Reviewerly/exHarmony huggingface dataset:
Multiresolution Interpretable Contourlet Graph Network for Image Classification

Modeling contextual relationships in images as graph inference is an interesting and promising research topic. However, existing approaches only perform graph modeling of entities, ignoring the intrinsic geometric features of images. To overcome this problem, a novel multiresolution interpretable contourlet graph network (MICGNet) is proposed in this article. MICGNet delicately balances graph representation learning with the multiscale and multidirectional features of images, where contourlet is used to capture the hyperplanar directional singularities of images and multilevel sparse contourlet coefficients are encoded into graph for further graph representation learning. This process provides interpretable theoretical support for optimizing the model structure. Specifically, first, the superpixel-based region graph is constructed. Then, the region graph is applied to code the nonsubsampled contourlet transform (NSCT) coefficients of the image, which are considered as node features. Considering the statistical properties of the NSCT coefficients, we calculate the node similarity, i.e., the adjacency matrix, using Mahalanobis distance. Next, graph convolutional networks (GCNs) are employed to further learn more abstract multilevel NSCT-enhanced graph representations. Finally, the learnable graph assignment matrix is designed to get the geometric association representations, which accomplish the assignment of graph representations to grid feature maps. We conduct comparative experiments on six publicly available datasets, and the experimental analysis shows that MICGNet is significantly more effective and efficient than other algorithms of recent years.
Combined Estimation of the Mutual Position of the Master and Slave Vehicle by Ultrasonic Receivers and Gnss-Module Data

The problem of determining the position of master vehicle when implementing a scenario of robotic vehicle as a part of caravanning driving system is studied. The mentioned scenario is expected the slave vehicle to replicate the motion trajectory of master vehicle represented by human operated vehicle, by another robotic vehicle or by human operator. To provide reliable operation of robotic vehicle within autonomous convoy a reliable and precise assessment of master vehicle position and trajectory regardless of environment is required. Implementation of ultrasonic receivers and emitters within information and measurement complex to determine master-slave vehicle mutual position allows to function at any time of the day inside and outside the premises in the presence of dynamic obstacles, smoke, precipitation, etc. Estimation of position and trajectory of master vehicle is accomplished by means of measuring arrival time of ultrasonic wave emitted by active beacon prior to spatial array of ultrasonic receivers. Similar indirect distance measurement between bacon and each receiver allowed to receive a system of equations to be solved by implementation of Kalman filter with Raugh - Tung - Striebel smoother. Major disadvantage of the approach is 20-meter range, which is enough for complex maneuvering. To increase the range of the complex operation, a combined estimation of the master's position based on measuring data of the spatial array of ultrasonic receivers and the module of global navigation satellite system is proposed.
Geolocalized photovoltaic energy prediction methodology using machine learning

Knowing the photovoltaic (PV) energy produced at a given time and a specific position is crucial to handle renewable energy. Nowadays, Machine Learning (ML) is broadly used to predict energy production and consumption, but existing datasets cover only some regions in each country, which impedes the deployment of such systems in production. This paper proposes a novel generalized geo-localized methodology to predict photovoltaic production for the next hour and the next day at a given geographical point. This result is part of the deliveries of a Swiss Federal office of Energy (SFOE) project called Micro Storage Intelligent and Distributed (MSID).
A Robust Graphical Authentication System for the Shoulder Surfing Attack

The technique of determining whether a user is legitimate or not is known as authentication. It needs a secret field (password) that only the actual user knows. Every security system is created in such a way that it must include at least one authentication technique to safeguard an individual’s identity. There are numerous authentication methods available today, but each method has its own advantages and drawbacks that allow attackers to carry out various exploits including dictionary attacks and shoulder surfing attacks, among others. With some tweaking, graphic password authentication may have fewer drawbacks. A modified version of the graphical password technique is employed in this paper. The user is assigned a three-digit unique number in this that must be kept private. Each digit has a unique value that is determined by a set of computations and algorithms. This makes it difficult for the attacker to decrypt the password and makes it possible to prevent numerous cyber attacks like dictionary attacks and shoulder surfing attacks.
Spatial-Semantic Transformer for Spatial Relation Recognition

