Reviewerly/exHarmony huggingface dataset:
SG2Path: a two-branch semantic guided backbone network for semantic segmentation

Semantic segmentation is vital for computers to process image scene parsing. Semantic segmentation requires the output of high-resolution segmentation results that classify each pixel in an image, and high-resolution feature maps consume significant computational costs in deep learning networks. To trade-off the accuracy and speed of semantic segmentation models, researchers have proposed various different approaches to extract features by semantic segmentation backbone networks. In this paper, we propose a novel semantic-guided backbone network (SG2Path) based on a multi-branch backbone architecture, in which we design a semantic-guided upsampling module (SGUM) to better fuse high-resolution spatial features with low-resolution semantic features in different branches, which effectively solves the semantic misalignment problem between feature maps of different resolutions. Experiments on the Cityscapes dataset and the Visualization analysis of the model prove the advantages of our model in the semantic segmentation task and the significant application potential.
Using Grasshopper Optimization in Big Data

From algorithms that have been popular and presently used in meta-heuristic is the grasshopper optimization method, which has made many theoretical breakthroughs and is widely applied in numerous optimization issues across different fields such as image processing, machine learning, engineering design, control over wireless networking, power systems, and other things. In this study, we review the literature that is currently accessible on the grasshopper optimization technique and its extensions to the chaotic, binary, multi-objective scenarios and hybrid. Finally, the grasshopper optimization algorithm has proven superior to other optimization algorithms in most literature.
research on UAV-assisted path planning system based on deep learning

With the widespread use of First Person View (FPV) Drone in film and television shooting, terrain reconnaissance, etc, the operator simultaneously handles the target task and obstacle avoidance trajectory planning flight task under greater pressure, which is very likely to lead to accidents, so combining monocular visual depth-of-field algorithm with Unmanned Aerial Vehicle(UAV) dynamic information to provide the controller with trajectory-assisted indication during flight becomes the focus of this research paper. In this paper, by combining the artificial potential field with deep learning, we use deep learning to regress the target motion trajectory under the action of depth-of-field potential, effectively predict multiple possible obstacle avoidance trajectories under the current position by introducing UAV positional information and UAV viewpoint images, improve the computational speed by streamlining the network, and deploy the algorithm model to Jetson TX2 for experimental validation. The experimental results show that the channel detection achieves the design requirements and realizes the effect of UAV aircraft to assist the controller in high dynamic scenes.
Ensuring Information System Security by Selective Multifactor Authentication / Обеспечение безопасности информационных систем с помощью выборочной многофакторной аутентификации

Multi-factor Authentication (MFA) has emerged as an important security measure to protect sensitive information and prevent unauthorized access in an increasingly interconnected world. In this article, we introduced the multi-factor authentication system we developed, which is a security mechanism and not only offers multiple authentication methods for users to verify their identity, but does it all in a clearly fixed order, which increases the level of security on vulnerable websites and minimizes the loss of accounts. This toolkit is flexible in its structure and can be used both separately and as an integral system. One of the advantages is that users themselves choose the authentication system and their order. All this slightly complicates the authentication process, but guarantees security from cyber attacks.
A Hybrid Method for Enhancement of Both Contrast Distorted and Low-Light Images

Many different histogram equalization (HE)-based image enhancement methods have been developed to overcome the problems of low or high image brightness, contrast sensitivity, and difficulty in revealing details of dark areas under low-light environments. In this paper, a novel image enhancement method based on HE and adaptive gamma correction with weight distribution (AGCWD) is proposed for natural and effective image enhancement. In this method, histogram stretching is performed on Red–Green–Blue (RGB) color components of image, and then the color space of RGB image is converted to Hue–Saturation–Intensity (HSI) color space. The histograms of S and I components are divided into sub-histograms according to the exposure threshold. The underexposure regions are enhanced with a new AGCWD. Then, the color space of HSI image is converted to RGB color space. Finally, the HE is applied to the input image with the obtained image histogram map. Thus, the method has not only effectively prevented the over-enhancement of the contrast but also obtained the quality and natural enhanced image. The proposed method is compared with the most known contrast enhancement methods and low-light enhancement methods. Experimental results have supported that the proposed method outperforms other methods in terms of both visual perception and objective evaluation.
The Linking of Emotional Intelligence and Team Performance: The Mediating Role of Knowledge Sharing

