Applications of Internet of Things and Unmanned Aerial Vehicle in Smart Agriculture: A Review

With the rapid advancement of technology and decline in human ability, technology has become a part of every aspect of our lives. Agriculture and irrigation are two domains in which man's potential may be exploited to its fullest. To commercialise in the industry, a variety of sensors and electronics devices are employed to keep prices down in a few domains. In order to save money and enhance the abilities of agricultural experts, UAVs (unmanned aerial vehicles) can be used for reconnaissance, pesticide and insecticide application, and bioprocessing mistake detection. When it comes to this application, both single-mode and multi-mode UAV systems will work just fine. On the other hand, this chapter identifies the challenges and limitations of IoT and UAVs connection in remote locations, demonstrating several use cases of smart agriculture and the advantages and applications of using IoT and UAVs in agriculture.

MAINTAIN AGENT CONSISTENCY IN SURAKARTA CHESS USING DUELING DEEP NETWORK WITH INCREASING BATCH

Deep reinforcement learning usage in creating intelligent agents for various tasks has shown outstanding performance, particularly the Q-Learning algorithm. Deep Q-Network (DQN) is a reinforcement learning algorithm that combines the Q-Learning algorithm and deep neural networks as an approximator function. In the single-agent environment, the DQN model successfully surpasses human ability several times over. Still, when there are other agents in the environment, DQN may experience decreased performance. This research evaluated a DQN agent to play in the two-player traditional board game of Surakarta Chess. One of the drawbacks that we found when using DQN in two-player games is its consistency. The agent will experience performance degradation when facing different opponents. This research shows Dueling Deep Q-Network usage with increasing batch size can improve the agent's performance consistency. Our agent trained against a rule-based agent that acts based on the Surakarta Chess positional properties and was then evaluated using different rule-based agents. The best agent used Dueling DQN architecture with increasing batch size that produced a 57% average win rate against ten different agents after training for a short period. ABSTRAK: Pembelajaran Peneguhan Mendalam adalah terbaik apabila digunakan bagi mewujudkan ejen pintar dalam menyelesaikan pelbagai tugasan, terutama jika ia melibatkan algoritma Pembelajaran-Q. Algoritma Rangkaian-Q Mendalam (DQN) adalah Pembelajaran Peneguhan berasaskan gabungan algoritma Pembelajaran-Q dan rangkaian neural sebagai fungsi penghampiran. Melalui persekitaran ejen tunggal, model DQN telah beberapa kali berjaya mengatasi kemampuan manusia. Namun, ketika ejen lain berada dalam persekitaran ini, DQN mungkin kurang berjaya. Kajian ini melibatkan ejen DQN bermain papan tradisional iaitu Catur Surakarta dengan dua pemain. Salah satu kekurangan yang dijumpai adalah konsistensi. Ejen ini akan kurang bagus ketika berhadapan lawan berbeza. Kajian menunjukkan dengan penggunaan Rangkaian-Q Dwipertarungan Mendalam bersama peningkatan saiz kumpulan dapat meningkatkan konsistensi prestasi ejen. Ejen ini telah dilatih untuk melawan ejen lain berasaskan peraturan dan sifat kedudukan Catur Surakarta. Kemudian, ejen ini diuji berpandukan peraturan berbeza. Ejen terbaik adalah yang menggunakan rekaan DQN Dwipertarungan bersama peningkatan saiz kumpulan. Ianya berhasil memenangi permainan dengan purata 57% berbanding sepuluh agen lain melalui latihan jangka masa pendek.

Decision prioritization and causal reasoning in decision hierarchies

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

The Blunting of Occam’s Razor: Omnicompetent Reductionism Distorting Conceptions of Giftedness

Theoretical and practical work in gifted education has been dominated by mechanistic precision in measurements designed to select students for gifted programs and guide them through their development. Too much faith in mechanistic precision can become a form of dogmatism that obscures very important, less-measurable dimensions of human ability. This interdisciplinary analysis explores this form of dogmatism while illustrating some ways in which it misaligns gifted education with turbulent, complex 21st-century trends and issues.

CHARACTERISTICS OF ARTISTIC AND ACTIVITY CRITERION OF FORMATION OF CREATIVITY OF A CHILD OF PRESCHOOL AGE BY MEANS OF A MODERN ARTISTPLAYER

The article analyzes the contemporaneity and relevance of the point of creation of preschool children. In the framework of scientific and pedagogical research, creativity considers as a qualitative change in human ability that corresponds to the psychophysical and psycho-emotional processes of the preschool period of personality formation. With the help of the conducted experimental work based on children’s preschool institutions of Poltava region, the art-activity criterion of creativity of children of 5-7 years with the involvement of modern art-game material has been revealed. Particular attention has been paid to modern scientific research, which is devoted to the matter of children’s creativity, which is developing in creative activities. As a result of experimental work, we identified indicators of artistic and creative criteria of creativity of preschool children, namely the indicator of artistic and game literacy, the indicator of artistic and playful actions, and the indicator of improvisation and creative activity.Special attention should be paid to identifying the levels of indicators of artistic and creative criteria of creativity of preschool children with the involvement of contemporary art and game material in the experiment - high, medium, and low levels of creativity, their features, and quality examples. Our article contains a diagnosis of the problems faced by preschool children when playing, as a leading activity or when getting acquainted with the developmental potential of modern art and game material from the standpoint of an artistic and creative criterion of creativity. Prospects for further development in the direction of forming the creativity of preschool children using modern art and game material are aimed at preparing a curriculum in this thematic area.

