Supervised cross-modal factor analysis for multiple modal data classification

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods

Cold-formed steel sections with web openings subjected to web crippling under two-flange loading conditions — Part II : Parametric study and proposed design equations

A parametric study of cold-formed steel sections with web openings subjected to web crippling was undertaken using finite element analysis, to investigate the effects of web holes and cross-section sizes on the web crippling strengths of channel sections subjected to web crippling under both interior-two-flange (ITF) and end-two-flange (ETF) loading conditions. In both loading conditions, the hole was centred beneath the bearing plate. It was demonstrated that the main factors influencing the web crippling strength are the ratio of the hole depth to the flat depth of the web, and the ratio of the length of bearing plates to the flat depth of the web. In this paper, design recommendations in the form of web crippling strength reduction factors are proposed, that are conservative to both the experimental and finite element results

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity

Researching EMI policy and practice multilingually: reflections from China and Turkey

In the field of English medium instruction (EMI), multilingual research approaches are crucial to carrying out effective and ethically responsible research, because EMI policies and practices are inherently multilingual. This paper is a partial replication study that adopts a ‘researching multilingually’ analytical framework to interrogate the challenges and affordances of using multiple languages during two EMI research projects. In the project in Turkey, the lead researcher, who is an English-Turkish bilingual, analysed policy documents (n = 145) and interview data (n = 67) drawing on her knowledge of both languages. Additionally, 85 EMI classroom observations were conducted. In the project in China, the research team of two L1 English speakers and two L1 Chinese speakers investigated 93 bilingual policy documents and conducted interviews with 26 policy arbiters by drawing on both languages during data collection and analysis. Together, these reflections highlight how multilingual approaches can be utilised throughout the research process, from team formation, research design, data collection, data analysis, and presentation of findings in research reports

Intelligence Augmentation: Towards Building Human-Machine Symbiotic Relationship

Artificial intelligence, which people originally modeled after human intelligence, has made significant advances in recent years. These advances have caused many to fear that machines will surpass human intelligence and dominate humans. Intelligence augmentation (IA) has the potential to turn the tension between the two intelligence types into a symbiotic one. Although IA has not gained momentum until recent years, the idea that machines can amplify human abilities has existed for many decades. Expanded from a panel discussion on Intelligence Augmentation at the 2020 International Conference of Information Systems (ICIS), we define IA in light of its history and evolution and classify IA based on its capabilities, roles, and responsibilities. Based on reviewing the IA literature in terms of research themes, enabling technology, and applications, we identify key research issues, challenges, and future opportunities

A Data Compression Algorithm for Wireless Sensor Networks Based on an Optimal Order Estimation Model and Distributed Coding

In many wireless sensor network applications, the possibility of exceptions occurring is relatively small, so in a normal situation, data obtained at sequential time points by the same node are time correlated, while, spatial correlation may exist in data obtained at the same time by adjacent nodes. A great deal of node energy will be wasted if data which include time and space correlation is transmitted. Therefore, this paper proposes a data compression algorithm for wireless sensor networks based on optimal order estimation and distributed coding. Sinks can obtain correlation parameters based on optimal order estimation by exploring time and space redundancy included in data which is obtained by sensors. Then the sink restores all data based on time and space correlation parameters and only a little necessary data needs to be transmitted by nodes. Because of the decrease of redundancy, the average energy cost per node will be reduced and the life of the wireless sensor network will obviously be extended as a result

FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications

The miniaturized calcium imaging is an emerging neural recording technique that can monitor neural activity at large scale at a specific brain region of a rat or mice. It has been widely used in the study of brain functions in experimental neuroscientific research. Most calcium-image analysis pipelines operate offline, which incurs long processing latency thus are hard to be used for closed-loop feedback stimulation targeting certain neural circuits. In this paper, we propose our FPGA-based design that enables real-time calcium image processing and position decoding for closed-loop feedback applications. Our design can perform real-time calcium image motion correction, enhancement, and fast trace extraction based on predefined cell contours and tiles. With that, we evaluated a variety of machine learning methods to decode positions from the extracted traces. Our proposed design and implementation can achieve position decoding with less than 1 ms latency under 300 MHz on FPGA for a variety of mainstream 1-photon miniscope sensors. We benchmarked the position decoding accuracy on open-sourced datasets collected from six different rats, and we show that by taking advantage of the ordinal encoding in the decoding task, we can consistently improve decoding accuracy without any overhead on hardware implementation and runtime across the subjects.Comment: 11 pages, 15 figure