Multi-Query Optimization on RSS Feeds

Summary.— The use of syntactic, semantic and graphemic information by 52 readers in the first and second year of junior school was examined by means of a cloze task and an error‐detection task. The results from both tasks indicated that there is greater use of all three types of information in the second year compared with the first year, and by good readers compared with poor readers. No evidence was found for an inverse relationship between the use of contextual and graphemic cues. The use of semantic information is initially less effective than that of syntactic information, but increases to equal it by the second year. The children's pattern of responding in relation to grammatical classes was similar to that found in adults.

Recently, semantic segmentation based on RGB and thermal infrared (TIR) images has become a research hotspot because of its stability in the weak light environment. However, most of the current methods ignore the differences between the two modalities of data and do not use semantic information in multi-modal fusion. In this paper, we propose a novel Semantic-Guided Fusion Network (SGFNet) for RGB-Thermal semantic segmentation, which makes full use of semantic information in the multi-modal fusion. Our SGFNet consists of an asymmetric encoder with TIR branch and RGB branch and a decoder. We concentrate on enhancing the multi-modal feature representation in the encoder with a pattern of fusion and enhancement. Specifically, considering that TIR images are stable under weak light conditions, we first propose a Semantic Guidance Head to extract semantic information in the TIR branch. In the RGB branch, we propose a Multi-modal Coordination and Distillation Unit to fuse multi-modal features first. Then, we propose a Cross-level and Semantic-guided Enhancement Unit to enhance the fused features with cross-level information and semantic information. We arrange these two units at all stages of the RGB branch to generate features with strong representation abilities at different levels. For the decoder, to obtain large receptive fields and fine edges, we improve the Lawin ASPP decoder by introducing edge information extracted from the low-level features, proposing the edge-aware Lawin ASPP decoder. With our encoder and decoder working together, our SGFNet can identify objects accurately and segment objects finely. Extensive experiments on the MFNet dataset demonstrate the superior performance of the proposed SGFNet compared with state-of-the-art methods. The code and results of our method are available at https://github.com/kw717/SGFNet.

Cognitive info-communications is a rapidly developing scientific paradigm. It integrates the representations of different research fields within a common model, that promotes the fast transfer and synthesis of scientific results. In this paper, an ambient assisted living framework is considered and it is explained, what kind of advantages can be taken from the integration of the framework with the paradigm of cognitive info-communications. The ambient assisted living framework is based on the intelligent space concept, which is realized in the VirCA platform, and it is extended with the use of semantic information. In the other direction, it is explained, what kind of advantages can be taken from the use of semantic information in the case of a coginfocom system. It is presented as well, how the use of semantic information and the paradigm of cognitive info-communications can enhance the efficiency and the performance of an assisted living application.

Source code suggestion is the utmost helpful feature in the integrated development environments that helps to quicken software development by suggesting the next possible source code tokens. The source code contains useful semantic information but is ignored or not utilised to its full potential by existing approaches. To improve the performance of source code suggestion, the authors propose a deep semantic net (DeepSN) that makes use of semantic information of the source code. First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code‐embedding to automatically extract the top‐notch features of the source code and to learn useful semantic information. Next, the source code's long and short‐term context dependencies are captured by using long short‐term memory. We extensively evaluated the proposed approach with three baselines on ten real‐world projects and the results are suggesting that the proposed approach surpasses state‐of‐the‐art approaches. On average, DeepSN achieves 7.6% higher accuracy than the best baseline.

Power, size and weight are major constraining factors for UAVs, which have caused researchers to investigate alternative information representations, such as semantics, to reduce the computational load onboard UAVs. However, the connection between spatial and semantic information has not been fully explored and the literature on including semantic information for UAV navigation is limited. This paper provides a new taxonomy on spatial and semantic relations through consideration of a selected subset of the literature. Concepts of semantic information are related with spatial maps and two hypotheses for improving algorithms based on the literature are generated. The algorithms are tested in numerical simulations on the task of semantic mapping and the effects of influential parameters are examined. Results indicate that a defined hierarchical structure of semantic information enables improved map reproduction. The proposed algorithm exhibits the potential to improve the use of semantic information in UAV deployment scenarios, such as agriculture, search and rescue and industrial inspections.