The research that examined the connections between emotional intelligence, knowledge sharing, and team effectiveness is outlined in this article.&nbsp; The study also investigated the impact of emotional intelligence and knowledge sharing on team performance.&nbsp; Although these interactions are crucial for improving team performance, there aren't many empirical research that look at these interactions together. This study used data from 357 employees of SMEs in Vietnam to analyze those associations using SEM.&nbsp; The obtained data were analyzed using SPSS and AMOS Version 22. Results from a test of these relationships largely support our hypothesized model. The findings showed that through encouraging knowledge sharing, emotional intelligence indirectly improves team performance. Furthermore, knowledge sharing has a direct impact on how well a team performs. On the other side, there is a strong correlation between sharing knowledge and emotional intelligence. The study's findings, implications, and suggestions for further study are explored.
Rock Model Generation and its classification using Deep Learning Method

Abstract Summary Adaptive QSGS, Lattice Boltzmann Method, Deep Convolutional Neural Netwoks, Particle Swarm Optimization
Scattering wavelet transform for earthquake signals classification with a small training set

Summary One method for early earthquake warning (EEW) is to detect precursors with weak energy. Detection of a weak earthquake is a challenging task due to the corruption of noisy signals. However, obtaining and annotating a large number of records are time-consuming for every region. When only a small training set is available, the traditional deep learning methods may cause over-fitting to the data. We propose to use scattering wavelet transform combined with simple classifiers (linear or SVM) to perform classification on earthquake records. The designed wavelet filters can help reduce the over-fitting of the deep neural network on a small training set. The proposed method achieves 93% classification accuracy when only 100 training samples are provided, which is much higher than the FCN and CNN method.
On data quality in mineral potential modelling: A case study using random forest and fractal techniques

Summary Here we report the application of a transformed predictor map-based random forest (RF) technique to mineral potential modelling (MPM) of orogenic gold mineral systems in the well-endowed Granites-Tanami Orogen (GTO), Northern Territory, Australia. In this study, we compared a previously developed random forest (RF) model based on 19 predictor maps ( Roshanravan et al., 2020 ) against a new RF model employing an expanded set of 23 predictor maps with the four new predictors representing more detailed geophysical data not previously available. We found the newly constructed RF model performed better than the previous version of Roshanravan et al. (2020 ), illustrating the value of high-quality input data to better reflect the mappable ingredients of the targeted mineral deposit type. To further constrain the new RF modelling results, the gold prospective domains were delimited using the concentration-area (C-A) fractal technique. The resulting first order targets occupy only c. 2% of the study area while capturing 76% of the known gold occurrences. The area captured by the first order targets represents a &gt;50 times (i.e., an order of magnitude) reduction of the search space, the hallmark of a well-performing, practically useful targeting technique.
Pelatihan Bahasa Inggris untuk Anak Usia Dini dengan Fun English

The people of Nologaten RT 10 RW 04 needed help, especially regarding English. This is due to parents' lack of knowledge and education levels, so they could only guide their children with minimal access to effective learning materials for learners at an early age. Learning English from an early age is necessary so that in its later development, learners will easily master English. English Letters Department of Universitas Sanata Dharma designed a community service activity, i.e., English Training for Children at Nologaten RT 10 RW 04. The series of community service activities began in February 2022 to recruit volunteers. Then the program's implementation started in July 2022 and ended in August 2022; this community service activity is expected to further strengthen the image of Universitas Sanata Dharma as a university that is concerned with the interests of the community in general and will further enhance the 3C spirit (competence, conscience, compassion) among lecturers and student volunteers, as well as to make English Letters Department of Universitas Sanata Dharma give positive impact to society. The post-test results yield increasing scores of 28% compared to the previous pre-test in all three evaluation domains (cognitive, affective, and psychomotor). They are also interested in listening to others while speaking, showing good cooperation with other group members, starting a simple English conversation, and asking questions in correct grammar.
Cyber Attack Detection using Secret Sharing Schemes

