Search Goals Research Articles

Knowledge Graph-Based Integration of Conversational Advisors and Faceted Filtering

Abstract Modern e-commerce websites often provide users with a variety of components, such as faceted filters and conversational recommender systems, that act as product advisors to help them find relevant products. However, these components are often treated separately and presented as independent components, leading to increased cognitive load and disruption in the search process. Also, the reasoning behind the resulting product recommendations is often not transparent. To address these limitations, we propose a novel approach that relies on a knowledge graph structure to seamlessly integrate faceted filtering and conversational advisors based on graphical user interfaces (GUI). Concretely, the knowledge graph is used to suggest filter values and products based on the user’s answers in the advisor, and, conversely, to determine follow-up questions based on the user’s selected filter values. The user interface also visualizes and explains the underlying relationships between answers given to the advisor and relevant product features in the filter component in order to increase the transparency of the search process. We conducted two user studies with a total of 448 participants to compare a system that integrates the different components according to our approach with a baseline system in which the mechanisms operate separately. Sequence analysis of the logged interaction data provided insights into participants’ behavior as they interacted with both systems. The results indicate that displaying recommended products and related explanations directly in the filter component increases acceptance and trust in the system. Also, the combination of a conversational advisor with values displayed in a filter interface, along with explanations of the underlying relationships, significantly contributes to the knowledge and understanding of those product features that are important in terms of the current search goal.

Methods and Algorithms for Searching Similarities between Texts

The review of the analytical article is a comprehensive study of text analysis modern methods in order to identify and measure the degree of their similarity, which itself is a very important and relevant task, since it examines and analyzes the tools used to solve it. The introduction discusses the purpose of this work, the relevance of the problem, and the importance of developing effective methods for comparing texts. The main part of the article examines and analyzes such methods as “jaccard similarity”, “shingle algorithm”, “levenshtein distance”, “tf-idf” and “bm25”, “bert” and the use of neural networksseparately. The application of a particular method is illustrated by examples presented in tabular form and illustrations. When considering and analyzing the “jaccard similarity”, the methods of its application and limitations are considered. When analyzing the “shingles algorithm”, the advantages of the method in the context of similarity search are revealed. The publication discusses methods based on line spacingin detail, including levenshtein distance. In this case, special attention is paid to the scope of its application and its advantages over other methods. By reviewing statistical methods such as 'tf-idf' and 'bm25', theanalysis of their application and effectiveness in text similaritysearching is given. The article is not limited by analyzing only traditional methods, but it also covers modern ones, including 'bert' and the use of neural networks. These methods are compared with each other, their advantages and disadvantages of use are identified. The conclusion section provides a comparative analysis of all presented methods based on the principle of objectivity, highlighting their characteristics and areas of application. The importance of choosing the most appropriate method for text similaritysearching is noted, depending on the specific search goals, tasks and requirements, and a conclusion is given about the most used, vast and productive method i.e. The use of neural networks. The conclusions emphasize that the article, devoted to a comparative analysis of various methods for similarity searching between texts, has the main goal of developing recommendations to choose the optimal method.

The Concept of Truth in Criminology

The paper examines the concept of truth in criminology as a dichotomous concept in relation to lies. The author concludes that the concept of «truth» can act as problematic knowledge, containing probable true knowledge, partly corresponding to reality, in which the person accepting it believes. This understanding of the concept should be considered as the goal and result of search and cognitive activity in a criminal case, which is a component of subsequently obtaining reliable, true forensically significant information. The truth, in the noted understanding, can also act as a factor that significantly determines the search and cognitive activity of the investigator in criminal proceedings. From the point of view of communicative interaction, truth can be considered as information, the addressee of which seeks to convey information that he evaluates as true, in order to create in the addressee a complete and true idea of some reality, taking into account his own ideas about such completeness and truth. This definition is applicable to solving problems of procedural actions in which the main method of obtaining information is questioning, as well as to minimizing errors in obtaining true information during the production of these actions. In philosophical knowledge, truth is also studied as an ideal of ethics. Considering that the concept of ethics in criminology is highly debatable, consideration of truth as an ethical ideal so far seems inconclusive. The ideas set forth in the paper may be significant in the context of a comprehensive consideration of the concept of «truth» in criminology and the development of a theory of truth as a phenomenon of criminal proceedings as opposed to lies.