To what extent does semantic information play a functional role in visual word recognition? Theories of word recognition vary in the importance assigned to semantic information in visual lexical decision, with past research suggesting that the nature of the foils is a crucial determinant of semantic reliance. Here, we explored the conditions under which semantic variables influence lexical decision. Normal readers performed visual lexical decision tasks in which imageability and semantic priming were manipulated, with nonword foils varying systematically in their orthographic and phonological similarity to the real words. The effects of imageability and semantic priming increased in magnitude as nonword foils became progressively more wordlike. These findings provide a clear illustration of the flexible use of semantic information to support normal visual word recognition.

In the study of scholars, the research interests of scholars have strong personalized features, which can well reflect the interest preferences of scholars in specific research fields to serve the tasks of user profiling and academic recommendation of scholars. Research interest mining aims to mine topics that can be used to describe scholars' research interests directly from the text. The existing methods lack the comprehensive use of semantic information of academic papers and academic network information. In this paper, we construct a research interest mining method for scholars based on the text of their papers and citation networks. On the one hand, the LSI model and Doc2Vec model are used for text representation of scholars' papers, which can effectively extract the topics of papers and mine the semantic information in the text; on the other hand, the MinHash algorithm is used to reduce the complexity of citation network similarity calculation. Finally, the integration method is used for fusion of interest labels to obtain the final scholars' research interest labels. The experimental results demonstrate that the integrated approach can get higher accuracy rates.

Generative text summary is an important branch of natural language processing. Aiming at the problems of insufficient use of semantic information, insufficient summary precision and the problem of semantics-loss in the current generated text summary method, an enhanced semantic model is proposed based on dual-encoder, which can provide richer semantic information for sequence-to-sequence architecture through dual-encoder. The enhanced attention architecture with dual-channel semantics is optimized, and the empirical distribution and Gain-Benefit gate are built for decoding. In addition, the position embedding and word embedding are merged into the word embedding technology, and the TF-IDF(term frequency-inverse document frequency), part of speech, key score are added to word’s feature. Meanwhile, the optimal dimension of word embedding is optimized. This paper aims to optimize the traditional sequence mapping and word feature representation, enhance the model’s semantic understanding, and improve the quality of the summary. The LCSTS and SOGOU datasets are used to validate proposed method. The experimental results show that the proposed method can improve the performance of the ROUGE evaluation system by 10–13 percentage points compared with other listed algorithms. We can observe that the semantic understanding of the text summaries is more accurate and the generation effect is better, which has a better application prospect.

Aiming at improving the poor effect of data ontology semantic information query, based on the existing semantic retrieval and combined with natural semantic processing technology, a data ontology semantic information query method based on natural semantic understanding is proposed. The natural semantic pattern is used to define the corresponding data ontology extraction rules, and the synonyms of domain ontology are obtained from relevant data. Further, Jena reasoning is used, and the implicit knowledge is obtained by combining domain knowledge and custom reasoning rules. Based on the natural semantics, a word similarity algorithm is proposed to analyze the syntax and semantics of the natural semantic query input by users, and construct SPARQL query sentences to realize the natural semantic query. The accuracy deviation formula is proposed to measure the rationality of the sorting algorithm, which simplifies the semantic information query steps of the data ontology. Experiments show that this method can obtain semantic information and effectively improve the accuracy of semantic information of data ontology.

Today, large organisations and regulated markets are subject to the control of external audit associations, which require the submission of a huge amount of information in the form of predefined and rigidly structured reports. The compilation of these reports requires the extraction, transformation and integration of data from different heterogeneous operational databases. This task is usually performed by developing a software ad hoc for each report, or by adopting a data warehouse and analysis tools, which are now established technologies. Unfortunately, the data warehousing process is notoriously long and error prone, and is therefore particularly inefficient when the output of the data warehousing is represented by a limited number of reports. This article presents “MMBR”, an approach that can generate a multidimensional model from the structure of expected reports as data warehouse output. The approach is able to generate the multidimensional model and populate the data warehouse by defining a knowledge base specific to the domain. Although the use of semantic information in data storage is not new, the novel contribution of our approach is represented by the idea of simplifying the design phase of the data warehouse, making it more efficient, by using an industry-specific knowledge base and a report-based approach.