We propose a model along with its algorithm, which can detect suspicious activities and safeguard sensitive information within a complicated infrastructure of an organization based on the secret sharing schemes. The model provides a flexible multifactor authentication methodology to increase the overall security of the system.
VideoAdviser: Video Knowledge Distillation for Multimodal Transfer Learning

Multimodal transfer learning aims to transform pretrained representations of diverse modalities into a common domain space for effective multimodal fusion. However, conventional systems are typically built on the assumption that all modalities exist, and the lack of modalities always leads to poor inference performance. Furthermore, extracting pretrained embeddings for all modalities is computationally inefficient for inference. In this work, to achieve high efficiency-performance multimodal transfer learning, we propose VideoAdviser, a video knowledge distillation method to transfer multimodal knowledge of video-enhanced prompts from a multimodal fundamental model (teacher) to a specific modal fundamental model (student). With an intuition that the best learning performance comes with professional advisers and smart students, we use a CLIP-based teacher model to provide expressive multimodal knowledge supervision signals to a RoBERTa-based student model via optimizing a step-distillation objective loss—first step: the teacher distills multimodal knowledge of video-enhanced prompts from classification logits to a regression logit—second step: the multimodal knowledge is distilled from the regression logit of the teacher to the student. We evaluate our method in two challenging multimodal tasks: video-level sentiment analysis (MOSI and MOSEI datasets) and audio-visual retrieval (VEGAS dataset). The student (requiring only the text modality as input) achieves an MAE score improvement of up to 12.3% for MOSI and MOSEI. Our method further enhances the state-of-the-art method by 3.4% mAP score for VEGAS without additional computations for inference. These results suggest the strengths of our method for achieving high efficiency-performance multimodal transfer learning.
Color image steganalysis based on quaternion discrete cosine transform

&lt;abstract&gt; &lt;p&gt;With the rapid development and application of Internet technology in recent years, the issue of information security has received more and more attention. Digital steganography is used as a means of secure communication to hide information by modifying the carrier. However, steganography can also be used for illegal acts, so it is of great significance to study steganalysis techniques. The steganalysis technology can be used to solve the illegal steganography problem of computer vision and engineering applications technology. Most of the images in the Internet are color images, and steganalysis for color images is a very critical problem in the field of steganalysis at this stage. Currently proposed algorithms for steganalysis of color images mainly rely on the manual design of steganographic features, and the steganographic features do not fully consider the internal connection between the three channels of color images. In recent years, advanced steganography techniques for color images have been proposed, which brings more serious challenges to color image steganalysis. Quaternions are a good tool to represent color images, and the transformation of quaternions can fully exploit the correlation among color image channels. In this paper, we propose a color image steganalysis algorithm based on quaternion discrete cosine transform, firstly, the image is represented by quaternion, then the quaternion discrete cosine transform is applied to it, and the coefficients obtained from the transformation are extracted to design features of the coeval matrix. The experimental results show that the proposed algorithm works better than the typical color image steganalysis algorithm.&lt;/p&gt; &lt;/abstract&gt;
Information management in school libraries: a systematic review of the literature

The study approaches information management focused on the context of school libraries through a systematic literature review carried out in the following databases: CAPES, EBSCO and SciELO journals, using the descriptors "information management" AND " school library. Thus, the general objective of this research proposes to carry out a systematic literature review on studies related to information management in school libraries. Therefore, we intend to discuss the concept of information management in the context of school libraries; carry out a mapping of articles that deal with the object of study; and analyze the works that approach the proposed theme. The searches resulted in 9 articles, from which the information considered relevant for analysis was extracted. The results point to the importance of information management in the context of school libraries so that knowledge and information can be managed in a planned, organized, interdisciplinary and intentional way.
Nurses' role in the health education process against falls in parkinsonian elderly

