Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and the Future of Accounting and Auditing Services

Blockchain (BC) is one of the utmost advances in recent years. Side by side with artificial intelligence (AI), it is an emergent trend holding an underlying transformative force. Hence, BC is a topic that concerns all organizations at any corner of the globe, regardless of its typology and dimension. Nonetheless, the notion of blockchain is often misunderstood and diminished to the strict use of technology to secure virtual transactions involving digital currencies (e.g., Bitcoin or Ethereum); however, BC technology carries a greater potential to be explored transversally across global supply chains, having a wide range of applications ranging from agriculture, to financial services, or healthcare, being furthermore entangled with other emergent disruptive technologies, such as artificial intelligence (AI), big data analytics (BDA), or the Internet of Things (IoT), which are already shaping the future of business and humanity. In short, these technologies are profoundly changing the pre-established conventions at the workplace. The huge potential of blockchain has captured the attention of scholars from various fields, some aligning synergies and resources toward interdisciplinary research projects, racing to explore the multiple streams of applicability of this phenomenon. This research sheds some light on the impact of BC on the overlooked organizational functions of accounting and auditing as we delve into the learning-responsiveness of individuals and organizations to grasp their capabilities built for their present and future.
Night-Time Monitoring System for Getting-up Action of Older Person Based on Single Camera and Infrared Reflective Sheet

As the impact of population aging becomes increasingly serious, more problems emerge with regard to medical and long-term care for the older individuals, which require solution. In long-term care, accidental falls frequently occur and are a significant cause of serious injuries and bed confinement due to leg fractures. Older people are especially prone to fall out of bed when they try to get up on their own. To reduce the occurrence of such accidents, one important solution is to detect the getting-up behavior of older individuals and to alert nursing staff to come and check. Although many studies have proposed various solutions such as the use of wearable devices and vision-based sensors, there are many issues in practical application. The complexity of device installation, high initial cost, and maintenance problems have restricted most care facilities to using fall prevention systems with mediocre results, such as pressure pad sensors. In this paper, we propose a fall prevention system based on a single camera (with infrared function) and infrared reflective sheets (IR sheets). The system detects the IR sheets placed on the shoulders of an individual and obtains position data. The relative positions of the IR sheets can be used to identify the state of the person on the bed. To improve the identification ability of the system, we propose to define an identification area. To identify the state more accurately, we propose to establish sub-areas within the identification area. We conducted experiments by recruited 19 subjects. The data of 6 subjects were used to construct the sub-areas. The other 13 subjects participated in testing the ability of the system in identifying the various states of the person in bed. Compared with the performance of other studies, our experimental results demonstrate that our system has a high identification rate, in addition to being low-cost and easy to set up.
Development of New Personality Test Method using OpenPose

There are two types of personality test methods in common use: verbal and non-verbal, each with its advantages and disadvantages. If a new personality test method that can compensate for the disadvantages of both methods is developed, it would be used worldwide. To develop a new method to estimate human personality, we have attempted to measure human motions using the optical motion capture system (OMC) and group them by feature points. However, it was found that OMC is inconvenient due to camera calibration and attachment of infrared markers to subjects. In this paper, to make it more efficient, we compared OpenPose, which can detect the position of human joints with a video camera, to OMC. As a result, it was confirmed that OpenPose is more effective than OMC in analyzing human motion. In addition, we attempted transfer learning for classifying test scores of a personality test based on “Big Five personality traits” from walking trajectory graphs from OpenPose.
MORPHOLOGICAL AND SYNTACTICAL ERRORS ANALYSIS ON STUDENTS WRITING SKILL AT PAMULANG UNIVERSITY, SOUTH TANGERANG, BANTEN

The aim of this research is to find out the Morphological and Syntactical errors made by the students in their written texts, the number of errors, and the types of errors that most often appear. The method which is used in this research is descriptive method. Descriptive research method refers to the method used to describe the data and the characteristics used to describe the population. The purpose of using the descriptive method is to obtain systematic, factual and accurate data that can provide an actual picture of the data that is the research material. Descriptive research methods should not be used in making causal relationships where one variable affects other variables. The source of the data was taken from the writings of 10 first semester students of the Faculty of Letters, Pamulang University, South Tangerang, Banten. In collecting data, the students were asked to write an essay in English. Students are given the freedom of topics to be written. In conclusion, from the 69 errors identified, there were 13 errors in the Morphological category and there were 56 errors in the Syntactical category from the total data found. From these results, it can be concluded that students make greater mistakes in the Syntactical category.
THE CURIOSITY EFFECT ON STUDENTS' READING COMPREHENSION

