Innovation is a cornerstone for an organization’s survival and success in the global competitive landscape in the VUCA world. The New Product Development (NPD) process is a crucial part of the portfolio and Innovation Management (IM) process. The leadership of an organization has a disproportionate impact on the outcome of innovation activities. Their involvement in IM and NPD is critical for success, considering they make strategic decisions to allocate resources for business growth. The leadership team demands a holistic picture of ideas before making decisions at early stages. The leadership challenge in decision making is that they have a limited time to make decisions by understanding many related aspects and insights quickly. The visual approaches have been vital in management practices to understand the situation and aid in decision-making by supporting cognitive processes. The fundamental problem in using visual representation is hidden expectations of leadership teams to represent needed elements to aid in strategic decision-making by leadership at the early stage of innovation. Also, the configuration of elements and interplay is another issue. The core challenge lies in understanding the effectiveness of currently used visual representations and then improving them by identifying needed elements and their configuration and placement in the visual representation. The paper takes literature review, expert interviews, and brainstorming approaches to distill the challenges to the concrete research questions and objectives. Providing solutions to the open research questions and challenges can significantly enhance the quality and speed of innovation-related decision-making.
Innovation is a cornerstone for an organization’s survival and success in the global competitive landscape in the VUCA world. The New Product Development (NPD) process is a crucial part of the portfolio and Innovation Management (IM) process. The leadership of an organization has a disproportionate impact on the outcome of innovation activities. Their involvement in IM and NPD is critical for success, considering they make strategic decisions to allocate resources. The leadership team demands a holistic picture related to ideas before making decisions at early stages. The leadership challenge in decision making is that they have a limited time to make decisions by understanding many related aspects and insights quickly. The visual approaches have been vital in management practices to understand the situation and aid in decision-making by supporting cognitive processes. The fundamental problem in using visual tools is understanding the needed elements in the visual tools to aid in strategic decision-making by leadership at the early stage of innovation. The answer to this challenge can significantly enhance the quality and speed of innovation-related decision-making. The research aims to understand the usage of visual representations utilized by organization leaders in the decision-making process at the early stage of innovation within the agile stage-gate process for new product development. From the understanding of visual representations, the research aims to derive a framework of the visual representation with its needed elements and relationships. The framework is mainly intended for practitioners to deploy in an organizational setting.
Artificial Intelligence has made a significant contribution to autonomous vehicles, from object detection to path planning. However, AI models require a large amount of sensitive training data and are usually computationally intensive to build. The commercial value of such models motivates attackers to mount various attacks. Adversaries can launch model extraction attacks for monetization purposes or step-ping-stone towards other attacks like model evasion. In specific cases, it even results in destroying brand reputation, differentiation, and value proposition. In addition, IP laws and AI-related legalities are still evolving and are not uniform across countries. We discuss model extraction attacks in detail with two use-cases and a generic kill-chain that can compromise autonomous cars. It is essential to investigate strategies to manage and mitigate the risk of model theft.
Kabaddi is a contact team sport of Indian-origin. It is a highly strategic game and generates a significant amount of data due to its rules. However, data generated from kabaddi tournaments has so far been unused, and coaches and players rely heavily on intuitions to make decisions and craft strategies. This paper provides a quantitative approach to the game of kabaddi. The research derives outlook from an analysis performed on data from the 3rd Standard-style Kabaddi World Cup 2016, organised by the International Kabaddi Federation. The dataset, which consists of 66 entries over 31 variables from 33 matches, was manually curated. This paper discusses and provides a quantitative perspective on traditional strategies and conceptions related to the game of kabaddi such as attack and defence strategies. Multiple hypotheses are built and validated using student’s t-test. This paper further provides a quantitative approach to profile an entire tournament to gain a general understanding of the strengths of various teams. Additionally, team-specific profiling, through hypotheses testing and visualisation, is presented to gain a deeper understanding of team’s behaviour and performance. This paper also provides multiple models to forecast the winner. The model-building includes automatic feature selection techniques and variable importance analysis techniques. Generalised linear model with and without an elastic net, recursive partitioning and regression tree, conditional inference tree, random forest, support vector machine (linear and radial) and neural network-based models are built and presented. Ensemble models use generalised linear model and random forest model techniques as ensemble method to combine outcome of a generalised linear model with the elastic net, random forest, and neural network-based models. The research discusses the comparison between models and their performance parameters. Research also suggests that ensemble technique is not able to boost up accuracy. Models achieve 91.67%-100% accuracy on cross-validation dataset and 78.57%-100% on test set. Results presented can be used to design in-game real-time winning predictions to improve decision-making. Results presented can be used to design agent and environments to train artificial intelligence via reinforced learning model.
