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(4) A novel ‵multi-robot map update‵ in case of dynamic obstacles in the map is proposed, such that robots update other robots about the positions of dynamic obstacles in the map.
In this work, we propose a localisation strategy in which robots have no access to centralised information.
In this paper, we propose a new collision avoidance method among multiple autonomous mobile robots.
These results seem to demonstrate a promising potential for guide robots designed to build rapport with customers in a shopping mall.
It is shown that real robots can adaptively avoid collision with each other and to obstacles in a complicated situation.
Experiments using a real robotic system show that the method can autonomously prevent collisions between the moving robots themselves and between the robots and the environment.
The results show that the PLANET approach to mobile Web search robots significantly reduces the amount of data transferred via the Internet and that it enables the robots to work more efficiently than the robots in the conventional stationary scheme whenever nontrivial amounts of HTML files are processed.

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What is Long Short-Term Memory?
4 answers
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) extensively utilized for historical time series prediction. LSTM networks are equipped with memory cells that enable the model to make long-term predictions, crucial for capturing complex changes in historical time series data. Hyperparameter optimization is a key challenge in maximizing LSTM performance, especially for users at varying expertise levels. Researchers have proposed innovative methods like incorporating additional memory cells and utilizing optimization algorithms such as the whale optimization algorithm (WOA) to enhance LSTM models for tasks like short-term load forecasting. These approaches aim to improve the accuracy and efficiency of LSTM-based predictions by addressing data processing, hyperparameter selection, and model optimization challenges.
How to define the optimal pricing and credit granting strategy for low-income people?
5 answers
To define the optimal pricing and credit granting strategy for low-income individuals, a combination of differentiated subsidies on commodities and credit-based congestion pricing (CBCP) can be considered. By offering travel credits to low-income users to offset toll payments, and implementing subsidies on goods consumed in higher proportion by low-income households, a more equitable system can be established. Additionally, leveraging peer groupings among low-income earners can help pool resources, improve risk mitigation, and enhance credit quality levels. Partnering with innovative citizen sector organizations can also aid in effectively reaching low-income markets and maximizing social impact. By integrating these approaches, a comprehensive strategy can be formulated to address the pricing and credit needs of low-income populations.
What are some of the barriers to instructional technology integration specifically in the integration of AIED?
4 answers
Barriers to instructional technology integration, particularly in the integration of Artificial Intelligence in Education (AIED), include technophobia, lack of time, absence of planning, lack of incentives, lack of evaluation, work saturation, intermittent power supply, lack of skills to use technologies, intermittent Internet connectivity, simplification leading to behaviorism, information cocoon from algorithmic recommendations, teachers' AI anxiety, ethical concerns, and emotional deficiencies. These barriers hinder the effective adoption and utilization of AIED in educational settings, emphasizing the need for addressing these challenges to enhance the integration of technology in teaching and learning processes.
What are the most commonly used methods for detecting and preventing cyberbullying?
5 answers
The most commonly used methods for detecting and preventing cyberbullying include traditional machine learning models, deep learning approaches, and natural language processing techniques. Traditional machine learning models have been widely employed in the past, but they are often limited to specific social networks. Deep learning models, such as Long Short Term Memory (LSTM) and 1DCNN, have shown promising results in detecting cyberbullying by leveraging advanced algorithms and embeddings. Additionally, the integration of Natural Language Processing (NLP) with Machine Learning (ML) algorithms, like Random Forest, has proven effective in real-time cyberbullying detection on platforms like Twitter. These methods aim to analyze social media content, language, and user interactions to identify and prevent instances of cyberbullying effectively.
What is the taxonomic classification of bamboo leaves?
5 answers
Bamboo leaves used in products can be taxonomically classified to the genera Phyllostachys and Pseudosasa from the temperate "woody" bamboo tribe (Arundinarieae). The temperate bamboos, part of the Bambusoideae subfamily, are morphologically diverse and have a complex taxonomy, with the Arundinaria clade being a significant lineage within this group. Additionally, a hierarchical classification approach utilizing the K nearest neighbor algorithm has been proposed for effective discrimination of bamboo species, which can have implications for the conservation of Giant Pandas. Molecular phylogenetic analyses have been conducted to understand the relationships among temperate woody bamboo species, emphasizing the importance of chloroplast DNA markers and complete plastomes in determining taxonomic classifications within this group.
How the channel can be estimated in irs-assisted mmWave multiuser MIMO system?
5 answers
Channel estimation in IRS-assisted mmWave multiuser MIMO systems can be achieved through various innovative approaches. One method involves leveraging deep learning for two-stage channel estimation, where the sparsity of the mmWave massive MIMO channel in the angular domain is exploited using a convolutional neural network, followed by channel reconstruction through a least squares problem. Another technique utilizes a machine learning-based channel predictor to estimate and predict user-IRS channels efficiently, reducing training pilot signals and enhancing data rates. Additionally, a peak detection-message passing algorithm can estimate angle, delay parameters, and channel gain by exploiting the array steering vector properties, particularly effective in low SNR scenarios. These methods showcase the diverse strategies available for accurate and efficient channel estimation in IRS-assisted mmWave multiuser MIMO systems.
How to improve the accuracy of LLM model?
