Reviewerly/exHarmony huggingface dataset:
Handwriting removal method based on CNN

This paper proposes a handwriting removal method based on Convolutional Neural Network (CNN). In image preprocessing, to avoid the influence of lighting and color on paper document images, illumination equalization is realized based on Contrast Limited Adaptive Histogram Equalization (CLAHE). And then the OTSU threshold segmentation method is used for adaptive threshold segmentation to obtain a binary image. In terms of datasets production, word region segmentation is realized based on edge detection, morphological processing, and contour fitting to produce test set and training set images. which include 2800 handwriting and printing. Then it is applied to the training and testing of the CNN model, the classification accuracy reached 98.25 %. Finally, the handwriting region in paper document image is fitted and removed. The experimental results show that the algorithm can better remove the handwriting content in paper document image, it is helpful to standardize the processing of paper documents.
Penguatan Literasi Numerasi untuk Siswa Sekolah Dasar di SDN Merjosari 4 Malang

The ability to read and count is needed by the next generation. Unfortunately, the importance of numeracy literacy to be implemented in schools has yet to be optimized by some schools. One of them is SDN Merjosari 4 Malang. There are not many activities that can be done to increase numeracy literacy in the school. After discussions with the headmaster, it was agreed that several activities would be carried out at school, namely (1) Forum Group Discussion (FGD) with teachers at SDN Merjosari 4 to strengthen Numerical Literacy, and (2) Carry out activities that could optimize Numerical Literacy at SDN Merjosari 4 Malang. The focus of Numeracy Literacy strengthening activities is given to grades 2, 3, and 4. The series of activities planned to be carried out in community service have all been carried out properly. Students feel interested and happy with the activities that have been carried out and hope that activities like this will be carried out in the future.
MSYOLOF: Multi-input-single-output encoder network with tripartite feature enhancement for object detection

Object detection under one-level feature is a challenging task, which requires that object representations at different scales can be extracted on a single feature map. However, existing object detectors using a one-level feature suffer from inadequate of different-scale object representations resulting in low accuracy for multi-scale object detection, especially for smaller objects. To address the problem above-mentioned, a novel object detector named MSYOLOF, is proposed to construct an effective single feature map for detecting objects of different scales. In the proposed network, three modules are proposed to bring considerable improvements, namely Feature Pyramid Pooling (FPP), Feature Perception Enhancement (FPE), and Dual Branch Receptive Field (DBRF). Firstly, the FPP module aggregates contextual information from various regions to improve the network's ability to achieve global information, which strengthens the model's understanding of the overall scene. Then, the FPE module utilizes coordinate attention to construct a residual block to obtain orientation-aware and position-sensitive information, making the network efficient in accurately locating and identifying objects of interest. Third, by rethinking the Dilated Encoder of YOLOF, the DBRF module reduces information loss and mitigates the problem of being sensitive only to large objects when dilated convolution utilizes large expansion rates. Extensive experiments are conducted on COCO benchmark to validate the effectiveness of our network, which exhibits superior performance compared to other state-of-the-art networks.
Data Leakage Detection Using Secret Key Exchange

Data leakage is a significant problem in many industries and institutions. While there are different methods for protecting data using various encryption algorithms, this technology doesn't provide complete security. In some cases, a data distributor may give sensitive data to trusted agents (third parties), and some of the data may be leaked in an unauthorized place. In such cases, the distributor must assess the likelihood that the leaked data came from one or more of the agents, as opposed to having been independently. This paper proposes strategies for allocating data across agents to improve the probability of identifying leaks in the event of data leakage. The proposed methods do not rely on altering the released data like watermarks. Instead, they focus on distributing the data in a way that makes it easier to identify the source of the leak. The paper highlights that data leakage is a significant problem that can lead to a loss of money, damage to reputation, and more. It can occur due to security vulnerabilities, poor data protection practices, or accidentally by a user. Therefore, it's essential to take measures for avoiding data leakage and protect data. Using the proposed strategies, the distributor can improve chances of identifying the source of the leak and taking appropriate action to prevent data leakage.
Hand Gesture Recognition For Human-Robot Cooperation In Manufacturing Applications

