Search results for: autonomous intelligence

Authors: Nazim Mir-Nasiri, Hudyjaya Siswoyo Jo

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The paper describes conceptual design, control strategies, and partial simulation for a new fully autonomous lower limb wearable exoskeleton system for human motion enhancement that can support its weight and increase strength and endurance. Various problems still remain to be solved where the most important is the creation of a power and cost efficient system that will allow an exoskeleton to operate for extended period without batteries being frequently recharged. The designed exoskeleton is enabling to decouple the weight/mass carrying function of the system from the forward motion function which reduces the power and size of propulsion motors and thus the overall weight, cost of the system. The decoupling takes place by blocking the motion at knee joint by placing passive air cylinder across the joint. The cylinder is actuated when the knee angle has reached the minimum allowed value to bend. The value of the minimum bending angle depends on usual walk style of the subject. The mechanism of the exoskeleton features a seat to rest the subject’s body weight at the moment of blocking the knee joint motion. The mechanical structure of each leg has six degrees of freedom: four at the hip, one at the knee, and one at the ankle. Exoskeleton legs are attached to subject legs by using flexible cuffs. The operation of all actuators depends on the amount of pressure felt by the feet pressure sensors and knee angle sensor. The sensor readings depend on actual posture of the subject and can be classified in three distinct cases: subject stands on one leg, subject stands still on both legs and subject stands on both legs but transit its weight from one leg to other. This exoskeleton is power efficient because electrical motors are smaller in size and did not participate in supporting the weight like in all other existing exoskeleton designs.

Keywords: energy efficient system, exoskeleton, motion enhancement, robotics

Procedia PDF Downloads 364

Authors: Weichen Chang

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To create a guided teaching method for AI generative drawing design, this paper develops a set of teaching models for AI generative drawing (DCAI), which combines learning modes such as problem-solving, thematic inquiry, phenomenon-based, task-oriented, and DFC . Through the information security AI picture book learning guided programs and content, the application of participatory action research (PAR) and interview methods to explore the dual knowledge of Context and ChatGPT (DCAI) for AI to guide the development of students' AI learning skills. In the interviews, the students highlighted five main learning outcomes (self-study, critical thinking, knowledge generation, cognitive development, and presentation of work) as well as the challenges of implementing the model. Through the use of DCAI, students will enhance their consensus awareness of generative mapping analysis and group cooperation, and they will have knowledge that can enhance AI capabilities in DCAI inquiry and future life. From this paper, it is found that the conclusions are (1) The good use of DCAI can assist students in exploring the value of their knowledge through the power of stories and finding the meaning of knowledge communication; (2) Analyze the transformation power of the integrity and coherence of the story through the context so as to achieve the tension of ‘starting and ending’; (3) Use ChatGPT to extract inspiration, arrange story compositions, and make prompts that can communicate with people and convey emotions. Therefore, new knowledge construction methods will be one of the effective methods for AI learning in the face of artificial intelligence, providing new thinking and new expressions for interdisciplinary design and design education practice.

Keywords: artificial intelligence, task-oriented, contextualization, design education

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

Procedia PDF Downloads 118

Authors: Felix Liedel

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The rise of artificial intelligence has numerous and far-reaching consequences. In addition to the obvious consequences for entire professions, the increasing interaction with chatbots also has a wide range of social consequences and implications. We are already increasingly used to interacting with digital chatbots, be it in virtual consulting situations, creative development processes or even in building personal or intimate virtual relationships. A media-theoretical classification of these phenomena has so far been difficult, partly because the interactive element in the exchange with artificial intelligence has undeniable similarities to human-to-human communication but is not identical to it. The proposed study, therefore, aims to reformulate the concept of symbolic interaction in the tradition of George Herbert Mead as symbolic interactivity in communication with chatbots. In particular, Mead's socio-psychological considerations will be brought into dialog with the specific conditions of digital media, the special dispositive situation of chatbots and the characteristics of artificial intelligence. One example that illustrates this particular communication situation with chatbots is so-called consensus fiction: In face-to-face communication, we use symbols on the assumption that they will be interpreted in the same or a similar way by the other person. When briefing a chatbot, it quickly becomes clear that this is by no means the case: only the bot's response shows whether the initial request corresponds to the sender's actual intention. This makes it clear that chatbots do not just respond to requests. Rather, they function equally as projection surfaces for their communication partners but also as distillations of generalized social attitudes. The personalities of the chatbot avatars result, on the one hand, from the way we behave towards them and, on the other, from the content we have learned in advance. Similarly, we interpret the response behavior of the chatbots and make it the subject of our own actions with them. In conversation with the virtual chatbot, we enter into a dialog with ourselves but also with the content that the chatbot has previously learned. In our exchanges with chatbots, we, therefore, interpret socially influenced signs and behave towards them in an individual way according to the conditions that the medium deems acceptable. This leads to the emergence of situationally determined digital identities that are in exchange with the real self but are not identical to it: In conversation with digital chatbots, we bring our own impulses, which are brought into permanent negotiation with a generalized social attitude by the chatbot. This also leads to numerous media-ethical follow-up questions. The proposed approach is a continuation of my dissertation on moral decision-making in so-called interactive films. In this dissertation, I attempted to develop a concept of symbolic interactivity based on Mead. Current developments in artificial intelligence are now opening up new areas of application.

