Machine Comprehension Comparison using Latest Advancements in Deep Learning

This study compares websites that take live data into account using search engine optimization (SEO). A series of steps called search engine optimization can help a website rank highly in search engine results. Static websites and dynamic websites are two different types of websites. Static websites must have the necessary expertise in programming compatible with SEO. Whereas in dynamic websites, one can utilize readily available plugins/modules. The fundamental issue of all website holders is the lower level of page rank, congestion, utilization, and exposure of the website on the search engine. Here, the authors have studied the live data of four websites as the real-time data would indicate how the SEO strategy may be applied to website page rank, page difficulty removal, and brand query, etc. It is also necessary to choose relevant keywords on any website. The right keyword might assist to increase the brand query while also lowering the page difficulty both on and off the page. In order to calculate Off-page SEO, On-page SEO, and SEO Difficulty, the authors examined live data in this study and chose four well-known Indian university and institute websites for this study: www.caluniv.ac.in, www.jnu.ac.in, www.iima.ac.in, and www.iitb.ac.in. Using live data and SEO, the authors estimated the Off-page SEO, On-page SEO, and SEO Difficulty. It has been shown that the Off-page SEO of www.caluniv.ac.in is lower than that of www.jnu.ac.in, www.iima.ac.in, and www.iitb.ac.in by 9%, 7%, and 7%, respectively. On-page SEO is, in comparison, 4%, 1%, and 1% more. Every university has continued to keep up its own brand query. Additionally, www.caluniv.ac.in has slightly less SEO Difficulty compared to other websites. The final computed results have been displayed and compared.

<span lang="EN-US">Search engine optimization (SEO) is an important internet marketing strategy and process that facilitates maximizing an intended website’s visibility with search engine results. It is widely employed nowadays to improve traffic volume or quality from search engines to a particular website. Even though a significant number of publications imply the essential aspects of SEO, only a few provide generalized ideas to deal with the complex structure of the web. Also, the critical issues of content quality, site popularity, keyword density, and publicity factors were not much considered in the traditional ranking algorithms during SEO processes. This has negatively influenced the retrieval rate in the existing SEO techniques, and consequently, inadequate search results were obtained through search engines. Hence, the study considers web page ranking as a theoretical basis for the research and addresses these limitations in the existing system. It further improves SEO performance by introducing a unique web-page ranking strategic design to gain higher page rank results. The results of the investigational study show that the proposed system effectively contributes towards SEO with an improved page ranking strategy and also provides higher accuracy in calculating the importance score of web pages which is comparable with popular ranking algorithms such as hyperlink-induced topic search (HITS) and PageRank.</span>

As the Internet grows in popularity, hundreds of thousands of people use search engines such as Google and Yahoo to search for information. However, search engine users are most interested in some result pages. Therefore, selling a website with search engine results is an important task in website development. Search engine optimization (SEO) needs to complete this task. However, from time to time, the black hat SEO strategy behind search engines is used, and the page rank of search engine results is higher than it deserves. This research paper implies a new methodology for search engine optimization (SEO) without sandboxing via serps such as Google and Bing. Previously, rulebooks were based solely on the number of backlinks on a page. This technique employs a secure link building technique where link speed is key, without sacrificing website optimization. Today's algorithm updates are considered. This white paper describes the characteristics of search engine PageRank algorithms, SEO techniques, and strategies for ranking keywords.

Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes.

SEO stands for Search Engine Optimization. It is a technique that searches various web pages for specified keywords and ranks these Web pages according to some parameters. They are used to feed pages to search engines.  The main importance of SEO is that it helps to find the relevant data and increase the rank of a webpage in search engines’ results. In our paper, we develop a new algorithm M-HITS (Modified HITS) to provide the page rank. M-HITS Algorithm is a new version of HITS algorithm. It is developed by extending the properties of HITS algorithm.

To be at the top in the world of www, every website needs to be standardized and well format as per standards defined. SEO has many new algorithms & indexing ways that helps user to get best results for their searches. Search Engine Optimization (SEO) is important for websites to improve the rank for search results and get more page views resulting into large user traffic. The search engine ranks provide the better and optimized result to user's query, which help them to view the exact contents they are looking for from list of popular web pages among the number of pages available on the web. My case study will focus on some advanced techniques that are helpful to the website owner for better page rank in search result. This will focus on simple modern SEO techniques that can be add on to your website designing which will indirectly help into SEO page rank.

Search engine plays a vital role in improving website traffic. Through Search Engine Optimization (SEO) techniques, website can be friendlier to search engine. This paper minutely analyzes PageRank (PR) algorithm and puts forward strategy based on SEO from the aspects of keywords, website development and page links. Keywords optimization is focused on keywords selection, density and distribution. The point of website optimization is title, Meta tag, domain name etc. Optimization in page links is concerned with inbound links and outbound links.

Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.

Invited Papers.- Turning the Web into a Database: Extracting Data and Structure.- Analysis and Improvement of Minimally Supervised Machine Learning for Relation Extraction.- Information Retrieval.- Using Evidences Based on Natural Language to Drive the Process of Fusing Multimodal Sources.- An Experimental Comparison of Explicit Semantic Analysis Implementations for Cross-Language Retrieval.- Term Extraction.- CITOM: Incremental Construction of Topic Maps.- Schema-Matching with Data Dictionaries.- Requirements Analysis: Concept Extraction and Translation of Textual Specifications to Executable Models.- Information Extraction.- Score-Based Approach for Anaphora Resolution in Drug-Drug Interactions Documents.- Relation Extraction for Monitoring Economic Networks.- Classification of Text.- Real-Word Typo Detection.- The Impact of Semantic and Morphosyntactic Ambiguity on Automatic Humour Recognition.- Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining.- Classification of Documents.- Analyzing Document Collections via Context-Aware Term Extraction.- AIR: A Semi-Automatic System for Archiving Institutional Repositories.- Protein-Protein Interactions Classification from Text via Local Learning with Class Priors.- Interfaces to Knowledge Bases.- Natural Language Interfaces: What Is the Problem? - A Data-Driven Quantitative Analysis.- Semantic Mapping between Natural Language Questions and SQL Queries via Syntactic Pairing.- Search Interface to a Mayan Glyph Database Based on Visual Characteristics.- Using Semantic Models of Natural Language.- Temporal Expression Identification Based on Semantic Roles.- Evaluation of Arabic Machine Translation System Based on the Universal Networking Language.- Quality Assessment of Knowledge Sources.- Quality of Subsumption Hierarchies in Ontologies.- Evaluating Semantic Relations by Exploring Ontologies on the Semantic Web.- On the Assessment of Text Corpora.- Poster Papers.- Improving Semantic Web Applications with Navigational Semantics.- Hybrid Algorithm for Word-Level Alignment of Parallel Texts.- A Hybrid Approach for Data Mart Schema Design from NL-OLAP Requirements.- Spreadsheet Information Retrieval through Natural Language.- Characterizing Weblog Corpora.- Improving Full Text Search with Text Mining Tools.- Cognos: A Natural Interaction Knowledge Management Toolkit.- Doctoral Symposium Papers.- Specialized Entailment Engines: Approaching Linguistic Aspects of Textual Entailment.- Natural Language Processing for Spreadsheet Information Retrieval.- Use Speech for User's Group Identification.

Introduction: Preoperative risk stratification of kidney cancer patients is critical for treatment selection and improving mortality rates. Current clinical approaches such as cancer grading are limited by inter-reviewer variability, require invasive biopsy procedures, and might not capture the full extent of patient risk. We sought to evaluate a deep learning (DL) model to quantify patient risk and prognosticate overall survival in kidney cancer patients via CT scans. Methods: This retrospective study used preoperative contrast CT scans from 4 public multi-institutional collections with date of death/censoring available. A deep learning (DL) network architecture was developed to analyze input CT scans via a convolutional feature extractor and a Cox proportional hazards-based cost function. The DL model assigned a risk score to each patient, with the median risk used as a cutoff for classifying patients as high-risk or low-risk. Model performance for prognosticating overall survival (OS) was evaluated separately in training, internal validation, and external validation cohorts via Kaplan-Meier analysis in terms of concordance index and hazard ratios, with risk groups compared via Wilcoxon log-rank tests (α = 0.05). Results: In the training/internal validation cohort (N=418, 8 institutions) and external validation cohort (N=102, 1 institution), the median age was 60 and 59 years, the male/female ratios were 62%/38% and 75%/25%, with a median follow up of 2.8 and 3.1 years, respectively. The DL model yielded a training c-index of 0.66 (95% CI: 0.63-0.68), internal validation c-index of 0.73 (95% CI: 0.70-0.76) and an external validation c-index of 0.67 (95% CI: 0.64-0.70). Kaplan-Meier analysis of associated survival curves based on DL-determined high- and low-risk patients demonstrated statistically significant separation in all cohorts: training (p = 0.003, HR = 8.63, 95% CI: 8.28-8.98), internal validation (p = 0.027, HR = 4.89, 95% CI: 4.52-5.26), and external validation (p = 0.01, HR = 6.54, 95% CI: 6.18-6.90). Baseline clinicopathological characteristics were not significantly different between DL high and low risk patients in external validation (chi-square testing; ISUP grade p=0.375, age p=0.824, race p=0.567, and sex p=0.440). ISUP grade was also not a significant predictor of OS in internal validation (HR=1.57, p=0.211, 95% CI: 1.28-1.85) or external validation (p = 0.465, HR=0.16, 95% CI: 0.33-0.74). Conclusion: A deep learning model based on pre-operative CT scans was found to accurately prognosticate overall survival in kidney cancers in a multi-institutional setting, independent of clinicopathological factors. This model is being validated on larger cohorts, and in the context of targeted therapies. Citation Format: Brennan Flannery, Mohsen Hariri, Thomas DeSilvio, Amir Sadri, Jane Nguyen, Erick M. Remer, Smitha Krishnamurthi, Satish E. Viswanath. Deep learning based risk stratification of pre-operative CT scans is prognostic of overall survival in kidney cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7379.

