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ORCIDIBM Journal of Research and Development, Volume 63
Volume 63, Number 1, January - February 2019
- Bryan Wiltgen
, Ashok K. Goel:
A computational theory of evaluation in creative design. 1:1-1:11 - Lav R. Varshney:
Mathematical limit theorems for computational creativity. 2:1-2:12 - S. Agrawal, Anush Sankaran, Anirban Laha, Saneem A. Chemmengath, Disha Shrivastava, Karthik Sankaranarayanan:
What is deemed computationally creative? 3:1-3:12 - Elham Khabiri, Ying Li, Pietro Mazzoleni, Dharmesh Vadgama:
Cognitive color palette creation using client message and color psychology. 4:1-4:10 - Pegah Karimi, Mary Lou Maher, Kazjon Grace, Nicholas Davis:
A computational model for visual conceptual blends. 5:1-5:10 - Anupama Ray, P. Agarwal, C. K. Maurya, Gargi Dasgupta:
Creative tagline generation framework for product advertisement. 6:1-6:10 - Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schörgendorfer, Yi-Min Chee:
A big data approach to computational creativity: The curious case of Chef Watson. 7:1-7:18 - Pablo Gervás, Eugenio Concepción, Carlos León, Gonzalo Méndez, Pablo Delatorre:
The long path to narrative generation. 8:1-8:10 - Pedro Martins, Hugo Gonçalo Oliveira, João Gonçalves, António Cruz, Amílcar Cardoso, Martin Znidarsic, Nada Lavrac, Simo Linkola, Hannu Toivonen, Raquel Hervás, Gonzalo Méndez, Pablo Gervás:
Computational creativity infrastructure for online software composition: A conceptual blending use case. 9:1-9:17
Volume 63, Numbers 2/3, March - May 2019
- R. Viswanathan, Diptiman Dasgupta, Srinivasa Raghavan Govindaswamy:
Blockchain Solution Reference Architecture (BSRA). 1:1-1:12 - Yacov Manevich, Artem Barger, Yoav Tock:
Endorsement in Hyperledger Fabric via service discovery. 2:1-2:9 - Fabrice Benhamouda, Shai Halevi, Tzipora Halevi:
Supporting private data on Hyperledger Fabric with secure multiparty computation. 3:1-3:8 - Venkat S. K. Balagurusamy, Cyril Cabral, Srikumar Coomaraswamy, Emmanuel Delamarche, Donna N. Dillenberger, Gero Dittmann, Daniel Friedman, Onur Gökçe, Nigel Hinds, Jens Jelitto, Andreas Kind, Ashwin Dhinesh Kumar, Frank Libsch, Joseph W. Ligman, Seiji Munetoh, Chandra Narayanaswami, Abhilash Narendra, Arun Paidimarri, Miguel Ángel Prada-Delgado, James T. Rayfield, Chitra K. Subramanian, Roman Vaculín:
Crypto anchors. 4:1-4:12 - Donna Dillenberger, Petr Novotný, Q. Zhang, Praveen Jayachandran, H. Gupta, Sandeep Hans, Dinesh C. Verma, Shreya Chakraborty, J. J. Thomas, M. M. Walli, Roman Vaculín, Kanthi K. Sarpatwar:
Blockchain analytics and artificial intelligence. 5:1-5:14 - Takaaki Tateishi, Sachiko Yoshihama, Naoto Sato, Shin Saito:
Automatic smart contract generation using controlled natural language and template. 6:1-6:12 - Chandra Narayanaswami, Raj Nooyi, Srinivasa Raghavan Govindaswamy, R. Viswanathan:
Blockchain anchored supply chain automation. 7:1-7:11 - Francisco Curbera, Daniel M. Dias, Vahan Simonyan Simonyan, Woong A. Yoon, Alex Casella:
Blockchain: An enabler for healthcare and life sciences transformation. 8:1-8:9
Volume 63, Numbers 4/5, July - September 2019
- Avinash Balakrishnan, Djallel Bouneffouf, Nicholas Mattei, Francesca Rossi:
Using multi-armed bandits to learn ethical priorities for online AI systems. 1:1-1:13 - Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush R. Varshney, Murray Campbell, Moninder Singh, Francesca Rossi:
