Reviewerly/exHarmony huggingface dataset:
BT Pool: The Best Template Pool for Target Tracking in Scale Space

During terminal guidance, the tracked target always enlarges, so the task of target tracking needs to solve the problem that the information between target and template exist a big biases. This paper proposed a new conception: Best Template Pool (BT Pool). The method adjust template to three scale, and then chooses the suitable tracking box while object changes from small to big. After experiment, it was found that there is a max score template to select before change the template. Therefore, it needs BT Pool to save. The simulated results show that BT Pool can make many tracking methods better in scale space from far to near.
Research on Korean Translation Computer Intelligent Proofreading Algorithm Based on Multi-Strategy Analysis

In the research and development of practical MT (machine translation) system, the translation of complex long sentences is a major problem. Aiming at the Korean translation proofreading system of syntax and phrases, which attaches importance to accuracy calibration and can not solve the problem of poor coherence, an intelligent proofreading system based on improved phrase translation model is proposed. In addition, the overall system is designed. Through the overall architecture of the system, the software and hardware of the system are designed, and the hardware design is mainly the search module, so as to improve the search accuracy. By designing the same knowledge representation form for different languages, the universality of the algorithm for different language translation systems is realized. The results show that the system can meet the proofreading needs of users and improve the proofreading efficiency.
Data Mining and Analysis of Business Administration Based on Cloud Computing

Because of the emergence of the information age, the amount of data generated by the whole society is increasing rapidly. How to quickly and effectively mine valuable information from massive data is one of the hot issues concerned by all sectors of society. A large number of DM technologies based on cloud computing realize the optimization of resource allocation and utilization, have the characteristics of virtualization and practicability, and meet the requirements of fast and accurate DM. Cloud computing is a modern information science and technology that has been widely promoted and applied based on the Internet in recent years. Second, it mainly publishes modern information science and technology to centrally process, exchange and share information data through the Internet. It is different from other computing methods in the processing and calculation of enterprise financial information data, It can not only centrally store and calculate the data in the daily financial accounting work of the enterprise, but also create a safer and more stable financial management environment for the enterprise than before, so that the operation of important internal accounting information data breaks the limitations of region and time, thus indirectly improving the overall work efficiency of enterprise financial management. This paper will focus on the specific role of cloud computing in enterprise management accounting and its practical application path.
CTMSN-EHI: A Context-Aware Time-Decay Multi-Head Self-Attention Network with Enhanced History Information for Multi- Turn Spoken Language Understanding

Contextual information is beneficial for spoken language understanding (SLU) to comprehend users' utterance. However, to exploit global dialog contextual information, some of the existing methods use multi-head self-attention network, in which the vital importance of recent context isn't considered. Furthermore, they also fail to fully utilize other contextual information, such as the dialog state in state tracker. To deal with these issues, we propose a multi-task joint model based on the context-aware time-decay muiti-head self-attention network with enhanced history information (CTMSN-EHI) to perform the intent classification, dialog act detection and slot filling tasks. Specifically, CTMSN exerts a time-decay mechanism to the multi-head self-attention network, enhancing the capability of capturing recent context in dialog system from utterance and token level, while the enhanced history information (EHI) refers to an enhanced sentence constructed from dialog state of previous turn and system acts of current turn, which facilitates to understand users' utterance from the perspective of the dialog system. And we experiment a lot on the Simulated Dialog Dataset, whose results demonstrate the effectiveness of our proposed method.
New Applications in Computing Using Intuitionistic Fuzzy Set Approach

Intuitionistic fuzzy set theory plays key role for dealing with uncertainty in various mathematical problems. Intuitionistic fuzzy set handles both membership and nonmembership function during solution of real-life problem. In this paper we present the definition and operations of an intuitionistic fuzzy set. Furthermore, based on the analysis of several operations on intuitionistic fuzzy set in the study, we provide some new notions and operations. We also prove some new results based on new operations on intuitionistic fuzzy set.
TB-Transformer: Integrating Mouse Trace with Object Bounding-box for Image Caption

