Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis e.g. in increasing the classification accuracy. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification accuracy. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification accuracy of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy subjects performed 6 sessions of 15 gait trials for one day. For each trial, two force plates recorded the 3D ground reaction forces (GRF). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each individual preprocessing step were analyzed and compared with respect to their prediction accuracy in a six-session classification using Support Vector Machines, Random Forest Classifiers and Multi-Layer Perceptrons. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

The determination of the computational complexity of multi-agent pathfinding on directed graphs has been an open problem for many years. For undirected graphs, solvability can be decided in polynomial time, as has been shown already in the eighties. Further, recently it has been shown that a special case on directed graphs is solvable in polynomial time. In this paper, we show that the problem is NP-hard in the general case. In addition, some upper bounds are proven.

With the burning desire and passion to equip ourselves and others with Artificial Intelligence skills, the mission and vision of global AI6 resonated with Azeez Oluwafemi and I the moment it was announced. We were quick to kick-start AI6 in our local community, Lagos, Nigeria on January 6th 2018. We organized and facilitated 16 weeks of study group from January till April and then from August till November. As ambassadors, we played a pivotal role in empowering the AI community in our city to get updated with the latest methods in artificial intelligence, and be able to implement the most cutting-edge AI models out there. For our first cycle, we walked our participants through Computer Vision from Stanford CS231n and Fast AI deep learning for coders.

Co-Founder and Group CEO, AntWorks -- Asheesh is the co-founder and CEO of AntWorks, a global leader in AI and Robotics. He believes humane, responsible AI is the future, and is excited by its limitless applications to solve for issues t… (show all) Asheesh is the co-founder and CEO of AntWorks, a global leader in AI and Robotics. He believes humane, responsible AI is the future, and is excited by its limitless applications to solve for issues that impact business, our lives and the planet we inhabit. Prior to boarding the entrepreneurial ship, Asheesh headed Infosys BPO – Asia Pacific, Japan and the Middle East. His experience over twenty years has also spanned across leadership roles in large ITeS organizations, such as Mphasis, TCS and WNS, having worked extensively across the UK and the United States.

Latest Strategic Study Released on Global Software for Autonomous Cars Market with forecast till 2025, the report comprises of historical data and estimation of the Global Software for Autonomous Cars Market. The following Industry is shown to progress with a noteworthy rise in the Compound Annual Growth Rate (CAGR) during the forecast period owing to various factors driving the market. Some of the key players mentioned in this research are "Alphabet, Delphi Automotive, Intel, NVIDIA, QNX Software Systems, Tesla, Apple, Autotalks, Cisco, Cohda Wireless, Covisint, DeepMap & Nauto", etc. Rapid Growth Factors In addition, the market is growing at a fast pace and the report shows us that there are a couple of key factors behind that. The most important factor that's helping the market grow faster than usual is the tough competition. Business Strategies Key strategies in theGlobal Software for Autonomous Cars Market that includes product developments, partnerships, mergers and acquisitions, etc discussed in this report.

Smart cities are no longer a utopian dream of the future. Thanks to a slew of innovative and game-changing technologies, they are already active and growing quickly. Smart cities could be described as the junction between three main areas, namely digital transformation, environmental sustainability and economic performance. They can be described as a framework made up of connected technologies designed to address the challenges of rapid urbanisation and promote more sustainable, smarter practices. And the more urbanisation soars, standards of living sustainability become more than pipe dreams, but rather calls for action.

Canadian immigration officials deny travel visas to a large number of AI researchers and research students scheduled to attend the NeurIPS and Black in AI workshop, event organizers said. Among the people who have been denied entry is Tẹjúmádé Àfọ njá, co-organizer of the NeurIPS Machine Learning workshop for the developing world. NeurIP Information Processing Systems (NeurIPs) is the world's largest annual international AI conference, according to the AI Index 2018 report. The conference is scheduled to be held from December 8 to 14 in Vancouver, Canada. On Tuesday, Black in AI co-founder and Google AI researcher Timnit Gebru said that 15 of the 44 attendees who planned to join the workshop on December 9 were denied entry.

Good day, The biggest AI bootcamp in Nigeria is here! Will you be part of the best of the best who will make it to the all-expense paid residential Artificial Intelligence Bootcamp?... This was a mail I received on September 24 from Data Science Nigeria. And below is a snippet of what I got on Data Science Nigeria's website today. I started my programming journey back in September, 2018 with the most highly rated course on Udemy courtesy of my mentor, Fakorede Abiola.

At Hot Chips 2019, Intel revealed new details of upcoming high-performance artificial intelligence (AI) accelerators: Intel Nervana neural network processors, with the NNP-T for training and the NNP-I for inference. Intel engineers also presented technical details on hybrid chip packaging technology, Intel Optane DC persistent memory and chiplet technology for optical I/O. Myriad X is the first VPU to feature the Neural Compute Engine - a dedicated hardware accelerator for running on-device deep neural network applications. Interfacing directly with other key components via the intelligent memory fabric, the Neural Compute Engine is able to deliver industry leading performance per Watt without encountering common data flow bottlenecks encountered by other architectures. Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated (NASDAQ: QCOM), announced that it is bringing the Company's artificial intelligence (AI) expertise to the cloud with the Qualcomm Cloud AI 100. Built from the ground up to meet the explosive demand for AI inference processing in the cloud, the Qualcomm Cloud AI 100 utilizes the Company's heritage in advanced signal processing and power efficiency. Our 4th generation on-device AI engine is the ultimate personal assistant for camera, voice, XR and gaming – delivering smarter, faster and more secure experiences. Utilizing all cores, it packs 3 times the power of its predecessor for stellar on-device AI capabilities. With the open-source release of NVDLA's optimizing compiler on GitHub, system architects and software teams now have a starting point with the complete source for the world's first fully open software and hardware inference platform. The next generation of NVIDIA's GPU designs, Turing will be incorporating a number of new features and is rolling out this year. Nvidia launched its second-generation DGX system in March. In order to build the 2 petaflops half-precision DGX-2, Nvidia had to first design and build a new NVLink 2.0 switch chip, named NVSwitch.

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