Reviewerly/exHarmony huggingface dataset:
Multi Loss Function for Cross-Modality Person Re-Identification

For cross-modality person re-identiflcation, the intra-class difference between visible images and infrared images of the same identity is large, and how to reduce this intra-class difference has become the key of cross-modality person re-identification. Therefore, we proposed a multi-loss function for cross-modality person re-identification. Firstly, the global attention mechanism was embedded in the Resnet50 network to retain non-local feature information. Secondly, generalized-mean pooling is used to increase feature information extraction for different fine-grained regions by adjusting parameters. Finally, we design a new total loss function to supervise network learning and improve model accuracy. The proposed method achieves an average accuracy of 54.18% and 78.40% in the SYSU-MM01 and RegDB datasets. The experimental results show that the proposed method can effectively improve the accuracy of cross-modality person re-identiflcation.
Social Recommendation Based on Preference Disentangle Aggregation

Graph Neural Networks (GNNs) have further promoted the development of social recommendation, but traditional social aggregation models combined with GNNs usually take the highly entangled and complex representations of nodes. However, due to the diversity of node preferences in social recommendation, the establishment of connectivity relationships between nodes just needs to be based on a part of preferences, so it is unreasonable to consider all the preferences of neighboring nodes when aggregating. To address this issues, we propose a novel social recommendation framework based on preference disentangled aggregation (PDARec). This method first maps the coarse-grained comprehensive representation of nodes to more fine-grained latent preference subspaces, then aggregates the information of neighboring nodes in separate latent preference subspaces. At the same time, to ensure the independence of multiple latent preference subspaces, the model uses a preference regularizer to measure the distance of each space. Experiments on two open datasets shows that the proposed social recommendation method based on preference disentangled aggregation produces a better potential representation of users (items) and effectively improves the performance of the recommendation compared with five baseline models, and the metrics RMSE and MAE had dropped 1%~2%.
Service Partition Method Based on Particle Swarm Fuzzy Clustering

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>c</mi> </math> -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.
Sign Language Recognition Based on Spatial-Temporal Graph Convolution-Transformer

This paper reports on sign language recognition based on human body part tracking. Tracking-based sign language recognition has practical advantages, such as robustness against variations in clothes and scene backgrounds. However, there is still room for improving feature extraction in tracking-based sign language recognition. In this paper, a tracking-based continuous sign language word recognition method called Spatial-Temporal Graph Convolution-Transformer is presented. Spatial-temporal graph convolution is employed to improve framewise feature extraction using tracking points, while Transformer enables the model to recognize word sequences of arbitrary lengths. Besides the model design, the training strategy also has an impact on the recognition performance. Multi-task learning, which combines connectionist temporal classification and cross-entropy losses, is employed to train the proposed method in this study. This training strategy improved the recognition performance by a significant margin. The proposed method was evaluated statistically using a sign language video dataset consisting of 275 types of isolated words and 120 types of sentences. The evaluation results show that STGC-Transformer with multi-task learning achieved 12.14% and 2.07% word error rates for isolated words and sentences, respectively.
Cervical Precancerous Classification System based on Texture Features and Support Vector Machine

Cervical cancer is one of the female reproductive health diseases being a significant issue globally because of the large number of new cases and deaths, particularly among women in developing countries. Cervical cancer can be avoided if detected early. The Pap smear screening procedure is used in industrialized nations to detect cervical cancer early. However, limited human resources, a significant time commitment, high prices, and insufficient infrastructure make it less successful in developing countries. With three types of cervical cell images: Normal, Low-grade Squamous Intraepithelial Lesion (LSIL), and High-grade Squamous Intraepithelial Lesion (HSIL), this study offers a classification system for cervical cell images using an image processing technique called Gray Level Co-occurrence Matrix (GLCM) and a Support Vector Machine (SVM) classification method (HSIL). With HSIL class as positive data and LSIL and Normal as negative data, the classification system used three SVM models: Cubic, Quadratic, and Fine Gaussian. SVM classification accuracy was 97.5 percent for 3.54s using the GLCM feature extraction approach.
Conversational Recommender System Based on Functional Requirements and Technical Specifications