Diminished episodic memory and diminished use of semantic information to aid recall by individuals with autism spectrum disorder (ASD) are both thought to result from diminished relational binding of elements of complex stimuli. To test this hypothesis, we asked high-functioning adults with ASD and typical comparison participants to study grids in which some cells contained drawings of objects in non-canonical colours. Participants were told at study which features (colour, item, location) would be tested in a later memory test. In a second experiment, participants studied similar grids and were told that they would be tested on object-location or object-colour combinations. Recognition of combinations was significantly diminished in ASD, which survived covarying performance on the Color Trails Test (D'Elia et al. Color trails test. Professional manual. Psychological Assessment Resources, Lutz, 1996), a test of executive difficulties. The findings raise the possibility that medial temporal as well as frontal lobe processes are dysfunctional in ASD.

The paradigm of intelligent space integrates the knowledge of several scientific fields. The intelligent space approach can be successfully applied for home care of handicapped and elderly people. The use of semantic information proved to be an efficient method for the knowledge integration of different domains. In this paper, a semantic intelligent space framework is presented which can handle the capabilities and needs of the single intelligent entities in order to realize cooperation for solving complex tasks. This semantic framework is extended with the representation of the further domain of emotional-behavioral systems. It is described, what kind of benefit can be realized based on the results of this domain, then the semantic representation of the domain is explained. It is decribed as well, how the original framework can be extended in order to cope with the representation.

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential applications in various fields, including medical imaging and robotics. Its compatibility with other machine learning methods for visual semantic analysis allows for integration into existing workflows. These aspects emphasize the significance of our contributions in advancing interactive image-segmentation techniques and their practical utility in real-world applications.

This Ph.D. work proposes and implements an unprecedented approach to increase the performance of agents operating in competitive scenarios that involve a data stream of information related to the dynamics of their adversaries’ behavior. Therefore, such agents must have the ability to detect, in real-time, eventual changes in the opponents’ behavior and, based on this information, adapt their decision-making processes in order to improve their abilities to deal with problems for which were designed. The RTS StarCraft: BroodWar game was used as a case study. The proposed approach was developed based on the following actions: 1) Extension and improvement of M-DBScan, which is a successful behavior change detection algorithm for data stream scenarios, in order to increase its accuracy of detecting behavior change, as well as being able to associate to each of these behaviors, a semantic that represents it; 2) Implementation of a StarCraft player agent whose decision-making module operates as follows: the information regarding the meanings of the opponent’s behavior provided by the extended versions of M-DBScan, proposed here, will be used to guide the agent in the execution of adequate actions considering the current opponent behavior. The proposed approach was validated through experiments conducted in order to evaluate the following metrics: accuracy in detecting changes in the opponent’s behavior and in assigning meanings to such behaviors; the win rate of the developed agent in tournaments where it faces different opponents in the game Starcraft. With the experiments carried out, it was possible to demonstrate the gain in the agent’s winning rate, due to the use of semantic information about the behavior change and the use of an adequate set of behaviors, which are relevant to the context of the problem.

In this paper, we introduce a new method that explores spatio-temporal-theme correlations between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors. Particularly, we employ a basic two-phase framework in pattern recognition (i.e. feature extraction and detection) with the novel improvement that concerns the use of semantic information acquired from social sensors to automatically label the low-level features extracted from physical sensors. Moreover, by symbolizing the trend component of time-series data, the proposed method has an ability to interpret event's patterns to help users get insights of how events happen. Differentiating from conventional supervised learning methods whose training data are labeled manually and in an off-line mode, the proposed method can collect labels for training data automatically and in an on-line mode. Moreover, after running for a certain time, a training stage can run parallel with the detecting stage when an event model is totally built. After that, the training stage continues learning to increase the accuracy of the event model by nonstop collecting new samples with labels from streaming data. The problem of environmental factors and particularly air pollution impacts on asthma exacerbation is considered for evaluating the proposed method. The experimental results show that the proposed method can probably detect the prevalence of asthma risks in a specific spatio-temporal context as well as help users understand how a change in the surrounding environment (e.g. weather condition and air pollution) can influence their health (e.g. asthma attack) by interpreting detected event's patterns.