MCR-Tree: An Efficient Index for Multi-dimensional Core Search

Core models are well-known cohesive subgraph models for graph analytics that have been extensively studied. These models, including (α, β)-core, (k, l)-core, and k -core, have multiple parameters, which are referred to as multi-dimensional cores. The goal of core search is to retrieve subgraphs from a graph that satisfy the semantics of a given core model. In the literature, various indexes have been proposed to accelerate core search for different core models. However, existing indexes suffer from several limitations, such as significant redundancy, lack of scalability with respect to the number of parameters, limited generality, and inadequate consideration of index maintenance. To address these limitations, in this paper, we thoroughly investigate the problem of multi-dimensional core search. In particular, we propose a novel index called MCR-Tree, which can be applied to different core models. The MCR-Tree projects all vertices into a multi-dimensional space by leveraging the skyline corenesses, which are indexed by an R-tree. Furthermore, the MCR-Tree integrates the connectivity information of subgraphs into the nodes of the R-tree to facilitate multi-dimensional core search. Subsequently, an efficient branch-and-bound algorithm is designed to perform multi-dimensional core search by traversing the MCR-Tree. Additionally, we discuss how to maintain the MCR-Tree for graph updates. Extensive experiments demonstrate that the MCR-Tree is up to two orders of magnitude smaller than existing indexes and the MCR-Tree-based core search method is up to an order of magnitude faster than existing algorithms.

Top-down suppression of negative features applies flexibly contingent on visual search goals.

Visually searching for a frequently changing target is assumed to be guided by flexible working memory representations of specific features necessary to discriminate targets from distractors. Here, we tested if these representations allow selective suppression or always facilitate perception based on search goals. Participants searched for a target (i.e., a horizontal bar) defined by one of two different negative features (e.g., not red vs. not blue; Experiment 1) or a positive (e.g., blue) versus a negative feature (Experiments 2 and 3). A prompt informed participants about the target identity, and search tasks alternated or repeated randomly. We used different peripheral singleton cues presented at the same (valid condition) or a different (invalid condition) position as the target to examine if negative features were suppressed depending on current instructions. In all experiments, cues with negative features elicited slower search times in valid than invalid trials, indicating suppression. Additionally, suppression of negative color cues tended to be selective when participants searched for the target by different negative features but generalized to negative and non-matching cue colors when switching between positive and negative search criteria was required. Nevertheless, when the same color - red - was used in positive and negative search tasks, red cues captured attention or were suppressed depending on whether red was positive or negative (Experiment 3). Our results suggest that working memory representations flexibly trigger suppression or attentional capture contingent on a task-relevant feature's functional meaning during visual search, but top-down suppression operates at different levels of specificity depending on current task demands.

Transformer-Empowered Multi-Modal Item Embedding for Enhanced Image Search in E-commerce

Over the past decade, significant advances have been made in the field of image search for e-commerce applications. Traditional image-to-image retrieval models, which focus solely on image details such as texture, tend to overlook useful semantic information contained within the images. As a result, the retrieved products might possess similar image details, but fail to fulfil the user's search goals. Moreover, the use of image-to-image retrieval models for products containing multiple images results in significant online product feature storage overhead and complex mapping implementations. In this paper, we report the design and deployment of the proposed Multi-modal Item Embedding Model (MIEM) to address these limitations. It is capable of utilizing both textual information and multiple images about a product to construct meaningful product features. By leveraging semantic information from images, MIEM effectively supplements the image search process, improving the overall accuracy of retrieval results. MIEM has become an integral part of the Shopee image search platform. Since its deployment in March 2023, it has achieved a remarkable 9.90% increase in terms of clicks per user and a 4.23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.

Tension and emotional arousal and their relationship to the performance of some basic skills on the floor movement apparatus in the technical gymnastics for women


 Search goal to:1- To identify the level of tension, emotional arousal, and the performance of some ground motor skills among the students of the second stage in the technical gymnast. 2- To identify the extent of the relationship of tension and emotional arousal with the performance of some ground motor skills among the students of the second stage in the technical gymnast. The descriptive survey method was used to suit the nature of the research problem. The research community consisted of the students of the second stage in the College of Physical Education and Sports Sciences. As for the research sample, they were deliberately chosen from the students of the second stage in the artistic gymnast game, and their number reached (50) students. He also used a set of aids, including a measure of stress and emotional arousal.The most important results reached are:1- There are significant differences for each of the psychological tension and emotional arousal among the artistic gymnast players when the degree of tension and arousal is high among the players.2- There is a correlation between the performance of some motor skills, emotional arousal, and psychological tension, and a statistical significance of the artistic gymnast players. Attic recommended the researcher Ali1- Emphasis on increasing attention to the psychological aspect in general, and arousal and psychological tension in particular, among gymnastik players, in order to achieve a better performance.
 