The online shopping is very inviting for people to save travel time and attained cost. When a person seeks to buy something, the easier way to find the product is by asking directly to the seller. However, when the person tries to buy things online, the communication with the seller can be limited and not effective for the seller. Among these people who try to buy product, some of them might be familiar with the specification of the product they want, but some people also might not be familiarwith the specification of the product. Our previous study has compared two models, the first one used functional requirement and the second one used technical specification. That study suggests combining the functional requirement andTechnical Specification into one model. To address this problem, this research proposes a conversational recommender system based on functional requirements and technical specifications using knowledge- based method. We use ontology to represent the system knowledge. To make the chatbot able to understand the user's message, the system is implemented on chatbot by using DialogFlow as the natural language processing (NLP) tool. To evaluate the system, we use user satisfaction evaluation method to reach the success rate of the system based on user's subjective opinion. Based on the result of the evaluation, it is concluded that theusers are satisfied with the chatbot with score above 83 % on 7/8 statements.
Design of practical teaching quality evaluation system based on rough set and big data mining

The article establishes a practical teaching quality evaluation index system from five aspects: practical teaching goal, practical teaching content, practical teaching process, practical teaching implementation guarantee, and practical teaching effect. Through large sample surveys and big data mining, combined with factor analysis to build a complete index system for practical teaching quality evaluation. On this basis, the rough set reduction method is adopted to reduce the complete index system and establish a scientific practical teaching quality evaluation system consisting of 5 first-level indicators, 21 second-level indicators, and 72 observation indicators.
Comparison of performance of the popular approaches to implementing parallel crawlers

This research aims to compare the efficiency of the several popular parallel programming approaches used for an I/O-heavy workload: process pools, thread pools, and asynchronous I/O using the M:N model. As a model task, parallel Web crawlers were used, implemented using the C++ language. Their performance was measured using a wall-time approach. We tackled the inherent noise of this method using randomization and statistical hypothesis testing. The results of the experiment showed that all approaches are plausible for crawlers, though the epoll-based one is substantially faster than the two others and the thread-based is faster than the process-based.
Eye Movement

This chapter focuses on eye movement from the perspective of human-computer interaction. The first section offers general information on the anatomy and physiology of the human eye, and outlines the key types of eye movement. It also presents the method of eye tracking and fields in which it can be applied. The chapter goes on to discuss issues pertaining to cognitive load, and how its intensity in relation to human-computer interaction and hypertext reading can be determined using oculomotor measurements. The final section is dedicated to the employment of eye tracking technology as a method of interaction. It presents methods that use the Gaussian function as a potential solution to the Midas touch problem. It also includes examples of solutions to registration errors during the use of head-mounted eye-trackers.
Visual Analysis of Heterogenous Air Pollution Data

Some studies have shown that air pollution is one of the important risk factors for human health. In order to find out the transmission mode, space-time patterns, main pollution causes and potential mode of air pollution, we analyzed the multi- granularity and multi-dimensional data, and designed a visual analysis scheme of air pollution data, which integrated time, space and relevant factors, and used many kinds of visual charts, including map, calendar chart, single axis scatter chart and so on. Based on these, a data-driven visual analysis system is designed and implemented, and a variety of visual analysis models are established. The interactive methods such as filtering, scaling and exploration can assist users to carry out the data analysis tasks such as data overview, key data tracking, comparing of correlation, analytic reasoning, hypothesis testing and potential mode discovery, and realize the analysis of key causes, spatiotemporal evolution and transmission mode of air pollution. The system is simple and efficient, which can provide valuable reference for in-depth analysis of air pollution data and evaluation of air pollution control measures.
AOM4JADE: An Aspect-Oriented Monitoring Approach for JADE-based Multi-Agent Applications

In Multi-Agent System (MAS), agents coordinate their behaviour aiming to achieve some collective goals. However, heterogeneous nature of agents and their autonomy make the coordination task exceedingly difficult and likewise the achievement of the ultimate system target. Consequently, it’s unavoidable to proceed with monitoring techniques in order to analyze and understand the dynamic behaviour of such a system. JAVA Agent DEvelopment framework (JADE) is a JAVA-based framework intensively used in academia to implement MAS. Aspect-Oriented Programming (AOP) paradigm has proved its effectiveness to support software monitoring especially regarding MAS specificities. The combination of monitoring and AOP results in the appearance of a new technique known as Aspect-Oriented Monitoring (AOM). In this paper, we introduce AOM4JADE, an Aspect-Oriented Monitoring Approach for JADE-based Multi-Agent Applications. Proposed approach investigates three JADE foundations: FIPA-ACL-based Agents’ communication, agents’ behaviours and agents’ lifecycle. For each concept, we specify the integration of monitoring related aspects with JADE architecture using Aspect UML class diagram.
An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.
Profiling HPC Applications with Low Overhead and High Accuracy

