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Perennial horticultural crop production is sensitive to temperature, water availability, solar radiation, air pollution, and CO_2. The value of perennial horticultural crops is derived from not only the quantity but also the quali...
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Perennial horticultural crop production is sensitive to temperature, water availability, solar radiation, air pollution, and CO_2. The value of perennial horticultural crops is derived from not only the quantity but also the quality of the harvested product. Perennial crop production is not easily moved as the climatic nature of a region changes due to many socio-economic factors including long reestablishment periods, nearness to processing plants, availability of labor, and accessible markets. Twodeciduous temperate fruit crops (apple and grape), two evergreen subtropical crops (citrus and coffee), and two tropical crops (banana/plantain and cacao) were selected as representative case studies. We evaluated the literature affecting the productionof these crops to provide an overview of the potential impacts of climate change. The literature survey identified limiting factors and provide information in assessing future climate change impacts. Although lack of data precludes a comprehensive assessment of CO_2 responses and interactions with other abiotic (and biotic) factors for most of the crops analyzed, the response of these crops to a doubling of atmospheric CO_2 is evaluated. The CO_2 fertilization effect may be amplified and sustained longer for perennial horticultural crops if other resources (e.g., nutrients and water availability) are amply supplied, and if proper management options (e.g., spacing, pruning, thinning) are practiced to facilitate the prolonged CO_2 effects. This will likely require maintaining intensive and environmentally sustainable cropping systems. In addition, the positive CO_2 effect may be negated by the detrimental effects of extreme temperatures on phenology, carbon sinks, reproductive physiology, and changes inthe disease/pest complex in the agroeco-system. There is a lack of information on the yield and quality responses of perennial horticultural crops to elevated CO_2 and the interaction with warming temperatures. Innovative research, modeling, and field trials for low-input cropping systems that integrate existing knowledge to capitalize on the benefits of elevated CO_2, while minimizing the input and costs, and temperature stresses are required to improve understanding in these crop species' responses to climate change and will better address adaptation and mitigation needs in these highly important and complex cropping systems.
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Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare ...
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Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.
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Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several f...
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Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions increases the diversity and complexity of classification problems. In this paper, we have proposed a novel and intelligent approach for the classification of human facial emotions. The proposed approach comprises customized ResNet18 by employing transfer learning with the integration of triplet loss function (TLF), followed by SVM classification model. Using deep features from a customized ResNet18 trained with triplet loss, the proposed pipeline consists of a face detector used to locate and refine the face bounding box and a classifier to identify the facial expression class of discovered faces. RetinaFace is used to extract the identified face areas from the source image, and a ResNet18 model is trained on cropped face images with triplet loss to retrieve those features. An SVM classifier is used to categorize the facial expression based on the acquired deep characteristics. In this paper, we have proposed a method that can achieve better performance than state-of-the-art (SoTA) methods on JAFFE and MMI datasets. The technique is based on the triplet loss function to generate deep input image features. The proposed method performed well on the JAFFE and MMI datasets with an accuracy of 98.44% and 99.02%, respectively, on seven emotions; meanwhile, the performance of the method needs to be fine-tuned for the FER2013 and AFFECTNET datasets.
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Most existing privacy-control methods in mobile computing support only binary and static privacy controls; therefore, it is usually difficult for mobile users to make use of effective privacy controls by considering both the neces...
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Most existing privacy-control methods in mobile computing support only binary and static privacy controls; therefore, it is usually difficult for mobile users to make use of effective privacy controls by considering both the necessity of an application and the types and quality of private information to be provided to the application under dynamic usage scenarios. In this paper, we define a quality of private information (QoPI) model to represent various types and quality levels of users' private information required by mobile applications. Using the QoPI model, we can also represent contextual properties that might affect the selection of the types and quality of private information in dynamic mobile computing situations. Users' common privacy-control patterns can be characterized, represented, and managed by using this model, and we can assist users in achieving context-aware and personalized privacy control. We evaluate the effectiveness of using the QoPI model by analyzing the data that we collected from users while allowing them to consider practical mobile computing situations. The analysis results show that the users actively utilized the fine-grained (multilevel) privacy controls supported by using the QoPI model, and their privacy-control patterns could be effectively collected and predicted based on this model. The results also show that consideration of contextual properties is essential for improving the accuracy and time performance of predicting an appropriate QoPI level to be used when a user accesses a mobile application.
