics.The proposed methods provide a useful automated tool for co-locating lesions between the CC and MLO views even in challenging cases.Our methods can aid clinicians to establish lesion correspondence quickly and accurately in the dual-view X-ray mammography, improving diagnostic capability.
The dawn of a text-dependent society

Recent developments in the field of deepfakes bring new threats that take advantage of the fact that it is increasingly difficult to distinguish between real and artificial media. Nowadays, mostly as fake news or disinformation; however, there are still unexplored areas such as using deepfakes to spoof voice verification. We present a real-world use case for spoofing voice authentication in a customer care call center. Based on this scenario, we evaluate the feasibility of attacking such a system and create an attacker profile. For this purpose, we examine three available speech synthesis tools and discuss their usability. We use these tools and acquired knowledge to generate a dataset including deepfake speech and assess the resilience of voice biometrics systems against deepfakes. We prove that voice biometrics systems are indeed vulnerable to deepfake powered attacks. The most significant outcome is the proposal of text-dependent verification as a novel countermeasure for presented attacks. Text-dependent verification provides higher security than text-independent verification and can be used today as the simplest protection method against deepfakes.
Design of Online Learning Early Warning Model Based on Artificial Intelligence

Students have produced a large number of learning logs and a large number of educational data through online learning. In order to extract useful information from these massive information, support teachers’ teaching activities, improve teaching quality, provide learners with personalized learning support, and teach students according to their aptitude, educational big data analysis has attracted extensive attention of educators. Under the impact of informatization, education and information technology are deeply integrated, and network education is in the ascendant. At the same time, the theories and methods of artificial intelligence are gradually applied to the field of education. Learning analysis, data mining, and other analysis methods provide effective analysis methods for online learning process and learning effect. This paper studies the design of online learning early warning model based on artificial intelligence. When the system is implemented, based on the existing evaluation index system, the online learning early warning model of analytic hierarchy process is introduced to give different evaluation indexes unequal weights to ensure the fairness of evaluation. On the basis of understanding the artificial intelligence algorithm, the corresponding detection indicators should also be selected according to the objectives of the online learning early warning model and the needs of the actual work of network operation and maintenance. Then, follow the corresponding data flow of artificial intelligence technology and analyze data preprocessing, feature selection, and model training.
Heuristic Reordering Strategy for Quantum Circuit Mapping on LNN Architectures

Because of the connection constraints of quantum devices, the quantum gate cannot operate directly on nonadjacent qubits. Quantum circuit mapping transforms a logical quantum circuit to a circuit that satisfies the connection constraints by adding SWAP gates for nonadjacent qubits. Global and local heuristic reordering strategies are proposed in this paper for quantum circuit mapping over linear nearest neighbor (LNN) architectures, which are one-dimensional topology structures, to reduce the number of SWAP gates added. Experiment results show that the average improvements of the two methods are 13.19% and 15.46%, respectively. In this paper, we consider the quantum circuit mapping problem for linear nearest neighbor (LNN) architectures. We propose a global heuristic qubit reordering optimization algorithm and a local heuristic qubit reordering optimization algorithm. Compared with the other algorithm results, the average improvements of the two methods for quantum cost are 13.19% and 15.46%, respectively. The two methods apply to the realization of quantum circuit neighboring over one-dimensional quantum architectures and can be extended to algorithms that work for other quantum architectures of different topologies.
Vision Shared and Representation Isolated Network for Person Search

Person search is a widely-concerned computer vision task that aims to jointly solve the problems of pedestrian detection and person re-identification in panoramic scenes. However, the pedestrian detection focuses on the consistency of pedestrians, while the person re-identification attempts to extract the discriminative features of pedestrians. The inevitable conflict greatly restricts the researches on the one-stage person search methods. To address this issue, we propose a Vision Shared and Representation Isolated (VSRI) network to decouple the two conflicted subtasks simultaneously, through which two independent representations are constructed for the two subtasks. To enhance the discrimination of the re-ID representation, a Multi-Level Feature Fusion (MLFF) module is proposed. The MLFF adopts the Spatial Pyramid Feature Fusion (SPFF) module to obtain diverse features from the stem network. Moreover, the multi-head self-attention mechanism is employed to construct a Multi-head Attention Driven Extraction (MADE) module and the cascaded convolution unit is adopted to devise a Feature Decomposition and Cascaded Integration (FDCI) module, which facilitates the MLFF to obtain more discriminative representations of the pedestrians. The proposed method outperforms the state-of-the-art methods on the mainstream datasets.