The traditional image caption algorithms output the image description text based on the image features. However, the predicted results often lack crucial semantic and spatial information. To solve the above problems, this paper proposes an image caption model based on multimodal data integration, which improves the ability of the image caption model to capture critical semantic information by adding mouse trace and object bounding box as additional information sources of the model. Firstly, the mouse traces are segmented, transformed into bounding boxes, and aligned with a text word mark. Secondly, we utilize the object detection network to learn the image features and predict the bounding box used to supplement image features. Finally, the multi-head attention mechanism is used to encode, integrate, and decode the image, caption, and mouse trace, and predict the image caption results. To verify the effectiveness of this method, the proposed method was tested on ADE20k, Flickr30k, and MSCOCO data sets based on localized narrative annotation achieving CIDEr scores of 1.280, 1.983, and 1.355. Compared with the existing methods, the proposed method has obvious advantages.
A Study on Exploit Development

A program or collection of instructions designed to exploit the flaws in other applications is known as an exploit. Exploit development is identifying vulnerabilities in apps and software and determining how to gain control of a system; thus, exploit development is critical for the security of digital assets and sensitive data stored on computer systems. Many flaws require an attacker to launch a series of shady actions to set up an exploit. Most of the vulnerabilities occur due to faults in software or system architecture. Attackers write code to exploit these holes to infect the machine with various types of malware which is often built on the collection of exploits and can easily be hidden in plain view of different websites. The hidden code on a website may instantly identify the operating system and programs that are running on a user’s machine, as well as the user’s browser type, and check for any vulnerabilities. In today’s world, the internet is the major cause of spreading many malware, viruses, and embedded scripts. Hence our problem statement will revolve around the detection of malicious and benign URLs and thus testing them to get the type of vulnerability.
Difference-Adaptive Knowledge Distillation for Resource-Constrained Embedded Devices

Deep Neural Network (DNN) models have brought significant performance gains to machine learning tasks. Nevertheless, the huge storage and computational costs of high-performance models severely limit their deployment on resource-limited embedded devices. Knowledge distillation (KD) is the mainstream approach for models compression, but the existing KD framework has some limitations. First, the student model underperforms when there is a considerable capacity gap between the student and teacher models, which reduces the model compression rate in knowledge distillation. Second, compared with training from scratch, the billions of additional forward propagation of the complex teacher model is time-consuming, resulting longer train time in the distillation process. This paper proposes AKD, an efficient difference-adaptive knowledge distillation framework. AKD consists of two modules. The distillation module uses a modified auxiliary teaching architecture as the backbone, and the adaptive module introduces a difference-adaptive method, which can adjust the teacher model adaptively according to the continuously improving representation ability of the student model. Experimental results on different kinds of datasets and models indicate that AKD not only improves the efficiency of distillation, but also enhances the performance of the compact student model and raise the compression rate.
A Novel Scheme For Feature Selection Using Filter Approach

This paper proposes a wrapper based scheme for feature selection in unsupervised data sets. This scheme first ranks features in a dataset as per their Laplacian scores. Subsequently, subsets of features are selected considering feature's rank. Each selected feature subset is then tested for classification accuracy. In order to achieve reasonable satisfactory results, feature subsets are iteratively tested for accuracy as well as cardinality using incremental approach. Four real benchmark datasets namely colon, leukaemia-l, lung-discrete & warpPIE10 have been used for the experiments in this paper. Accuracy above 80% and number of features reduced to less than 20% of the total number of features in each dataset justify the potential of the proposed scheme to be applied to other large datasets as well.
Hand Gesture Detection Using Convexity Hull and Convolutional Neural Network

Since the beginning of mankind, communication has always played a key role in everyday life and at the same time has pushed mankind to achieve targets for more convenient modes of communication. From the invention of the telephone to us having access to touch screen smartphones - we have come a long way. Unfortunately, we haven't been able to catch up at the same pace for people with speech impairment. While multiple studies on sign language recognition have been performed, there are still multiple constraints and limitations which are to be tackled. In this paper, application of convexity hull algorithm on hand gesture recognition has been proposed, where we further discuss the role of convexity defect and application of cosine rule in convexity hull. However, different skin tones and hand signs with similar gestures can cause false prediction thus to combat these limitations, CNN model has been proposed through which accuracy of 90% was achieved.
A Multi-Layer PBFT Consensus Algorithm with Inter-group Supervision