Formation of the Idea of Natural Law in Ancient Greece and Ancient Rome

The article analyzes the formation of the idea of natural law, which has an important theoretical and applied significance, as it makes it possible to better understand the essence of law, its connection with egalitarian and humanistic teachings. The research is based on modern philosophical worldview approaches, such general scientific research methods as axiological, anthropological, phenomenological, comparative-historical, comparative-legal, system-structural, hermeneutical, functional, institutional, as well as formal-legal method are used. The article examines the works of representatives of the Milesian school founded by Thales in the first half of the 6th century BC, whose analysis of human consciousness, human ability to create, transform the world, formulate ideas and implement them led to the idea of a universal Logos, a universal divine Mind, and the Law of Nature. The article reveals the contribution of sophists to the development of the idea of the natural law who justified the differences between natural and human law, defended the idea of equality of all people, called for not discriminating against citizens, depending on their origin, and denied slavery. The role of representatives of the stoicism school in substantiating the idea of natural law based on awareness of the fundamental difference between human nature and nature, justifying the existence of the unchangeable law of nature (lex naturale) in the form of common sense, equality of all people, recognition of slavery contrary to human nature, the need for recognition of human rights by law to preserve human dignity is highlighted. The article examines the influence of the ideas of the philosophers of Ancient Greece on the development of Roman law, the role of the Scipio group in this influence, and the essence of the then rational understanding of natural law as a true law, namely, common sense, which, in accordance with nature, concerns all people, is unchangeable and eternal

Human latent-state generalization through prototype learning with discriminative attention

People cannot access the latent causes giving rise to experience. How then do they approximate the high-dimensional feature space of the external world with lower-dimensional internal models that generalize to novel examples or contexts? Here, we developed and tested a theoretical framework that internally identifies states by feature regularity (i.e., prototype states) and selectively attends to features according to their informativeness for discriminating between likely states. To test theoretical predictions, we developed experimental tasks where human subjects first learn through reward-feedback internal models of latent states governing actions associated with multi-feature stimuli. We then analyzed subjects’ response patterns to novel examples and contexts. These combined theoretical and experimental results reveal that the human ability to generalize actions involves the formation of prototype states with flexible deployment of top-down attention to discriminative features. These cognitive strategies underlie the human ability to generalize learned latent states in high-dimensional environments.

Skateboarders’ Representations of Materiality: A Case Study of Rodney Mullen and Spike Jonze

Physical activity is commonly conceived of in terms of its human involvement – as a test of, and testament to, human ability. However, physical activity does not exist without the contributions of countless non-human agencies, such as equipment and environments, with which the athletes work closely and form relationships. As such, athletes have a unique understanding of non-human agency. In this article I analyse the power of non-human agency in skateboarding through the representations of the professional skateboarder Rodney Mullen and filmmaker Spike Jonze. I examine their lectures, interviews, and films to show the ways in which skateboarders experience, practice, and represent the principles of actor-network theory (ANT). Skateboarders utilise and manipulate the often-unanticipated potential of non-human tools and urban landscapes and translate them into a collaborative result. Skateboarding is a trial-and-error experiment of testing, innovating, and adapting possibilities and limitations set by a network of mediators including people and ‘things’. Mullen and Jonze commonly depict skateboarding as the product of networks rather than independent human action. Their representations reveal how skateboarders perceive and act out their role as humans within networks alongside non-human agencies such as skateboards and obstacles, and which combine to produce skateboarding.

Bootstrapping Ordinal Thinking

In this chapter, the authors apply cognitive neuroscience, gene–culture co-evolution, and extended cognition to account for the evolution of an unusual neurologically grounded trait—the ability to arrange items in ordered sequences. Cognitive neuroscience strongly suggests that the human ability to conceive of and use ordinal sequences such as alphabets and calendars relies on dedicated neural resources, yet ordinal sequences such as these do not exist in nature. There is thus the provocative possibility that ordinal thinking evolved as a specific response to cultural phenomena. But which, and how? Applying the perspectives of extended cognition and gene–culture co-evolution (neuronal recycling in particular), the authors explore the likelihood that ordinal cognition arose through the manipulation of material artifacts, with stringing beads for thousands of generations being one possible scenario.