Reviewerly/exHarmony huggingface dataset:
Attribute-Guided Global and Part-Level Identity Network for Person Re-Identification

Most of the person re-identification (re-ID) algorithms based on deep learning mainly learn the global feature representation of pedestrians, while ignoring the important role of fine-grained pedestrian attribute features on re-ID tasks. Pedestrian attributes are middle-level semantic features, which have invariance in different poses, camera views, and illumination conditions. Considering the robustness and promotion of pedestrian attributes for person re-ID task, we propose an Attribute-guided Global and Part-level identity Network (AGPNet), which consists of a global identity task, a part-level identity task, and a pedestrian attributes learning task. AGPNet takes advantage of perceived semantic information of pedestrian attributes and deploys them as guidance to attend to human body regions and learn robust feature representation in the feature representation construction stage. Extensive experiments on two large-scale person re-ID datasets (Market-1501 and DukeMTMC-reID) show the effectiveness of our method, which is competitive with the state-of-the-art algorithms.
Meta-Information Fusion of Hierarchical Semantics Dependency and Graph Structure for Structured Text Classification

Structured text with plentiful hierarchical structure information is an important part in real-world complex texts. Structured text classification is attracting more attention in natural language processing due to the increasing complexity of application scenarios. Most existing methods treat structured text from a local hierarchy perspective, focusing on the semantics dependency and the graph structure of the structured text independently. However, structured text has global hierarchical structures with sophisticated dependency when compared to unstructured text. According to the variety of structured texts, it is not appropriate to use the existing methods directly. The function of distinction information within semantics dependency and graph structure for structured text, referred to as meta-information, should be stated more precisely. In this article, we propose HGMETA, a novel meta-information embedding frame network for structured text classification, to obtain the fusion embedding of hierarchical semantics dependency and graph structure in a structured text, and to distill the meta-information from fusion characteristics. To integrate the global hierarchical features with fused structured text information, we design a hierarchical LDA module and a structured text embedding module. Specially, we employ a multi-hop message passing mechanism to explicitly incorporate complex dependency into a meta-graph. The meta-information is constructed from meta-graph via neighborhood-based propagation to distill redundant information. Furthermore, using an attention-based network, we investigate the complementarity of semantics dependency and graph structure based on global hierarchical characteristics and meta-information. Finally, the fusion embedding and the meta-information can be straightforwardly incorporated for structured text classification. Experiments conducted on three real-world datasets show the effectiveness of meta-information and demonstrate the superiority of our method.
Evaluating College Students’ Comprehensive Quality by the AHP Algorithm

For the full rank of appraisement, college students act a central party in the instructive fabric of colleges and universities. The common attribute teaching should settle the reciprocal expert valuation agreeing to the specifying goals of training. Establishing a practical and energetic system for appraising the extensive rank of college students is a topical valuable for investigation. This writing confers a mandate supported on Analytic Hierarchy Process (AHP) to exactly rank the compendious degree, college students, frame estimation indicators, calculative crushing, and generate rising wherefore to distinct mayor leagues. Taking the full attribute valuation of electronic computer greater combine, a college as a represent, the import and implementation of this precept are utter details. Through the analysis of the passable state of the thorough nature appraisement, college students, alluring Taiyuan University of Science and Technology as an instance, a large property valuation dummy was established with the assistance of analytic hierarchy outgrowth. A reasonable valuation of students foresees a notional base.
Application of Image Denoising Algorithm and Data Mining in Psychological Teaching Quality Evaluation

In order to improve the psychological teaching quality of college students, this paper combines image denoising algorithm and data mining algorithm to construct a psychological teaching quality evaluation system. The denoising algorithm is mainly used to identify the behavior and expressions of the students so as to explore the psychological state of the students. Moreover, this paper combines the data mining algorithm to carry out the quantitative analysis of students’ psychological state and analyzes and introduces the calibration principle of Dirckx method in detail to improve it and then forms a new calibration method. In addition, based on the arctangent function, this paper proposes a new two-frame random phase-shift fringe image phase shift extraction algorithm. The research results show that the image denoising algorithm and data mining proposed in this paper have a good effect in the evaluation method of psychological teaching quality and can play a good role in the improvement of students’ mental health.
OpenCBD: A Network-Encrypted Unknown Traffic Identification Scheme Based on Open-Set Recognition