Design and research of automated warehouse simulation platform based on virtual visualization framework

The automated storage and retrieval system (AS/RS) has been developed rapidly and has been widely used in various fields. To achieve the ultimate goal of improving the efficiency of warehouse operation, this article studies the problem of goods location- allocation on the premise of a comprehensive analysis of the efficiency of automated warehouses and the overall stability of shelves. The mathematical model is established. Traditional Tabu search algorithms depend on Tabu table construction to avoid multiple continuous cycles that may occur in the direct search process. In this article, the improved algorithm can directly search and store in the Tabu table to prevent the algorithm from infinite search. A complete solution in that Tabu list continues to be passed to act as a point solution to replace the iteratively update product until a set iteration interval of the product is completed. The machine learning system can get the optimal solution. The improved algorithm is applied to solve the model, and the running results and the performance of the algorithm itself are obtained. The improved Tabu Search algorithm has more advantages in the process of solving the model because it reduces the size of the whole problem. The algorithm also uses the penalty optimization function as a measure of each solution. The greater the penalty is, the worse the solution is, and the smaller the penalty is, the better the solution is. The operation process of the material in and out of the warehouse in one day is studied, and the validity of the optimization scheme of goods location allocation is verified by substituting specific data for the solution. Finally, the case of goods in and out of the warehouse is simulated by using simulation software, and the running state and efficiency of the stacker are displayed according to the data. The Tabu direct search table is combined with the structure and function of the principle of amnesty direct search to achieve the goal of direct search and decision-making. The equilibrium coefficient of its operation is calculated by using the data, which proves that the scheme is feasible and has practical significance and research value.

Improving First-stage Retrieval of Point-of-interest Search by Pre-training Models

Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve the effectiveness and efficiency of POI search, most existing approaches are based on a multi-stage pipeline that consists of an efficiency-oriented retrieval stage and one or more effectiveness-oriented re-rank stages. In this article, we focus on the first efficiency-oriented retrieval stage of the POI search. We first identify the limitations of existing first-stage POI retrieval models in capturing the semantic-geography relationship and modeling the fine-grained geographical context information. Then, we propose a Geo-Enhanced Dense Retrieval framework for POI search to alleviate the above problems. Specifically, the proposed framework leverages the capacity of pre-trained language models (e.g., BERT) and designs a pre-training approach to better model the semantic match between the query prefix and POIs. With the POI collection, we first perform a token-level pre-training task based on a geographical-sensitive masked language prediction and design two retrieval-oriented pre-training tasks that link the address of each POI to its name and geo-location. With the user behavior logs collected from an online POI search system, we design two additional pre-training tasks based on users’ query reformulation behavior and the transitions between POIs. We also utilize a late-interaction network structure to model the fine-grained interactions between the text and geographical context information within an acceptable query latency. Extensive experiments on the real-world datasets collected from the Didichuxing application demonstrate that the proposed framework can achieve superior retrieval performance over existing first-stage POI retrieval methods.

Sharing Model Framework for Zero‐Shot Sketch‐Based Image Retrieval

AbstractSketch‐based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero‐shot learning. Given a sketch as a query, the search goal is to retrieve the corresponding photographs in a zero‐shot scenario. In this paper, we divide the aforementioned challenging work into three tasks and propose a sharing model framework that addresses these problems. First, the weights of the proposed sharing model effectively reduced the modality gap between sketches and photographs. Second, semantic information was used to handle different label spaces during the training and testing stages. The sketch and photograph domains share semantic information. Finally, a memory mechanism is used to reduce the intrinsic variety in sketches, even if they all belong to the same class. Sketches and photographs dominate the embeddings in turn. Because sketches are not limited by language, our ultimate goal is to find a method to replace text searches. We also designed a demonstration program to demonstrate the use of the proposed method in real‐world applications. Our results indicate that the proposed method exhibits considerably higher zero‐shot SBIR performance than do other state‐of‐the‐art methods on the challenging Sketchy, TU‐Berlin, and QuickDraw datasets.