Keywords: artificial intelligence, chatbot, media theory, symbolic interactivity

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

Procedia PDF Downloads 114

Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Michael Mederle, Heinz Bernhardt

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The Management of machine fleets or autonomous vehicle control will considerably increase efficiency in future agricultural production. Especially entire process chains, e.g. harvesting complexes with several interacting combine harvesters, grain carts, and removal trucks, provide lots of optimization potential. Organization and pre-planning ensure to get these efficiency reserves accessible. One way to achieve this is to optimize infield path planning. Particularly autonomous machinery requires precise specifications about infield logistics to be navigated effectively and process optimized in the fields individually or in machine complexes. In the past, a lot of theoretical optimization has been done regarding infield logistics, mainly based on field geometry. However, there are reasons why farmers often do not apply the infield strategy suggested by mathematical route planning tools. To make the computational optimization more useful for farmers this study focuses on these influencing factors by expert interviews. As a result practice-oriented navigation not only to the field but also within the field will be possible. The survey study is intended to cover the entire range of German agriculture. Rural mixed farms with simple technology equipment are considered as well as large agricultural cooperatives which farm thousands of hectares using track guidance and various other electronic assistance systems. First results show that farm managers using guidance systems increasingly attune their infield-logistics on direction giving obstacles such as power lines. In consequence, they can avoid inefficient boom flippings while doing plant protection with the sprayer. Livestock farmers rather focus on the application of organic manure with its specific requirements concerning road conditions, landscape terrain or field access points. Cultivation of sugar beets makes great demands on infield patterns because of its particularities such as the row crop system or high logistics demands. Furthermore, several machines working in the same field simultaneously influence each other, regardless whether or not they are of the equal type. Specific infield strategies always are based on interactions of several different influences and decision criteria. Single working steps like tillage, seeding, plant protection or harvest mostly cannot be considered each individually. The entire production process has to be taken into consideration to detect the right infield logistics. One long-term objective of this examination is to integrate the obtained influences on infield strategies as decision criteria into an infield navigation tool. In this way, path planning will become more practical for farmers which is a basic requirement for automatic vehicle control and increasing process efficiency.

Keywords: autonomous vehicle control, infield logistics, path planning, process optimizing

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Authors: A. Ribeiro, N. Valério, C. Vilarinho, J. Araujo, J. Carvalho

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Res2ValHUM project involves institutions from the Spanish Autonomous Region of Galicia and the north of Portugal (districts of Porto and Braga) and has as overall objectives of promotion of composting as an process for the correct managing of organic waste, valorization of compost in different fields or applications for the constitution of products with high added value, reducing of raw materials losses, and reduction of the amount of waste throw in landfills. Three main actions were designed to achieve the objectives: development of a management tool to improve collection and residue channeling for composting, sensibilization of the population for composting and characterization of the chemical and biological properties of compost and humic and fulvic substances to envisage high-value applications of compost. Here we present the cooperative activity of Galician and northern Portuguese institutions to valorize organic waste in both regions with common socio-economic characteristics and residue management problems. Results from the creation of the resource manage tool proved the existence of a large number of agricultural wastes that could be valorized. In the North of Portugal, the wastes from maize, oats, potato, apple, grape pomace, rye, and olive pomace can be highlighted. In the Autonomous Region of Galicia the wastes from maize, wheat, potato, apple, and chestnuts can be emphasized. Regarding the isolation and identification of microbial consortia from compost samples, results proved microorganisms belong mainly to the genus Bacillus spp. Among all the species identified in compost samples, Bacillus licheniformis can be highlighted in the production of humic and fulvic acids.

