1,721,009 research outputs found
Identifying skylines in cloud databases with incomplete data
No full textSkyline queries is a rich area of research in the database
community. Due to its great benefits, it has been integrated
into many database applications including but not limited to
personalized recommendation, multi-objective, decision support
and decision-making systems. Many variations of skyline
technique have been proposed in the literature addressing
the issue of handling skyline queries in incomplete database.
Nevertheless, these solutions are designed to fit with centralized
incomplete database (single access). However, in many realworld database systems, this might not be the case, particularly
for a database with a large amount of incomplete data distributed
over various remote locations such as cloud databases. It is
inadequate to directly apply skyline solutions designed for the
centralized incomplete database to work on cloud due to the
prohibitive cost. Thus, this paper introduces a new approach
called Incomplete-data Cloud Skylines (ICS) aiming at processing
skyline queries in cloud databases with incomplete data. This
approach emphasizes on reducing the amount of data transfer and domination tests during skyline process. It incorporates sorting
technique that assists in arranging the data items in a way where
dominating data items will be placed at the top of the list helping
in eliminate dominated data items. Besides, ICS also employs
a filtering technique to prune the dominated data items before
applying skyline technique. It comprises a technique named local
skyline joiner that helps in reducing the amount of data transfer
between datacenters when deriving the final skylines. It limit
the amount of data items to be transferred to only those local
skylines of each relation. A comprehensive experiment have
been performed on both synthetic and real-life datasets, which
demonstrate the effectiveness and versatility of our approach in
comparison to the current existing approaches. We argue that our
approach is practical and can be adopted in many contemporary
cloud database systems with incomplete data to process skyline
queries
Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique
No full textDue to the rapid emergence and evolution of AI applications, the utilization of smart imaging devices has increased significantly. Researchers have started using deep learning models, such as CNN, for image classification. Unlike the traditional models, which require a lot of features to perform well, CNN does not require any handcrafted features to perform well. It uses numerous filters, which extract required features from images automatically for classification. One of the issues in the horticulture industry is fruit classification, which requires an expert with a lot of experience. To overcome this issue an automated system is required which can classify different types of fruits without the need for any human effort. In this study, a dataset of a total of 26,149 images of 40 different types of fruits was used for experimentation. The training and test set were randomly recreated and divided into the ratio of 3:1. The experiment introduces a customized head of five different layers into MobileNetV2 architecture. The classification layer of the MobileNetV2 model is replaced by the customized head, which produced the modified version of MobileNetV2 called TL-MobileNetV2. In addition, transfer learning is used to retain the pre-trained model. TL-MobileNetV2 achieves an accuracy of 99%, which is 3% higher than MobileNetV2, and the equal error rate of TL-MobileNetV2 is just 1%. Compared to AlexNet, VGG16, InceptionV3, and ResNet, the accuracy is better by 8, 11, 6, and 10%, respectively. Furthermore, the TL-MobileNetV2 model obtained 99% precision, 99% for recall, and a 99% F1-score. It can be concluded that transfer learning plays a big part in achieving better results, and the dropout technique helps to reduce the overfitting in transfer learning
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Cross-lingual sparse-MoE distillation for efficient low-resource assamese–english and bodo–english translation
No full textAbstract Neural machine translation (NMT) for low-resource languages such as Assamese and Bodo has seen dramatic quality improvements with large multilingual models like Multilingual Bidirectional and Auto-Regressive Transformer (mBART50) and IndicTrans2 multilingual Transformer model, but their parameter counts (often billion) make real-time, on-device deployment infeasible. Although Assamese and Bodo are not among mBART50’s pretraining languages, we first fine-tuned mBART50 on the AI4Bharat Samanantar Assamese–English and IndicTrans2-derived Bodo–English corpora to enable cross-lingual adaptation from related Indo-Aryan and Tibeto-Burman languages. We propose a novel two-stage approach that combines sparse Mixture-of-Experts (MoE) architectures with cross-lingual knowledge distillation to yield a 400-million-parameter student model that retains translation quality within approximately one Bilingual Evaluation Understudy (BLEU) point of its 1.3-billion-parameter teacher while reducing active computation per token by approximately four-fold. Our student uses a twelve-layer Transformer encoder–decoder: the first half of encoder and decoder layers remain standard, while the latter half incorporate sparsely activated Mixture-of-Experts (MoE) feed-forward blocks (four experts in the encoder with top-two gating; two experts in the decoder with top-one gating) and learnable language-prefix embeddings. We perform cross-lingual knowledge distillation, transferring both hard and soft labels from the fine-tuned mBART50 teacher on the AI4Bharat Samanantar Assamese–English corpus and IndicTrans2-derived Bodo–English data, with evaluation on the FLORES-200 multilingual benchmark. On a 10,000-sentence test set, our student achieves 34.5 BLEU compared with 35.2 BLEU for the teacher in Assamese–English, and 31.2 compared with 32.0 in Bodo–English, while running inference at approximately 24 ms per sentence on an RTX 3050 laptop GPU–about 280% faster than the dense teacher. To our knowledge, this is the first demonstration of cross-lingual MoE-based distillation for Indic NMT, enabling efficient, high-quality translation at the edge
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning
Full text linkIn the era of advancing artificial intelligence (AI), its application in agriculture has become increasingly pivotal. This study explores the integration of AI for the discriminative classification of corn diseases, addressing the need for efficient agricultural practices. Leveraging a comprehensive dataset, the study encompasses 21,662 images categorized into four classes: Broken, Discolored, Silk cut, and Pure. The proposed model, an enhanced iteration of MobileNetV2, strategically incorporates additional layers—Average Pooling, Flatten, Dense, Dropout, and softmax—augmenting its feature extraction capabilities. Model tuning techniques, including data augmentation, adaptive learning rate, model checkpointing, dropout, and transfer learning, fortify the model's efficiency. Results showcase the proposed model's exceptional performance, achieving an accuracy of ~96% across the four classes. Precision, recall, and F1-score metrics underscore the model's proficiency, with precision values ranging from 0.949 to 0.975 and recall values from 0.957 to 0.963. In a comparative analysis with state-of-the-art (SOTA) models, the proposed model outshines counterparts in terms of precision, recall, F1-score, and accuracy. Notably, MobileNetV2, the base model for the proposed architecture, achieves the highest values, affirming its superiority in accurately classifying instances within the corn disease dataset. This study not only contributes to the growing body of AI applications in agriculture but also presents a novel and effective model for corn disease classification. The proposed model's robust performance, combined with its competitive edge against SOTA models, positions it as a promising solution for advancing precision agriculture and crop management
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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