Original Query Research Articles

Return to Play Outcomes in Elite Athletes after Cervical and Lumbar Motion Preservation Spine Surgery: A Systematic Review.

Systematic Review. The primary purpose of this article was to survey the present literature and report on return to play outcomes in elite athletes after undergoing motion preservation spinal surgery. For elite performance, athletes require adequate mobility and throughout the trunk, torso, and spine to achieve maximal force production. Therefore, elite athletes that have failed conservative treatment may seek to undergo motion preserving surgical options such as total disc arthroplasty and lumbar microdiscectomy. Individual studies have reported on return to play (RTP) outcomes following individual motion preservation surgical procedures, no systematic reviews have formally to reported on RTP outcomes, post-operative performance, and reoperation rates on these procedures in elite athletes. A systematic review was conducted from inception until February 2024 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. One reviewer queried PubMed for relevant studies that reported on RTP outcomes in elite athletes following motion preservation spinal surgery (MPSS) based on title and abstract (n=1,404). After the original search query, an additional reviewer screened full length articles. A total of 11 studies met the inclusion criteria. Special consideration was given to RTP rates, post-operative performance, and reoperation rates. A total of 612 elite athletes from the National Basketball Association, Major League Baseball, National Football League, National Hockey League, and other professional sporting organizations underwent cervical and lumbar MPSS to treat various spinal pathologies. Various motion sparing techniques were used to treat various pathologies. After undergoing MPSS, RTP rates ranged from 75-100% for lumbar cases and 83.3-100% for cervical cases. Most athletes successfully returned to sport between. Post-operative performance varied with some athletes performing at the same level prior to surgery and some performing at a decreased level. Motion preservation spinal surgery is a feasible option when properly indicated. Future studies are needed to compare return to sport rates, post-operative performance, and re-operation rates between motion preservation spinal surgery to spinal arthrodesis.

Examining Implications of Search Query Construction on Sentiment Analysis: The Case of Brandwatch

Consistent with other fields, advertising research is currently facing a replication crisis, evoking methodological concerns regarding the reproducibility of research findings. To address this crisis, we systematically reviewed seven computational advertising studies that collected and analyzed the sentiment of tweets. Upon identifying five studies that reported replicable data collection processes, we utilized the original search queries to recollect tweets using a third-party platform, Brandwatch. We also replicated data collection using iterative complex Boolean (ICB) operators, which sought to enhance the resulting dataset precision. Then, we juxtaposed the effects of search query design (original vs. ICB) using sentiment analysis. Together, the findings suggest that how a query is written can affect measures of precision, engagement, sentiment, and recall. Tests of precision are recommended to mitigate the effects of variance in retrieval relevance.

A modified Vector Space Model for semantic information retrieval

In this research, we present a modified Vector Space Model which focuses on the semantic relevance of words for retrieving documents. The modified VSM resolves the problem of the classical model performing only lexical matching of query terms to document terms for retrievals. This problem also restricts the classical model from retrieving documents that do not have exact match of query terms even if they are semantically relevant to the query. In the modified model, we introduced a Query Relevance Update technique, which pads the original query set with semantically relevant document terms for optimised semantic retrieval results. The modified model also includes a novel tf−p which replaces the tf−idf technique of the classical VSM, which is used to compute the Term Frequency weights. The replacement of the tf−idf resolves the problem of the classical model penalising terms that occur across documents with the assumption that they are stop words, which in practice, there are usually such words which carry relevant semantic information for documents’ retrieval. We also extended the cosine similarity function with a proportionality weight pqd, which moderates biases for high frequency of terms in longer documents. The pqd ensures that the frequency of query terms including the updated ones are accounted for in proportionality with documents size for the overall ranking of documents. The simulated results reveal that, the modified VSM does achieve semantic retrieval of documents beyond lexical matching of query and document terms.

Query Refinement for Diverse Top-k Selection

Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing recognition of the need to diversify their outcomes. In this paper, we define and study the problem of modifying the selection conditions of an ORDER BY query so that the result of the modified query closely fits some user-defined notion of diversity while simultaneously maintaining the intent of the original query. We show the hardness of this problem and propose a mixed-integer linear programming (MILP) based solution. We further present optimizations designed to enhance the scalability and applicability of the solution in real-life scenarios. We investigate the performance characteristics of our algorithm and show its efficiency and the usefulness of our optimizations.

Multi cascaded transformer network and hybrid heuristic-aided optimal bi-clustering mechanism for patent retrieval system using query expansion

To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance.