Keywords: agricultural wastes, Bacillus licheniformis, Bacillus spp., humic-acids, fulvic-acids

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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Authors: Abolfazl Zaraki, Yoshikatsu Hayashi, Harry Thorpe, Vincent Strong, Gisle-Andre Larsen, William Holderbaum

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Thanks to the high maneuverability of the cable-driven hyper-redundant manipulators (HRMs), this class of robots has shown a superior capability in highly confined and unstructured space applications. Although the large number of degrees of freedom (DOF) of HRMs enhances the motion flexibility and the robot’s reachability range, it highly increases the complexity of the kinematic configuration which makes the kinematic control problem very challenging or even impossible to solve. This paper presents our current progress achieved on the development of a kinematic-based leader-follower control system which is designed to control not only the robot’s body posture but also to control the trajectory of the robot’s movement in a semi-autonomous manner (the human operator is retained in the robot’s control loop). To obtain the forward kinematic model, the coordinate frames are established by the classical Denavit–Hartenburg (D-H) convention for a hyper-redundant serial manipulator which has a controlled cables-driven mechanism. To solve the inverse kinematics of the robot, unlike the conventional methods, a leader-follower mechanism, based on the sequential inverse kinematic, is followed. Using this mechanism, the inverse kinematic problem is solved for all sequential joints starting from the head joint to the base joint of the robot. To verify the kinematic design and simulate the robot motion, the MATLAB robotic toolbox is used. The simulation result demonstrated the promising capability of the proposed leader-follower control system in controlling the robot motion and trajectory in our confined space application.

Keywords: hyper-redundant robots, kinematic analysis, semi-autonomous control, serial manipulators

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Authors: Li Yong

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Using the data of 135 financial institutions in China from 2018 to 2022, this paper deeply analyzes the underlying theoretical mechanism of artificial intelligence (AI) technology promoting financial development and examines the impact of AI technology on the digital transformation performance of financial enterprises. It is found that the level of AI technology has a significant positive impact on the development of finance. Compared with the impact on the expansion of financial scale, AI technology plays a greater role in improving the performance of financial institutions, reflecting the trend characteristics of the current AI technology to promote the evolution of financial structure. By investigating the intermediary transmission effects, we found that AI technology plays a positive role in promoting the performance of financial institutions by reducing operating costs and improving customer satisfaction, but its function in innovating financial products and mitigating financial risks is relatively limited. In addition, the promotion of AI technology in financial development has significant heterogeneity in terms of the type, scale, and attributes of financial institutions.

Keywords: artificial intelligence technology, financial development, China, heterogeneity

Procedia PDF Downloads 56

Authors: Han-Yu Cheng

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This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment.

Keywords: generative AI, anime aesthetics, Japanese anime illustration, art consumption

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Authors: Ying Zhu

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Recent years have witnessed a boom of foreign investments in the field of artificial intelligence (AI). Foreign investments provide critical capital for AI development but also trigger national security concerns of host states. A notable example is an increasing number of cases in which the Committee on Foreign Investment in the United States (CFIUS) has denied Chinese acquisitions of US technology companies on national security grounds. On July 19, 2018, the Congress has reached a deal on the final draft of a new provision to strengthen CFIUS’s authority to review overseas transactions involving sensitive US technology. The question is: how to reconcile the emerging tension between, on the one hand, foreign AI investors’ expectations of a predictable investment environment, and on the other hand, host states’ regulatory power on national security? This paper provides a methodology to reconcile this tension under international investment law. Based on an examination, the national security exception clauses in international investment treaties and the application of national security justification in investor-state arbitration jurisprudence, the paper argues that a traditional interpretation of the national security exception, based on the necessity concept in customary international law, fails to take into account new risks faced by countries, including security concerns over strategic industries such as AI. To overcome this shortage, the paper proposes to incorporate an integrated national security clause in international investment treaties, which includes a two-tier test: a ‘self-judging’ test in the pre-establishment period and a ‘proportionality’ test in the post-establishment period. At the end, the paper drafts a model national security clause for future treaty-drafting practice.