As the parallel scale of HPC applications represented by earth system models becomes larger and the computing cost becomes higher, the performance of HPC applications is increasingly critical. Profiling HPC applications accurately helps to model the applications and find the performance bottlenecks. However, due to the complexity of HPC applications, the diversity of programming languages, the differences of individual programming habits, and multiple architectures, accurate profiling becomes very tough. In this paper, we propose LPerf: a low-overhead and high-accuracy profiler for HPC applications. To reduce the profiling overhead and improve the profiling accuracy, we propose a preprocessing method which can automatically instrument with tunable granularity thus significantly reducing the run-time overhead of profiling, an aggregated caller-callee relationship which is used to locate relationship of functions efficiently, and a profiling-aware method which can precisely calculate running time of functions. The experimental results show that the error rate of profiling reaches 0.02%, and the overhead reaches 1.6%, in the earth system model named CAS-ESM. Compared with the baselines, the precision, accuracy, and overhead of LPerf have reached the state of the art.
Rule-based Validation Module on EssenceBoard Method Editor

This paper proposed the development of a validation module on EssenceBoard method editor. The main reason for this development is because the validation that EssenceBoard can do is still not complete. Moreover, there is no feature to add custom validation rules for each organization. It is common knowledge that the validation of each method can vary depending on the organization’s needs. EssenceBoard also does not provide examples of methods that can be used in general cases. These deficiencies become the basis for the development process. We propose a rule-based validation, where rules can be applied to validate the method. Users can add new rules and edit existing ones as needed, following the format and syntax provided. In addition, EssenceBoard will provide examples of software development methods for some common cases. Later, method engineers can further develop the method example into a new method according to their needs. Testing has been carried out on the validation module to check the validity of the new rules and the added functionality: (1) adding and editing rules and (2) using examples of existing methods to create new methods. The test results show that the validation module function correctly; it can detect errors in the editing method to improve the method validation.
DeSa COVID-19: Deep salient COVID-19 image-based quality assessment

This study offers an advanced method to evaluate the coronavirus disease 2019 (COVID-19) image quality. The salient COVID-19 image map is incorporated with the deep convolutional neural network (DCNN), namely DeSa COVID-19, which exerts the n-convex method for the full-reference image quality assessment (FR-IQA). The glaring outcomes substantiate that DeSa COVID-19 and the recommended DCNN architecture can convey a remarkable accomplishment on the COVID-chestxray and the COVID-CT datasets, respectively. The salient COVID-19 image map is also gauged in the minuscule COVID-19 image patches. The exploratory results attest that DeSa COVID-19 and the recommended DCNN methods are very good accomplishment compared with other advanced methods on COVID-chestxray and COVID-CT datasets, respectively. The recommended DCNN also acquires the enhanced outgrowths faced with several advanced full-reference-medical-image-quality-assessment (FR-MIQA) techniques in the fast fading (FF), blocking artifact (BA), white noise Gaussian (WG), JPEG, and JPEG2000 (JP2K) in the distorted and undistorted COVID-19 images. The Spearman's rank order correlation coefficient (SROCC) and the linear correlation coefficient (LCC) appraise the recommended DCNN and DeSa COVID-19 fulfillment which are compared the recent FR-MIQA methods. The DeSa COVID-19 evaluation outshines 2.63% and 2.62% higher compared the recommended DCNN, and 28.53% and 29.01% esteem all of advanced FR-MIQAs methods on SROCC and LCC measures, respectively. The shift add operations of trigonometric, logarithmic, and exponential functions are mowed down in the computational complexity of the DeSa COVID-19 and the recommended DCNN. The DeSa COVID-19 more superior the recommended DCNN and also the other recent full-reference medical image quality assessment methods.
Roadmap for Visibility-based Target Tracking: Iterative Construction and Motion Strategy