The purpose of this study was to find out whether curriculum has a significant effect on students' reading comprehension or not. This study uses a quantitative ex post facto method. The research instrument used consisted of tests and questionnaires which were distributed to the sample. The population in this study were students of class X from SMA NEGERI 1 BANGUN PURBA (200 students) and the sample of this study was 60 students. The researcher took the sample using proportional stratified sampling technique, in which the sample was divided into three groups proportionally: high, medium, and low curiosity. The results of this study indicate that curriculum has a significant effect on reading comprehension of students in class X. It is shown from the average score of students with high curriculum (83.5) which has a higher score than the average score of students with low curiosity (60). It is also shown from the results of statistical calculations that the value of tp (7.150) &gt; tt (2.086) at the significance level (α = 0.05). From these calculations, the alternative hypothesis is accepted and the null hypothesis is rejected.
PERFORMANCE EVALUATION OF INFORMATION RETRIEVAL SYSTEM USING VECTOR SPACE MODEL: A COMPARATIVE ANALYSIS

The increasing use of the internet has created a vast amount of digital information and it is expanding extremely fast. Therefore, Information retrieval becomes a challenging task to fetch relevant information for users. The aim of this paper was to examine and evaluate the performance of the Information retrieval system through eight experiments to test all the features that can be used in a vector space model. These experiments were compared to show the best and the worst implemented features. The features are represented by applying (tf.idf, stop words, stemming), (tf.idf, No- stop words, stemming), (tf.idf, No- stop words, No-stemming), (tf.idf, stop words, No-stemming), (tf, stop words, stemming), (tf, No- stop words, stemming), (tf, No- stop words, No-stemming), (tf, stop words, No-stemming). Results showed that using stop words, stemming approach, and tf.idf improve the performance of the system. However, when tf was used without using stop words and stemming approaches the performance of the system is declined. In addition, results showed that stop words have a significant effect on the system while the stemming approach has no noticeable effect particularly with tf.
Unsupervised SAR Despeckling by Combining Online Speckle Generation and Unpaired Training

Speckle suppression is a crucial preliminary step for synthetic aperture radar (SAR) image processing. Supervised despeckling approaches trained on synthetic datasets usually perform poorly in practice due to the unavailability of clean SAR images. Besides, the spatial correlation of speckle is rarely considered in many methods based on the fully developed speckle assumption. In this paper, we propose an unsupervised despeckling method to address these issues by combining online speckle generation and unpaired training. The method consists of two branches: the stop-gradient branch and the unpaired branch. First, the stop-gradient branch learns to generate the spatially correlated speckle. Then, the unpaired branch combines the generated speckle with the unpaired optical image to form pairs of training data for network parameter updates. More specifically, in order to generate the more realistic speckle in the stop-gradient branch, we design a speckle correction module with three SAR speckle priors: the threshold prior, the unit mean prior, and the correlation prior coupled with the weighted patch-shuffle. In the unpaired training, a hybrid loss function is employed, which takes spatial smoothness and detail protection into consideration. Afterwards, we combine the stop-gradient branch with the unpaired branch by the Siamese network to achieve alternate optimization of speckle generation and speckle removal. Finally, the optimization process in our method is analyzed theoretically. Qualitative and quantitative experiments demonstrate that the proposed method is comparable to the supervised despeckling approaches on synthetic datasets and outperforms several state-of-the-art unsupervised methods on real SAR datasets.
The main problems and countermeasures of English teaching in higher education based on VAR model

Abstract This paper first analyzes the current situation and characteristics of English teaching in higher education and explores the current classroom model of English teaching in higher education. Then, it combines the multiple regression model and VAR method to construct an improved multiple autoregressive model of English teaching based on VAR and apply it to higher vocational English teaching. Finally, the improved model was tested for smoothness and co-integration, and the problems of English teaching in higher education were analyzed using impulse responses. The results showed that when the number of teacher-student ratios increased, the teaching quality response was -3, the English teaching time increased, and the English teaching quality response was around 4.5. The best-fit teaching model yielded a positive and stable response of around 4.5.
The Effect of Moral Global Leadership Moderation on the Relationship between Achievement Motivation, Smart Work, and Performance

This study aims to examine the relationship between Achievement Motivation (AM), Smart Work (SW), and human resources (HR) performance. It questions how moral global leadership (MGL) could moderate the relationship between AM, SW, and HR performance. A theoretical model was developed and tested on sample data representing 219 employees, educators, and educational staff of Islamic Higher education (IHE). The data were collected through surveys and applied to structural equation modeling using SEM-PLS. This study found that AM and SW significantly affect HR performance. While MGL substantially moderates the relationship between AM, SW, and HR performance. This study contributes to the literature on MGL, AM, and SW in creating HR performance that has yet to be studied so far. This study offers the concept of MGL, which plays a central role in moderating the relationship between AM, SW, and HR performance.
The Effect Analysis of the Application of CAI to Japanese Oral Teaching in Colleges