Human-robot cooperation plays an increasingly important role in manufacturing applications. Together, humans and robots display an exceptional skill level that neither can achieve independently. For such cooperation, hand gesture communication using computer vision has been proven to be the most suitable due to the low cost of implementation and flexibility. Therefore, this work focuses on the hand gesture classification problem in view of human and robot collaboration. To facilitate collaboration, six of the most common gestures applicable in manufacturing applications were selected. The first part of the research was devoted to creating an image dataset using the proposed acquisition system. Then, pre-trained neural networks were designed and tested. In this step, the feature extraction approach was adopted, which utilises the representations learned by a previous network to extract meaningful features. The results suggest that all developed pre-trained networks attained high accuracy (above 98,9%). Among them, the VGG19 demonstrated the best performance, achieving accuracy equal to 99,63%. The proposed approach can be easily adapted to recognise a more extensive or different set of gestures. Utilising the proposed vision system and the developed neural network architectures, the adaptation demands only acquiring a set of images and retraining the developed networks.
The National Educators' View on Information Literacy (IL) Implementation

For the past 25 years, IL instruction has been formally incorporated into the Malaysian curriculum.However, it appears that the implementation of Information Literacy Education (ILE) is developing and progressing more slowly than may be anticipated.Determining the elements influencing the adoption of IL education in Malaysian primary schools, and examining how these aspects are supporting or impeding the process, are the objectives of this qualitative study.The national primary schools representing one of Malaysia's two public school types were the subjects of case studies.For a deeper understanding of the implementation challenges that surfaced during the implementation, school teachers and other significant stakeholders took part in in-depth, semi-structured interviews that were followed by documentary analysis.The examination of the interview transcripts with the teaching staff has revealed a number of elements that both help and impede the implementation of IL instruction.According to interview data, the majority of participants had favourable opinions of IL education.They believed that IL education was significant and had the ability to increase the fun and engagement of teaching and learning activities.The participants did, however, also mention that there were issues with IL implementation; these issues appear to be related to the ecology of the school.
Appraisal of Implementation of Inclusive Entrepreneurship Education Programme in Public Universities in South-South, Nigeria (During Post Covid-19 Pandemic Era)

The study was conducted on appraisal of the implementation of inclusive entrepreneurship education programme in public universities in the post-covid-19 pandemic era in South-South, Nigeria. The study utilized two research questions and hypotheses. Descriptive survey research design was adopted for the research. The population of the study consisted of 19,862 final year students in universities in SouthSouth, Nigeria. A multistage sampling technique was used to draw a sample of 984 students. Instrument titled Appraisal of the Implementation of Inclusive Entrepreneurship Education Programme Questionnaire (AIIEEPQ) was used for data collection. Descriptive statistics and independent t-test analyses were used to analyse the data. The result was tested at 0.05 level of significance. The findings revealed that quality of course content has not significantly enhanced the implementation of inclusive education programme in post-covid19 pandemic era while students’ disposition has significantly enhanced the implementation of inclusive education programme in post-covid19 pandemic era. It was concluded that quality of the course content in the study of Inclusive Entrepreneurship Education has not successfully helped students in the practice of entrepreneurial activities but what has sustained them remains the positive attitude they developed in course of their study, chiefly based on what improved version of life they hope to make of themselves and others through the empowerment as entrepreneurs. Based on the conclusion, it was recommended among others that government and higher institutions of learning should in addition to training, upgrade and implement their entrepreneurship curriculum content to accommodate today’s entrepreneurship skills as they also pertain to the criteria of inclusiveness.
ANALYSIS OF CRIMINAL ACTS OF A GROUP OF SEXUAL LIBERTIES WHICH ARE CONSIDERED CYBERCRIME

The authors analyze the state of the cyber crime (VTK) area, which is tangent with a group of crimes against sexual liberties. The need for analysis comes from the specific position of the criminal codex group of crimes against sexual liberties. As a rule, they carry a significant social danger, and thus significant attention to the societal - social environment, as well as pressure to solve crimes and punish perpetrators. The authors give a view of the status of this area of criminality in the Republic of Serbia, compare it to the current state of criminality over a period of 10 years and 5 years respectively, and show that although these acts are not so prevalent in numbers, they carry many additional specifics. The number of both considered criminal acts (more crimes within the wider areas) from the group of crimes against gender freedom and those of this group VTK is very small compared to the other. For the aforementioned reasons, these acts, both in the group of acts against gender freedom and when they occur in the VTK, carry significant public attention, pressure on the law enforcement agencies, additional problems related to victimological aspects as well as possible additional problems of the penological nature. The specifics that such acts carry are reflected in the special position of the criminal acts before the law enforcement agencies and the courts, which require increased attention from the prosecution authorities. From the analyzed data, the authors draw conclusions regarding trends in this area while combining with VTK areas. Key words: cybercrime, paedophiles, sexual criminal acts, law enforcement
Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models

