Reviewerly/exHarmony huggingface dataset:
A text extraction framework of financial report in traditional format with OpenCV

Authors:
raw_author_name: Jiaxin Wei
raw_affiliation_string: Department of Finance and Commerce, Qinghai Higher Vocational and Technical Institute, Haidong, China
raw_author_name: Jin Yang
raw_affiliation_string: Office of Academic Research, Qinghai Higher Vocational and Technical Institute, Haidong, China
raw_author_name: Xinyang Liu
raw_affiliation_string: Department of Finance and Commerce, Qinghai Higher Vocational and Technical Institute, Haidong, China

Abstract: Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework.
Verifiable quantum protocol for dynamic secure multiparty summation based on homomorphic encryption

Authors:
raw_author_name: Mei Luo
raw_affiliation_string: Hefei University of Technology, School of Mathematics, Hefei, Anhui, 230009, CHINA
raw_author_name: Fulin Li
raw_affiliation_string: Hefei University of Technology, School of Mathematics, Hefei, 230009, CHINA
raw_author_name: Li Liu
raw_affiliation_string: Hefei University of Technology, School of Mathematics, Hefei, Anhui, 230009, CHINA
raw_author_name: Shixin Zhu
raw_affiliation_string: Hefei University of Technology, School of Mathematics, Hefei, Anhui, 230009, CHINA

Abstract: Abstract The research of quantum secure multiparty computation is a subject of great importance in modern cryptography. In this study, we construct a veriﬁable quantum protocol for dynamic secure multiparty summation based on the cyclic property of d-level MUBs. Our protocol can realize dynamic parameter update in the aspect of members and secret inputs, improving the practicality of the protocol. Moreover, a veriﬁcation mechanism for result checking by applying ElGamal homomorphic encryption is given, and further enables the detectability of cheating behaviors, making our protocol safer. The security analysis proves the proposed protocol not only can resist a range of typical attacks from outside and inside, but also is secure against dishonest revoked participant attack which has been neglected in previous dynamic quantum summation protocols. From a theoretical perspective, compared with existing summation protocols, the protocol provides better practicability, higher privacy protection, and higher efﬁciency.&amp;#xD;
Image semantic segmentation model based on CBAMUNet

Authors:
raw_author_name: guo qian
raw_affiliation_string: University of Sanya (China)

Abstract: Mainstream image semantic segmentation networks can only extract local features of an image. Their receptive field is limited to the range of convolutional kernel size, which impacts the distinguishability of extracted features by the encoder. This issue leads to inaccurate segmentation, loss of small targets, and other related problems. This paper presents a solution to the aforementioned challenges by proposing the use of a CBAMUNet image semantic segmentation model. The model combines CBAM attention module and UNet En-decoder network. Firstly, the UNet network has been enhanced to eliminate the need for cropping of input images to reduce feature loss. Secondly, the CBAM module has been integrated into its skip connection to enable the UNet network to extract more efficient features and enhance the segmentation effect. And the model uses a new loss function. Data augmentation is also used to improve the generalization ability of the model. Through the comparison experiments with UNet, it is found that CBAMUNet is able to significantly improve the segmentation effect of images, and increase the pixel precision(PA) and the mean intersection over union(mIoU) of the semantic segmentation of images.
Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

Authors:
raw_author_name: Ajay Kumar

Abstract: &amp;nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&amp;#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.
Using a fuzzy cognitive map to assess interventions to reduce antimicrobial resistance in a Swedish One Health system context under potential climate change conditions

Authors:
raw_author_name: Shannon E Majowicz

Abstract: Abstract Introduction: Antimicrobial resistance (AMR) is a growing One Health crisis that can be impacted by other challenges of sustainable development, such as climate change, but few interventions have been assessed with a systems-wide lens. The objectives of this study were to use a previously defined fuzzy cognitive map (FCM) of the Swedish One Health system to: 1) identify areas in the system to target interventions; and 2) test the potential ability and viability of interventions to reduce AMR under a changing climate. Methods: The FCM, based on participatory modelling workshops and literature scan, was used to assess the sustainability of eight interventions under potential climate change conditions. Network metrics were calculated to describe the system structure and identify highly impactful nodes. Results: The network metrics identified high-leverage nodes including alternative productions systems and good farming practices. None of the scenarios evaluated were able to adequately reduce AMR within the system. Conclusions: Overall, fuzzy cognitive mapping provides an innovative way to analyse the AMR system, identify high-leverage interventions, and examine potential impact of interventions using a broader systems lens.
The development of higher-order thinking skill-based immune system learning tools

Authors:
raw_author_name: Nur Subkhi
raw_author_name: Sunarti Sunarti
raw_author_name: Idah Hamidah