Abstract In this paper, we first analyze the requirements for teaching spoken Japanese in colleges and universities, design a computer-assisted teaching system using C/S and three-layer hybrid architecture, and create a business flow diagram of the system. The second step is to conduct a technical analysis of the computer-assisted teaching system, and Bayesian theory is employed to model the complex system with multi-factor synergy. The effect of the computer-assisted system on Japanese-speaking teaching courses in colleges and universities was analyzed. The results indicated that the students dictated 4,500 words, while the lowest dictated as little as 300 words, but overall, they were more balanced. The prompt word ratio was directly proportional to the number of repetitions by observing the number of single dictations of students.
WebCollectives: A light regular expression based web content extractor in Java

Conventional web crawling methods typically involve a sequence of distinct steps for downloading and extracting web content. A noteworthy limitation of these conventional crawling approaches is their lack of a focus-based crawling strategy. The software introduced in this paper, known as WebCollectives, introduces a straightforward crawling approach by integrating content extraction into a hierarchical regular expression definition model. Furthermore, it streamlines the crawling process through a pipeline-oriented framework, emphasizing focus-based link extraction. This crawler employs either a configurable Selenium mechanism or a direct HTTP GET method to fetch web pages. Subsequently, it undergoes an extraction process based on hierarchical regular expressions. Notably, Selenium allows for adaptable JavaScript functions to navigate web pages effectively. The content extraction generates XML structures from diverse types of content. Comparative analysis with the standard DOM (Document Object Model) reveals that the proposed approach yields significant improvements in extraction efficiency and requires fewer lines of code. Specifically, it outperforms non-recursive standard DOM hierarchy definitions in terms of both extraction speed and code complexity.
Latent Wander: an Alternative Interface for Interactive and Serendipitous Discovery of Large AV Archives

Audiovisual (AV) archives are invaluable for holistically preserving the past. Unlike other forms, AV archives can be difficult to explore. This is not only because of its complex modality and sheer volume but also the lack of appropriate interfaces beyond keyword search. The recent rise in text-to-video retrieval tasks in computer science opens the gate to accessing AV content more naturally and semantically, able to map natural language descriptive sentences to matching videos. However, applications of this model are rarely seen. The contribution of this work is threefold. First, working with RTS (Télévision Suisse Romande), we identified the key blockers in a real archive for implementing such models. We built a functioning pipeline for encoding raw archive videos to the text-to-video feature vectors. Second, we designed and verified a method to encode and retrieve videos using emotionally abundant descriptions not supported in the original model. Third, we proposed an initial prototype for immersive and interactive exploration of AV archives in a latent space based on the previously mentioned encoding of videos.
Dual-branch Pattern and Multi-scale Context Facilitate Cross-view Geo-localization

Cross-view geo-localization aims to locate the target image of the same geographic location from different viewpoints, which is a challenging task in the field of computer vision. Due to the interference of similar images and the surrounding environment of the target building, the matching accuracy is significantly reduced when facing complex scenes. To solve this problem, we propose a cross-view geo-localization method based on dual-branch pattern and multi-scale context to provide a solution for challenging dataset with numerous distractors. This method exploits a Transformer feature extraction network to reduce the loss of fine-grained features. Meanwhile, a dual-branch structure is designed to capture image semantic information and local context information bidirectionally, which can effectively deal with the problem of more interference items in satellite images and improve the accuracy of geographic location tasks in complex scenes. After quantitative experimental verification, both recall rate (Recall) and image retrieval average precision (AP) indicators have been significantly improved on benchmark dataset University-1652 and challenging dataset University-160K, our method can achieve advanced cross-view geo-localization performance.
AFPN: Attention-guided Feature Partition Network for Cross-view Geo-localization

Cross-view geo-localization is to retrieve images of the same geographic target from different platforms. Since drones have received increasing attention in recent years because of their ability to capture high-quality multimedia data from the sky, we focus on image retrieval from the drone platform to the satellite platform in this paper. We propose an attention-guided feature partition network (AFPN) which leverages learnable spatial attention maps to divide the global high-level feature map into the class-aware foreground and the class-agnostic background feature in an end-to-end learning manner. Our backbone is based on the powerful vision transformer to model long-range global dependencies between patches. Data augmentation and multiple sampling strategies are also adopted in our experiments. Our method achieves Recall@1 accuracy at 95.60% on University-1652 and 94.48% on University-160k, and ranks 2nd in the ACMMM23 Multimedia Drone Satellite Matching Challenge.
Why Don't You Speak?: A Smartphone Application to Engage Museum Visitors Through Deepfakes Creation