Teaching AI agents ethical values using reinforcement learning and policy orchestration. 2:1-2:9 - Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney:
Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. 3:1-3:9 - Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang:
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. 4:1-4:15 - Biplav Srivastava, Francesca Rossi:
Rating AI systems for bias to promote trustable applications. 5:1-5:9 - Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, Darrell Reimer, John T. Richards, Jason Tsay, Kush R. Varshney:
FactSheets: Increasing trust in AI services through supplier's declarations of conformity. 6:1-6:13 - Daphne L. Coates, Andrew Martin:
An instrument to evaluate the maturity of bias governance capability in artificial intelligence projects. 7:1-7:15 - Maria Y. Rodriguez, Diane DePanfilis, Paul Lanier:
Bridging the gap: Social work insights for ethical algorithmic decision-making in human services. 8:1-8:8 - Katharina Simbeck:
HR analytics and ethics. 9:1-9:12
Volume 63, Number 6, November - December 2019
- Minsik Cho, Ulrich Finkler, Mauricio J. Serrano, David S. Kung, Hillery C. Hunter:
BlueConnect: Decomposing all-reduce for deep learning on heterogeneous network hierarchy. 1:1-1:11 - David E. Womble, Mallikarjun Shankar, Wayne Joubert, J. Travis Johnston, Jack C. Wells, Jeffrey A. Nichols:
Early experiences on Summit: Data analytics and AI applications. 2:1-2:9 - Saugata Ghose, Amirali Boroumand, Jeremie S. Kim, Juan Gómez-Luna, Onur Mutlu:
Processing-in-memory: A workload-driven perspective. 3:1-3:19 - Saibal Mukhopadhyay, Yun Long, Burhan Ahmad Mudassar, C. S. Nair, Bartlet H. DeProspo, Hakki Mert Torun, M. Kathaperumal, V. Smet, Duckhwan Kim, Sudhakar Yalamanchili, Madhavan Swaminathan:
Heterogeneous integration for artificial intelligence: Challenges and opportunities. 4:1 - Subramanian S. Iyer, SivaChandra Jangam, Boris Vaisband:
Silicon interconnect fabric: A versatile heterogeneous integration platform for AI systems. 5:1-5:16 - Alon Amid, Kiseok Kwon, Amir Gholami, Bichen Wu, Krste Asanovic, Kurt Keutzer:
Co-design of deep neural nets and neural net accelerators for embedded vision applications. 6:1-6:14 - Evangelos Eleftheriou, Manuel Le Gallo, S. R. Nandakumar, Christophe Piveteau, Irem Boybat, Vinay Joshi, Riduan Khaddam-Aljameh, Martino Dazzi, Iason Giannopoulos, Geethan Karunaratne, Benedikt Kersting, Milos Stanisavljevic, Vara Prasad Jonnalagadda, Nikolas Ioannou, Kornilios Kourtis, Pier Andrea Francese, Abu Sebastian:
Deep learning acceleration based on in-memory computing. 7:1-7:16 - Hung-Yang Chang, Pritish Narayanan, Scott C. Lewis, Nathan C. P. Farinha, Kohji Hosokawa, Charles Mackin, Hsinyu Tsai, Stefano Ambrogio, An Chen, Geoffrey W. Burr:
AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed. 8:1-8:14 - Elliot J. Fuller, Yiyang Li, Christopher H. Bennett, Scott T. Keene, Armantas Melianas, Sapan Agarwal, Matthew J. Marinella, Alberto Salleo, A. Alec Talin:
Redox transistors for neuromorphic computing. 9:1-9:9 - Shubham Jain, Aayush Ankit, Indranil Chakraborty, Tayfun Gokmen, Malte J. Rasch, Wilfried Haensch, Kaushik Roy, Anand Raghunathan:
Neural network accelerator design with resistive crossbars: Opportunities and challenges. 10:1-10:13
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