Keywords: foreign investment, artificial intelligence, international investment law, national security exception

Procedia PDF Downloads 143

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 177

Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Viridiana Edith Ardura Perea, José Luis Bermúdez Alcocer

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The purpose of this paper is to establish the influence of shading on a Building Integrated Photovoltaic (BIPV) system´s performance in an urban context. The PV systems of the Science Center of Complexity (Centro de Ciencias de la Complejidad) Building based in the Main Campus of the National and Autonomous University of Mexico (UNAM) in Mexico City was taken as case study.  The PV systems are placed on the rooftop and on the south façade of the building.  The south-façade PV system, operating as sunshades, consists of two strings:  one at the ground floor and the other one at the first floor.  According to the building’s facility manager, the south-façade PV system generates 42% less electricity per kilowatt peak (kWp) installed than the one on the roof.  The methods applied in this study were Solar Radiation Analysis (SRA) simulations performed with the Insight 360 Plug-in from Revit 2018^® and an on-site measurement using specialized tools.  The results of the SRA simulations showed that the shading casted by the PV system placed on the first floor on top of the PV system of the ground floor decreases its solar incident radiation over 50%.  The simulation outcome was compared and validated to the measured data obtained from the on-site measurement.  In conclusion, the loss factor achieved from the shading of the PVs is due to the surroundings and the PV system´s own design.  The south-façade BIPV system’s deficient design generates critical losses on its performance and decreases its profitability.

Keywords: building integrated photovoltaics design, energy analysis software, shading losses, solar radiation analysis

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Michael K. O. Ayomoh, Oyindamola A. Omotuyi

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Autonomous mobile robots deployed to explore or operate in a never explored or an abandoned sub-surface mine requires extreme effectiveness in coordination and communication. In a bid to transmit information from the depth of the mine to the external surface in real-time and amidst diverse physical, chemical and virtual impediments, the concept of unified cooperative robots is seen to be a proficient approach. This paper presents an effective [human → robot → task] coordination framework for effective exploration of an abandoned underground mine. The problem addressed in this research is basically the development of a globalized optimization model premised on time series differentiation and geometrical configurations for effective positioning of the two classes of robots in the cooperation namely the outermost stationary master (OSM) robots and the innermost dynamic task (IDT) robots for effective bi-directional signal transmission. In addition, the synchronization of a vision system and wireless communication system for both categories of robots, fiber optics system for the OSM robots in cases of highly sloppy or vertical mine channels and an autonomous battery recharging capability for the IDT robots further enhanced the proposed concept. The OSM robots are the master robots which are positioned at strategic locations starting from the mine open surface down to its base using a fiber-optic cable or a wireless communication medium all subject to the identified mine geometrical configuration. The OSM robots are usually stationary and function by coordinating the transmission of signals from the IDT robots at the base of the mine to the surface and in a reverse order based on human decisions at the surface control station. The proposed scheme also presents an optimized number of robots required to form the cooperation in a bid to reduce overall operational cost and system complexity.

Keywords: sub-surface mine, wireless communication, outermost stationary master robots, inner-most dynamic robots, fiber optic

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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Authors: Chastiti Mediafira Wulolo, Tri Legionosuko, Suhirwan, Yusuf

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Terrorism and radicalization have become a common threat to every nation in this world. As a part of the asymmetric warfare threat, terrorism and radicalization need a complex strategy as the problem solver. One such way is by collaborating with the international community. The Our Eyes Initiative (OEI), for example, is a cooperation pact in the field of intelligence information exchanges related to terrorism and radicalization initiated by the Indonesian Ministry of Defence. The pact has been signed by Indonesia, Philippines, Malaysia, Brunei Darussalam, Thailand, and Singapore. This cooperation mostly engages military acts as a central role, but it still requires the involvement of various parties such as the police, intelligence agencies and other government institutions. This paper will use a qualitative content analysis method to address the opportunity and enhance the optimization of OEI. As the result, it will explain how OEI takes the opportunities as the strategy for counter-terrorism by building it up as the regional cooperation, building the legitimacy of government and creating the legal framework of the information sharing system.