Abstract Spatial relation recognition, which aims to predict a spatial relation predicate, has attracted increasing attention in the computer vision study. During tackling this problem, modeling spatial relation of the subjects and objects is of great importance. We find that only using spatial features leads to poor results in predicting the spatial relation. To overcome these challenges, we propose an effective spatial attention module to enhance spatial features using semantic features. After identifying the importance of spatial attention mechanism, we propose a spatial transformer module with encoder layers to recognize unseen spatial relation based on spatial attention mechanism. Extensive experiments on the benchmark dataset (SpatialSense) show that, by using refined spatial feature, our spatial transformer model and spatial attention model achieve state-of-the-art performance on overall accuracy.
Computer Area Localization Algorithm Based on FCN

Computer area localization algorithms are an important area of research aimed at accurately determining object locations. However, traditional regional localization algorithms have certain limitations in the face of complex scenes and changing environments. To overcome these problems, this paper proposes a region localization algorithm based on a FCN. Through experimental evaluation and data analysis, this paper finds that the regional localization algorithm based on FCN has obvious advantages over traditional algorithms. Experimental results show that the algorithm shows better performance in terms of accuracy rate and error distance. Specifically, the algorithm based on the FCN has achieved a positioning accuracy of 70%, which is much higher than the 30% of the traditional algorithm. In this paper, its superiority in localization tasks is verified through experimental evaluation and data analysis. The algorithm not only improves the accuracy and precision of positioning, but also has strong robustness and generalization ability. This provides a more accurate and reliable positioning solution for practical application scenarios, and provides strong support for the development of autonomous driving, intelligent navigation and other fields.
Morphology-Assisted Sindhi Text Analysis for Natural Language Processing Applications

Objectives: Understanding word construction and internal structure, especially in the Sindhi language, requires knowledge of the linguistic field known as morphology. In this study, Sindhi morphology is examined with particular attention paid to its structure, function, nature, word categories, and writing system. Natural Language Processing (NLP) relies on morphological analysis to identify words and their grammatical features, enabling applications like spell checkers and machine translation. A comparative analysis is done to comprehend how Sindhi Morphology developed. Because research on morphology analysis lack proper classification and cover both modern and conventional methodologies, Sindhi morphology variances present difficulties for computerization. Methods: Morphological analysis is crucial in Natural Language Processing (NLP) domains like spell checkers and gadget translation, studying word production and phrase shape using morphemes, the smallest grammatical elements in a language. Morphemes are the building blocks of words and are divided into free and fixed morphemes. Findings: Sindhi’s rich morphology and complexity enable borrowing and lending of words, but ambiguity is high due to similar patterns and vowel deletions. Morphological analysis influences semantic and syntactic analysis. Computerization is challenging due to prefixes, suffixes, and stem positions. Primary and secondary words can be subdivided into compound and complicated terms. The language uses initial, middle, and end writing styles. Novelty: This research aims to develop an automatic Sindhi morphological analyzer for future NLP applications, ensuring compatibility with existing Information Technology world applications. It will help understand Sindhi word structure and benefit software developers in developing Sindhi natural language and speech processing applications. Keywords: Sindhi Morphology; Morphological Analysis; NLP; Communication and information sharing; machine Learning
A Survey on Regression-Based Crowd Counting Techniques

Traditional detect and count strategy can’t well handle the extremely crowded footage in computer vision-based counting task. In recent years, deep learning approaches have been widely explored to tackle this challenge. By regressing visual features to density map, the total crowd number can be predicted while avoids the detection of their actual positions. Efforts of improving performance distribute at various phases of the detecting pipeline, such as feature extraction and eliminating deviation of regressed density map etc. In this article, we conduct a thorough review on the most representative and state-of-the-art techniques. The efforts are systematically categorized into three topics: the evolving of front-end network, the handling of unbalanced density map prediction, and the selection of loss function. After the evaluation of most significant techniques, innovations of the state-of-the-art are inspected in detail to analyze specific reasons to achieve high performances. As conclusion, possible directions of enhancement are discussed to provide insights of future research.
Adaptive path following control for miniature unmanned aerial vehicle confined to three-dimensional Dubins path: From take-off to landing

This study proposes a method for resolving the challenge of controlling miniature fixed-wing unmanned aerial vehicles (MAVs) along a predetermined three-dimensional (3D) Dubins path while using models with uncertainty and when experiencing external wind disturbances. We provide a multilayered structure that incorporates both guiding and control at the same time. In the guidance layer, a modified vector-field-based approach is presented to enable the MAV to follow a 3D Dubins path, including the takeoff, cruise, and landing processes with three different types of route segments. Then, an adaptive sliding model controller is used for the analysis and management of both wind disturbances and system uncertainties. Finally, both simulated scenarios and in-flight trials demonstrate the applicability of the methodology and the efficiency of the proposed approach.
Machine Learning Technique for Phishing Website Detection