Self-reconstruction network for fine-grained few-shot classification

Metric-based methods are one of the most common methods to solve the problem of few-shot image classification. However, traditional metric-based few-shot methods suffer from overfitting and local feature misalignment. The recently proposed feature reconstruction-based approach, which reconstructs query image features from the support set features of a given class and compares the distance between the original query features and the reconstructed query features as the classification criterion, effectively solves the feature misalignment problem. However, the issue of overfitting still has not been considered. To this end, we propose a self-reconstruction metric module for diversifying query features and a restrained cross-entropy loss for avoiding over-confident predictions. By introducing them, the proposed self-reconstruction network can effectively alleviate overfitting. Extensive experiments on five benchmark fine-grained datasets demonstrate that our proposed method achieves state-of-the-art performance on both 5-way 1-shot and 5-way 5-shot classification tasks. Code is available at https://github.com/liz-lut/SRM-main.

Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion

Deep incremental hashing has become a subject of considerable interest due to its capability to learn hash codes in an incremental manner, eliminating the need to generate codes for classes that have already been learned. However, accommodating more classes requires longer hash codes, and regenerating database codes becomes inevitable when code expansion is required. In this paper, we present a unified deep hash framework that can simultaneously learn new classes and increase hash code capacity. Specifically, we design a triple-channel asymmetric framework to optimize a new CNN model with a target code length and a code projection matrix. This enables us to directly generate hash codes for new images, and efficiently generate expanded hash codes for original database images from the old ones with the learned projection matrix. Meanwhile, we propose a pairwise-label-based incremental similarity-preserving loss to optimize the new CNN model, which can incrementally preserve new similarities while maintaining the old ones. Additionally, we design a double-end quantization loss to reduce the quantization error from new and original query images. As a result, our method efficiently embeds both new and original similarities into the expanded hash codes, while keeping the original database codes unchanged. We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance.

Transferable Video Moment Localization by Moment-Guided Query Prompting

Video moment localization stands as a crucial task within the realm of computer vision, entailing the identification of temporal moments in untrimmed videos that bear semantic relevance to the supplied natural language queries. This work delves into a relatively unexplored facet of the task: the transferability of video moment localization models. This concern is addressed by evaluating moment localization models within a cross-domain transfer setting. In this setup, we curate multiple datasets distinguished by substantial domain gaps. The model undergoes training on one of these datasets, while validation and testing are executed using the remaining datasets. To confront the challenges inherent in this scenario, we draw inspiration from the recently introduced large-scale pre-trained vision-language models. Our focus is on exploring how the strategic utilization of these resources can bolster the capabilities of a model designed for video moment localization. Nevertheless, the distribution of language queries in video moment localization usually diverges from the text used by pre-trained models, exhibiting distinctions in aspects such as length, content, expression, and more. To mitigate the gap, this work proposes a Moment-Guided Query Prompting (MGQP) method for video moment localization. Our key idea is to generate multiple distinct and complementary prompt primitives through stratification of the original queries. Our approach is comprised of a prompt primitive constructor, a multimodal prompt refiner, and a holistic prompt incorporator. We carry out extensive experiments on Charades-STA, TACoS, DiDeMo, and YouCookII datasets, and investigate the efficacy of the proposed method using various pre-trained models, such as CLIP, ActionCLIP, CLIP4Clip, and VideoCLIP. The experimental results demonstrate the effectiveness of our proposed method.

Consumer protection and sustainable development: key green research areas

Motivation: Consumers are an important link in the socio-economic system. In the literature, there is a noticeable increase in interest on the part of researchers in shaping consumer attitudes and behaviour in various markets, including the issue of pro-environmental attitudes and behaviour. In the context of contemporary transformations and challenges of sustainable development, the importance of solutions aimed at providing consumers with adequate protection against a variety of unfair market practices misleading consumers on the issue of green products and services is growing. The motivation for undertaking this research was the need to highlight the complexity of green issues around consumer protection and, at the same time, to address them from a sustainability perspective. This can be an important step in discussing and setting further research directions in this area. Aim: The aim of this article is to identify key green research areas undertaken around consumer protection issues while addressing them from a sustainable development perspective. The implementation of this objective consisted of a literature review of the subject listed in the Scopus database and the Web of Science database. The methods adopted were a Structured Literature Review with original Q1-Q4 queries and a Classic Literature Review. Results: In economic reality, it can be observed that an increasing number of economic actors are offering products and services that are labelled green. They support the transformation of the current economy toward a green economy. However, the analyses show that consumers are increasingly confronted with the phenomenon of greenwashing. This requires the creation of new solutions to protect consumers against such unfair practices. The results of the considerations undertaken show the interdisciplinary character of the undertaken considerations around the issue of consumer protection in the field of greenwashing.

