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This document discusses the intersection of artificial intelligence (AI) and deep learning with sensor technologies. It highlights the applications of AI and deep learning in various fields such as healthcare, manufacturing, agriculture, transportation, and environmental monitoring. The document also provides a summary of twelve research articles and reviews that explore the integration of AI and deep learning with sensor data for different purposes, including lung cancer screening, reinforcement learning, point cloud sampling, anomaly detection, human activity recognition, semantic segmentation, face recognition, audio super-resolution, industrial control systems, traffic flow prediction, weed detection, and medical diagnostics. These contributions demonstrate the potential of AI and deep learning in optimizing sensor data utilization and solving complex real-world problems. [Extracted from the article]

The addition of animated pedagogical agents (APAs) in computer-assisted learning (CAL) systems could successfully enhance students' learning motivation and engagement in learning activities. Conventionally, the APA incorporated multimedia materials are constructed through the cooperation of teachers and software programmers. However, the thinking and technical gap between two roles, a concept/content provider (teacher) and an implementer (software programmer), may lead to the concept distortion and loss in the final teaching material implementations. This often results in preventing a teacher from preparing his or her own APA-based multimedia learning systems. This study aims to develop a courseware to facilitate the CAL through the combination of the two roles into a single one and then teachers can use it freely by themselves. The courseware can help teachers to develop appropriate multimedia instructional materials incorporating APAs, which attracts students' attention and enhances their interest in learning. Furthermore, in this study two separate trial experiments were conducted to measure the teachers' satisfaction with the courseware and students' learning motivation after using APA-based multimedia learning systems. The results show that the participating teachers were satisfied with the courseware and would like to apply it in their instruction. Moreover, the participating students were strongly motivated by the customized APA-based multimedia learning systems. Therefore, the proposed courseware could give teachers a good opportunity to add pedagogical value to the curriculum, so that students' learning interest and motivation could be enhanced. [ABSTRACT FROM PUBLISHER]

Animated agents are virtual characters who demonstrate facial expressions, gestures, movements, and speech to facilitate students’ engagement in the learning environment. Our research developed a courseware that supports a XML-based markup language and an authoring tool for teachers to script animated pedagogical agents in teaching materials. The design of the markup language refers to Mayer's segmenting principle that is efficient for assisting a teacher to edit teaching contents into a set of segments and further organize the segments in a given lesson in class. This paper details a case study of applying the proposed courseware for elementary students in English education, and further investigates how the instructional materials scripted using the courseware affect the learning achievement of students. Two groups of participants from an elementary school in Taiwan enrolled in this experiment. The experimental group used animated agent-based instructional materials in their English classroom, whereas the control group participated in a traditional curriculum. Post-test results revealed that the experimental group outperformed the control group. Our experiment demonstrated that the developed courseware provides an opportunity for scripting animated pedagogical agents as appropriate instructional tools in a computer-assisted language learning environment to foster elementary students’ English learning. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents a new knowledge-based system for extracting and identifying text-lines from various real-life mixed text/graphics compound document images. The proposed system first decomposes the document image into distinct object planes to separate homogeneous objects, including textual regions of interest, non-text objects such as graphics and pictures, and background textures. A knowledge-based text extraction and identification method obtains the text-lines with different characteristics in each plane. The proposed system offers high flexibility and expandability by merely updating new rules to cope with various types of real-life complex document images. Experimental and comparative results prove the effectiveness of the proposed knowledge-based system and its advantages in extracting text-lines with a large variety of illumination levels, sizes, and font styles from various types of mixed and overlapping text/graphics complex compound document images. [Copyright &y& Elsevier]

The degree of collocational knowledge influences learners' competence in the production of L2 speech and text. However, collocation learning is complex because learners might co-produce words incorrectly according to L1 inference. This study aims at the design of an online video-assisted collocation learning system, VACLS, that integrated video captioning and concordancing to foster collocational knowledge acquisition. English collocations with three patterns, namely verb–noun, verb–adverb, and verb–preposition, were selected as target collocations in VACLS. While learning with VACLS, the learners are able to watch videos with full captions and then looked for additional knowledge and instances of the target collocations via an online concordancer incorporated in VACLS. Besides the development of VACLS, effectiveness of the system and learners' perception were also evaluated. 20 university EFL students in Taiwan enrolled the experiment. After 3 weeks of learning with the aid of VACLS, the learning outcome for collocation retention was significant and the participants with less initial collocational knowledge exhibited greater improvement in collocation retention after learning VACLS. Besides, these learners highly agreed VACLS is helpful for English collocation acquisition. Thus the VACLS can be useful for English educators and EFL/ESL learners. [ABSTRACT FROM AUTHOR]

In this new era, it is no longer impossible to create a smart home environment around the household. Moreover, users are not limited to humans but also include pets such as dogs. Dogs need long-term close companionship with their owners; however, owners may occasionally need to be away from home for extended periods of time and can only monitor their dogs' behaviors through home security cameras. Some dogs are sensitive and may develop separation anxiety, which can lead to disruptive behavior. Therefore, a novel smart home solution with an affective recommendation module is proposed by developing: (1) an application to predict the behavior of dogs and, (2) a communication platform using smartphones to connect with dog friends from different households. To predict the dogs' behaviors, the dog emotion recognition and dog barking recognition methods are performed. The ResNet model and the sequential model are implemented to recognize dog emotions and dog barks. The weighted average is proposed to combine the prediction value of dog emotion and dog bark to improve the prediction output. Subsequently, the prediction output is forwarded to a recommendation module to respond to the dogs' conditions. On the other hand, the Real-Time Messaging Protocol (RTMP) server is implemented as a platform to contact a dog's friends on a list to interact with each other. Various tests were carried out and the proposed weighted average led to an improvement in the prediction accuracy. Additionally, the proposed communication platform using basic smartphones has successfully established the connection between dog friends. [ABSTRACT FROM AUTHOR]

Stock forecasting is a significant and challenging task. The recent development of web technologies has transformed the communication channel to allow the public to share information over the web such as news, social media contents, etc., thus causing exponential growth of web data. The massively available information might be the key to revealing the financial market's unexplained variability and facilitating forecasting accuracy. However, this information is usually in unstructured natural language and consists of different inherent meanings. Although a human can easily interpret the inherent messages, it is still complicated to manually process such a massive amount of textual data due to the constraint of time, ability, energy, etc. Due to the different properties of text sources, it is crucial to understand various text processing approaches to optimize forecasting performance. This study attempted to summarize and discuss the current text-based financial forecasting approaches in the aspect of semantic-based, sentiment-based, event-extraction-based, and hybrid approaches. Afterward, the study discussed the strength and weakness of each approach, followed with their comparison and suitable application scenarios. Moreover, this study also highlighted the future research direction in text-based stock forecasting, where the overall discussion is expected to provide insightful analysis for future reference. [ABSTRACT FROM AUTHOR]

Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity. [ABSTRACT FROM AUTHOR]

Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models. [ABSTRACT FROM AUTHOR]