Keywords: our eyes initiative, terrorism, counter-terrorism, ASEAN, cooperation, strategy

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Mattias Bergstrom

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Significance of the Study: This research will provide new insights into the risks with digital embedded infrastructure. Through this research, we will analyze each risk and its potential negation strategies, especially for AI and autonomous automation. Moreover, the analysis that is presented in this paper will convey valuable information for future research that can create more stable, secure, and efficient autonomous systems. To learn and understand the risks, a large IoT system was envisioned, and risks with hardware, tampering, and cyberattacks were collected, researched, and evaluated to create a comprehensive understanding of the potential risks. Potential solutions have then been evaluated on an open source IoT hardware setup. This list shows the identified passive and active risks evaluated in the research. Passive Risks: (1) Hardware failures- Critical Systems relying on high rate data and data quality are growing; SCADA systems for infrastructure are good examples of such systems. (2) Hardware delivers erroneous data- Sensors break, and when they do so, they don’t always go silent; they can keep going, just that the data they deliver is garbage, and if that data is not filtered out, it becomes disruptive noise in the system. (3) Bad Hardware injection- Erroneous generated sensor data can be pumped into a system by malicious actors with the intent to create disruptive noise in critical systems. (4) Data gravity- The weight of the data collected will affect Data-Mobility. (5) Cost inhibitors- Running services that need huge centralized computing is cost inhibiting. Large complex AI can be extremely expensive to run. Active Risks: Denial of Service- It is one of the most simple attacks, where an attacker just overloads the system with bogus requests so that valid requests disappear in the noise. Malware- Malware can be anything from simple viruses to complex botnets created with specific goals, where the creator is stealing computer power and bandwidth from you to attack someone else. Ransomware- It is a kind of malware, but it is so different in its implementation that it is worth its own mention. The goal with these pieces of software is to encrypt your system so that it can only be unlocked with a key that is held for ransom. DNS spoofing- By spoofing DNS calls, valid requests and data dumps can be sent to bad destinations, where the data can be extracted for extortion or to corrupt and re-inject into a running system creating a data echo noise loop. After testing multiple potential solutions. We found that the most prominent solution to these risks was to use a Peer 2 Peer consensus algorithm over a blockchain to validate the data and behavior of the devices (sensors, storage, and computing) in the system. By the devices autonomously policing themselves for deviant behavior, all risks listed above can be negated. In conclusion, an Internet middleware that provides these features would be an easy and secure solution to any future autonomous IoT deployments. As it provides separation from the open Internet, at the same time, it is accessible over the blockchain keys.

Keywords: IoT, security, infrastructure, SCADA, blockchain, AI

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences. Humanity has never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

Procedia PDF Downloads 356

Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: Selcuk Aslan, Dervis Karaboga, Celal Ozturk

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Artificial Bee Colony (ABC) algorithm is one of the most successful swarm intelligence based metaheuristics. It has been applied to a number of constrained or unconstrained numerical and combinatorial optimization problems. In this paper, we presented a parallelized version of ABC algorithm by adapting employed and onlooker bee phases to the Compute Unified Device Architecture (CUDA) platform which is a graphical processing unit (GPU) programming environment by NVIDIA. The execution speed and obtained results of the proposed approach and sequential version of ABC algorithm are compared on functions that are typically used as benchmarks for optimization algorithms. Tests on standard benchmark functions with different colony size and number of parameters showed that proposed parallelization approach for ABC algorithm decreases the execution time consumed by the employed and onlooker bee phases in total and achieved similar or better quality of the results compared to the standard sequential implementation of the ABC algorithm.

Keywords: Artificial Bee Colony algorithm, GPU computing, swarm intelligence, parallelization

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Authors: Günther Schuh, Christina König, Toni Drescher

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One of the key factors for the competitiveness and market success of technology-driven companies is the timely provision of information about emerging technologies, changes in existing technologies, as well as relevant related changes in the market's structures and participants. Therefore, many companies conduct technology intelligence (TI) activities to ensure an early identification of appropriate technologies and other (weak) signals. One base activity of TI is technology monitoring, which is defined as the systematic tracking of developments within a specified topic of interest as well as related trends over a long period of time. Due to the very large number of dynamically changing parameters within the technological and the market environment of a company as well as their possible interdependencies, it is necessary to focus technology monitoring on specific indicators or other criteria, which are able to point out technological developments and market changes. In addition to the execution of a literature review on existing approaches, which mainly propose patent-based indicators, it is examined in this paper whether indicator systems from other branches such as risk management or economic research could be transferred to technology monitoring in order to enable an efficient and focused technology monitoring for companies.