The encryption of network traffic promotes the development of encrypted traffic classification and identification research. However, many existing studies are only effective for closed-set experimental data, that is to say, only for traffic of known classes, while there are often lots of unknown classes traffic in the real environment of open sets, and many studies have difficulty identifying the traffic of unknown classes and can only misclassify them as known classes. How to identify unknown traffic and classify known traffic in an open-collection environment is one of the focuses of traffic analysis research. Considering these problems, this paper proposes a novel solution, which applies the open-set recognition method to the unknown traffic identification, and constructs a model based on deep learning and ensemble learning. The method constructs a model based on a convolutional neural network and a transformer encoder and then uses a three-stage training and testing process, combined with a novel loss function, to generalize to the open space to form OpenCBD. Experiments on public datasets show that the proposed method is significantly better than other open-set identification methods. It can not only distinguish known traffic from unknown traffic but also identify specific classes of known traffic.
A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud

To effectively protect citizens’ property from the infringement of fund-raising fraud, it is necessary to investigate the dissemination, identification, and causation of fund-raising fraud. In this study, the Susceptible Infected Recovered (SIR) model, Back-Propagation (BP) neural network, Fault tree, and Bayesian network were used to analyze the dissemination, identification, and causation of fund-raising fraud. Firstly, relevant data about fund-raising fraud were collected from residents in the same area via a questionnaire survey. Secondly, the SIR model was used to simulate the dissemination of victims, susceptibles, alerts, and fraud amount; the BP neural network was used to identify the data of financial fraud and change the accuracy of the number analysis of neurons and hidden layers; the fault-tree model and the Bayesian network model were employed to analyze the causation and importance of basic events. Finally, the security measures of fund-raising fraud were simulated by changing the dissemination parameters. The results show that (1) for the spread of the scam, the scale of the victims expands sharply with the increase of the fraud cycle, and the victims of the final fraud cycle account for 12.5% of people in the region; (2) for the source of infection of the scam, the initial recognition rate of fraud by the BP neural network varies from 90.9% to 93.9%; (3) for the victims of the scam, reducing fraud publicity, improving risk awareness, and strengthening fraud supervision can effectively reduce the probability of fraud; and (4) reducing the fraud rate can reduce the number of victims and delay the outbreak time. Improving the alert rate can reduce victims on a large scale. Strengthening supervision can restrict the scale of victims and prolong the duration of fraud.
Tweets Topic Classification and Sentiment Analysis Based on Transformer-Based Language Models

People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such ‘big data’ allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts’ topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes.
Leveraging Blockchain Technology and Tokenizing Green Assets to Fill the Green Finance Gap

Clean energy projects have difficulties accessing finance. The transition to clean energy and accelerating investments in green projects require a game-changing approach, groundbreaking infrastructure, and pioneering green financing strategies. This article discusses the potential of blockchain technology in filling the green investment gap. Use cases related to the application of blockchain in green projects are analyzed. Blockchain technology can provide security, transparency, auditability, and traceability and help fill the green finance gap.
AP-TLB-IGWO: Adult-pup teaching–learning based interactive grey wolf optimizer for numerical optimization

