Reviewerly/exHarmony huggingface dataset:
Position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion

Object tracking plays an important role in the computer vision field and has many applications such as video surveillance and vehicle navigation. But the occlusion problem is one of the most challenging problems in the applications. Although there are many approaches in the object tracking field that focus on dealing with occlusion scenes, the occlusion with large size barriers and long occlusion time still cannot be solved. To handle the problems, this paper proposes a reliable tracking method based on particle filter focus on long-term full occlusion with large size barriers. In this paper the large size is defined as pixel width from 350 to 600 in fixed resolution images. and the long term is defined as occlusion frame number from 180 to 600. First, this paper proposed a particle position reset module to replace the resampling process during the occlusion periods to solve the problem of losing the target after occlusion. In addition, a hybrid feature based likelihood model is proposed for the occlusion happening and ending judgments. Experiments on the extreme occlusion situation sequences demonstrate the reliability and accuracy of the proposed work on these challenging scenes. The algorithm finally implements the average 92% success rate at the tested sequences.
Vision-based fall detection system for the elderly using image processing and deep learning

Population aging is happening in developed and some developing countries, this means the proportion of elderly people in society is increasing dramatically. The elderly often suffers from bone and joint diseases that make their daily live difficult. Therefore, accidental falls are a major cause of loss of autonomy, injuries among the elderly. Healthcare surveillance systems need to be improved to take care of these elderly due to the lack of nurses, this paper represents a solution to this problem. By using a vision-based architecture, the healthcare surveillance system can detect fall accidents in people's daily life activities and then notify the nurse to have in-time assistance. Yolov3-tiny is applied to detect humans in the frame, bounding boxes are generated to visualize the detection, then humans are tracked using the Kalman filter algorithm. AlphaPose is applied to generate key points from detected persons. Each keypoint coordinate will change frame after frame when persons in the frame are moving, then keypoint coordinate states are fed into ST-GCN as input, and the ST-GCN model will predict the probability of the person's activity (fall or not fall). Our model is more effective at detecting and predicting falls, distinguishing it from many other proposed models in the past. Some of our improvement include fall detection in low lighting conditions, multi-person fall detection, and fall detection when the human body is partially occluded. The experimental results show that our proposed vision-based surveillance system achieves high accuracy of 99.08%, precision of 98.84%, and recall of 98.03%.
Frequent Itemset Mining Algorithm Based on Linear Table

Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number of scans to the original dataset. Furthermore, operations such as pruning and grouping are also used to optimize the algorithm. For different datasets, the algorithm shows different mining speeds. (1) In sparse datasets, the algorithm achieves an average 45% improvement in mining speed over the bit combination algorithm, and a 2-3 times improvement for the classic FP-growth algorithm. (2) In dense datasets, the average improvement over the classic FP-growth algorithm is 50-70%. For the bit combination algorithm, there are dozens of times of improvement. In fact, the algorithm that integrates bit combinations with bitwise AND operation can effectively avoid recursive operations and it is beneficial to the parallelization. Further analysis shows that the linear table is easy to split to facilitate the data batch mining processing.
Convolutions vs. Sequences: Understanding performances of neural-based methods for automatic Baybayin script recognition

Common approaches to vision-based tasks such as character and object recognition use Convolutional Neural Networks (CNNs) due to their practicality in processing images and theoretical grounding. In this work, we take a different perspective in the task of Baybayin script recognition by exploring Vision Transformers, a new paradigm for processing images inspired by the Transformer model. We compare performances of CNNs and ViT and analyzed model confidence on a set of test images using Local Interpretable Model-Agnostic Explanations (LIME). Results show that, performance-wise, convolution-based architectures (CNNS) still outperform sequence-based methods (ViT) for discriminating Baybayin scripts with a nearly doubled performance of 84.5% to 48.8% in accuracy respectively.
Optimizing the performance of a server-based classification for a large business document flow

The document categorization problem in the case of a large business document flow is considered. Textual and visual embeddings were employed for classification. Textual embeddings were extracted via OCR Tesseract. The Viola and Jones method was applied to generate visual embeddings. This paper describes the performance optimization technology for the implemented classification algorithm. Servers with Intel CPUs were used for the algorithm execution. For single-threaded implementation, high-level and low-level optimizations were performed. High-level optimization was based on the parametrization of the recognition algorithms and the employment of intermediate data. Low-level optimization was carried out via compiler tools allowing for an extended set of SIMD instructions. The implementation of parallelization with several multithreaded applications on multiple servers was also described. The proposed solution was tested using own test data sets of business documents. The proposed method can be applied in modern information systems to analyze the content of a large flow of digital document images.
On the invariance of the digital description of a handwritten signature