Assessing the capacity of numerical understanding of vision-and-language models over images and texts is crucial for real vision-andlanguage applications, such as systems for automated medical image analysis.We provide a visual reasoning dataset focusing on numerical understanding in the medical domain.The experiments using our dataset show that current vision-and-language models fail to perform numerical inference in the medical domain.However, the data augmentation with only a small amount of our dataset improves the model performance, while maintaining the performance in the general domain.* Equal Contribution. 1 https://radiopaedia.org/cases/haemorrhagic-intracranialmetastases-from-breast-cancer
Business communication and emotional intelligence: Two sides of the same coin

This article aims to investigate the mechanisms of business communication and its relations with the principles of emotional intelligence in the workplace. The survey was conducted in the municipality of Tangará da Serra-MT between the years 2022 and 2023, with a total of 724 workers from local commerce and service companies. In methodological terms, the investigation is characterized by a quantitative approach based on random collection of statistical data, with explanatory objectives regarding the phenomenon of communication in the professional environment. The results indicate the importance of developing the socio-emotional competencies present in the concept of emotional intelligence, focusing on the dynamics of business communication in order to allow the creation of healthy environments of interaction and cooperation. In addition, the research reinforces the evidence that most communication problems are related to the lack of emotional control of employees and their managers.
ANALISIS KELAYAKAN BUKU TEKS SISWA IPA KURIKULUM 2013 PADA MATERI PENCEMARAN LINGKUNGAN KELAS VII DITINJAU DARI KELENGKAPAN DAN KETEPATAN ISI

This study aims to identify the completeness and accuracy of the contents of the 2013 curriculum science student textbooks on environmental pollution material in grade VII. The method used in this research is a qualitative approach. From the results of the analysis, it was found that the completeness of the contents of the 2013 curriculum science student textbook on environmental pollution material for grade VII was considered incomplete, this was due to the fact that there were still some sub-materials that had not been included. As for the accuracy of the contents of the book, it can be said that it is quite in accordance with the indicators of KD achievement from KI-1, KI-2, KI-3 and KI-4. This is evidenced by the suitability of the content of the book which is sufficient to meet the KD achievement indicators from KI. Based on the results of the analysis, it can be said that the student textbook is feasible as teaching material but still requires improvement. Keywords: Student Textbooks, Completeness of Content, Accuracy of Content, Teaching Materials, 2013 Curriculum
PELATIHAN REFERENCE MANAGER BAGI MAHASISWA AKUNTANSI

The lack of understanding of Accounting students about reference managers motivated this training. The objectives of implementing this training program are: a). Provide training to Accounting students who are taking their thesis on how to properly cite scientific papers using a reference manager, especially the Mendeley; b). Train and assist Accounting students in making citations and bibliography. The method used adopts the steps in the action research, namely planning, acting, observing and reflecting. The reference manager training activity for Accounting students can be said to be successful because through this training participants claimed to be helped in installing and running reference managers, especially Mendeley. Participants are also able to simulate the correct way of doing citations. Participants considered that this activity was very useful for them, especially for those who were taking their thesis because it could make it easier for them to manage their references.
Enhancing Multi-Behavior Recommendations Through Capturing Dynamic Preferences

Multi-behavior recommendation has gained significant attention in recent years for its ability to outperform single-behavior models. Current research related to multi-behavior models leaves room for improvement in the following two areas. First, the noise carried by individual behaviors and the additional noise generated during behavior processing is often overlooked, and these can ultimately degrade recommendation performance. Second, the specific time period of behavioral interactions and the frequency of interactions within that time period are also not taken into account. To address the above limitations, we propose a multi-behavior recommendation model integrating dynamic preferences (MB-DP) that captures dynamic interests while smoothing and denoising multi-behavior information. MB-DP extracts low and high-order semantics from various behaviors and unifies the measurements to generate interaction predictions. Additionally, it analyzes the interaction time and frequency of each behavior using gated recurrent units to capture the dynamic preferences of users and improve the prediction values. Extensive experimental results on two real-world datasets show that MB-DP significantly improves recommendation performance compared to the state-of-the-art baselines.
A Stable Lifting Convolutional Autoencoder for Anomaly Detection of Turbopump Bearings of Liquid Rocket Engine

Anomaly detection, which could not only identify potential risks early but also offer the first time for remaining useful lift prediction, plays a crucial role in assuring the safe operation of major equipment, including liquid rocket engines. However, due to the complicated modulation phenomena caused by speed variations, existing anomaly detection techniques for vibration signals with stationary speeds would fail on varying-speed signals. Simultaneously, the turbopump bearings run under transient rotating speeds. Thus, motivated by the outstanding performance of redundant second generation wavelet transform in non-stationary feature extraction, a stable lifting convolutional autoencoder (LiftingCAE) is presented. First, a lifting decomposition-based encoder is introduced to layer-by-layer decompose the components of various scales. Then, stable loss is suggested to extract latent features by minimizing the interference information whereas maximizing the health state-dependent features. Finally, the decoder based on lifting reconstruction is utilized to model health data through fusing the features of different scales. The proposed LiftingCAE was validated by vibration signals collected on a turbopump bearing test rig working in a cryogenic environment, and was compared to some state-of-the-art methods. The results show the effectiveness and superiority of LiftingCAE in detecting turbopump bearing anomalies.
Multi-source Navigation Sensor Backend Information Fusion Technology Based on D-S Evidence Theory