Bio-inspired optimization technique acts as the fundamental backbone of any AI assisted expert system and Grey wolf optimizer (GWO) is a noteworthy example of one such technique. GWO is new in nature but gaining its popularity due to its better exploration and avoidance of stagnation at local minima. However it needs a more intelligent and balanced exploration and exploitation management procedure. Also for the problems with nonzero solution in the search space GWO needs extra attention as it has a tendency of convergence at the origin of the coordinate system. In order to mitigate these challenges and for the better representation of the existing system, an adult-pup teaching–learning based interactive grey wolf optimization (AP-TLB-IGWO) algorithm is proposed in this paper. With a new structural framework, the proposed algorithm focuses on better generalization, search procedure and diversification. Here both the adult wolves and the pups act as the search agents. They both concurrently and independently explore the entire search space for optimal results. Energy driven hunting procedure is introduced in the update equation of GWO. Information sharing takes place between adult and pup wolves and prospective agents are identified. The performance of the proposed technique is tested on 23 well known classical benchmark functions, CEC2014 problem set, CEC2017 problem set and five real world optimization problems and compared with several established optimization techniques. The experimental results and related analysis proved that the proposed AP-TLB-IGWO is providing significantly promising results in comparison with existing techniques.
Empirical analysis of the impact of queries on watermarked relational databases

Robust relational database watermarking techniques aim to preserve the watermark in the protected assets despite daily updates and malicious attacks. Nevertheless, most techniques do not analyze the root of operations that can degrade the embedded watermark signal, being that the case of Structured Query Language (SQL) queries for relational data. In this paper, we perform an empirical analysis of the role played by different types of SQL queries in the persistence and degradation of a watermark signal in a relational data set. According to the type of operation, we present a set of alternatives for the watermark detection process, contributing to the extraction of an ownership copyright signals threshold. Moreover, our proposal allows data owners to consider variations for the selected watermarking technique, according to the suspected malicious operation performed in the copy of available data. Finally, we give evidence of the importance of some features to be considered when selecting a watermark source, making possible the resilience against update operations that affect a high amount of protected data and the enhancement of a partially detected watermark.
AFS Graph: Multidimensional Axiomatic Fuzzy Set Knowledge Graph for Open-Domain Question Answering

Open-domain question answering (QA) tasks require a model to retrieve inference chains associated with the answer from massive documents. The core of a QA model is the information filtering ability and reasoning ability. This article proposes a semantic knowledge reasoning graph model based on the multidimensional axiomatic fuzzy set (AFS), which can generate the knowledge graph (KG) and build reasoning paths for reading comprehension tasks through unsupervised learning. Moreover, taking advantage of the interpretable AFS framework enables the proposed model to have the ability to learn and analyze the semantic relationships between candidate documents. Meanwhile, the utilization of the multidimensional AFS acquires semantic descriptions of candidate documents more concise and flexible. The similarity degree between paragraphs is calculated according to the AFS description to generate the graph. Interpretable chains of reasoning provided by the AFS knowledge graph (AFS Graph) will serve as the basis for the answer prediction. Compared with the previous methods, the AFS Graph model presented in this article improves interpretability and reasoning ability. Experimental results show that the proposed model can achieve the state-of-the-art performance on datasets of HotpotQA, SQuAD, and Natural Questions Open.
Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects

Dialect variation spans different linguistic levels of analysis. Two examples include the typical phonetic realisations produced and the typical range of intonational choices made by individuals belonging to a given dialect group. Taking the modeling principles of a specific automatic accent recognition system, the work here characterises and observes the variation that exists within these two levels of analysis among eight Arabic dialects. Using a method that has previously shown promising performance on English accent varieties, we first model the segmental level of analysis from recordings of Arabic speakers to capture the variation in the phonetic realisations of the vowels and consonants. In doing so, we show how powerful this model can be in distinguishing between Arabic dialects. This paper then shows how this modeling approach can be adapted to instead characterise prosodic variation among these same dialects from the same speech recordings. This allows us to inspect the relative power of the segmental and prosodic levels of analysis in separating the Arabic dialects. This work opens up the possibility of using these modeling frameworks to study the extent and nature of phonetic and prosodic variation across speech corpora.
Incidence of Sulphide Mineralization in Porcellanite of the Chopan Formation from the Vindhyan Supergroup, Eastern India