The article presents results of our experiments carried out to study the invariance of the digital description of the image of a handwritten signature presented on paper. The description is built on the basis of a normalized image of the signature, digitized in the visible range of the electromagnetic spectrum by a scanner, with subsequent calculation of the distribution of its local features. The variability of this representation of the signature under different conditions simulating a change in its color, orientation on paper, line thickness and dimensions has been experimentally studied. It is shown that the digital description of the handwritten signature image, previously proposed by the authors, is sufficiently invariant with respect to the listed conditions for its execution to perform the off-line signature verification procedure.
Certificateless Broadcast Encryption with Authorization Suitable for Storing Personal Health Records

Abstract Cloud medical treatment provides real-time data sharing in a cost-effective method, making it more practical to create, collect and manage vast amounts of personal health records (PHR) of patients. However, health information is considered highly sensitive. How to securely store and dynamically process massive patients’ PHR data in a public cloud environment has become one of the most important challenges. Therefore, we introduce a novel solution to the problems of privacy exposure, data security and flexible access of storage modules in medical systems. In this paper, we present a privacy-preserving certificateless broadcast encryption with authorization for the PHR system, which is the best approach to effectively solve the above problems and avoid key escrow. In our work, users (patients) outsource their encrypted data to the cloud server and reallocate data accessing rights of recipients through an authorization set, sharing with a group of authorized receivers (doctors) in a secure and efficient manner. In addition, it is shown to be capable of achieving both plaintext confidentiality and receiver anonymity under the random oracle model. Moreover, the experimental evaluation shows that the proposed scheme enjoys low computational and communication overhead, indicating the feasibility and practicality of the scheme.
Effects of goal orientation on online learning: A meta-analysis of differences in Korea and US

This meta-analysis examines the correlation between goal orientation and related variables in online learning to examine the influence of individual goal orientation on online learning as well as the differences arising from diverse cultural backgrounds. The study analyzed 27 papers from 2000 to 2022, comprising 8 US and 19 Korean studies. The average effect size of goal orientation and related variables, such as learning satisfaction, self-efficacy, and task value, were also analyzed. In addition, moderating effect according to the country and type of goal orientation was examined to evaluate differences arising from cultures. It was found that learning immersion, learning participation, and intention to continue learning, showed a high average effect size with goal orientation, with the other related variables also demonstrating a significant average effect size. There was no moderating effect of the state in the relationship between goal orientation and academic achievement, although a moderating effect existed according to the type of goal orientation. Based on the results of this study, we analyzed the variables that can reinforce learning along with goal orientation in online learning situations. Therefore, our findings will help formulate various educational support directions that can lead students to successfully gain knowledge through online learning, which has been growing expeditiously in the wake of the COVID-19 pandemic.
A General Survey on Lossy Compression Algorithms for Online Learning Images in Cloud Environments

This paper presents the important inferences derived from the literature survey. A study of the literature available on the current problem reveals a large number of available techniques. Some of these techniques are mentioned. All these techniques suffer from various drawbacks like measuring image quality parameters and how much storage space occupy in the cloud environment. An extensive survey was made on the issues such as image compression and its features, image compression methods, existing compression algorithms, new proposed compression algorithm and their impact, the various levels of image compression in BTC were analyzed.
An overview: Big data analysis by deep learning and image processing