Abstract: &#x0D; &#x0D; &#x0D; &#x0D; Learning tools can assist teachers in facilitating achievement during the learning process. At that time, the learning tools utilized still relied upon the pre-existing 2013 curriculum, lacking the incorporation of higher-order thinking Skills (HOTS) indicators for critical thinking skills, particularly in the context of immune system material. Therefore, research was conducted to create a learning tool immunity that was based on HOTS. The objectives of this study were as follows: 1) To analyze the requirement of HOTS-based biology learning tools in the immune system material for class XI in SMA/MA; 2) To design the product development for HOTS-based biology learning tools in class XI immune system material in SMA/MA; 3) To validating HOTS-based biology learning tool products in class XI immune system material in SMA/MA. This study employed the ADDIE (Analysis, Design, Development, Implementation, Evaluation) a model for instructional design. However, this research was conducted only up to the development stage of the ADDIE model. The sample comprised five experts and three practitioners, namely biology learning experts, lecturers’ teachers, and language experts. The purposive sampling technique was employed in this research. The instruments used included product validation sheets and interview sheets. Data collection methods involved distributing product validation sheets and conducting interviews with validators. This research produces a HOTS-based immunity learning tool product which generally falls into the very valid category with a percentage of 94.56%. It means that the development product is valid for use in biology learning at the SMA/MA level.&#x0D; &#x0D; &#x0D; &#x0D;
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

Authors:
raw_author_name: Hao-Ren Yao
raw_affiliation_string: National Institutes of Health, Bethesda, USA
raw_author_name: Nairen Cao
raw_affiliation_string: Boston College, Chestnut Hill, USA
raw_author_name: Katina Russell
raw_affiliation_string: Georgetown University, Washington, USA
raw_author_name: Der-Chen Chang
raw_affiliation_string: Georgetown University, Washington, USA
raw_author_name: Ophir Frieder
raw_affiliation_string: Georgetown University, Washington, USA
raw_author_name: Jeremy T. Fineman
raw_affiliation_string: Georgetown University, Washington, USA

Abstract: Learning Electronic Health Records (EHRs) representation is a preeminent yet under-discovered research topic. It benefits various clinical decision support applications, e.g., medication outcome prediction or patient similarity search. Current approaches focus on task-specific label supervision on vectorized sequential EHR, which is not applicable to large-scale unsupervised scenarios. Recently, contrastive learning has shown great success in self-supervised representation learning problems. However, complex temporality often degrades the performance. We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems. Unlike the state-of-the-art, we do not change the graph structure to construct augmented views. Instead, we use Kernel Subspace Augmentation to embed nodes into two geometrically different manifold views. The entire framework is trained by contrasting nodes and graph representations on those two manifold views through the commonly used contrastive objectives. Empirically, using publicly available benchmark EHR datasets, our approach yields performance on clinical downstream tasks that exceeds the state-of-the-art. Theoretically, the variation in distance metrics naturally creates different views as data augmentation without changing graph structures. Practically, our method is non-ad hoc and confirms superior performance on commonly used graph benchmark datasets.
Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

Authors:
raw_author_name: Kaifan Zou
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Xiaorong Guan
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Zhong Li
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Huibin Li
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Changlong Jiang
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
raw_author_name: Zihui Zhu
raw_affiliation_string: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: To improve soldiers’ combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> smart, and information <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm’s motion costs.
Robust Testing of AI Language Model Resiliency with Novel Adversarial Prompts

Authors:
raw_author_name: Yulia Kumar
raw_affiliation_string: Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA
raw_author_name: J. Jenny Li
raw_affiliation_string: Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA
raw_author_name: Patricia Morreale
raw_affiliation_string: Department of Computer Science and Technology, Kean University, Union, NJ 07083, USA

Abstract: In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.
Learning noisy transition matrix using a neural network

Authors:
raw_author_name: Yongliang Miao
raw_affiliation_string: Beijing normal university-HongKong baptist university united international collage
raw_author_name: Kuntian Tang
raw_affiliation_string: Guangdong university of Finance and Economics
raw_author_name: Chen Wang
raw_affiliation_string: South China normal university
raw_author_name: Yuqi Cao
raw_affiliation_string: No.59 Middle School

Abstract: In the field of deep learning, it is crucial to know the accurate distribution of dataset. However, to obtain a high quality of dataset using traditional methods is prone to be both costly and inefficient. Using deep learning methods to estimate the noisy transition matrix provides a feasible way, as the result of its essential function of learning to denote the relationship between clean labels and noisy labels and building a statistically consistent classifier. The major difficulty of learning the noisy transition matrix stems from the unavailability of the distribution of clean data and noisy data. In this paper, we propose a practical and convenient method to study a combination of augumentation and a novel loss function, only leveraging the already known clean labels to aid in learning the noisy transition matrix in the whole dataset. Finally, Through the experiment, the result demonstrates a superior performance and generalization capabilities of the proposed method.
Investigation related to database design for letter of credit