We consider the problem of generating a fixed path for a mobile observer in a polygonal environment that can maintain a line-of-sight with an unpredictable target. In contrast to purely off-line or on-line techniques, we propose a hierarchical tracking strategy in which an off-line path generation technique based on a RRT is coupled with an online feedback-control technique to generate trajectories for the mobile observer.
CDNFinder: Detecting CDN-hosted Nodes by Graph-Based Semi-Supervised Classification

As a crucial internet infrastructure, Content Delivery Network (CDN) is widely deployed. Detecting CDN-hosted nodes from network traffic is important for Quality of Service (QoS), malware detection and firewall rule-sets. Current researches use hand-crafted rules, classification or clustering methods. However, those methods relying on plaintext are limited by the invisibility of plaintext due to encryption, as well as the limitations of DNS Resource Records, such as unreliability. Besides, those methods don&#x0027;t dig the structural information of domains and IPs. To overcome those shortcomings, we present CDNFinder, a novel method to detect CDN-hosted nodes by graph-based semi-supervised classification. Based on the active datasets collected in 10 vantage points, we construct the graph and extract innovative attributes. By modifying Graph Neural Network (GNN), CDNFinder outperforms classical machine learning methods, especially in recall rate (around 98%). Meanwhile, CDNFinder shortens the runtime of classical GNN algorithm by about 31% with no loss in metrics.
Distributed ledgers to support revenue-sharing business consortia: a Hyperledger-based implementation

Private distributed ledgers (DLs) appear to be more successful than public blockchains at supporting innovative business ecosystems in the form of enterprise consortia. In fact, by untying transactions from the native cryptocurrencies of public blockchains, consortium DLs can smoothly interface with the various sectors of the traditional economy with the capability to innovate it. Furthermore, eliminating mining greatly increases the computational efficiency of these technologies compared to public blockchains. So far, consortium DLs have been used primarily for traceability and document management. Now the time is ripe to move forward, impacting business and profitability. We describe here a consortium model based on distributed ledger technology that applies the old principle that “unity is strength” by deploying the economic method of Revenue Sharing as a smart contract. We illustrate the model through a prototype implementation on Hyperledger Fabric, the most widely adopted among the platforms for consortium distributed ledgers.
BLINK-CON: A HANDS FREE MOUSE POINTER CONTROL WITH EYE GAZE TRACKING

The working of Blink-Con, a framework for intelligent vision directed mobilization of onscreen mouse-cursor, without engaging any auxiliary assistance, is illustrated in this paper. Blink-Con is framed keeping in mind the corporal accessibility limitations of a specially abled person in operating, controlling and interacting with physical devices. In this work the system explored are technically dominated in color spaces, precise ROI detection, closed loop sequential interaction and eventually a system gets constructed and attained where the GUI onscreen pointer (mouse cursor) gets directed by interpreting significant eye gaze and blink. The algorithm for eye blink detection from 2-D image feed been classified in two major step for accomplishing a heuristic based region of interest (ROI) identification and tracking the ROI for rapid detection of volunteered blinks. The sequence of volunteered blinks are taken as trigger of robust mouse mobilization function input, that results in highly optimized smooth mouse-travel, divulging an adaptable mouse control performance with only eye blink. The experimental results are favorable to the system viability for multi facet use of the algorithms.
Solving the Olum 2 cipher: a new approach to cryptanalysis of transposition ciphers

Olum 2 is one of two ciphers created more than 75 years ago by mathematician Paul Olum to challenge his Manhattan Project officemate, physicist Richard Feynman. In this manuscript, I describe the first successful decryption of Olum 2 using a novel approach to cryptanalysis of transposition ciphers. To decrypt Olum 2, I generated the bigrams and trigrams for all possible transposition intervals. I then identified transposition intervals with multiple bigrams and trigrams that occur frequently in English. I calculated the ratios of their English frequencies to the frequencies of bigrams and trigrams generated by a random reordering of the ciphertext. This enabled me to identify letter sequences with the highest probability of being true cipher message components rather than occurring by chance. In Olum 2, Professor Olum divided the message into sections of thirty-five letters and applied a rotating key to change the order of transposition for each successive section. His strategy not only confounded Professor Feynman but also proved impervious to several decryption programs in use today that assume a uniform transposition has been applied throughout the cipher. The decryption methods described in this manuscript can assist in the decryption of other ciphers employing a variety of transposition methods.