Representation Learning and Learning from Limited Labeled Data for Community Question Answering

The subject matter of this article is a HybridAttention mechanism integrated into a deep neural architecture for Network Intrusion Detection Systems (NIDS). This study aims to develop and study a HybridAttention mechanism based on a combination of global (self-attention) and local (dynamic local attention) models to improve the quality of traffic classification in real-time NIDS. The tasks to be solved are as follows: analyzing the applicability of existing attention mechanisms in network intrusion detection; integrating various attention types into a CNN-BiGRU architecture; developing a HybridAttention mechanism based on dynamic window alignment; optimizing the model using Optuna; and experimentally evaluating its performance on benchmark datasets using standard classification metrics. The methods used are: deep learning modeling with CNN-BiGRU architecture, integration of various attention mechanisms, including a novel HybridAttention, hyperparameter optimization using Optuna, and performance evaluation based on standard classification metrics. The results of this study show that the proposed HybridAttention mechanism demonstrates superiority over individual types of attention in all key metrics. The model achieved up to 99.85% accuracy on the NSL-KDD dataset training data and demonstrated strong generalization on the UNSW-NB15 dataset, achieving up to 98.06% accuracy in multi-class classification and up to 99.20% in binary classification. The proposed model also outperformed state-of-the-art approaches for processing unbalanced data and detecting various types of attacks. Conclusions. The scientific novelty of the results obtained is as follows: a HybridAttention mechanism combining self-attention and dynamic local attention was developed to enhance sequential pattern recognition in network traffic; the CNN-BiGRU architecture was improved by integrating multiple attention modules; systematic hyperparameter optimization using Optuna improved generalization on imbalanced data; and the proposed model outperformed existing approaches on benchmark datasets in detecting both known and novel cyberattacks.

With the diversification of network traffic and the popularization of encrypted communication, traditional traffic classification methods have been difficult to cope with the challenges of new encryption protocols and hidden traffic. Deep learning, as a powerful feature extraction and classification tool, has been widely used in network traffic classification. In this paper, a deep network traffic classification model based on multi-scale feature fusion and attention mechanism (MSF-AttNet) is proposed. Features of different time scales are extracted through multi-scale convolution, and the adaptive weighting of channel attention and temporal self-attention mechanisms is combined to significantly improve the classification accuracy and interpretability of the model. Experiments show that MSF-AttNet outperforms traditional methods and other deep learning models on the ISCX VPN-nonVPN dataset.

Using deep learning models on small scale datasets would result in overfitting. To overcome this problem, the process of pre-training a model and fine-tuning it to the small scale dataset has been used extensively in domains such as image processing. Similarly for question answering, pre-training and fine-tuning can be done in several ways. Commonly reading comprehension models are used for pre-training, but we show that other types of pre-training can work better. We compare two pre-training models based on reading comprehension and open domain question answering models and determine the performance when fine-tuned and tested over BIOASQ question answering dataset. We find open domain question answering model to be a better fit for this task rather than reading comprehension model.

Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in gamma-band frequencies (25–100 Hz). Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to integrate these concepts into a single framework of attention. Here, we aim to provide such a unified framework by expanding the normalization model of attention with a multi-level hierarchical structure and a time dimension; allowing the simulation of a recently reported backward progression of attentional effects along the visual cortical hierarchy. A simple cascade of normalization models simulating different cortical areas is shown to cause signal degradation and a loss of stimulus discriminability over time. To negate this degradation and ensure stable neuronal stimulus representations, we incorporate a kind of oscillatory phase entrainment into our model that has previously been proposed as the “communication-through-coherence” (CTC) hypothesis. Our analysis shows that divisive normalization and oscillation models can complement each other in a unified account of the neural mechanisms of selective visual attention. The resulting hierarchical normalization and oscillation (HNO) model reproduces several additional spatial and temporal aspects of attentional modulation and predicts a latency effect on neuronal responses as a result of cued attention.