Authors:
raw_author_name: Yanchen Guo
raw_affiliation_string: Shenzhen University

Abstract: The "Letter of Credit" is a fundamental payment mechanism in global trade, and its efficiency has significantly declined due to the impact of the COVID-19 pandemic. This document delves deeply into the development and execution of a comprehensive database for managing Letters of Credit (L/Cs). It is designed with a real-world case study derived from the International Settlement Department of the Bank of China. The central goal of this database is to enhance the efficient management and simplification of the complex procedures inherent in L/C transactions. The development process follows a systematic approach, commencing with the establishment of three fundamental assumptions to delineate the scope of the study. Subsequently, the database design progresses through stages involving Entity-Relationship Diagram (ERD) development, relational model construction, and normalization procedures. These steps collectively culminate in the creation of an intelligently structured database comprising thirteen distinct tables. In conclusion, this paper not only delves into the design and implementation of the L/C database but also showcases its practicality through real-world scenarios. The database, fueled by meticulous design and thoughtful implementation, is poised to revolutionize the management of L/C operations, offering enhanced efficiency and accuracy in international trade transactions.
Suspicious Activity Tracking

Authors:
raw_author_name: Prof. Mrs. A. H. Renushe

Abstract: Abstract: Our offering includes a deep neural network model that can identify firearms in photographs and a machine learning and computer vision pipeline that can detect abandoned luggage in order to identify potential gun-based crime and circumstances involving abandoned luggage in surveillance film. Unusual behavior the technique of identifying undesired human activity in locations and circumstances is called detection. To do this, footage is converted into frames, and the processed frames are then used to analyze the people's sports. YOLOv3 is used to find a niche, in dubious sports like lock breaking and bag snatching, among others. Our gadget has a superb processing pace in addition to appropriate accuracy of detection. It is harder for computers to detect things in videos compared to images because of issues like blurriness or things getting blocked. They propose a solution called Shot Video Object Detector, which is a faster kind of detector for videos. It works by combining information from nearby frames to make better guesses about where objects are. Unlike other methods, Shot Video Object Detector does this by figuring out how things move between frames and then using that info to combine features. It also creates new features by borrowing information directly from neighboring frames using a special structure. Automated surveillance in public areas plays a crucial role in upholding law and order and proactively identifying potential risks to the public. Not only does the procedure automatically identify and detect known crooks, but it also tracks people's and things' movements and uses machine learning algorithms to alert the authorities to any questionable activity
Didactic Use of the Publisher Processor to Enhance Meaningful Learning in Peruvian Secondary School Students

Authors:
raw_author_name: Silvia Georgina Aguinaga Doig
raw_author_name: Osmer A. Campos-Ugaz
raw_author_name: Ronald M. Hernández
raw_author_name: Yen Marvin Bravo Larrea
raw_author_name: Carlos Luy-Montejo

Abstract: The coronavirus pandemic has changed the course of education from face-to-face to remote. In response to this context, we investigated the positive effect of the didactic use of the Publisher processor to enhance meaningful learning in the competence "explain the physical world based on scientific knowledge about matter and energy, biodiversity, earth and universe" of the science and technology area in fifth grade students of secondary education of a Public Educational Institution of Chiclayo (Peru). For this purpose, a pre-experimental design with pre- and post-test was used with a sample of 102 students selected in a non-probabilistic way. A program based on activities incorporated in learning experiences proposed by the Ministry of Education (Peru) was applied. As a result, 72% of the students increased their academic performance after the application of the program, improving up to five (5) points compared to the initial test. Thus, the development of didactic material using the Publisher processor strengthens learning, which highlights the importance of information technologies in the achievement of active learning and the need to use them in the processes of mediation, reinforcement and teacher feedback in virtual and/or face-to-face scenarios.
Research on the intersection of natural language processing and deep learning

Authors:
raw_author_name: Hanwen Cai
raw_affiliation_string: East China University of Science and Technology

Abstract: In the past ten years, Natural Language Processing (NLP) has made many surprising progress due to the rapid development of Deep Learning(DL) and further explored the possibility of future development. This article briefly introduces the NLP field, the basic structure of DL, and the impact of the combination of DL and NLP on the NLP field. Finally, it reviews the limitations of the two under the constraints of current science and technology and looks forward to the possibility and direction of their future development. Appropriately applying DL to NLP can indeed bring great progress to the core areas and applications of NLP. Still, at the same time, the development of NLP will also be limited by the shortcomings of DL. It is necessary to continuously optimize the DL model while taking advantage of the advantages of DL. The most critical factor to promote the development of DL and NLP in the future.