The Game Changer - Smartphone Shopping Applications: R. Deepika, V. Karpagam
Award-winning creative innovation studio, Holition, has published their latest research on in-store retail user experience surrounding facial recognition and other technology-based points of sales. Research, conducted in collaboration with University College London Interaction Centre, aims to address crucial pain points in consumer experience that involve a new generation of digital technologies. UX Series Volume I entails pioneer studies surrounding customers interactions with Holitions own FACE application, using facial recognition software to aid consumers journeys in-store. FACE specialises in makeup applications, using a plethora of technologies to analyse the consumers, skin tone, shade, skin type, facial features, and more in an effort to ease logistical issues of shopping in-store for and trying on makeup products. The findings underline the apps potential to support consumers whilst shopping and to drive purchase intentions. Holition is an award winning digital creative studio specialising in emerging technologies crafting premium 3D digital experiences for a growing network of pioneering digital luxury organisations. Our clients include Richemont, LVMH, Swatch Group and Kering Group across the emerging digital fashion and retail sectors. Holitions international deployments have been installed at premium retail stores including Selfridges, Harrods, Dover Street Market in London, Isetan in Tokyo, and Bloomingdales in New York. The Holition team are experts in the areas of digital retail and are widely invited to be thought leaders at global conferences and events.
Seminar paper from the year 2016 in the subject Business economics - Business Management, Corporate Governance, grade: 1,3, University of Applied Sciences Neu-Ulm , language: English, abstract: The following research paper states out the analysis of the potential of a big data based shopping application for a sales manager of a store department in Germany. Based on a theoretical-conceptual analysis the paper gives a theoretical background regarding the necessary customer data in sales, big data, mobile shopping applications and the store departments. Considering the importance of big data in commerce and the rising amount of data generated by mobile applications, the paper at hand presents which data can be tracked, which analysis can be conducted with the data and what are potential activities for a sales manager to achieve mentioned aims in the different marketing policies and the overarching aim to increase profit. The findings of the analysis demonstrate that the implementation of a mobile shopping app offers many activities to achieve or support sales and marketing goals but the complex situation of store departments also needs to be taken into account.
Personalization is ubiquitous from search engines to online-shopping websites helping us find content more efficiently and this book focuses on the key developments that are shaping our daily online experiences. With advances in the detection of end users emotions, personality, sentiment and social signals, researchers and practitioners now have the tools to build a new generation of personalized systems that will really understand the users state and deliver the right content. With leading experts from a vast array of domains from user modeling, mobile sensing and information retrieval to artificial intelligence, human-computer interaction (HCI) social computing and psychology, a broad spectrum of topics are covered. From discussing psychological theoretical models and exploring state-of-the-art methods for acquiring emotions and personality in an unobtrusive way, as well as describing how these concepts can be used to improve various aspects of the personalization process and chapters that discuss evaluation and privacy issues. Emotions and Personality in Personalized Systems will help aid researchers and practitioners develop and evaluate user-centric personalization systems that take into account the factors that have a tremendous impact on our decision-making - emotions and personality.