Enhancing learning and exploratory search with concept semantics in online healthcare knowledge management systems: An interactive knowledge visualization approach

Healthcare is a knowledge intensive process, which requires large amount of information from various healthcare knowledge providers. However, this growing information makes challengeable for users in finding useful healthcare information to support their healthcare decision making. Knowledge management and knowledge management systems are drawing increasing attention in healthcare community. This paper proposes an online healthcare knowledge management framework and system prototype, featured with integrating knowledge learning and exploratory search for solving problems efficiently. Inspired by the knowledge to action framework in healthcare and the iterative process in software engineering, an iterative and incremental knowledge management model is designed to organize the activities during the knowledge learning and searching procedure. Supported by the knowledge-enhanced infrastructure of online healthcare information designed, domain knowledge learning and task knowledge learning environment can work collaboratively and interactively to improve the result of learning and exploratory search. Knowledge base also provides helpful information in an interactive way for solving problems. Given exploiting probabilistic information retrieval model and concept semantics described in Description Logics, a search intent discovery method and search intent guided learning and searching mechanism are designed within this environment for achieving the learning and search goal. The results of the experiments have proved the efficacy of the methods and process model used in the leaning-focused healthcare knowledge management system.

Some Tactical Features of the Search in the Investigation of Terrorist and Extremist Crimes

The article is devoted to the peculiarities of the search in criminal cases of the crimes of terrorist and extremist orientation. The preparatory and working stages of the investigative action are considered, taking into account the need to implement tactical search techniques. When investigating of terrorist and extremist crimes, a search is an unsafe investigative action for its participants, and because of this, recommendations have been developed aimed at suppressing possible threats from persons involved in the committing of crimes. It is noted that these persons may not only attempt on the life and health of the participants of the investigative action, but also provide other counteraction to the search. The importance of participation in the search actions of specialists, as well as their use of technical and forensic means, is determined. Recommendations aimed at improving the efficiency of the search in tactical forensic terms are proposed. It is concluded that it is necessary to plan a search and carefully prepare for it in order to achieve search goals in the detection and investigation of terrorist and extremist crimes.

How do people find pairs?

Humans continuously scan their visual environment for relevant information. Such visual search behavior has typically been studied with tasks in which the search goal is constant and well-defined, requiring relatively little interplay between memory and orienting. Here we studied a situation in which the target is not known in advance, and instead, memory needs to be dynamically updated during the actual search. Observers compared two simultaneously presented arrays of objects for any matching pair of items-a task that requires continuous comparisons between what is seen now and what was seen a few moments ago. To manipulate the balance between memorizing and scanning, we ran two versions of the task. In an eye-tracking version, the objects were continuously available and could be scanned with relative ease. The results suggested that observers preferred scanning over memorizing. In a mouse-tracking version, perceptual availability was limited, and scanning was slowed. Now observers substantially increased their memory use. Thus, the results revealed a flexible and dynamic interplay between memory and perception. The findings aid in further bridging the research fields of attention and memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Automatic Life Detection Based on Efficient Features of Ground-Penetrating Rescue Radar Signals.

Good feature engineering is a prerequisite for accurate classification, especially in challenging scenarios such as detecting the breathing of living persons trapped under building rubble using bioradar. Unlike monitoring patients' breathing through the air, the measuring conditions of a rescue bioradar are very complex. The ultimate goal of search and rescue is to determine the presence of a living person, which requires extracting representative features that can distinguish measurements with the presence of a person and without. To address this challenge, we conducted a bioradar test scenario under laboratory conditions and decomposed the radar signal into different range intervals to derive multiple virtual scenes from the real one. We then extracted physical and statistical quantitative features that represent a measurement, aiming to find those features that are robust to the complexity of rescue-radar measuring conditions, including different rubble sites, breathing rates, signal strengths, and short-duration disturbances. To this end, we utilized two methods, Analysis of Variance (ANOVA), and Minimum Redundancy Maximum Relevance (MRMR), to analyze the significance of the extracted features. We then trained the classification model using a linear kernel support vector machine (SVM). As the main result of this work, we identified an optimal feature set of four features based on the feature ranking and the improvement in the classification accuracy of the SVM model. These four features are related to four different physical quantities and independent from different rubble sites.

Using Genetic Algorithm for DNA Profile Matching

The DNA is used in forensic investigations to identify suspects and victims at crime scenes. However, manual matching of DNA profiles is difficult and error-prone, especially in large databases. In Iraq, technology for DNA matching is limited, making manual matching the only option. Regenerate. In this work, we propose a Genetic Algorithm (GA) for DNA dataset matching to provide simple and user-friendly software to be used by law enforcement agencies in Iraq. The genetic algorithm is a type of heuristic search method used in computing science and artificial intelligence. It is based on the theory of natural selection and evolutionary biology and is used to find the best solutions to search problems. Genetic algorithm is robust for searching through big, complicated datasets. Thus, in this paper, the GA is the algorithm of choice to achieve the goal of DNA matching search. The used dataset is actual data that have been collected from the Ministry of Interior at the Basra Investigation Center. Finally, the python simulation results show 100% accuracy where the proposed method managed to find the DNAs under consideration precisely.

