Description Of Items Research Articles

Item Matching Model in E-Commerce: How Users Benefit

Abstract Research purpose. During the last decades, e-commerce sales have been rocketing, and this tendency is expected to increase over the following years. Due to the digital nature of e-commerce, one actual item can be sold on various e-commerce platforms, which leads to the exponential growth of the number of propositions. At the same time, the title and description of this item might differ. All these facts make more complicated for customers the process of searching on online platforms and change business approaches to the development of competitive strategy by e-commerce companies. The research question is how we can apply a machine learning algorithm to detect, based on the product information such as title and description, whether the items are actually relevant to the same product. Methodology. We suggest an approach that is based on a flexible textual data pipeline and the usage of a machine-learning model ensemble. Each step of the data processing is adjustable in dependence on domain issues and data features because we can achieve better results in solving the item-matching task. Findings. The item-matching model is developed. The proposed model is based on the semantic closeness of text descriptions of items and the usage of the core of keywords to present the reference item. Practical implications. We suggest an approach to improving the item searching process on different e-commerce platforms by dividing the process into two steps. The first step is searching for the related items among the set of reference items according to user preferences. The reference item description is created based on our item-matching model. The second step is surfing proposals of similar items on chosen e-commerce platforms. This approach can benefit buyers and sellers in various aspects, such as a low-price guarantee, a flexible strategy of similar products shown, and appropriate category-choosing recommendations.

Dysphagia, mealtime behaviors, and eating ability of persons living with dementia: A systematic review of psychometric properties of instruments

AbstractBackgroundIt is critical to use validated instruments to assess dysphagia, mealtime behaviors, and eating ability of persons living with dementia to evaluate the efficacy of mealtime care interventions. However, the characteristics and psychometric quality of such instruments are unknown. This systematic review aims to synthesize the quantity and quality of instruments that assess dysphagia, mealtime behaviors, and eating ability of persons living with dementia.MethodWe searched Pubmed, CINAHL, AgeLine, PsychINFO, and Cochrane Library for records published from January 1980 to July 30th, 2021. Records were eligible if they included any instrument that measures concepts of interest (i.e., dysphagia, mealtime behaviors, eating ability). Instruments were excluded if they assessed nutritional status only. Eligible instruments originally developed or later modified to measure concepts of interest were identified from eligible records. Eight characteristics of eligible instruments will be extracted: 1) development process, 2) concept/construct being operationalized, 3) sample and setting, 4) administration method, 5) description of items, 6) scoring format/interpretation, 7) reliability, and 8) validity. The Psychometric Assessment for Self‐report and Observational Tools (PAT), a newly developed psychometric quality assessment tool, will be used to evaluate the psychometric quality of eligible instruments.ResultIn total, 11,074 records were reviewed. Seventy‐nine eligible instruments were identified from 174 records. Fifteen instruments assessed swallowing ability; 5 assessed mastication ability; 6 assessed dysphagia; 24 assessed mealtime behaviors; 7 assessed eating ability; 22 assessed two or more concepts of interest.ConclusionA large number of instruments were developed and/or used to assess dysphagia, mealtime behaviors, and eating ability of persons living with dementia. All instruments’ psychometric properties will be further evaluated to inform the application and adaption of appropriate instruments for practice and research.

PENGARUH PENERAPAN MODEL PEMBELAJARAN THINK PAIR SHARE TERHADAP PEMAHAMAN KONSEP MATEMATIS SISWA KELAS X DPIB SMK NEGERI 5 PADANG

This research is motivated by students' understanding of mathematical concepts is still low. The purpose of this study was to find out whether students' understanding of mathematical concepts by applying the Think Pair Share learning model is better than students' understanding of mathematical concepts by conventional learning in class X DPIB students of SMK Negeri 5 Padang. This research is a quasi-experimental research with a quasi-experimental design with a posttest only control group design with total sampling as research subjects. The sample of this research is class X students of SMK Negeri 5 Padang. This study used a final test instrument in the form of a description item with reliability = 0.54. The data analysis technique used is the t-test. Based on data collection techniques and data analysis performed tcount=2.28 and ttable=1.67 because tcount > ttable then the hypothesis is accepted, thus it can be concluded that student learning outcomes on students' understanding of mathematical concepts by applying the Think Pair Share learning model are better than student learning outcomes on students' understanding of mathematical concepts by applying conventional learning models.