Multi-source navigation sensors can provide high-precision navigation solutions. To achieve back-end information fusion, this paper presents a fault detection algorithm and an information fusion algorithm. The fault detection algorithm is designed to detect anomalies in multisource navigation sensor data, while the information fusion algorithm is used to integrate data from multiple sensors for obtaining more accurate and reliable navigation information. These two algorithms are based on the D-S evidence theory and Murphy’s improved method, which effectively detect and judge abnormal data and handle conflicts and uncertainties among multiple sources of information, thereby outputting more reliable navigation information. By applying the algorithms to actual flight data, this study demonstrates their effectiveness in abnormal situations, such as abnormal inertial attitude angle, constant difference between inertial pitch angle and heading pitch angle, and sudden changes in GPS output position information.
Qualified Remote Signature through a Hybrid Cloud Architecture

There are two basic electronic signature scenarios involving an individual signing a digital document: by means of her private key and qualified certificate stored on a qualified cryptographic device (usually an USB token), or by means of a private key and cloud certificate stored in the protected remote environment of a Qualified Service Provider. The simplest case is when both the document and the cryptographic device are located on the person’s computer. But what if the person needs to sign a document located on a remote location - let’s say on a document management platform - via a cloud service, but she wants to sign it using the USB token available on her computer? For the digital document to be signed on the remote platform, it must be downloaded, signed and reuploaded. This paper proposes a different approach in which the document is signed with a qualified signature, without downloading its content and without using a cloud-based private key and certificate, and still respecting the qualified signature regulations and requirements.
Geophysical and geological framework of the southern edge of the Parnaíba Basin, NE Brazil

The application of geophysical techniques to highlight and characterize magnetic and gravity anomalies was carried out along a research area at the southern edge of the Parnaíba Basin.This study was conducted through qualitative and quantitative interpretations of magnetic and gravity data, supported by surface geological information, reflection seismic data, stratigraphic well data, field data, and magnetic susceptibility and density measurements of rock samples collected in outcrops.The study used the 3D magnetization vector inversion method, and 4 regional transects were performed for 2.5D modeling of magnetic and gravity data.The investigation revealed the configuration of the structural framework of the southern edge of the Parnaíba Basin.The characterization of magnetic anomalies allowed the definition of four main domains.Three magnetic zones were fragmented by depth, in the intervals of 20 km to 30 km, 5 km to 8 km and 0.5 km to 1 km, which demonstrate the association of magnetic anomalies with shallow and deep crustal structures.The magnetic susceptibility data show good correlation with the main structural discontinuities, and in particular with the Transbrasilian Zone.The results show that the maximum thickness of the sedimentary cover in the basin is approximately 2 km.No features associated with grabens or rifts were identified along the transects, and the presence of gravity and pseudo-gravity lows are closely related to less dense lithological units of the upper crust, such as granitic masses and supracrustal sequences.The reactivations of the Transbrasilian Zone during the Mesozoic generated deep structures in the crust that promoted the rise of basaltic rocks and the intrusion of kimberlitic bodies.Geophysical and geological framework of the southern edge of the Parnaíba Basin, NE Brazil
A divide-and-conquer control strategy with decentralized control barrier function for luggage trolley transportation by collaborative robots

Abstract This article focuses on the luggage trolley transportation problem, an essential part of robotic autonomous luggage trolley collection. To efficiently address the nonholonomic constraints derived from the formation of two collaborative robots and a queue of luggage trolleys, we propose a comprehensive framework consisting of a global planning method and a real-time divide-and-conquer control strategy. The popular Hybrid A * algorithm generates a feasible path as the global planner. A model predictive controller is designed to track this path stably and in real time. To maintain the formation so that the whole queue of robots and luggage trolleys does not split, a safety filter that consists of a discrete-time control Lyapunov function and a decentralized control barrier function is implemented in the transportation process. Finally, we conduct real-world experiments to verify the effectiveness of the proposed method on three representative paths, and the results show that our approach can achieve robust performance. The demonstration video can be found at https://www.youtube.com/watch?v=iPiT8BfLIpU .