This book focuses on ubiquitous indoor localization services, specifically addressing the issue of floor plans. It combines computer vision algorithms and mobile techniques to reconstruct complete and accurate floor plans to provide better location-based services for both humans and vehicles via commodity smartphones in indoor environments (e.g., a multi-layer shopping mall with underground parking structures). After a comprehensive review of scene reconstruction methods, it offers accurate geometric information for each landmark from images and acoustics, and derives the spatial relationships of the landmarks and rough sketches of accessible areas with inertial and WiFi data to reduce computing overheads. It then presents the authors recent findings in detail, including the optimization and probabilistic formulations for more solid foundations and better robustness to combat errors, several new approaches to promote the current sporadic availability of indoor location-based services, and a holistic solution for floor plan reconstruction, indoor localization, tracking, and navigation. The novel approaches presented are designed for different types of indoor environments (e.g., shopping malls, office buildings and labs) and different users. A valuable resource for researchers and those in start-ups working in the field, it also provides supplementary material for students with mobile computing and networking backgrounds. Dr. Ruipeng Gao is currently a lecturer in the School of Software Engineering, Beijing Jiaotong University, China. He received his Ph.D degree in computer science from Perking University in 2016. His advisor is Prof. Jason Cong. Dr. Gaos research interests include mobile computing and sensor networks. He has published over 16 research papers on indoor localization area, including ACM MobiCom, ACM MobiSys, ACM SenSys, IEEE INFOCOM, IEEE ICC, IEEE GlobeCom, and IEEE Transactions on Mobile Computing. A portion of this monograph is from his Ph.D thesis, which has won the 2016 Outstanding PhD Dissertation Award from both Perking University and the National Innovation Center for Energy-efficient Synergy Computing in China. Dr. Fan Ye is currently an assistant professor in the ECE Department, Stony Brook University. He received the BE and MS degrees from Tsinghua University, and the PhD degree from the Computer Science Department, UCLA. He has published more than 60 peer reviewed papers that have received more than 7,000 citations according to Google Scholar. He has 21 granted/pending US and international patents/applications. His research interests include mobile sensing platforms, systems and applications, Internet-of-Things, indoor location sensing, wireless, and sensor networks. Dr. Guojie Luo is currently an associate professor in the School of EECS, Peking University, China. He received the BS degree in computer science from Peking University in 2005, and the MS and PhD degrees in computer science from UCLA, in 2008 and 2011, respectively. He received the 2013 ACM SIGDA Outstanding PhD Dissertation Award in Electronic Design Automation and the 10-year Retrospective Most Influential Paper Award at ASPDAC 2017. He is currently an assistant professor in the School of EECS, Peking University. His research interests include FPGA design automation, FPGA acceleration for imaging and sensing, and design technologies for 3D ICs. Prof. Jason Cong is a Distinguished Chancellors Professor at the Computer Science Department, also with joint appointment from the Electrical Engineering Department, of University of California, Los Angeles, the director of Center for Domain-Specific Computing (CDSC), the director of VLSI Architecture, Synthesis, and Technology (VAST) Laboratory, and a distinguished visiting professor at Peking University. He served as the chair the UCLA Computer Science Department from 2005 to 2008. Dr. Congs research interests include synthesis of VLSI circuits and systems, programmable systems, novel computer architectures, nano-systems, and highly scalable algorithms. He has over 400 publications in these areas, including 10 best paper awards, three 10-Year Most Influential Paper Awards, and one inducted to the FPGA and Reconfigurable Computing Hall of Fame. He was elected to an IEEE Fellow in 2000 and ACM Fellow in 2008, and the National Academy of Engineering in 2017.
The book will provide: 1) In depth explanation of rough set theory along with examples of the concepts. 2) Detailed discussion on idea of feature selection. 3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations. 4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each. 5) In depth investigation of various application areas using rough set based feature selection. 6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs 7) Program files of various representative Feature Selection algorithms along with explanation of each. The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers. Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality. Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms. Dr Summair Raza has PhD specialization in Software Engineering from National University of Science and Technology (NUST), Pakistan. He completed his MS from International Islamic University, Pakistan in 2009. He is also associated with Virtual University of Pakistan as Assistant Professor. He has published various papers in international level journals and conferences. His research interests include Feature Selection, Rough Set Theory, Trend Analysis, Software Architecture, Software Design and Non-Functional Requirements. Dr Usman Qamar has over 15 years of experience in data engineering both in academia and industry. He has Masters in Computer Systems Design from University of Manchester Institute of Science and Technology (UMIST), UK. His MPhil and PhD in Computer Science are from University of Manchester. Dr Qamars research expertise are in Data and Text Mining, Expert Systems, Knowledge Discovery and Feature Selection. He has published extensively in these subject areas. His Post PhD work at University of Manchester, involved various data engineering projects which included hybrid mechanisms for statistical disclosure and customer profile analysis for shopping with the University of Ghent, Belgium. He is currently an Assistant Professor at Department of Computer Engineering, National University of Sciences and Technology (NUST), Pakistan and also heads the Knowledge and Data Engineering Research Centre (KDRC) at NUST.