A graph-based approach for minimising the knowledge requirement of explainable recommender systems

Traditionally, recommender systems use collaborative filtering or content-based approaches based on ratings and item descriptions. However, this information is unavailable in many domains and applications, and recommender systems can only tackle the problem using information about interactions or implicit knowledge. Within this scenario, this work proposes a novel approach based on link prediction techniques over graph structures that exclusively considers interactions between users and items to provide recommendations. We present and evaluate two alternative recommendation methods: one item-based and one user-based that apply the edge weight, common neighbours, Jaccard neighbours, Adar/Adamic, and Preferential Attachment link prediction techniques. This approach has two significant advantages, which are the novelty of our proposal. First, it is suitable for minimal knowledge scenarios where explicit data such as ratings or preferences are not available. However, as our evaluation demonstrates, this approach outperforms state-of-the-art techniques using a similar level of interaction knowledge. Second, our approach has another relevant feature regarding one of the most significant concerns in current artificial intelligence research: the recommendation methods presented in this paper are easily interpretable for the users, improving their trust in the recommendations.

Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual Sources

Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users’ reviews, and users’ profiles. Incorporating diverse sources of information is a reasonable approach to improving recommendation accuracy. However, most studies primarily use the utility matrix, and when they use textual sources they do not integrate them with the utility matrix. This is due to the risk of combined information causing noise and reducing the effectiveness of good sources. To overcome this challenge, in this study we propose a novel method that utilizes the Transformer Model, a deep learning model that efficiently integrates textual and utility matrix information. The study suggests feature extraction techniques suitable for each information source and an effective integration method in the Transformer model. The experimental results indicate that the proposed model significantly improves recommendation accuracy compared to the baseline model (MLP) for the Mean Absolute Error (MAE) metric, with a reduction range of 10.79% to 31.03% for the Amazon sub-datasets. Furthermore, when compared to SVD, which is known as one of the most efficient models for recommender systems, the proposed model shows a decrease in the MAE metric by a range of 34.82% to 56.17% for the Amazon sub-datasets. Our proposed model also outperforms the graph-based model with an increase of up to 108% in Precision, a decrease of up to 65.37% in MAE, and a decrease of up to 59.24% in RMSE. Additionally, experimental results on the Movielens and Amazon datasets also demonstrate that our proposed model, which combines information from the utility matrix and textual sources, yields better results compared to using only information from the utility matrix.

Development of the Young Disability Questionnaire (spine) for children with spinal pain: field testing in Danish school children

ObjectiveThe objective of this study was to finalise the development of the Young Disability Questionnaire (YDQ-spine) to measure the consequences of neck, midback and low back pain, relevant for schoolchildren...

FilmovCinema: A Cosine Similarity-Driven Movie Suggestion Engine

Recommendation-based systems are utilized for various purposes such as suggesting web pages, books, restaurants, TV shows, movies, and so on. One of the primary objectives of movie recommendation systems is to provide movie suggestions to users based on their interests, saving them time in scouring the internet for suitable movies from a vast pool of options. Content-based recommendation systems analyze item descriptions and predict movies that a user may prefer, based on the features present in previously selected movies. These systems may utilize one or more features to recommend movies, such as the movie genre, director, or actors. In this paper, we propose a recommendation system that incorporates the features of cast, keywords, crew, and genres. We integrate a column outlining all four features that make it a powerful piece of a movie recommendation system top of Form.