Online tourist information search strategies

Technological advances continue to significantly change tourist information search behaviour. This paper revisits online tourist information search strategies by reconceptualising them as a combination of goals, approaches and online sources. Based on pre-trip and post-trip interviews and trip planning diaries, the paper investigates how tourists search for information online. An online tourist information search strategy framework is developed by considering how tourists integrate approaches and sources to meet their information search goals. The key contributions of the study are the redefinition of tourist information search strategy in an online context, a classification of sources that more suitably reflects online information-scapes, and the identification of interesting and often complex search patterns that suggest changes in information search strategies over the course of trip planning.

INSTALLING LINUX OS OVER NETWORK IN AREA OF ITCGH (USING,” KICKSTART”)

Here in this monoscript installing operating system over the network, you will commonly find four solutions ways for these four problems Such as Cost savings, centralization, timekeeping, and security which has always challenged IT centers in Afghanistan as well as in Ghor province. This search goal is an possabel OS placement concept for IT centers. Using one of the OS spread methods over a network called “Kickstart”. One of the most problematic obstacles against IT centers every day is IT centers' computers which are fundamental tools in many disciplines of study and work. In many cases, they are offered to users inside an open room to be used freely for their needs. ITCGh (Information Technology Center Ghor ) is one example. During my year at the university, it was possible to observe the technological dependence of a tool used to deploy OS all along with the computers. Kickstart is a well defined program we can work with this in the form of Command-line (CLI) and Graphical User Interface (GUI). It response the queries while OS installation. Finally, DHCP, TFTP, FTP, xinetd, and syslinux are the protocols that we need to configure in our Server System.

Explicating affordances in travel information search: investigating device use in relation to goals and personal characteristics

Despite increasing attention to technology development and its impact on travel information search, research about information search behaviours across multiple devices (i.e., smartphones, laptops, tablets, and desktop computers) has received limited attention. Guided by technology affordance theory, this paper examines the link between device use and information search goals, and the relationship between personal characteristics and device use. The study is based on two independent studies. Data were collected using a mixed-methods approach utilising an online survey and an online trip planning diary. The findings revealed that information searchers were essentially multi-device users and switched between devices based on their information search goals. Moreover, personal characteristics such as demographic characteristics, experience and personality influenced device use. The findings of the research shed light on the elusive concept of affordance to enhance the understanding of the complexity of device usage during travel information search.

Assessing Information Search by Task Outcome

People do not search information as such but seek to get a job done or to manage a situation by the help of search results. Therefore, the ultimate goal of information search is to advance task performance. It should be evaluated accordingly, i.e. by its contribution to task outcome. This implies an extended notion of search process, which also covers the use of information in search results for task outcome. For measuring the effect of search to task outcome we propose both indirect and direct indicators which measure search success.

EFFECT OF THİAMİNE ON MORPHİNE ANALGESİA AND TOLERANCE İN RATS

Absract: The latest research have demonstrated that inflammation, oxidative stress and apoptosis plays a majör role in morphine analgesia and tolerance development. This search goals to examine the possible role of thiamine use on oxidative stress, inflammation and apoptosis in the development of morphine analgesia and morphine tolerance in rats. 
 Methods: Thirty-six male Wistar rats were used in this study. The rats were severed into six groups: saline, 100 mg/kg thiamine, 5 mg/kg morphine, thiamine + morphine, morphine tolerance and thiamine + morphine tolerance. The resulting analgesic effect was measured by hot plate and tail movement analgesia tests. TAS and TOS, inflammation parameters, and apoptosis protein levels of the dorsal root ganglion tissues sample were measured using an ELISA kit. 
 Results: When thiamine was given alone, it did not show anti-nociceptive effect (p>0.05). In addition, thiamine enhanced the analgesic effect of morphine (p < 0.05) and also significantly reduced tolerance to morphine (p < 0.05). However, it reduced TOS when administered with a single dose of morphine and tolerance induction (p < 0.05). In addition, thiamine reduced apoptosis protein levels after tolerance development (p < 0.05). 
 Conclusion: Consequently, these results may attain by reducing TOS, inflammation, and apoptosis.