In order to solve the problem of poor scalability of the Practical Byzantine Fault Tolerance (PBFT) algorithm widely used in blockchain, many variant PBFT-based consensus algorithms reduce the communication complexity by running consensus protocol in a hierarchical grouping structure. However, it also brings greater challenges to ensuring consistency for the whole network. In this paper, we innovatively add the inter-group supervision mechanism to the multi-layer PBFT consensus process, the representative nodes of each consensus group check the consensus messages in other groups to prevent the malicious primary from breaking inter-group consistency. And a group number assignment algorithm based on VRF is designed to ensure the effective implementation of supervision. Next, to maximize the scalability of the system model, an objective function of optimizing the consensus structure is analyzed. This objective function can find a hierarchical grouping structure with minimum communication complexity while maintaining 1/3 Byzantine fault tolerance. At last, a hierarchical strategy based on node reputation is designed, which further strengthen the security of the multi-layer consensus model. The performance of the protocol is evaluated by simulations, and the results show that it can effectively reduce the communication complexity of the system while ensuring the Byzantine fault tolerance.
Building Ontology for Toxic words

Many online social media platforms have particular community guidelines for comment sections. The platforms that maintain commentary sections in various posts, videos, and blogs need to adhere to these guidelines. These comment sections may have specific comments that fail to satisfy the rules and regulations to maintain societal norms of communication. These comments are classified as toxic comments. Google's Perspective API defines toxic comments as comments that are rude, offensive, and likely to make someone leave the conversation. In this paper, we have built a toxic words ontology, which is as per our knowledge, first Ontology built on toxic words. This Ontology consists of toxic words and their antonyms and synonyms in increasing order of their toxicity levels. Traversing this ontology, we can find the best-suited word with less toxicity and similar meaning. This is a dynamic ontology and new words can be added easily. Thus letting us convey messages in a civil manner. We propose to reduce toxicity in the most straightforward way. After studying several papers, we found out that the toxicity mainly occurs because of use of toxic words. We also observed that use of less toxic synonyms or no toxic synonyms has huge effects on toxicity score given by the Perspective API, and results section proves that.
Dual chain authentication and key agreement protocol based on blockchain Technology in Edge Computing

Under the cloud edge end network architecture of the edge computing mode, the computing services of the cloud server located in the trusted environment are sunk to the edge nodes for processing, which can solve the heavy computing bottleneck problem in the cloud center. However, the geographical characteristics of the wide distribution of edge nodes bring new security risks and cross-domain authentication problems to terminal device authentication. In this paper, blockchain technology is applied to the edge computing mode. By building a blockchain on edge nodes, changing the certifi-cation locations from the cloud center to the edge nodes. We propose a dual-chain authentication and key protocol protocol based on blockchain technology, which solves the security problems and few latency problems in some scenarios, including cross-domain problems caused by location changes between the same devices. Eventually, we have analyzed the security of our proposed protocol through simulation software and compare its performance with the related works. Authentication security is guaranteed under dual chain and a few authentication efficiency is improved.
Knowledge Graph Assisted Automatic Writing of Football Match News

The football news style generated by the extraction method will be colloquial and informal, the background information in the live text is lacking, and the football news generation combined with the template lacks the generalization ability and flexibility. This paper proposes an extraction and generation model integrating knowledge atlas to realize automatic writing of football match news. The extraction model BertSum based on BERT pre training is adopted, and combined with the time information in the football match, candidate sentences are extracted from the live text to form the first press release. The players' names in the first news draft are replaced with special tags, and then the first news draft is rewritten using the generative model PGNet. The coverage mechanism is combined to reduce the duplication of generation. At the same time, a knowledge graph is built to supplement the background knowledge that is missing in the live text broadcast, so as to improve the quality of news generation. The evaluation results show that the proposed method can effectively generate football match news from live text broadcast, and the extraction generation method integrating knowledge graphs is obviously better than the single extraction or generation method.
Exploring the Feasibility of Transformer Based Models on Question Relatedness

Professional question answering communities, such as Stack Overflow, are becoming a significant aspect of many intellectual endeavors. As a result, developing a strategy for swiftly locating relevant questions and answers can effectively assist experts in issue solving. Various detection models have been presented to address the problem of question-relatedness prediction, but all are sub-optimal since they cannot effectively capture the long-distance dependency of a long sequence. With the invention of the self-attention mechanism and transformer, a better approach to dealing with question-relatedness prediction has just emerged. The primary objective of this study is to investigate the feasibility of the transformer-based model for question-relatedness problems. We turn the question relatedness problem into a text classification problem and introduce a representative transformer-based model, namely Bidirectional Encoder Representation from Transformers (BERT), to deal with text classification. In our experiment, we show that BERT outperforms both SoftSVM and BiDotLSTM, implying that the transformer-based model has considerable potential to address the challenge in the question-relatedness problem. Furthermore, since we can re-formulate text classification problem into a link prediction problem, we discuss the possibility of incorporating the transformer-based approach and graph representation learning.
Analysis of Histogram and Energy Curve-based Thresholding using Cuckoo Search