NEAT—Named Entities in Archaeological Texts: A semantic approach to term extraction and classification

Abstract The lack of annotated datasets affects the development of Natural Language Processing applications and heavily impacts the access to textual data, in particular for specific domains and specific languages. In this paper, we propose a methodology to annotate texts concerning domain-specific knowledge, to provide a reliable source of data for the task of Named Entity Recognition (NER) in the domain of archaeology for the Italian laguage. This method integrates syntactic and semantic information from several structured sources to annotate entities’ mentions in unstructured texts. Furthermore, we make use of an ontology to label entities with the specific type they refer to. By using a corpus made up of item descriptions from Europeana’s Archaeology Collection, we first test our proposed methodology on a mock dataset composed of 1,000 texts. After several steps of improvements, we use the final process to create a complete dataset composed of 5,000 descriptions. The resulting dataset, Named Entities in Archaeological Texts has a total of 41,002 spans of texts annotated with their domain-specific entity classification according to the CIDOC Conceptual Reference Model.

Arabic Captioning for Images of Clothing Using Deep Learning.

Fashion is one of the many fields of application that image captioning is being used in. For e-commerce websites holding tens of thousands of images of clothing, automated item descriptions are quite desirable. This paper addresses captioning images of clothing in the Arabic language using deep learning. Image captioning systems are based on Computer Vision and Natural Language Processing techniques because visual and textual understanding is needed for these systems. Many approaches have been proposed to build such systems. The most widely used methods are deep learning methods which use the image model to analyze the visual content of the image, and the language model to generate the caption. Generating the caption in the English language using deep learning algorithms received great attention from many researchers in their research, but there is still a gap in generating the caption in the Arabic language because public datasets are often not available in the Arabic language. In this work, we created an Arabic dataset for captioning images of clothing which we named "ArabicFashionData" because this model is the first model for captioning images of clothing in the Arabic language. Moreover, we classified the attributes of the images of clothing and used them as inputs to the decoder of our image captioning model to enhance Arabic caption quality. In addition, we used the attention mechanism. Our approach achieved a BLEU-1 score of 88.52. The experiment findings are encouraging and suggest that, with a bigger dataset, the attributes-based image captioning model can achieve excellent results for Arabic image captioning.

DEVELOPING HOTS-BASED OPEN ENDED TEST ON GEOMETRY FOR JUNIOR HIGH SCHOOL

This study aims to develop HOTS-based open ended questions on Geometry material in junior high schools that are standardized based on the criteria of validity, reliability, level of difficulty, and discriminatory power. This research uses Research and Development research type. The research model is modified from the McIntire & Miller model. The research subjects were class VII students at SMP Negeri 2 Pontianak. The instruments used in this research are validation sheets and documentation. Data analysis techniques were carried out quantitatively and qualitatively. Based on the results of the research and discussion it can be concluded that the development procedure includes: 1) defining the test universe, audience, and purpose; 2) developing a plan; 3) conduct pilot test; 4) composing the test items; 5) validation the test; 6) complete the test. The development results obtained 15 item description questions with the results of the analysis including: 1) the reliability of the test was in the very high category, namely 0.95; the validity of the items in the average category is very high, namely 0.71; the average item difficulty level is in the moderate category, namely 0.45; and the discriminating power of the items in the average category is very good, namely 0.43.

CORE-CERT Items as a Minimal Requirement for Replicability of Exercise Interventions: Results From Application to Exercise Studies for Breast Cancer Patients.

Hacke, C, Knuth, J, Bucher, M, Weisser, B, and Schmidt, T. CORE-CERT items as a minimal requirement for replicability of exercise interventions: results from application to exercise studies for breast cancer patients. J Strength Cond Res 37(5): e346-e360, 2023-Exercise interventions have been demonstrated to be useful in the prevention and therapy for multiple different diseases. The Consensus on Exercise Reporting Template (CERT) has been developed as the reporting guideline for exercise studies. The main goals of CERT are to ensure completeness of reporting, to enable interpretation of exercise programs, and to allow extraction of data for meta-analyses and the development of guidelines. However, for clinicians, the most important aspect of good reporting might be the replicability of protocols for their patients. This study was designed to determine the core components of exercise interventions for breast cancer, which are considered as minimal requirement for replicability in practice. The original items of CERT were specified, subdivided, or extended by additional key items to develop a "CORE-CERT checklist." The original CERT and our CORE-CERT were then applied to 29 exercise RCTs for breast cancer obtained from the most frequently cited and most recent meta-analyses in current guidelines. The reporting quality using both templates were examined. Mean original CERT score was 11 of 19 (59%) of completed items and 13 of 19 (68%) of reporting completeness for CORE-CERT. Reporting quality using CORE-CERT items was approximately 8% higher, indicating a more precise description of items in CORE-CERT. Differences concerned exercise dosage, nonexercise components, supervision, and description of each exercise. We propose a novel CORE-CERT guideline necessary for the replicability of exercise interventions in clinical practice. The application of CORE-CERT demonstrated a slightly better but still insufficient reporting quality of exercise interventions for breast cancer.

