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1. Introducing a Teaching Framework for BDA Curricula With the SAP and ERPsim Games: Pedagogy and Assessment

   Wang, Ming; Zhan, Jun; Hu, Tao (August 2024, Journal of information systems education)

   This study introduces a modular teaching framework for business data analytics (BDA) curricula and programs. The framework integrates gamification features of the SAP business processes, ERPsim Games, and SAP data warehousing into the experiential learning of BDA curricula. The pedagogical practices of deploying the framework in an undergraduate BDA course are reported and assessed in virtual and face-to-face teaching modalities. The assessment shows that integrating the framework in business pedagogies enhances the BDA learning experience and teaching effectiveness. The paper concludes with the theoretical and practical implications of the study for business educators and practitioners in BDA learning, teaching, and training. The limitations and future research avenues of the study are discussed. 

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2. BDA: practical dependence analysis for binary executables by unbiased whole-program path sampling and per-path abstract interpretation

   https://doi.org/10.1145/3360563  

   Zhang, Zhuo; You, Wei; Tao, Guanhong; Wei, Guannan; Kwon, Yonghwi; Zhang, Xiangyu (October 2019, Proceedings of the ACM on Programming Languages)

   Binary program dependence analysis determines dependence between instructions and hence is important for many applications that have to deal with executables without any symbol information. A key challenge is to identify if multiple memory read/write instructions access the same memory location. The state-of-the-art solution is the value set analysis (VSA) that uses abstract interpretation to determine the set of addresses that are possibly accessed by memory instructions. However, VSA is conservative and hence leads to a large number of bogus dependences and then substantial false positives in downstream analyses such as malware behavior analysis. Furthermore, existing public VSA implementations have difficulty scaling to complex binaries. In this paper, we propose a new binary dependence analysis called BDA enabled by a randomized abstract interpretation technique. It features a novel whole program path sampling algorithm that is not biased by path length, and a per-path abstract interpretation avoiding precision loss caused by merging paths in traditional analyses. It also provides probabilistic guarantees. Our evaluation on SPECINT2000 programs shows that it can handle complex binaries such as gcc whereas VSA implementations from the-state-of-art platforms have difficulty producing results for many SPEC binaries. In addition, the dependences reported by BDA are 75 and 6 times smaller than Alto, a scalable binary dependence analysis tool, and VSA, respectively, with only 0.19% of true dependences observed during dynamic execution missed (by BDA). Applying BDA to call graph generation and malware analysis shows that BDA substantially supersedes the commercial tool IDA in recovering indirect call targets and outperforms a state-of-the-art malware analysis tool Cuckoo by disclosing 3 times more hidden payloads. 

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3. Task-Optimized Retinal-Inspired CNN Converges to Biologically-Plausible Functionality. 8th Workshop on Biological Distributed Algorithms (BDA), July 2021.

   Murray, K.; Wang, B.; Lynch, N. (January 2021, 8th Workshop on Biological Distributed Algorithms (BDA))

   Convolutional neural networks (CNN) are an emerging technique in modeling neural circuits and have been shown to converge to biologically plausible functionality in cortical circuits via task-optimization. This functionality has not been observed in CNN models of retinal circuits via task-optimization. We sought to observe this convergence in retinal circuits by designing a biologically inspired CNN model of a motion-detection retinal circuit and optimizing it to solve a motion-classification task. The learned weights and parameters indicated that the CNN converged to direction-sensitive ganglion and amacrine cells, cell types that have been observed in biology, and provided evidence that task-optimization is a fair method of building retinal models. The analysis used to understand the functionality of our CNN also indicates that biologically constrained deep learning models are easier to reason about their underlying mechanisms than traditional deep learning models. 

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4. Law and Economics and Tort Litigation Institutions: Theory and Experiments.

   Landeo, Claudia M. (January 2018, Research Handbook on Behavioral Law and Economics, K. Zeiler and J. Teitelbaum (eds.))
5. Raman Spectroscopy and Modeling and Simulation of Quantum Dots and Nanomaterials for Optoelectronic and Sensing Applications

   https://doi.org/10.32604/icces.2024.013296  

   Misra, Prabhakar; Alghamdi, Hawazin; Garcia-Sanchez, Raul; Mitchell, Wyatt; Powell, Allison; Vohra, Nikhil (January 2024, The International Conference on Computational & Experimental Engineering and Sciences)

   Semiconducting quantum dots (Q-dots) with strain-tunable electronic properties are good contenders for quantum computing devices, as they hold promise to exhibit a high level of photon entanglement. The optical and electronic properties of Q-dots vary with their size, shape, and makeup. An assortment of Q-dots has been studied, including ZnO, ZnS, CdSe and perovskites [1]. We have employed both Raman spectroscopy (to precisely determine their vibrational frequencies) and UV-VIS spectroscopy (to determine accurately their band gap energies). The electronic band structure and density of states of the ZnO and ZnS Q-dots have been investigated under strain using Density Functional Theory (DFT). The computer program SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms) was used to perform the DFT calculations via the linear combination of atomic orbitals (LCAO) method. The spin polarization of such systems may itself be used to encode information or influence the electronic properties of semiconducting Q-dots, which deserve special attention, as they have potential applications in lasers, photovoltaic cells, and imaging. In addition, we have investigated pristine and functionalized graphene nanoplatelets and metal oxides for sensing applications. 

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