Objective: To analyze the role of the nurse in the education and promotion of self-care in Parkinsonian elderly patients. Method: This is an integrative review study, carried out through a survey of data in the scientific bases: LILACS, SCIELO and MEDLINE. Results: Nine studies were selected for the research discussion. Discussion: Home health care is defined as providing services to patients at home. Care starts with the personal, initially through activities of daily living, such as personal hygiene, nutrition, exercise, clothing, as well as through guidelines for dressing and administering medications as needed. Care for the elderly is observed, carried out at home by relatives who normally do not receive the necessary training. Final Considerations: Nurses must implement a care system that describes all the necessary care procedures for patients, allow other team members to implement and monitor the patient's development and encourage self-care at each stage of the disease.
Leukemia Classification using a Convolutional Neural Network of AML Images

Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.
$$\textbf{k}$$NN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval

Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. As the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding; however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user’s multiple interests. To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval. kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct “interests” of the user. By querying each of a user’s mixture component in proportion to their mixture weights, we retrieve a high-diversity set of candidates reflecting elements from each of a user’s interests. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets. Accompanying this work, we open source a large Twitter follow-graph dataset ( https://huggingface.co/datasets/Twitter/TwitterFollowGraph ), to spur further research in graph-mining and representation learning for recommender systems.
Use of AI in Cloud-Based Certificate Authentication for Travel Concession

Certificate Authentication is a big challenging task in a socio-economic country like India. 28 states and 8 union territories with diversified cultures and languages make it a big voluminous task. There is an urgent need to automate this authentication task. This paper proposes the cloud-based certificate authentication. Google Cloud services are used to automate the authentication process. Vision API and flask framework are explored which allows developers to easily integrate vision detection features within applications including Image labeling, Face and landmark detection, Optical character recognition and Tagging of explicit contents. The proposed arrangement makes use of Cloud Vision API optical character recognition to infer the presence of required fields in scanned PDF. Recognized fields will be communicated in the output report.
Statistical Approaches to File Fragment Analysis

Metadata is a blanket term that encompasses the information describing a file’s myriad properties. A file’s metadata might capture its extension, headers, footers, signatures, length, and file type, among other details. This information, along with other markers, can be exploited by a forensic examiner to identify and categorize files, often with relative ease. However, the challenge that arises in digital forensics is that examiners could potentially be tasked with identifying file types using only a few remnant fragments on a storage device. These remnants left behind on a device could be a product of routine operating system activities or a misguided attempt to destroy evidence by a suspect. In addition, files are routinely transmitted over a network as fragments, sometimes with errors, and network forensic examiners might be called upon for file fragment identification in this scenario. It is also commonplace to find perpetrators maliciously and deliberately falsifying metadata, including headers and footers, in an effort to mislead and misdirect forensic examiners. To this end, in this research, we propose statistical techniques for file type identification and classification using file fragments. We demonstrate that byte frequency analysis and related techniques are quite potent in their ability to tackle these aforementioned challenges. In our work, we analyze fragments garnered from fourteen different files. We choose these file types in our analysis as they are commonly used for various purposes and with different structures. We evaluate the performance of our proposed framework using evaluation metrics such as accuracy, precision, recall, and F1-score. Our results show that our proposed framework can accurately identify and classify file types from file fragments. Our work is novel in that we have pushed the boundaries of what was considered feasible using fundamental statistical tools, as suggested by the body of work in the literature. Our objective is to develop the foundations of a framework for file-fragment analysis under the fabric of statistical analysis. We show that the auspice of our research is applicable in domains as disparate as file integrity analysis, malware detection, steganography, intrusion detection systems, security policy implementation, and of course, a big thrust in digital and cyber forensic analysis.
Impact of Robot Related User Pre-experience on Cognitive Load, Trust, Trustworthiness and Satisfaction with VR Interfaces

This paper investigates how user pre-experience impacts the quality of Human-Robot Interaction (HRI) when using a virtual reality (VR) interface to control a robot. The benefit lies in the fact that creating interfaces for these complex systems is a challenging process, and our aim is to shed light on one of the contributing factors. We aim to complement existing works by conducting a user study exploring the link between multiple psychological factors and users’ pre-experience with robot systems, video games and gamepads while interacting with a real robot through a VR interface. Results showed that experienced users reported less cognitive load, but also less trustworthiness of and satisfaction with the system and that these factors are correlated with system usability and satisfaction. We assume that this is based in pre-experienced users, having higher expectations and a more critical perception of new systems. These findings could guide the development of future interfaces tailored to specific user groups.