Lasso-based variable selection methods in text regression: the case of short texts

Communication through websites is often characterised by short texts, made of few words, such as image captions or tweets. This paper explores the class of supervised learning methods for the analysis of short texts, as an alternative to unsupervised methods, widely employed to infer topics from structured texts. The aim is to assess the effectiveness of text data in social sciences, when they are used as explanatory variables in regression models. To this purpose, we compare different variable selection procedures when text regression models are fitted to real, short, text data. We discuss the results obtained by several variants of lasso, screening-based methods and randomisation-based models, such as sure independence screening and stability selection, in terms of number and importance of selected variables, assessed through goodness-of-fit measures, inclusion frequency and model class reliance. Latent Dirichlet allocation results are also considered as a term of comparison. Our perspective is primarily empirical and our starting point is the analysis of two real case studies, though bootstrap replications of each dataset are considered. The first case study aims at explaining price variations based on the information contained in the description of items on sale on e-commerce platforms. The second regards open questions in surveys on satisfaction ratings. The case studies are different in nature and representative of different kinds of short texts, as, in one case, a concise descriptive text is considered, whereas, in the other case, the text expresses an opinion.

Faculty Wellness of a Higher Education Institution in the New Normal Time: Basis for the Personal and Professional Development Program

This study aimed to describe the status of faculty wellness in terms of the eight dimensions of wellness such as emotional, environmental, financial, intellectual, occupational, physical, social, and spiritual. Also, the challenges encountered by the faculty on the eight dimensions of wellness during the new normal time. The mixed methods of research were utilized, the constructed and validated questionnaire were responded by 277 faculty members of the HEI and ten randomly selected faculty members were interviewed. The results reveal that the physical wellness got the rating of occasionally while the other seven wellness got the rating of frequently, which implies that majority of the faculty were frequently practicing or doing the item descriptions of the eight dimensions of wellness. For the challenges encountered, the findings show that the spiritual challenge had a rating of seldom while the other ten challenges got the rating of sometimes, which means that the faculty encountered most of the challenges on the other seven dimensions of wellness, and the ranked first three were physical, occupational, and social. The results of the interview present the faculty’s good and not that good experiences with coping mechanisms during the new normal time. The researchers concluded that the status of the eight dimensions of faculty wellness should always be checked and monitored to maintain a smart, healthy, and responsive faculty. Eight personal and professional development programs were proposed to maintain and enhance the faculty wellness of the faculty in the HEI.

The Persuasiveness of Menu Item Descriptions: The Influence of Language on Consumer Menu Choice in Kenyan Casual Dining Restaurants

This study investigated the influence of descriptive language on consumers' menu item choices in casual dining restaurants. The study was anchored on the stimulus-organization-response theory. The study focused on customers who patronize casual dining restaurants in Kenya and adopted a cross-sectional survey design. A cluster sampling method followed by simple random sampling methods was used to select respondents from the target population. Four hundred and fifteen participants (n = 415) were drawn using simple random sampling from five Kenyan universities. The data collection tool included a questionnaire with a response rate of 69.1%. Data were analyzed using descriptive and inferential statistics. The study results demonstrated that consumer item choice is influenced by various factors. The results showed that there was a weak association (r = 0.36, p < 0.05) between descriptive language and consumers' menu item choices. The F-value (F (1) = 48.308, p < 0.05) implies that the way menu items were described had a significant effect on which item a consumer chooses. R-square = 0.32, meaning the model is not a perfect predictor of the menu item choice. The study recommends that restaurant owners should focus on creating unique menu descriptions and using new technologies in restaurant menu development.