Rag-Fusion: A New Take on Retrieval Augmented Generation

Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context.

VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT

Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also incurs significant computational costs. To tackle these challenges, we propose VTG-GPT, a GPT-based method for zero-shot VTG without training or fine-tuning. To reduce prejudice in the original query, we employ Baichuan2 to generate debiased queries. To lessen redundant information in videos, we apply MiniGPT-v2 to transform visual content into more precise captions. Finally, we devise the proposal generator and post-processing to produce accurate segments from debiased queries and image captions. Extensive experiments demonstrate that VTG-GPT significantly outperforms SOTA methods in zero-shot settings and surpasses unsupervised approaches. More notably, it achieves competitive performance comparable to supervised methods. The code is available on GitHub.

A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.

Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.

Efficient Evaluation of Arbitrary Relational Calculus Queries

The relational calculus (RC) is a concise, declarative query language. However, existing RC query evaluation approaches are inefficient and often deviate from established algorithms based on finite tables used in database management systems. We devise a new translation of an arbitrary RC query into two safe-range queries, for which the finiteness of the query's evaluation result is guaranteed. Assuming an infinite domain, the two queries have the following meaning: The first is closed and characterizes the original query's relative safety, i.e., whether given a fixed database, the original query evaluates to a finite relation. The second safe-range query is equivalent to the original query, if the latter is relatively safe. We compose our translation with other, more standard ones to ultimately obtain two SQL queries. This allows us to use standard database management systems to evaluate arbitrary RC queries. We show that our translation improves the time complexity over existing approaches, which we also empirically confirm in both realistic and synthetic experiments.

Estimating the Cardinality of Queries Based on a Sample from a Full Outer Join of Tables

Cardinality estimation (CardEst) plays an important role in creating high-quality query execution plans in the DBMS. In the last decade, a large number of methods have been developed: traditional methods (histograms, samples), machine learning methods based on queries or data. But all of them are based on different restrictions and assumptions, and the cardinality estimation with their help worsens with an increase in the number of joined tables. The article proposes two new methods based on the theory of approximate calculation of aggregates and allowing to remove most of the restrictions. Method 1 fetches blocks after executing the subqueries of the original query and does not require a preliminary analysis of the filter conditions. The condition for joining tables can be arbitrary (not necessarily the equality of attributes). Method 2 allows you to calculate the probabilities of reading blocks based on the metadata accumulated in the process of populating the database. Metadata takes up little memory, and the overhead of maintaining it is low. Method 2, in contrast to method 1, takes into account the true cardinality of the connection of neighboring blocks in the sample obtained from metadata. Therefore, the prospect opens up for a more accurate assessment of the cardinality of joining a large number of tables. Method 1 (EVACAR method) is implemented and compared with modern machine learning methods BayesCard, DeepDB, FLAT on a special STATS test. The results of the experiments confirmed the effectiveness of the EVACAR method. The EVACAR method is more accurate or its maximum q-error is comparable to machine learning methods for 75-88 % of evaluated queries (subplans). In the future, it is planned to implement the 2nd method for assessing the cardinality of queries.

Transformer-Based Visual Object Tracking with Global Feature Enhancement

With the rise of general models, transformers have been adopted in visual object tracking algorithms as feature fusion networks. In these trackers, self-attention is used for global feature enhancement. Cross-attention is applied to fuse the features of the template and the search regions to capture the global information of the object. However, studies have found that the feature information fused by cross-attention does not pay enough attention to the object region. In order to enhance cross-attention for the object region, an enhanced cross-attention (ECA) module is proposed for global feature enhancement. By calculating the average attention score for each position in the fused feature sequence and assigning higher weights to the positions with higher attention scores, the proposed ECA module can improve the feature information in the object region and further enhance the matching accuracy. In addition, to reduce the computational complexity of self-attention, orthogonal random features are introduced to implement a fast attention operation. This decomposes the attention matrix into the product of a random non-linear function between the original query and key. This module can reduce the spatial complexity and improve the inference speed by avoiding the explicit construction of a quadratic attention matrix. Finally, a tracking method named GFETrack is proposed, which comprises a Siamese backbone network and an enhanced attention mechanism. Experimental results show that the proposed GFETrack achieves competitive results on four challenging datasets.

