Image retrieval with hierarchical matching pursuit

A novel representation of images for image retrieval is introduced in this paper, by using a new type of feature with remarkable discriminative power. Despite the multi-scale nature of objects, most existing models perform feature extraction on a fixed scale, which will inevitably degrade the performance of the whole system. Motivated by this, we introduce a hierarchical sparse coding architecture for image retrieval to explore multi-scale cues. Sparse codes extracted on lower layers are transmitted to higher layers recursively. With this mechanism, cues from different scales are fused. Experiments on the Holidays dataset show that the proposed method achieves an excellent retrieval performance with a small code length.Comment: 5 pages, 6 figures, conferenc

Insights Into Privacy Protection Research in AI

This paper presents a systematic bibliometric analysis of the artificial intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023. The analysis highlights IEEE Transactions on Knowledge and Data Engineering and IEEE Access journals as highly influential, the former being an early contributor and the latter emerging as a pivotal source. The study demonstrates substantial disparities in scientific productivity across countries. Specifically, the top 10 countries collectively accounted for 74.8% of the articles, with China and the USA making up nearly half of the total contribution (46.1%). In contrast, regions in Africa and South America exhibited lower scientific production. The evolution of privacy preservation research is reflected, shifting from an algorithm-oriented approach to a focus on data orientation, and subsequently, to privacy solutions centered around Cloud Computing. In recent years, there has been a shift towards embracing Federated Learning and Differential Privacy. The analysis brings to light emerging themes and identifies research gaps, notably a global disparity in research output and a lag in ethical and legal inquiry. It asserts that enhanced interdisciplinary collaboration is imperative to formulate comprehensive privacy solutions for AI. Specifically, the paper imparts invaluable insights that are pivotal for effectively addressing the evolving privacy concerns in the era of AI and big data

An analytical and numerical study of magnetic spring suspension and energy recovery mechanism

As the automotive paradigm shifts towards electric, limited range remains a key challenge. Increasing the battery size adds weight, which yields diminishing returns in range per kilowatt-hour. Therefore, energy recovery systems, such as regenerative braking and photovoltaic cells, are desirable to recharge the onboard batteries in between hub charge cycles. While some reports of regenerative suspension do exist, they all harvest energy in a parasitic manner, and the predicted power output is extremely low, since the majority of the energy is still dissipated to the environment by the suspension. This paper proposes a fundamental suspension redesign using a magnetically-levitated spring mechanism and aims to increase the recoverable energy significantly by directly coupling an electromagnetic transducer as the main damper. Furthermore, the highly nonlinear magnetic restoring force can also potentially enhance rider comfort. Analytical and numerical models have been constructed. Road roughness data from an Australian road were used to numerically simulate a representative environment response. Simulation suggests that 10’s of kW to >100 kW can theoretically be generated by a medium-sized car travelling on a typical paved road (about 2–3 orders of magnitude higher than literature reports on parasitic regenerative suspension schemes), while still maintaining well below the discomfort threshold for passengers (<0.315 m/s 2 on average)

Influencing Factors of Entrepreneurial Intention among Engineering Students in Sichuan, China

Purpose: The purpose of this study is to investigate the key influencing factors of entrepreneurial intention of engineering students in Sichuan, China. The conceptual framework proposes Entrepreneurship Education (EE), Personal Attitudes (PA), Perceived Behavioral Control (PBC), Subjective Norms (SN), Entrepreneurial Self-efficacy (ESE), Entrepreneurial Creativity (EC) and Entrepreneurial Intention (EI). Research design, data and methodology: A quantitative research method (N=693) was adopted to issue questionnaires to engineering students in Xihua University. Nonprobability sampling technique includes judgmental sampling, stratified random sampling, and convenience sampling. Confirmatory factor analysis (CFA) and structural equation model (SEM) was used for data analysis and model measurement, including factor loading, reliability, validity and model fit. Results: The results illustrate Entrepreneurship Education (EE) was affected by entrepreneurial self-efficacy (ESE), perceived behavioral control (PBC) and personal attitude (PA). Entrepreneurial self-efficacy (ESE) had an effect on entrepreneurial creativity (EC). Personal attitude (PA) and entrepreneurial creativity (EC) significantly affected entrepreneurial intention (EI). Whereas ESE, PBC and SN did not significant to EI. Conclusions: Out of nine hypotheses, only six were supported to meet the research objectives. Therefore, it is suggested to carry out effective reform of entrepreneurship education in combination with the national construction of new engineering for improving students' entrepreneurial intention