This work presents the occurrence of sulphide mineralization for the first time in the Middle Porcellanite Member belonging to the Porcellanite Formation of the Semri Group in and around Amrora-Barwari-Kupa areas, Garhwa district, Jharkhand. The Porcellanite Formation has been categorised into four mappable units i.e. lower Porcellanite Member; middle Porcellanite Member; Amrora Member, and upper Porcellanite Member. Out of the four members, sulphide mineralization has been mostly observed in the middle Porcellanite Member which is represented by grey to black, fine-grained, thinly laminated porcellanite and made up of quartz and few grains of feldspar. Petrographic study shows that it comprises of devitrified glass, quartz shards, K-feldspar, plagioclase, and perthite phenocrysts as essential minerals and accessories are sericite, calcite, chlorite, opaque, and devitrified groundmass. It consists of sulphide minerals like chalcopyrite, pyrite, pyrrhotite, and grains of arsenopyrite and gold. Based on the field observations and ore petrographic study, three zones of sulphide mineralization have been marked within this unit. The longest sulphide mineralization zone exposed to the northeast of Amroa village is 750m long and 300m wide having an E-W trend. In this mineralization zone, specks of pyrite, chalcopyrite, and gold are observed which occur as dissemination. The second and third mineralization zones are 360m long and 150m wide having N-S trend and 300m long and 100m wide having NW-SE trend respectively which have mainly been noticed to the southeast of Barwari and northeast of Kupa village. The sulphide mineralization is mainly associated with shearing and faulting which has been corroborated by the petrography and field evidences.
LAG: Layered Objects to Generate Better Anchors for Object Detection in Aerial Images

You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated uniformly and affected by the overall data distribution. In this paper, we are inspired by experiments under different anchors settings and proposed the Layered Anchor Generation (LAG) algorithm. In the LAG, objects are layered by their diagonals, and then anchors of each layer are generated by analyzing the diagonals and aspect ratio of objects of the corresponding layer. In this way, anchors of each layer can better match the detection range of each layer. Experiment results showed that our algorithm is of good generality that significantly uprises the performance of You Only Look Once version 3 (YOLOv3), You Only Look Once version 5 (YOLOv5), You Only Learn One Representation (YOLOR), and Cascade Regions with CNN features (Cascade R-CNN) on the Vision Meets Drone (VisDrone) dataset and the object DetectIon in Optical Remote sensing images (DIOR) dataset, and these improvements are cost-free.
Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things (IoT) cloud to improve transmission efficiency, which has created a novel paradigm for improving the speed of common connected objects in IoT. However, although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services, security concerns, such as a lack of reliability and collusion attacks, still exist in the outsourcing computation. In this paper, we propose a reliable, anti-collusion outsourcing computation and verification protocol, which uses distributed storage solutions in response to the issue of centralized storage, leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy. Moreover, we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum, which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks. The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure, reliable, and more effective compared with state-of-the-art approaches.
Automated Model Inference for Gaussian Processes: An Overview of State-of-the-Art Methods and Algorithms

Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default instantiations yield acceptable analytical quality in terms of model accuracy, GPM inference algorithms automatically search for an application-specific model fitting a particular dataset. State-of-the-art methods for automated inference of GPMs are searching the space of possible models in a rather intricate way and thus result in super-quadratic computation time complexity for model selection and evaluation. Since these properties only enable processing small datasets with low statistical versatility, various methods and algorithms using global as well as local approximations have been proposed for efficient inference of large-scale GPMs. While the latter approximation relies on representing data via local sub-models, global approaches capture data's inherent characteristics by means of an educated sample. In this paper, we investigate the current state-of-the-art in automated model inference for Gaussian processes and outline strengths and shortcomings of the respective approaches. A performance analysis backs our theoretical findings and provides further empirical evidence. It indicates that approximated inference algorithms, especially locally approximating ones, deliver superior runtime performance, while maintaining the quality level of those using non-approximative Gaussian processes.