Paediatric trauma related with electric micro-mobility devices (review)

It is known that micro-mobility has a positive impact by reducing congestion and emissions. However, with the increase in traffic of these vehicles, the number of related collisions and accidents has increased, and the number of injured and killed people, including children, has risen accordingly. The purpose of the review study was to conduct a comprehensive study of several issues related to the safety of using electric micro-mobility devices for both adults and children through a systematic literature review; to determine the forensic aspects of injuries to operators of electric micro-mobility devices and pedestrians, and to investigate the mechanism of injury. A systematic, thematic bibliographic review was used to conduct this review study. The Scopus database was chosen as the main portal for searching for publications (www.scopus.com). At the same time, a limited search in the Google Scholar database was used for original queries. The article highlights the aspects related to the safety of using electric micro-mobility devices for children and adults, the peculiarities of injuries, the statistical correlation between the frequency of injuries and the use of basic protective equipment, and the characteristic types of injuries for specific micro-mobility devices. Despite all the work done, its results are mostly statistical and do not provide a holistic view of the mechanism of injury and forensic assessment of the consequences of injury. There is also an open issue related to the study of the peculiarities of injuries to operators and pedestrians, and the establishment of differential trauma criteria specific to operators and pedestrians.

Symmetric Graph-Based Visual Question Answering Using Neuro-Symbolic Approach

As the applications of robots expand across a wide variety of areas, high-level task planning considering human–robot interactions is emerging as a critical issue. Various elements that facilitate flexible responses to humans in an ever-changing environment, such as scene understanding, natural language processing, and task planning, are thus being researched extensively. In this study, a visual question answering (VQA) task was examined in detail from among an array of technologies. By further developing conventional neuro-symbolic approaches, environmental information is stored and utilized in a symmetric graph format, which enables more flexible and complex high-level task planning. We construct a symmetric graph composed of information such as color, size, and position for the objects constituting the environmental scene. VQA, using graphs, largely consists of a part expressing a scene as a graph, a part converting a question into SPARQL, and a part reasoning the answer. The proposed method was verified using a public dataset, CLEVR, with which it successfully performed VQA. We were able to directly confirm the process of inferring answers using SPARQL queries converted from the original queries and environmental symmetric graph information, which is distinct from existing methods that make it difficult to trace the path to finding answers.

An efficient long-text semantic retrieval approach via utilizing presentation learning on short-text

Although the short-text retrieval model by BERT achieves significant performance improvement, research on the efficiency and performance of long-text retrieval still faces challenges. Therefore, this study proposes an efficient long-text retrieval model based on BERT (called LTR-BERT). This model achieves speed improvement while retaining most of the long-text retrieval performance. In particular, The LTR-BERT model is trained by using the relevance between short texts. Then, the long text is segmented and stored off-line. In the retrieval stage, only the coding of the query and the matching scores are calculated, which speeds up the retrieval. Moreover, a query expansion strategy is designed to enhance the representation of the original query and reserve the encoding region for the query. It is beneficial for learning missing information in the representation stage. The interaction mechanism without training parameters takes into account the local semantic details and the whole relevance to ensure the accuracy of retrieval and further shorten the response time. Experiments are carried out on MS MARCO Document Ranking dataset, which is specially designed for long-text retrieval. Compared with the interaction-focused semantic matching method by BERT-CLS, the MRR@10 values of the proposed LTR-BERT method are increased by 2.74%. Moreover, the number of documents processed per millisecond increased by 333 times.

Energy Sector’s Green Transformation towards Sustainable Development: A Review and Future Directions

The energy sector’s green transformation recently gained major scientific attention, due to the role of the energy sector in the economy. The energy sector, similarly to the other economic sectors, faces sustainable development (SD) challenges. This review paper’s goal is to explore the areas of the green energy sector transformation towards SD context distinguished in the scientific literature review. The adopted method in this paper is bibliometric research of the scientific publications indexed in Scopus. There were two original queries formulated, and their results were analyzed in the VOSviewer program in the form of bibliometric maps and tables. A comparison of the proposed original queries’ results points to the importance of the journal subject area indexed in the Scopus database. There are publications important for the energy sector green transformation not included in the energy subject area in this database. The vast number of publications dealing with cross-disciplinary subjects revolving around green transformation in the energy sector is the cause of the multiple side topics covering the areas of the SD. The study identifies keyword-specified areas around the topic of green transformation towards SD in the energy sector. In this study, the limitations of the employed methods and the theoretical, methodical, and empirical implications of the research were presented. Presented results can inspire other researchers who are looking for a research gap or describing the state of the art. The future possible research avenues were also addressed.