Student Pharmacists’ Perspectives Regarding a Virtually Delivered Research Proposal Course in the Doctor of Pharmacy Curriculum

This study aimed to assess third-year student pharmacists' perspectives regarding a virtually delivered research proposal course. A 23-item questionnaire was distributed to third-year student pharmacists enrolled in a research proposal course over three weeks in April 2021. The questionnaire contained 15 Likert-scale items, seven descriptive items, and a free-text item for additional comments about the course. Items were summarized using descriptive statistics. Fifty-four student pharmacists (response rate = 40.9%) participated in the survey. The student pharmacists surveyed generally had a positive perception of the virtually delivered research proposal course with median scores ≥ 4 (indicating agreement) for the majority (13/15) of survey items. Students did not agree that there was no difference in their motivation to succeed in the virtual course versus an in-person course and did not agree that they were more likely to pursue a career that involves undertaking a research project. This study found that student pharmacists generally had a positive perception of a virtually delivered research proposal course. These findings offer some support for the provision of an online, virtually delivered research proposal course for student pharmacists. Further research with a larger sample of students from multiple pharmacy schools is needed to improve the generalizability of the results.

Uncertainty-Aware Multiview Deep Learning for Internet of Things Applications

As an essential approach in many Internet of Things (IoT) applications, multiview learning synthesizes multiple features to achieve more comprehensive descriptions of data items. Most of the previous studies on multiview learning have been dedicated to increasing the prediction accuracy, while ignoring the reliability of the decision. This would limit their deployment in high-risk IoT and industrial applications such as the automated vehicle. Although a trusted multiview classification model has been proposed recently, it cannot well deal with the highly complementary multiview data. In this work, we present an evidential multiview deep learning (EMDL) method to make reliable decisions. EMDL first seeks view-specific evidence of each category, which could be termed as the amount of support to each category collected from data. It then dynamically fuses different views at the evidence level to construct the multiview common evidence and makes reliable prediction accordingly (strong evidence indicates high prediction confidence). In particular, we establish a degradation layer to learn the mappings from the common evidence (comprehensive information) to view-specific evidences (partial information) for evidence fusion. It aims to explicitly model consistent and complementary relations in multiview data at the evidence level. We apply EMDL on a synthetic toy dataset and five real-world datasets (three datasets are related to industrial scenarios). Experiments show that EMDL outperforms state-of-the-art baseline methods.

Automatic Classification of Construction Work Codes in Bill of Quantities of National Roadway Based on Text Analysis

The construction work classification code is a crucial index for consistently collecting unit cost data from the bill of quantities (BOQ). Appropriate classification codes assigned to detailed construction work item descriptions are required to verify project cost and for quality control. However, the codification system is complicated and time consuming for estimators to follow. This study proposes a framework to recognize the text of detailed construction work item descriptions in the BOQ and automatically assign the most similar work classification code. The automatic assignment algorithm was designed to score the similarity of the tokenized words in the work item description based on whether they contain the same word. The framework was experimented with using the national roadway BOQ; this BOQ is used for the procurement process in South Korea. It transformed work item descriptions from unstandardized BOQs into standardized data with more than 91.12% accuracy. The results of this study can be used as a prerequisite step for standardizing construction life cycle cost data and the automatic generation of BOQs.