In recent years, optimization has become the most notable area of research. Optimization is applied to spot the most favourable and ideal solution that provides the best results under certain constraints. Nature-inspired algorithms can be used to find out the most favourable solution among the computed solutions. Several nature-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Honey Bee Optimization (HBO), Lion Optimization Algorithm (LOA), etc. can be employed to procure the most favourable solutions in less time. Cuckoo search has emerged as the uncomplicated and the most prolific and productive algorithm to fix the real optimization issues that are extremely non-linear. Image Segmentation is said to be the most demanding task for recollecting non-converging analogous regions. The major challenge is to calculate the non-converging analogous regions. An immense requisite is there to initiate an intelligent image segmentation technique that fulfils both the above challenges and offers the most favorable results for image segmentation. In this paper, the author has appertained the population-based cuckoo search algorithm. This paper compares Energy Curve-based Thresholding using Cuckoo Search and Histogram -based Thresholding using Cuckoo Search.
Challenges and Approaches of Code-mixed Hate Speech Detection

The online platform and social media are very eye catchy for internet users. Platforms like YouTube, Twitter, Instagram, etc., are higher in demand due to their brilliant services. Users of these sights frequently comment on others' posts which may contain toxic speech. Some platforms also raise concerns about emerging of this activity. As the increase of hate speech is just next to impossible to control, the need to detect these contents through automated hate speech detection technologies arises. In this work, we focused on multi-lingual issues, especially Indo-European code-mixed languages. At first, we identified some issues related to code-mixed Indian languages. Then, we focused on the available solutions to this problem. We have gone through the works performed on machine learning and deep learning techniques and identified the limitations of those works. We have analyzed the present solutions and gone through the comparative studies of those. Our implementation is conducted on code-mixed twitter datasets providing several perceptions on hate speech. We have performed the experimental work on HASOC 2021 dataset. Our work contributes to the field of hate speech detection by comparing feature extraction and classifier algorithms (Machine Learning and Deep Learning). More specifically, the proposed work aimed at distinguishing Hate and Non-Hate speech from normal text.
Text-Based Sentiment Analysis of Students' Satisfaction: An Input to Strategic Plan

Sentiment analysis is the study of people's sentiments as expressed in written language. To ensure continuous improvement in higher education institutions (HEIs), it is critical to identify the sentiments of each stakeholder. City College of Calamba (CCC), as one of the recognized HEIs in the Philippines, utilized the traditional method to collect the sentiments of its stakeholders, which is the use of a suggestion box. Despite the pandemic, the suggestion box remains the primary platform for collecting the sentiments of its stakeholders and clients. In this paper, the most commonly used sentiment classification algorithms were Multinomial Nave Bayes (MNB) and Linear Support Vector Machine (LSVM). The main objective of the study is to analyzethe sentiments of CCC stakeholders retrieved from the suggestion box, and the results may serve as an input to the strategic plan of the institution to plan the necessary improvements. The method employed in the study has the following phases: data preprocessing, classification, validation, and evaluation. In terms of data classification, the most commonly used algorithms for sentiment classification were evaluated to identify the optimum algorithm for the sentiment analysis of the stakeholders and clients. The results revealed that the LSVM algorithm is the optimal algorithm for analyzing text sentiments. The results obtained using the LSVM algorithm may be used as input to the strengths and weaknesses analysis, which is vital to the strategic plan of the institution.
Progressive Heterogeneous Ensemble Learning for Cancer Gene Expression Classification

Ensemble learning is an extensively researched subject in machine learning due to its robust and reliable performance. Multiple machine learning models are combined in ensemble learning to improve performance and reliability. There are many algorithms and variations in ensemble learning, but most techniques focus on data space like Bagging, AdaBoost, etc., or feature space like Random Subspace, Attribute Bagging, etc. Traditionally an ensemble of the same learning algorithm is used, but to increase the diversity of base classifiers, using an ensemble of different learning algorithms will be more beneficial. Progressive Subspace Ensemble Learning (PSEL) is investigated in this paper, which combines both data sample and feature space at the same time and applies a sequential selection strategy to select the best classifiers. This work extends PSEL as a heterogeneous ensemble of classifiers to improve performance and reliability for cancer gene expression classification. The proposed work is tested for cancer gene expression classification and performs better than state-of-the-art ensemble techniques.