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Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas. We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches. After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years.[PUBLICATION ABSTRACT]

Computational syntactic processing is a fundamental technique in natural language processing. It normally serves as a pre-processing method to transform natural language into structured and normalized texts, yielding syntactic features for downstream task learning. In this work, we propose a systematic survey of low-level syntactic processing techniques, namely: microtext normalization, sentence boundary disambiguation, part-of-speech tagging, text chunking, and lemmatization. We summarize and categorize widely used methods in the aforementioned syntactic analysis tasks, investigate the challenges, and yield possible research directions to overcome the challenges in future work.

Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.

Italian riffle dace (Telestes muticellus, Bonaparte 1837) is a small-bodied Leuciscidae native to the Italian Peninsula, of which little is known about the ecology and individual movements in nature. Passive Integrated Transponder (PIT) telemetry is used to track fish movements and behaviour. The basic assumption is that the PIT-tagged organism's performances do not differ considerably from their natural behaviour. Here we present the first evaluation of potential tagging effects in the genus Telestes. The survival rate and tag retention were compared between two different tag implantation methods – injector gun and scalpel incision - and pit-tagging effects on swimming performance were evaluated. Five weeks after tagging, Italian riffle dace demonstrated high survival rates in all treatments: 94.8% for fish tagged with injector gun (n=58), 100% for scalpel incision method (n=58), and 98.3% for controls (n=58). The tag retention was 96.6% for gun treatment and 100% for scalpel treatment. Prolonged swimming performance, tested 22-23 days after tagging, showed a reduction in endurance (time-to-fatigue) for scalpel treatment (n=22) compared to the control group (n=21), while no difference in maximum swimming velocity was observed. We conclude that PIT tagging is a suitable technique for Italian riffle dace, showing high survival and PIT retention and no effect on maximum swimming speed. Significantly lower prolonged swimming performance, although likely less ecologically important, shows that tagging is not without costs. Potential biases need to be evaluated on a study-by-study basis, and future studies should explore behavioural tagging effects in nature.

Or maybe just clutter in-stream, but either way, Threads remains opposed to hashtags, as it has been from the start, with the Threads team keen to avoid the rampant spamming of popular tags in order to get more post reach. In July 2023, shortly after the launch of the app, Mosseri said that hashtags “come along with a lot of safety and integrity work that I think a lot of people underappreciate,” which is why he wasn’t sure that Threads should have them In August 2023, Mosseri said that he was still unsure about adding hashtags on Threads as they could “cause more trouble than they’re worth” In October 2023, Mosseri said that hashtags will not “meaningfully change the trajectory” of Threads’ development Then in late October, Threads rolled out topic tags, which enable users to add one topic to each update, in order to “make it easier for others to find and join in on the conversation” In January 2024, Mosseri said that hashtags may be “good to build” for Threads, in line with user requests, but that they won’t “noticeably grow Threads or Threads usage” In February this year, in a talk about Instagram, Mosseri said that hashtags “don’t work” to increase reach In March this year, Threads added topic tags on profiles to help showcase what you’re likely to post about In May this year, Mosseri once again said that hashtags don’t improve visibility on Instagram, but they are “a great way to let people know what a post is about and connect posts” So, to be clear, Mosseri (who, it’s also worth noting, is no longer in charge of Threads) doesn’t see much potential for hashtags, at least with respect to how a lot of people have used them in the past in increasing post reach. [...]Threads is still trying to work out a better alternative, with its latest Communities feature being another experiment in helping to guide topic-based discussion in the app. [...]they don’t, which means that, really, it depends on how people are searching for your topic of focus as to whether you should be adding hashtags or not. Because even if they aren’t clickable, if the search results displayed for each are different, then the specifics may matter for linking into relevant queries. [...]we don’t have any insight into how people are searching on Threads, because Threads doesn’t share data on keywords and/or search trends.

To examine the habitat usage of adult Pacific bluefin tuna (PBF), electronic tagging was conducted in the Sea of Japan during May and June of 2012−2017. Archival tags were internally implanted and pop-up satellite archival transmitting tags were deployed; data on the horizontal movements and diving behaviours of 36 individual PBF were successfully retrieved. In the summer spawning season, the tagged PBF were concentrated near Sado Island and Oki Island in the Sea of Japan, and they were distributed widely to the southwest (near Tsushima Island) or northeast (near the Tsugaru Strait) in the autumn and winter. We obtained the first long-term tracking record (246 d) for adult PBF, and this individual exhibited residency in a known spawning region during the spawning season in the proximity of warm-core eddy features. This fish spent most of the daytime below the thermocline between 30 and 150 m depths where the surface ambient temperature was 26.0 ± 1.5°C, but at night it ventured into the warm surface layer. Its whole-body heat transfer coefficient increased when it experienced warm waters (≥24°C), which we suggest is a physiological response to avoid overheating. The mean peritoneal cavity temperature was only 1.8°C higher than the ambient temperature, compared with 6.9°C higher during the cooler autumn–winter period. Our hypothesis is that the warm surface temperatures found in the spawning grounds induce a physiology–reproduction trade-off in adult PBF, which must behaviourally and physiologically thermoregulate their body temperature to gain spatial and temporal access to oceanographic conditions that may promote larval survivorship and growth.

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.