ANALISIS BUTIR SOAL KETRAMPILAN BERFIKIR TINGKAT TINGGI SISWA XI PADA MATERI SISTEM GERAK MANUSIA

Background: One of the skills that students should be have is higher-order thinking skills, especially critical thinking skills acquired during learning which are measured to find out student learning outcomes in the form of tests. Before the test, an analysis of the quality of the test or item analysis is carried out to produce a quality test and meet the test requirements, namely validity, reliability, objectivity, index of difficulty, and discriminatory power. Methods: This research uses a quantitative descriptive approach Results: The research showed that the results of the instrument for testing higher-order thinking skills, especially critical thinking skills in the subject of human movement systems for class XI students at MA Darul Ulum, were declared valid and reliable, then used to measure higher-order thinking skills. The average score of students' higher-order thinking skills for the descriptive item test instrument was 72. The highest score for the descriptive item test instrument achieved by students was 96 and the lowest score was 32. This shows that students' higher-order thinking skills were still lacking. The researcher re-developed the high-order thinking skills test instrument by adjusting the performance indicators to the questions. Conclusion: The test instrument for higher order thinking skills, especially critical thinking skills in the human movement system material for class XI students at MA Darul Ulum which was developed is valid, reliable, and feasible to use.

The Visit of Mālik bin Muʿāwiyah, King of Kindah and Maḏḥiǧ to the Himyarite King Šammar Yuharʿiš in Maʾrib

Sebe diline ait ve mB 2006 İ-54 (şek. 2) olarak adlandırılan kitabe, siglum/müstensih (yani yazar) kısaltması içermekte ve Yemen/Me’rib yakınlarındaki Avâm Tapınağı/Maḥrem Belkîs’ta Sebe tanrısı el-Makah’a adanmış bronz bir heykelciğin kaydını taşımaktadır (Maraqten 2015: 109–135).
 Öğenin Açıklaması: Heykelciğin alt kısmı şu an kayıptır ve yazıt tek bir yüze kazınmıştır. Yazıt, biraz aşağı eğilmiş ve çift, hafifçe kazınmış yatay çift çizgiler şeklinde oyulmuştur. Taşın yüzeyi parlatılmış; heykelciğin yapışık olduğu üst yüzey kırılmış ve ilk satır neredeyse tamamen kaybolmuş vaziyettedir. Sol alt köşe kırılmış ve taşın bazı kenar bölümleri hafifçe zarar görmüştür. Metin, ilk satırdaki birkaç kayıp harf dışında eksiksiz vaziyettedir. Yazıtın üst kısmı kısmen kaybolmuş olsa da transkripsiyonda gösterildiği üzere 11. satıra atıfta bulunarak yeniden oluşturulması mümkündür.
 Bu 14 satırlık yazıt; 9. AFSM’nin (American Foundation for the Study of Man/Amerikan İnsan Araştırmaları Vakfı) 2006 yılı sezonunda düzenlediği kazılarda, Avâm Tapınağının ek binasında keşfedildi ve zeminin döşenmesinde yeniden kullanıldı. Yazılı bloğun ölçüleri aşağıdaki gibidir: 35×22cm; harf yüksekliği: 1,5×1,7cm. Kavisli el-Makah sembolü satır bir ve ikinin başlangıcında göze çarpmaktadır; ancak üst kısmı kırılmıştır. Yazıtın bugünkü konumu Avâm Tapınağı’dır ve hala yerindedir

Fashion-Oriented Image Captioning with External Knowledge Retrieval and Fully Attentive Gates.

Research related to fashion and e-commerce domains is gaining attention in computer vision and multimedia communities. Following this trend, this article tackles the task of generating fine-grained and accurate natural language descriptions of fashion items, a recently-proposed and under-explored challenge that is still far from being solved. To overcome the limitations of previous approaches, a transformer-based captioning model was designed with the integration of external textual memory that could be accessed through k-nearest neighbor (kNN) searches. From an architectural point of view, the proposed transformer model can read and retrieve items from the external memory through cross-attention operations, and tune the flow of information coming from the external memory thanks to a novel fully attentive gate. Experimental analyses were carried out on the fashion captioning dataset (FACAD) for fashion image captioning, which contains more than 130k fine-grained descriptions, validating the effectiveness of the proposed approach and the proposed architectural strategies in comparison with carefully designed baselines and state-of-the-art approaches. The presented method constantly outperforms all compared approaches, demonstrating its effectiveness for fashion image captioning.