The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and swift responses critical for diverse applications, its devices' intrinsic resource limitations expose them to security risks. This research delves into the adversarial vulnerabilities of AI models on resource-constrained embedded hardware, with a focus on Model Extraction and Evasion Attacks. Our findings reveal that adversarial attacks from powerful host machines could be transferred to smaller, less secure devices like ESP32 and Raspberry Pi. This illustrates that adversarial attacks could be extended to tiny devices, underscoring vulnerabilities, and emphasizing the necessity for reinforced security measures in TinyML deployments. This exploration enhances the comprehension of security challenges in TinyML and offers insights for safeguarding sensitive data and ensuring device dependability in AI-powered edge computing settings.
Applications related to artificial intelligence, machine learning, and system identification simulations essentially use eigenvectors. Calculating eigenvectors for very large matrices using conventional methods is compute-intensive and renders the applications slow. Recently, Eigenvector-Eigenvalue Identity formula promising significant speedup was identified. We study the algorithmic implementation of the formula against the existing state-of-the-art algorithms and their implementations to evaluate the performance gains. We provide a first of its kind systematic study of the implementation of the formula. We demonstrate further improvements using high-performance computing concepts over native NumPy eigenvector implementation which uses LAPACK and BLAS.
The technologies and algorithms are growing at an exponential rate. The technologies are capable enough to solve technically challenging and complex problems which seemed impossible task. However, the trending methods and approaches are facing multiple challenges on various fronts of data, algorithms, software, computational complexities, and energy efficiencies. Nature also faces similar challenges. Nature has solved those challenges and formulation of those are available as Nature Inspired Algorithms (NIA), which are derived based on the study of nature. A novel method based on TRIZ to map the real-world problems to nature problems is explained here.TRIZ is a Theory of inventive problem solving. Using the proposed framework, best NIA can be identified to solve the real-world problems. For this framework to work, a novel classification of NIA based on the end goal that nature is trying to achieve is devised. The application of the this framework along with examples is also discussed.
Innovation is a cornerstone for an organization’s survival and success in the global competitive landscape in the VUCA world. The New Product Development (NPD) process is a crucial part of the portfolio and Innovation Management (IM) process. The leadership of an organization has a disproportionate impact on the outcome of innovation activities. Their involvement in IM and NPD is critical for success, considering they make strategic decisions to allocate resources for business growth. The leadership team demands a holistic picture of ideas before making decisions at early stages. The leadership challenge in decision making is that they have a limited time to make decisions by understanding many related aspects and insights quickly. The visual approaches have been vital in management practices to understand the situation and aid in decision-making by supporting cognitive processes. The fundamental problem in using visual representation is hidden expectations of leadership teams to represent needed elements to aid in strategic decision-making by leadership at the early stage of innovation. Also, the configuration of elements and interplay is another issue. The core challenge lies in understanding the effectiveness of currently used visual representations and then improving them by identifying needed elements and their configuration and placement in the visual representation. The paper takes literature review, expert interviews, and brainstorming approaches to distill the challenges to the concrete research questions and objectives. Providing solutions to the open research questions and challenges can significantly enhance the quality and speed of innovation-related decision-making.
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