Bachelor Thesis from the year 2013 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, grade: 2,3, International Business School Nürnberg, course: International Business & Management - Potential of Mobile Applications in CRM, language: English, abstract: A practice-oriented approach to the implementation of mobile devices and mobile CRM strategies into the current service-infrastructure of companies dealing with high complexity in CRM. Mobile is everywhere and it´s constantly growing. News are preferably read on the phone, shopping is done via iPad and the travel business has become a highly dynamic patchwork of mobile and online services. The customer has a choice - make it easy to be chosen! [...] Data is ubiquitous and cheap, analytical ability is scarce... The sexiest job in the next ten years will be statistician. (Quote: Google´s Chief Economist, Hal Varian) [...] it is essential for companies to act, not to react, predicting, not observing market developments, in order to ensure future success. A company needs to manage customer relations instead of products.
Learn the fundamentals of PLCs and how to control them using Arduino software to create your first Arduino PLC. You will learn how to draw Ladder Logic diagrams to represent PLC designs for a wide variety of automated applications and to convert the diagrams to Arduino sketches. A comprehensive shopping guide includes the hardware and software components you need in your tool box. You will learn to use Arduino UNO, Arduino Ethernet shield, and Arduino WiFi shield. Building Arduino PLCs shows you how to build and test a simple Arduino UNO-based 5V DC logic level PLC with Grove Base shield by connecting simple sensors and actuators. You will also learn how to build industry-grade PLCs with the help of ArduiBox. What Youll Learn Build ModBus-enabled PLCs Map Arduino PLCs into the cloud using NearBus cloud connector to control the PLC through the Internet Use do-it-yourself light platforms such as IFTTT Enhance your PLC by adding Relay shields for connecting heavy loads Who This Book Is For Engineers, designers, crafters, and makers. Basic knowledge in electronics and Arduino programming or any other programming language is recommended. Pradeeka Seneviratne is a software engineer with over 10 years of experience in computer programming and systems designing. He loves programming embedded systems such as Arduino and Raspberry Pi. Pradeeka started learning about electronics when he was at primary college by reading and testing various electronic projects found in newspapers, magazines, and books. Pradeeka is currently a full-time software engineer who works with highly-scalable technologies. Previously, he worked as a software engineer for several IT infrastructure and technology servicing companies, and he was also a teacher for information technology and Arduino development. He researches how to make Arduino-based unmanned aerial vehicles and Raspberry Pi-based security cameras and is the author of the Internet of Things with Arduino Blueprints [Packt Publishing]
ntees powerful and clear sound and offers an ideal public address solution for medium to large crowds. *Bluetooth is not yet available in all countries at this time.Ideal applications include:• Schools• Places of worship• Presentations, seminars and meetings• Shopping center & trade show presentations and promotions• Aerobic & fitness centers• Parades & fetes• Street performers• Recreational & social activities • Hobby & leisure clubs• Weddings & funeral services• AuctionsKey Features• Lightweight Class-D amplifier produces 190-watt audio output.• 1 titanium compression driver & 8 woofer.• Industry’s only remote wireless master level control with use of an optional ACT-32HR transmitter microphone.• Master volume with ´´Memory” and ´´Standard” mode options. ´´Memory” mode saves and recalls last volume level during next power on. ´´Standard” mode resets to zero level.• Up to 4 optional diversity receiver modules with 16 auto-scannable frequencies.• One-touch Scan & ACT sync button for fast and easy channel set-up.• Built-in Bluetooth music player for wireless audio streaming.• Retractable handle and sturdy wheels for easy transport.• Built-in storage compartment for 2 HH or BP transmitters.• A voice priority feature that mutes the music when wireless microphone is used.• Built-in AC switching power supply for universal usage and fast battery charge.• Smart 4-segment battery meter for accurate battery and charging status.• Industry’s only MTM-92 wireless transmitter option for interlinking multiple MA-708 portables wirelessly to extend transmission range & expand coverage.• Optional lithium rechargeable battery pack offering superior cycle life, half the weight and environmentally friendly• 3 power modes: AC, rechargeable battery and DC jack.• Suitable for tripod, floor or desk mount use• Available in black or white color.