Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, such as internet search and fashion retrieval. In this scenario, the input image serves as an intuitive context and background for the search, while the corresponding language expressly requests new traits on how specific characteristics of the query image should be modified in order to get the intended target image. This task is challenging since it necessitates learning and understanding the composite image-text representation by incorporating cross-granular semantic updates. In this paper, we tackle this task by a novel \underline{\textbf{B}}ottom-up cr\underline{\textbf{O}}ss-modal \underline{\textbf{S}}emantic compo\underline{\textbf{S}}ition (\textbf{BOSS}) with Hybrid Counterfactual Training framework, which sheds new light on the CIR task by studying it from two previously overlooked perspectives: \emph{implicitly bottom-up composition of visiolinguistic representation} and \emph{explicitly fine-grained correspondence of query-target construction}. On the one hand, we leverage the implicit interaction and composition of cross-modal embeddings from the bottom local characteristics to the top global semantics, preserving and transforming the visual representation conditioned on language semantics in several continuous steps for effective target image search. On the other hand, we devise a hybrid counterfactual training strategy that can reduce the model's ambiguity for similar queries.

Learned Indexes are a novel approach to search in a sorted table. A model is used to predict an interval in which to search into and a Binary Search routine is used to finalize the search. They are quite effective. For the final stage, usually, the lower_bound routine of the Standard C++ library is used, although this is more of a natural choice rather than a requirement. However, recent studies, that do not use Machine Learning predictions, indicate that other implementations of Binary Search or variants, namely k-ary Search, are better suited to take advantage of the features offered by modern computer architectures. With the use of the Searching on Sorted Sets SOSD Learned Indexing benchmarking software, we investigate how to choose a Search routine for the final stage of searching in a Learned Index. Our results provide indications that better choices than the lower_bound routine can be made. We also highlight how such a choice may be dependent on the computer architecture that is to be used. Overall, our findings provide new and much-needed guidelines for the selection of the Search routine within the Learned Indexing framework.

The development of emotion detection technology has emerged as a highly valuable possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of a large number of start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparison to social data. This study compares eight technologies; IBM Watson NLU, ParallelDots, Symanto-Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and NLP Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores that they delivered as well as the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.

Abstract: Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism. Modern FPGA devices can leverage high-bandwidth memory technologies, but when applications are memory-bound designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. This development process requires hardware design skills that are uncommon in domain-specific experts. In this paper, we propose an automated tool flow from a domain-specific language (DSL) for tensor expressions to generate massively-parallel accelerators on HBM-equipped FPGAs.

Succinct event description based on multiple documents is critical to news systems as well as search engines. Different from existing summarization or event tasks, Multi-document Event Summarization (MES) aims at the query-level event sequence generation, which has extra constraints on event expression and conciseness. Identifying and summarizing the key event from a set of related articles is a challenging task that has not been sufficiently studied, mainly because online articles exhibit characteristics of redundancy and sparsity, and a perfect event summarization needs high level information fusion among diverse sentences and articles. To address these challenges, we propose a two-phase framework for the MES task, that first performs event semantic graph construction and dominant event detection via graph-sequence matching, then summarizes the extracted key event by an event-aware pointer generator. For experiments in the new task, we construct two large-scale real-world datasets for training and assessment. Extensive evaluations show that the proposed framework significantly outperforms the related baseline methods, with the most dominant event of the articles effectively identified and correctly summarized.

Today within the search engine optimization world, typically it's extra difficult than ever to inform what's scorching and what's not relating to search engine optimization terminology, phrases, and phrases. As manufacturers and entrepreneurs begin to embrace Web3, the subsequent technology of the web phrases come and go. To make sure you are on prime of it, we tapped the minds of the trade's main search engine optimization and digital advertising and marketing professionals to dissect the over-used, underrated, and up-and-coming search engine optimization phrases. Identical to types change with the season, search engine optimization modifications with the algorithms and the trendy instances. What may need been final season's must-have buzzword simply may be this 12 months's crimson flag ready for a Google penalty. Are we nonetheless speaking about carrying black hats and white hats?

Alan Kent from Google revealed a video on search engine optimisation ideas for e-commerce websites, this consists of 8 ideas. You possibly can watch the video embedded beneath or simply learn my abstract of these ideas. Be sure your web page titles together with the model title, colour and sort of product is vital to have in your title. And ensure so as to add structured knowledge to your product web page. Additionally take into consideration your out of inventory merchandise.

Bob van Luijt is CEO of SeMI Technologies the company behind the open-source vector search engine Weaviate. A new ecosystem of smaller companies is ushering in a "third wave" of AI-first database technology. New search engines and databases brilliantly answer queries posed in natural language, but their machine-learning models are not limited to text searches. The same approach can also be used to search anything from images to DNA. Much of the software involved is open source, so it functions transparently and users can customize it to meet their specific needs.

Social Network Mining (SNM) has become one of the main themes in big data agenda. As a resultant network, we can extract social network from different sources of information, but the information sources were growing dynamically require a flexible approach. To determine the appropriate approach needs the data engineering in order to get the behavior associated with the data. Each social network has the resources and the information source, but the relationship between resources and information sources requires explanation. This paper aimed to address the behavior of the resource as a part of social network analysis (SNA) in the growth of social networks by using the statistical calculations to explain the evolutionary mechanisms. To represent the analysis unit of the SNA, this paper only considers the degree of a vertex, where it is the core of all the analysis in the SNA and it is basic for defining the relation between resources and SNA in SNM. There is a strong effect on the growth of the resources of social networks. In total, the behavior of resources has positive effects. Thus, different information sources behave similarly and have relations with SNA.

Brave is probably best known among hardcore geeks as one of Chrome's challengers. But for awhile now, the company has offered more than just a privacy-minded browser. A year ago, it launched the beta for a search engine, too--and now, on its first anniversary, Brave Search has hit a milestone of 2.5 billion queries, with a peak of 14.1 million queries in one day. For a nascent search engine, these numbers are big. As Brave claims in a blog post, it's won this achievement faster than Google (who took over a year to meet the same goal), plus run circles around DuckDuckGo. Its privacy-oriented rival took four years to cross the same threshold.