Big Data Analysis and Deep Learning are two fields of data science that are getting a proportion of interest nowadays. The relevance of big data has increased as a result of the massive amounts of domain-specific data that many public and private firms have gathered. This data may be useful for research on topics like national security, fraud, and medical informatics and detection. Huge volumes of data are analyzed by businesses like Google and Microsoft for business analysis and decision-making that affects both present and future technology. By using deep learning algorithms, high-level information is extracted. Multifaceted intellections are articulated as data illustrations using a tiered erudition approach. On the basis of comparably simpler abstractions created at the level before, sophisticated abstractions are learnt at a given level. The field of image processing is still in its infancy. Agriculture, textiles, transportation, and other fields have all made use of image alteration, image coding, compression, picture segmentation, and other technologies. Traditional image processing techniques, on the other hand, are unable to handle the huge quantity of image mockups available today. As a result, to raise the level and efficiency of image processing, a novel methodical area has emerged that focuses on exploring big data-based image processing technology and developing an image processing archetypal. The image processing prototypical based on big data offers recompenses including strong repeatability, high accuracy, extensive applicability, good flexibility, and a high potential for information reduction, according to existing big data research results. In this review, potential of Deep Learning and images processing to address some of the most challenging problems in Big Data Analytics, including the extraction of multifaceted designs from gigantic data sets, semantic indexing, data tagging, rapid facts repossession, and simplification of discriminative tasks will be explored. The integration and interaction of the three main topic is image processing, deep learning, and big data will be explained in this review. All three of these areas hold great promise for a variety of industries. The research issues highlighted in the integration and interplay of these broad domains are examined, as are some potential study avenues.
Virtual Musical Instruments with Python and OpenCV

There is an increasing need for musical aspirants to have access to cheaper musical instruments. This study explores the opportunities to utilize image recognition algorithms via OpenCV to port this technology into readily available modern devices, which will enable inexpensive yet authentic methods of playing a piano. Through OpenCV and Pygame libraries, one can set up a rigid camera that will trace the player’s fingers. The fingers if they cross or hover over a specific coordinate of a key, the piano note (.wav file) will be played by Pygame’s mixer module. This simple yet inexpensive option might help first-time musical aspirants experience music in an affordable and accessible way. Furthermore, this article explores the future scope of accommodating other musical instruments.
Interrelationship Between Human Development, Financial Development and Economic Growth: Empirical Evidences from Indian Economy

The interrelationship between human development, financial development and economic growth exhibited a very interesting yet peculiar fact that human development is the prime determinant of the development process bestowing significantly to economic growth as well as the development of the financial sector in the Indian economy. To explore short-run as well as long-run interactions between financial development, human development and economic growth of the Indian economy, the autoregressive distributed lag (ARDL) bound test has been employed. It is vital to state that financial development is detected as negatively impacting the growth of the Indian economy; alternatively, growth could have been additionally enhanced through auxiliary human development instead of emphasising financial development at large. Therefore, the need of the hour is to formulate concrete plans and policies to spearhead the development of human resources through the promotion of education and health facilities as an integral part of our policy frame which may axiomatically reinforce the potency of the financial system as well as economic growth in India.
On the Succinctness of Modal μ-Calculus Based on Covariant–Contravariant Refinement

The expressive power of logics is one of the major research topics in mathematical logic and computer science. One way of comparing the complexities of different formalisms (e.g. knowledge representation formalisms) stems from the perspective of representational succinctness. The concept of covariant–contravariant refinement (CC-refinement, for short) generalizes the concepts of refinement, simulation and bisimulation. We introduce an extension of the standard multi-agent modal [Formula: see text]-calculus system [Formula: see text] with CC-refinement operators ([Formula: see text]) and show that [Formula: see text] is equivalently expressive to [Formula: see text]. This paper presents the succinctness results of [Formula: see text] compared with [Formula: see text] from two viewpoints based on the sets of covariant and contravariant actions. We establish a family of CC-refinement formulas for comparing the succinctness based on the well-known parity symmetric alternating automata, which is often used to describe modal [Formula: see text]-calculus formulas.
Decoding Artificial Intelligence in Neuroscience: Applications Beyond Diagnosis

Background: Artificial Intelligence has witnessed exponential expansion in health care applications. The article pronounces the dynamic excellence AI is achieving in the healthcare discipline of neuroscience. Objective: The paper highlights basic concepts of AI and acmes the interdisciplinary collaboration of Computational neuroscience, Cognitive science, and AI. Also, the article draws out important findings related to AI in neuroscience amongst its diverse application in the various disciplines. An ephemeral overview of applications of AI-based constructs in neurological disorders namely Neuroinflammation, Schizophrenia, Parkinson’s disease, Epilepsy, Autism spectrum disorder, Alzheimer’s disease, Brain tumor and Anesthesiology has been demonstrated in the present work. Methods: The method includes the collection of data from different search engines like google Scholar, PubMed, ScienceDirect, SciFinder, etc. to get coverage of relevant literature for accumulating appropriate information regarding AI, neuroscience, and their linkages. Results: These considerations are made to expand the existing literature on the progressing role of AI in the management of neurological disorders. Conclusion: The exponential expansion in the development of AI-based systems might aid in addressing the prevailing limitations in the domain of neurological disorders and neuroscience.
A Hierarchical Feature Ensemble Deep Learning Approach for Software Defect Prediction