D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism

Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. Recent studies have shown that token labels can convey crucial task-specific information and enrich token semantics. However, as far as we know, due to completely abstain from sequence tagging mechanism, all prior span-based work fails to use token label in-formation. To solve this problem, we pro-pose Sequence Tagging enhanced Span-based Network (STSN), a span-based joint extrac-tion network that is enhanced by token BIO label information derived from sequence tag-ging based NER. By stacking multiple atten-tion layers in depth, we design a deep neu-ral architecture to build STSN, and each atten-tion layer consists of three basic attention units. The deep neural architecture first learns seman-tic representations for token labels and span-based joint extraction, and then constructs in-formation interactions between them, which also realizes bidirectional information interac-tions between span-based NER and RE. Fur-thermore, we extend the BIO tagging scheme to make STSN can extract overlapping en-tity. Experiments on three benchmark datasets show that our model consistently outperforms previous optimal models by a large margin, creating new state-of-the-art results.Comment: 10pages, 6 figures, 4 table

Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition

For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear complementary advantages, but few models have attempted to leverage these advantages in a single NER model as far as we know. In our previous work, we proposed a paradigm known as Bundling Learning (BL) to address the above problem. The BL paradigm bundles the two NER paradigms, enabling NER models to jointly tune their parameters by weighted summing each paradigm's training loss. However, three critical issues remain unresolved: When does BL work? Why does BL work? Can BL enhance the existing state-of-the-art (SOTA) NER models? To address the first two issues, we implement three NER models, involving a sequence labeling-based model--SeqNER, a span-based NER model--SpanNER, and BL-NER that bundles SeqNER and SpanNER together. We draw two conclusions regarding the two issues based on the experimental results on eleven NER datasets from five domains. We then apply BL to five existing SOTA NER models to investigate the third issue, consisting of three sequence labeling-based models and two span-based models. Experimental results indicate that BL consistently enhances their performance, suggesting that it is possible to construct a new SOTA NER system by incorporating BL into the current SOTA system. Moreover, we find that BL reduces both entity boundary and type prediction errors. In addition, we compare two commonly used labeling tagging methods as well as three types of span semantic representations

Modeling subauroral polarization streams during the 17 March 2013 storm

The subauroral polarization streams (SAPS) are one of the most important features in representing magnetosphere‐ionosphere coupling processes. In this study, we use a state‐of‐the‐art modeling framework that couples an inner magnetospheric ring current model RAM‐SCB with a global MHD model Block‐Adaptive Tree Solar‐wind Roe Upwind Scheme (BATS‐R‐US) and an ionospheric potential solver to study the SAPS that occurred during the 17 March 2013 storm event as well as to assess the modeling capability. Both ionospheric and magnetospheric signatures associated with SAPS are analyzed to understand the spatial and temporal evolution of the electrodynamics in the midlatitude regions. Results show that the model captures the SAPS at subauroral latitudes, where Region 2 field‐aligned currents (FACs) flow down to the ionosphere and the conductance is lower than in the higher‐latitude auroral zone. Comparisons to observations such as FACs observed by Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE), cross‐track ion drift from Defense Meteorological Satellite Program (DMSP), and in situ electric field observations from the Van Allen Probes indicate that the model generally reproduces the global dynamics of the Region 2 FACs, the position of SAPS along the DMSP, and the location of the SAPS electric field around L of 3.0 in the inner magnetosphere near the equator. The model also demonstrates double westward flow channels in the dusk sector (the higher‐latitude auroral convection and the subauroral SAPS) and captures the mechanism of the SAPS. However, the comparison with ion drifts along DMSP trajectories shows an underestimate of the magnitude of the SAPS and the sensitivity to the specific location and time. The comparison of the SAPS electric field with that measured from the Van Allen Probes shows that the simulated SAPS electric field penetrates deeper than in reality, implying that the shielding from the Region 2 FACs in the model is not well represented. Possible solutions in future studies to improve the modeling capability include implementing a self‐consistent ionospheric conductivity module from inner magnetosphere particle precipitation, coupling with the thermosphere‐ionosphere chemical processes, and connecting the ionosphere with the inner magnetosphere by the stronger Region 2 FACs calculated in the inner magnetosphere model.Key PointsSAPS simulation using BATS‐R‐US coupled with ring current model RAM‐SCBComparisons done with AMPERE, DMSP, and Van Allen Probes observationsCaptured the basic physics and mechanism of SAPSPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111134/1/jgra51638.pd