In this paper, we offer a gamification-based application for the cultural heritage sector that aims to enhance the learning and fruition of museum artworks. The application encourages users to experience history and culture in the first person, based on the idea that the artworks in a museum can tell their own story, thus improving the engagement of the museums and providing information on the artwork itself.
Dynamic Network for Language-based Fashion Retrieval

Language-based fashion image retrieval, as a kind of composed image retrieval, presents a substantial challenge in the domain of multi-modal retrieval. This task aims to retrieve the target fashion item in the gallery given a reference image and a modification text. Existing approaches primarily concentrate on developing a static multi-modal fusion module to learn the combined semantics of the reference image and modification text. Despite their commendable advancements, these approaches are still limited by a deficiency in flexibility, which is attributed to the application of a singluar fusion module across diverse input queries. In contrast to static fusion methods, we propose a novel method termed Dynamic Fusion Network (DFN) to compose the multi-granularity features dynamically by considering the consistency of routing path and modality-specific information simultaneously. In specific, our proposed method is consisted of two modules: (1) Dynamic Network. The dynamic network enables a flexible combination of the different operation modules, providing multi-granularity modality interaction for each reference image and modifier text. (2) Modality Specific Routers (MSR). The MSR generates precise routing decisions based on the distinct semantics and distributions of each reference image and modifier text. Extensive experiments on three benchmarks,\textiti.e., FashionIQ, Shoes and Fashion200K, demonstrate the effectiveness of our proposed model compared with the existing methods.
TC-OCR: TableCraft OCR for Efficient Detection &amp; Recognition of Table Structure &amp; Content

The automatic recognition of tabular data in document images presents a significant challenge due to the diverse range of table styles and complex structures. Tables offer valuable content representation, enhancing the predictive capabilities of various systems such as search engines and Knowledge Graphs. Addressing the two main problems, namely table detection (TD) and table structure recognition (TSR), has traditionally been approached independently. In this research, we propose an end-to-end pipeline that integrates deep learning models, including DETR, Cascade TabNet, and PP OCR v2, to achieve comprehensive image-based table recognition. This integrated approach effectively handles diverse table styles, complex structures, and image distortions, resulting in improved accuracy and efficiency compared to existing methods like Table Transformer. Our system achieves simultaneous table detection, table structure recognition, and table content recognition (TCR), preserving table structures and accurately extracting tabular data from document images. The integration of multiple models addresses the intricacies of table recognition, making our approach a promising solution for image-based table understanding, data extraction, and information retrieval applications. Our proposed approach achieves an IOU of 0.96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in the OCR Accuracy compared to the previous Table Transformer approach.
Drone Satellite Matching based on Multi-scale Local Pattern Network

\beginabstract In this technical report, we represent our solution for the ACMMM23 Multimedia Drone Satellite Matching Challenge. Our solution focuses on exploiting the feature aggregation strategy to develop a robust cross-view geo-localization system for Drone Satellite Matching. In particular, we propose an end-to-end framework named Multi-scale Local Pattern Network~(MLPN), which builds upon the LPN and incorporates a multi-scale aggregation block. LPN is employed to divide high-level features of different scales, and the multi-scale aggregation block, as the name implies, is utilized to aggregate the local features obtained by the division. Experiments show that MLPN can effectively match UAV images with satellite images and achieve a competitive accuracy on University160k. Additionally, in Multimedia Drone Satellite Matching Challenge, our solution achieves the fourth place. \endabstract
Video Referring Expression Comprehension via Transformer with Content-conditioned Query

Video Referring Expression Comprehension (REC) aims to localize a target object in videos based on the queried natural language. Recent improvements in video REC have been made using Transformer-based methods with learnable queries. However, we contend that this naive query design is not ideal given the open-world nature of video REC brought by text supervision. With numerous potential semantic categories, relying on only a few slowupdated queries is insufficient to characterize them. Our solution to this problem is to create dynamic queries that are conditioned on both the input video and language to model the diverse objects referred to. Specifically, we place a fixed number of learnable bounding boxes throughout the frame and use corresponding region features to provide prior information. Also, we noticed that current query features overlook the importance of cross-modal alignment. To address this, we align specific phrases in the sentence with semantically relevant visual areas, annotating them in existing video datasets (VID-Sentence and VidSTG). By incorporating these two designs, our proposed model (called ConFormer) outperforms other models on widely benchmarked datasets. For example, in the testing split of VID-Sentence dataset, ConFormer achieves 8.75% absolute improvement on [email protected] compared to the previous state-of-the-art model.