Keywords: technology forecasting, technology indicator, technology intelligence, technology management, technology monitoring

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Authors: Prakash Singh

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Employees who are satisfied at their place of work rate their leaders’ intrapersonal competencies as being high. They also believe that a leader’s intrapersonal competencies influence their sense of job satisfaction. Employees who indicate that they are unhappy at their place of work rate their leaders’ intrapersonal competencies as being low. They also believe that a leader’s intrapersonal intelligence influence their feeling of job satisfaction. The leader’s appropriate intrapersonal competencies are crucial to the creation of a motivated and satisfied employee team. In this study, the quantitative research method was used to determine the employees’ perceptions of their leaders’ intrapersonal competencies and their influence on their job satisfaction; the six competencies being self-awareness, self-confidence, self-expression, self-control, adaptability, and optimism. All the competencies of leaders identified in this quantitative study can therefore be described as intervening variables that influence an employee’s sense of job satisfaction. The number of responses that indicate that each of the intrapersonal competencies of a leader that will have an influence on an employee’s sense of job satisfaction, ranges from 93% (a leader’s sense of self-awareness) to 99% (a leader’s ability to be adaptable). As the responses are significantly similar, it can be stated that the respondents indicate that all the intrapersonal competencies of a leader can influence an employee’s sense of job satisfaction. The findings of this study strongly suggest that in order to be satisfied at work, employees prefer to be led by leaders who are confident in their leadership roles; who send out clear, unambiguous messages; who maintain self-control; who are adaptable and flexible;, who face the future with optimism and who support the establishment of a collegial working environment. Evidently, the findings corroborate the hypothesis that employees believe that the intrapersonal competencies of leaders have a positive influence on the employees’ sense of job satisfaction. This study’s findings, therefore, confirm that the key to the leaders’ self-knowledge is access to their own feelings and the ability to discriminate among them and draw upon them to guide behaviour in their organisations. This exploratory study makes a contribution to the emerging research being accomplished on leaders’ intrapersonal intelligence with more research still needing to be attempted to determine to what extent these competencies of leaders can reshape the organizational climate and culture.

Keywords: emotional intelligence, employees’ job satisfaction, leaders’ intrapersonal competencies, leaders’ self-knowledge

Procedia PDF Downloads 261

Authors: Dustin Schöder

Abstract:

The potential benefits of the utilization of highly automated and autonomous trucks in logistics operations are the subject of interest to the entire logistics industry. The benefits of the use of these new technologies were scientifically investigated and implemented in roadmaps. So far, reliable data and experiences from real life use cases are still limited. A German research consortium of both academics and industry developed a highly automated (SAE level 4) vehicle for yard operations at railports and container depots. After development and testing, a several month field test at the DUSS Terminal in Ulm-Dornstadt (Germany) and the nearby DB Intermodal Services Container Depot in Ulm-Dornstadt was conducted. The truck was piloted in a shuttle service between both sites. In a holistic automation approach, the vehicle was integrated into a digital communication platform so that the truck could move autonomously without a driver and his manual interactions with a wide variety of stakeholders. The main goal is to investigate the effects of highly automated trucks in the key processes of container loading, unloading and container relocation on holistic railport yard operation. The field test data were used to investigate changes in process efficiency of key processes of railport and container yard operations. Moreover, effects on the capacity utilization and potentials for smothering peak workloads were analyzed. The results state that process efficiency in the piloted use case was significantly higher. The reason for that could be found in the digitalized data exchange and automated dispatch. However, the field test has shown that the effect is greatly varying depending on the ratio of highly automated and manual trucks in the yard as well as on the congestion level in the loading area. Furthermore, the data confirmed that under the right conditions, the capacity utilization of highly automated trucks could be increased. In regard to the potential for smothering peak workloads, no significant findings could be made based on the limited requirements and regulations of railway operation in Germany. In addition, an empirical survey among railport managers, operational supervisors, innovation managers and strategists (n=15) within the logistics industry in Germany was conducted. The goal was to identify key characteristics of future railports and terminals as well as requirements that railports will have to meet in the future. Furthermore, the railport processes where automation and autonomization make the greatest impact, as well as hurdles and challenges in the introduction of new technologies, have been surveyed. Hence, further potential use cases of highly automated and autonomous applications could be identified, and expectations have been mapped. As a result, a highly detailed and practice-based roadmap towards a ‘terminal 4.0’ was developed.

Keywords: highly automated driving, autonomous driving, SAE level 4, railport operations, container depot, intermodal logistics, potentials of autonomization

Procedia PDF Downloads 74