Software defect prediction can detect modules that may have defects in advance and optimize resource allocation to improve test efficiency and reduce development costs. Traditional features cannot capture deep semantic and grammatical information, which limits the further development of software defect prediction. Therefore, it has gradually become a trend to use deep learning technology to automatically learn valuable deep features from source code or relevant data. However, most software defect prediction methods based on deep learning extraction features from a single information source or only use a single deep learning model, which leads to the fact that the extracted features are not comprehensive enough to affect the final prediction performance. In view of this, this paper proposes a Hierarchical Feature Ensemble Deep Learning (HFEDL) Approach for software defect prediction. Firstly, the HFEDL approach needs to obtain three types of information sources: abstract syntax tree (AST), class dependency network (CDN) and traditional features. Then, the Convolutional Neural Network (CNN) and the Bidirectional Long Short-Term Memory based on Attention mechanism (BiLSTM+Attention) are used to extract different valuable features from the three information sources and multiple prediction sub-models are constructed. Next, all the extracted features are fused by a filter mechanism to obtain more comprehensive features and construct a fusion prediction sub-model. Finally, all the sub-models are integrated by an ensemble learning method to obtain the final prediction model. We use 11 projects in the PROMISE defect repository and evaluate our approach in both non-effort-aware and effort-aware scenarios. The experimental results show that the prediction performance of our approach is superior to state-of-the-art methods in both scenarios.
Political Polarization in Times of Crisis: La grieta and Its Impact on Argentine Democracy during the COVID-19 Pandemic

The article discusses how the Argentine socio‑political polarization known as la grieta has influenced the country’s politics and impacted democratic standards during the Covid‑19 pandemic. It uses qualitative data to present the nature of Argentine socio‑political polarization, the outbreak of the Covid‑19 pandemic in Argentina and new areas of polarization that emerged in the country under the pandemic situation, mainly the quarantine measures and vaccination campaign. The paper also takes a look on potential challenges for the Argentine democracy in a post‑pandemic reality.
A Joint Model for Target-Oriented Opinion Words Extraction and Sentiment Classification

Target-oriented opinion word extraction and aspect-level sentiment classification are two highly relevant tasks in aspect-based sentiment analysis. Previous studies tend to separate them and focus on one of the tasks, which ignore the connection between opinion word extraction and sentiment classification, and result in the waste of useful connection information. In this paper, we propose a co-extraction model, in which the two tasks are formulated as a sequence labeling problem. The model involves two stacked Bi-LSTM modules and an information interaction component to generate all opinion-polarity pairs of the input sentences simultaneously. The experimental results show that our model achieves advanced results in target opinion word-polarity co-extraction. The performance of both tasks is stronger than the baseline, and the model is of low complexity and high operational efficiency.
TEVL: Trilinear Encoder for Video-language Representation Learning

Pre-training model on large-scale unlabeled web videos followed by task-specific fine-tuning is a canonical approach to learning video and language representations. However, the accompanying Automatic Speech Recognition (ASR) transcripts in these videos are directly transcribed from audio, which may be inconsistent with visual information and would impair the language modeling ability of the model. Meanwhile, previous V-L models fuse visual and language modality features using single- or dual-stream architectures, which are not suitable for the current situation. Besides, traditional V-L research focuses mainly on the interaction between vision and language modalities and leaves the modeling of relationships within modalities untouched. To address these issues and maintain a small manual labor cost, we add automatically extracted dense captions as a supplementary text and propose a new trilinear video-language interaction framework TEVL (Trilinear Encoder for Video-Language representation learning). TEVL contains three unimodal encoders, a TRIlinear encOder (TRIO) block, and a temporal Transformer. TRIO is specially designed to support effective text-vision-text interaction, which encourages inter-modal cooperation while maintaining intra-modal dependencies. We pre-train TEVL on the HowTo100M and TV datasets with four task objectives. Experimental results demonstrate that TEVL can learn powerful video-text representation and achieve competitive performance on three downstream tasks, including multimodal video captioning, video Question Answering (QA), as well as video and language inference. Implementation code is available at https://github.com/Gufrannn/TEVL .