The Internet has emerged as an indispensable tool in both our personal and professional life in our modern day. As a direct consequence of this, the number of customers who make their purchases over the Internet is quickly increasing. Internet users may be vulnerable to a wide variety of web threats because of this fact. These threats may result in monetary loss, fraudulent use of credit cards, loss of personal data, potential damage to a brand's reputation, and customer mistrust in e-commerce and online banking. Phishing is a sort of cyber threat that may be defined as the practice of imitating a genuine website for the purpose of stealing sensitive information such as usernames, passwords, and credit card numbers. This research focuses on strategies for detecting phishing attacks. This study apply a machine learning approach to detect a phishing attack. As a result, this study able to detect phishing with accuracy 94%.
Multimodal Pre-training Method for Vision-language Understanding and Generation

PDF HTML XML Export Cite reminder Multimodal Pre-training Method for Vision-language Understanding and Generation DOI: 10.21655/ijsi.1673-7288.00315 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like loss functions (masked language modeling and image-text matching) during pre-training. Despite their good performance in the understanding of downstream tasks, such as visual question answering, image-text retrieval, and visual entailment, these methods cannot generate information. To tackle this problem, this study proposes Unified multimodal pre-training for Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generation tasks. It expands existing pre-training paradigms and uses random masks and causal masks simultaneously, where causal masks are triangular masks that mask future tokens, and such pre-trained models can have autoregressive generation abilities. Moreover, several vision-language understanding tasks are turned into text generation tasks according to specifications, and the prompt-based method is employed for fine-tuning of different downstream tasks. The experiments show that there is a trade-off between understanding tasks and generation tasks when the same model is used, and a feasible way to improve both tasks is to use more data. The proposed UniVL framework attains comparable performance to recent vision-language pre-training methods in both understanding tasks and generation tasks. Moreover, the prompt-based generation method is more effective and even outperforms discriminative methods in few-shot scenarios. Reference Related Cited by
Sintigrafik Görüntülerden Tiroid Nodülleri için Bilgisayar Destekli Tanı Sistemi

In modern medicine, image recognition via segmentation of anatomical regions and automatic classification of diseases using medical images has a growing potential role in diagnosis of various diseases. Scintigraphy of thyroid is one of the established imaging modalities for diagnosis of thyroid gland disorders. In our study, the speckle noise was reduced in the scintigraphy images with the optimized Bayesian nonlocal mean filter. The thyroid gland was automatically segmented by local based active contour method and the thyroid gland pathologies were classified with convolutional neural networks (CNN). The proposed computer aided diagnosis (CAD) system was compared with Pyramid of Histograms of Orientation Gradients (PHOG), Gray Level Co occurrence Matrix (GLCM), Local Configuration Pattern (LCP) and Bag of Feature (BoF) methods. The common pathological patterns of scintigraphic images of the thyroid gland were successfully classified by CNN with an overall success rate of 91.19%. The comparative methods were PHOG, GLCM, LCP and BoF methods which provided overall success rates of 7.61%, 86.04%, 88.91% and 85.72% respectively. The proposed CNN based automatic diagnosis system provided promising results compared to handcrafted methods.
MKSS: An Effective Multi-authority Keyword Search Scheme for edge–cloud collaboration

The rapid development of cloud computing motivates Internet of Things users to outsource their data to enjoy value-added cloud services: data storage, data sharing, etc. To provide reliable but searchable results, searchable encryption (e.g., Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS)) become an area of active research. One major drawback of existing CP-ABKS designs lies in the private key being generated and distributed by a trusted Central Authority (CA), which is a single point of failure/attack. Another limitation is that the cloud server may return forged or incomplete query results to the data user. This paper proposes a novel Multi-authority Keyword Search Scheme for edge–cloud collaboration (MKSS). To protect the security of the private key, we design a private key generation algorithm based on secure multi-party computation. To preserve the integrity of search results, we construct an efficient verification algorithm that detects forged or incomplete results. We evaluate the security and performance of our protocol using real-world datasets to demonstrate robustness and efficiency. Our construction advances the existing body of research towards secure and efficient keyword search protocols in cloud systems.
Utility of Whole Slide Imaging for Intraoperative Consultation: Experience of a Large Academic Center