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Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variet...
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Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.
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Bacterial and yeast endophytes isolated from the Salicaceae family have been shown to promote growth and alleviate stress in plants from different taxa. To determine the physiological pathways through which endophytes affect plant...
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Bacterial and yeast endophytes isolated from the Salicaceae family have been shown to promote growth and alleviate stress in plants from different taxa. To determine the physiological pathways through which endophytes affect plant water relations, we investigated leaf water potential, whole-plant water use, and stomatal responses of rice plants to Salicaceae endophyte inoculation under CO_(2)enrichment and water deficit. Daytime stomatal conductance and stomatal density were lower in inoculated plants compared to controls. Leaf ABA concentrations increased with endophyte inoculation. As a result, transpirational water use decreased significantly with endophyte inoculation while biomass did not change or slightly increased. This response led to a significant increase in cumulative water use efficiency at harvest. Different endophyte strains produced the same results in host plant water relations and stomatal responses. These stomatal responses were also observed under elevated CO_(2)conditions, and the increase in water use efficiency was more pronounced under water deficit conditions. The effect on water use efficiency was positively correlated with daily light integrals across different experiments. Our results provide insights on the physiological mechanisms of plant-endophyte interactions involving plant water relations and stomatal functions.
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Segmenting objects of dynamic shapes and various colors is still challenging to the computer vision of natural images, because of slow computation, inaccuracy, and loss of information. In this paper, we propose a novel segmentatio...
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Segmenting objects of dynamic shapes and various colors is still challenging to the computer vision of natural images, because of slow computation, inaccuracy, and loss of information. In this paper, we propose a novel segmentation algorithm based on active contour models, to overcome these weaknesses. First, we apply a morphological gradient-based edge detector to an image, to extract its edge map. Because this step is performed directly on color images, it helps us avoid losing color characteristics, compared with gray-scale conversion. Second, this edge map will be used as a clue to provide both good edge information and good region information for an active contour, without a re-initialization model. As a result, our proposed algorithm allows the contour to be initialized more flexibly, evolves the contour faster, and segments the boundary of objects more precisely in color images. Results attained on diverse natural images show its promising performance, compared with other models, for both accuracy and computational time.
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Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (know...
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Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (known as the vocal burst) is still sparse. The vocal burst is concise and has meaningless content, which is harder to deal with than verbal speech. Therefore, in this paper, we proposed a self-relation attention and temporal awareness (SRA-TA) module to tackle this problem with vocal bursts, which could capture the dependency in a long-term period and focus on the salient parts of the audio signal as well. Our proposed method contains three main stages. Firstly, the latent features are extracted using a self-supervised learning model from the raw audio signal and its Mel-spectrogram. After the SRA-TA module is utilized to capture the valuable information from latent features, all features are concatenated and fed into ten individual fully-connected layers to predict the scores of 10 emotions. Our proposed method achieves a mean concordance correlation coefficient (CCC) of 0.7295 on the test set, which achieves the first ranking of the high-dimensional emotion task in the 2022 ACII Affective Vocal Burst Workshop & Challenge.
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Logs that record system information are managed in anomaly detection, and more efficient anomaly detection methods have been proposed due to their increase in complexity and scale. Accordingly, deep learning models that automatica...
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Logs that record system information are managed in anomaly detection, and more efficient anomaly detection methods have been proposed due to their increase in complexity and scale. Accordingly, deep learning models that automatically detect system anomalies through log data learning have been proposed. However, in existing log anomaly detection models, user logs are collected from the central server system, exposing the data collection process to the risk of leaking sensitive information. A distributed learning method, federated learning, is a trend proposed for artificial intelligence learning regarding sensitive information because it guarantees the anonymity of the collected user data and collects only weights learned from each local server in the central server. In this paper, we executed an experiment regarding system log anomaly detection using federated learning. The results demonstrate the feasibility of applying federated learning in deep-learning-based system-log anomaly detection compared to the existing centralized learning method. Moreover, we present an efficient deep-learning model based on federated learning for system log anomaly detection.
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Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acqui...
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Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.
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