5 answers
To enhance the accuracy of Large Language Models (LLMs), several strategies have been proposed. One approach involves utilizing a human evaluation framework to assess model answers across various dimensions like factuality, comprehension, reasoning, possible harm, and bias. Additionally, instruction prompt tuning has been introduced as a parameter-efficient method to align LLMs to new domains, showing improvements in comprehension, knowledge recall, and reasoning with model scale. Another method includes implementing a Selection-Inference (SI) framework that leverages pre-trained LLMs for logical reasoning tasks, resulting in significant performance enhancements without fine-tuning. Moreover, employing a natural approach in multiple-choice question answering tasks, along with ensuring high multiple choice symbol binding (MCSB) ability in LLMs, has shown promising results in improving accuracy and closing the gap with the state of the art.
Why can the technological interventions fail in fighting farm theft?
10 answers
Technological interventions in combating farm theft, while innovative and promising, can encounter several challenges that may lead to their failure. One primary reason is the complexity and high maintenance costs associated with some anti-theft systems, which can be burdensome for farmers to manage and sustain over time. Additionally, the effectiveness of these technologies can be significantly hindered by the lack of internet access and the knowledge on how to use these technologies, especially in rural areas of African countries where 3G/4G network coverage is sparse. Moreover, the practicality of implementing high-tech solutions like DNA technology for livestock identification in South Africa has been elusive for many potential livestock farmers, indicating a gap between the availability of technology and its accessibility or usability by the target population. Similarly, the reliance on surveillance systems and sensor networks for monitoring farm perimeters can be compromised by limitations such as the inability to distinguish between humans and animals or the resolution of images, which may not be sufficient to identify intruders accurately. The effectiveness of technological interventions is also affected by the farmers' attitudes and the police's perspectives towards farm crime prevention. While there is a general acknowledgment of the potential of technology in preventing farm crimes, concerns about the limited police resources and the efficacy of these methods have been raised. Furthermore, the situational crime prevention strategies, although developed for urban environments, suggest that increasing effort, risk, and reducing rewards for offenders through capable guardianship on farms can be challenging to achieve in rural settings. In essence, the failure of technological interventions in fighting farm theft can be attributed to a combination of factors including high costs, technological limitations, accessibility issues, and the broader socio-economic and resource constraints faced by farmers and law enforcement agencies.
What are the relationships between countermovement jump and repeated sprint?
5 answers
The relationship between countermovement jump (CMJ) and repeated sprint performance has been extensively studied in various sports contexts. Research indicates that CMJ performance can be affected by repeated sprint training, with changes observed during and post-training sessions. Additionally, asymmetries in jump height between limbs can increase following repeated sprint protocols, suggesting a link between sprint fatigue and inter-limb differences in jump performance. Furthermore, the level of repeat sprint ability (RSA) has been shown to influence changes in CMJ characteristics, emphasizing the importance of considering individual sprint capabilities when interpreting fatigue-induced alterations in neuromuscular performance. These findings highlight the interconnectedness between CMJ outcomes and repeated sprint activities, underscoring the relevance of monitoring jump performance as a metric for assessing acute fatigue in athletes undergoing sprint training protocols.
Mental health questionnaire on VR or XR devices?
4 answers
Virtual Reality (VR) and Extended Reality (XR) technologies offer innovative ways to administer mental health questionnaires. VR can enhance assessment by providing immersive environments for questionnaire administration. XR, including VR, can revolutionize mental health treatment by offering safe spaces for therapy and self-exploration. Studies have shown that VR can improve decision-making in healthcare and aid in diagnosing mental disorders. Additionally, research indicates that questionnaires administered by virtual humans in VR scenarios are practically equivalent to self-administered ones, suggesting the feasibility of using VR for questionnaire delivery. Therefore, utilizing mental health questionnaires on VR or XR devices can offer personalized, engaging, and effective assessment tools for individuals seeking mental health support.
What are the limitations of police patrol in combating farm theft?
10 answers
The limitations of police patrol in combating farm theft are multifaceted, reflecting challenges in resources, effectiveness, and strategic approaches. Firstly, the effectiveness of traditional and technological prevention methods, such as regular patrols and CCTV, is perceived as limited due to constrained police resources, highlighting a significant challenge in adequately addressing farm crime. This is compounded by the police's struggle to resource rural policing effectively against a backdrop of budget cuts, inadequate strategic guidance, and a lack of understanding of the impact of rural and farm crime, which further diminishes farmers' confidence in police efforts. Moreover, the failure of farmers to report crimes due to the inability to prove ownership of stolen stock and a lack of public knowledge about the extent and impact of crime victimization presents a major obstacle to the policing of agricultural crime. This issue is exacerbated by the outdated character of studies on routine police patrol, which fail to clearly establish the quantitative crime deterrent effects of such patrols, indicating a gap in contemporary understanding of their effectiveness. The Agricultural Crime, Technology, Information, and Operations Network (ACTION) initiative suggests that increasing guardianship measures and hardening targets may help reduce victimization, but these efforts are limited by the need for more comprehensive approaches to arrest and prosecute offenders. The physical, social, and cultural context of rural communities further complicates the policing and prevention of agricultural crime, as highlighted by the varied responses of rural police to property-related victimizations on farms. Additionally, a broad lack of police training, insight into farming issues, and wider organizational resource commitment hinders effective policing of farm business crime, despite some satisfaction and trust in the police among farmers. The complexity of the police officer patrol problem (POPP) in ensuring effective surveillance further underscores the challenges in combating farm theft through patrol alone. Lastly, while smart surveillance systems offer potential solutions, their effectiveness is limited by the resolution of images and the ability to distinguish between legitimate and dubious individuals.