In the United States, review of digital whole slide images (WSIs) using specific systems is approved for primary diagnosis but has not been implemented for intraoperative consultation.To evaluate the safety of review of WSIs and compare the efficiency of review of WSIs and glass slides (GSs) for intraoperative consultation.Ninety-one cases previously submitted for frozen section evaluation were randomly selected from 8 different anatomic pathology subspecialties. GSs from these cases were scanned on a Leica Aperio AT2 scanner at ×20 magnification (0.25 μm/pixel). The slides were deidentified, and a short relevant clinical history was provided for each slide. Nine board-certified general pathologists who do not routinely establish primary diagnoses using WSIs reviewed the WSIs using Leica Aperio ImageScope viewing software. After a washout period of 2-3 weeks, the pathologists reviewed the corresponding GSs using a light microscope (Olympus BX43). The pathologists recorded the diagnosis and time to reach the diagnosis. Intraobserver concordance, time to diagnosis, and specificity and sensitivity compared to the original diagnosis were evaluated.The rate of intraobserver concordance between GS results and WSI results was 93.7%. Mean time to diagnosis was 1.25 minutes for GSs and 1.76 minutes for WSIs (P < .001). Specificity was 91% for GSs and 90% for WSIs; sensitivity was 92% for GSs and 92% for WSIs.Time to diagnosis was longer with WSIs than with GSs, and scanning GSs and uploading the data to whole slide imaging systems takes time. However, review of WSIs appears to be a safe alternative to review of GSs. Use of WSIs allows reporting from a remote site during a public health emergency such as the COVID-19 pandemic and facilitates subspecialty histopathology services.
Research on Automatic Generation Algorithm of Phoneme Conversion Learning Corpus Based on KNN Algorithm

As for phoneme conversion, there is no good method all the time, so most of the current researches on speaker voice conversion only convert the phonetic spectrum. From the linguistic point of view, phonemes are the smallest unit of speech, and syllables and sentences are all composed of phonemes in a fixed way. In this paper, the research on automatic generation algorithm of phoneme conversion learning corpus based on KNN(K nearest neighbor) algorithm is carried out. In order to solve the problem that it is difficult to collect a large amount of speech data of the target user, this paper uses KNN algorithm to construct an average phoneme model, mixes the data in the previously established phoneme database, and obtains GMM (Gaussian Mixture Model) of phonemes through sufficient training. In 10 cases, the spectral distance ratio based on KNN is lower than that based on GMM, with an average reduction of 9.261%, which indicates that independent conversion of each phoneme by phoneme classification can effectively avoid GMM smoothing and obtain more information of the target speaker.
Evaluation of Environmental Conditions on Object Detection Using Oriented Bounding Boxes for AR Applications

The objective of augmented reality (AR) is to add digital content to natural images and videos to create an interactive experience between the user and the environment.Scene analysis and object recognition play a crucial role in AR, as they must be performed quickly and accurately.In this study, a new approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time.The approach is evaluated using two datasets: a real image dataset (DOTA dataset) commonly used for computer vision tasks, and a synthetic dataset that simulates different environmental, lighting, and acquisition conditions.The focus of the evaluation is on small objects, which are difficult to detect and recognise.The results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions.
A time-dependent regularization of the Redfield equation

We introduce a new regularization of the Redfield equation based on a replacement of the Kossakowski matrix with its closest positive semidefinite neighbor. Unlike most of the existing approaches, this procedure is capable of retaining the time dependence of the Kossakowski matrix, leading to a completely positive divisible quantum process. Using the dynamics of an exactly-solvable three-level open system as a reference, we show that our approach performs better during the transient evolution, if compared to other approaches like the partial secular master equation or the universal Lindblad equation. To make the comparison between different regularization schemes independent from the initial state, we introduce a new quantitative approach based on the Choi-Jamiołkowski isomorphism.
Interpersonal relationships in the nursing care of people with type 2 diabetes / Relação interpessoal no cuidado de enfermagem a pessoas com diabetes tipo 2

Objective: to analyze the effectiveness of the interpersonal relationship in nursing care and its relationship with general self- care and foot care in people with type 2 diabetes. Method: this cross-sectional, analytical study was conducted at a University Hospital with 150 people with type 2 diabetes who answered the Interpersonal Relationship in Nursing Care Questionnaire, a questionnaire on self-care activities with diabetes, and a questionnaire to assess adherence to feet self-care. Results: the results showed that the effectiveness of the interpersonal relationship in care ranged from moderate to high. The questionnaire scores were significant with nephropathy comorbidities, cerebrovascular accident, diet adherence, and receiving foot care guidance. Conclusion: interpersonal relationships in effective care can strengthen bonding, trust, and verbalization of feelings, improving diabetes education and adherence to self-care behaviors.