Reviewerly/exHarmony huggingface dataset:
Contrastive completing learning for practical text-image person ReID: Robuster and cheaper

Text-image person re-identification (TIReID) seeks to leverage textual descriptions for the retrieval of target pedestrians. Due to its versatility, TIReID has gained increasing attention. However, manual annotation of textual descriptions and identity labels can be time-consuming and costly, limiting its scalability in practical settings. Privacy concerns and poor data storage can lead to data loss or ineffectiveness, further exacerbating challenges in real-world scenarios. To address these limitations, we propose for the first time incomplete Text-image person re-identification (iTIReID), which comprises a small amount of complete pairwise data and a large amount of incomplete data, where all identity labels are unavailable. We introduce a novel Contrastive Completing Learning (CCL) framework for iTIReID, consisting of two stages: Pure Contrastive Learning (PCL) and Feature Completion Contrastive Learning (FCCL). In PCL, only complete pairwise data is utilized for training, which serves as a preliminary improvement of the model’s capacity and prepares for the upcoming feature completion stage. In FCCL, available features are used to complete missing modality features and facilitate effective training with incomplete data. During this process, Cross-modal Semantic Measure (CSM) is proposed to leverage intra-modality similarity to measure cross-modal similarity and filter out features with the highest semantic similarity, thereby circumventing modality discrepancy. Semantic-Weighted Generation (SWG) is proposed to generate approximate features based on the semantic similarity weight of the similar features. To fully leverage pairwise data for label-free training, we introduce the contrastive CMPM (CCMPM) loss for contrastive learning to achieve weakly supervised training. Experimental results verify the effectiveness of our proposed methods and demonstrate competitive performance compared to fully supervised methods using complete data.
On Size-Independent Sample Complexity of ReLU Networks

We study the sample complexity of learning ReLU neural networks from the point of view of generalization. Given norm constraints on the weight matrices, a common approach is to estimate the Rademacher complexity of the associated function class. Previously [GRS20] obtained a bound independent of the network size (scaling with a product of Frobenius norms) except for a factor of the square-root depth. We give a refinement which often has no explicit depth-dependence at all.
TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on region-of-interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with a transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multitask modeling. It allows for mutual training and optimization of classification, segmentation and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an adaptive global aggregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the suboptimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on the TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.
QoS‐enhanced load balancing strategies for metaverse‐infused VR/AR in engineering education 5.0

Abstract The evolution of augmented reality (AR) and virtual reality (VR) technologies has ushered in a new era of immersive experiences, with applications ranging from entertainment to education. The proposed framework introduces a fog layer with an innovative, improved geographic load‐balancing algorithm. It optimizes load distribution and provides quality of service (QoS) parameters that are important for enhancing user experiences for AR/VR applications. The iFogSim toolkit experimentally validates the framework in electroencephalogram‐based VR/AR gaming applications. Also, the proposed framework is tested in a diverse range of scenarios. Results show that the proposed algorithm improves QoS for AR/VR applications with noticeable improvements on average latency, jitter, and packet loss. Future research should aim to address these limitations for a more comprehensive understanding of the proposed framework's practicality and effectiveness. The development of immersive technologies will continue to expand in multiple sectors, and future research will focus on energy efficiency, security, and real‐world applications.
Building causal models for finding actual causes of unmanned aerial vehicle failures

Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause–effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause–effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause–effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.
Backpropagation through Back Substitution with a Backslash

.We present a linear algebra formulation of backpropagation which allows the calculation of gradients by using a generically written "backslash" or Gaussian elimination on triangular systems of equations. Generally, the matrix elements are operators. This paper has three contributions: (i) it is of intellectual value to replace traditional treatments of automatic differentiation with a (left acting) operator theoretic, graph-based approach; (ii) operators can be readily placed in matrices in software in programming languages such as Julia as an implementation option; (iii) we introduce a novel notation, "transpose dot" operator "\(\{\}^{T_\bullet }\)" that allows for the reversal of operators. We further demonstrate the elegance of the operators approach in a suitable programming language consisting of generic linear algebra operators such as Julia [Bezanson et al., SIAM Rev., 59 (2017), pp. 65–98], and that it is possible to realize this abstraction in code. Our implementation shows how generic linear algebra can allow operators as elements of matrices. In contrast to "operator overloading," where backslash would normally have to be rewritten to take advantage of operators, with "generic programming" there is no such need.KeywordsbackpropagationAutoDiffbackslashoperatorJuliagraphMSC codes05C50
Incorporating a Teach for Mastery System in a Computer Science Course

Teaching for Mastery is the idea that students should progress through material when they have conquered the previous material it depends on and each student may progress at their own pace.The paper documents the process and results of incorporating a teach for mastery system into a Computer Science Programming Language Design course.The course is aimed at junior and senior undergraduate students and its goal is to introduce them to the four main language families and to show them how to teach themselves a programming language.This teach for mastery system caused both student learning and student satisfaction to improve in a course that was already well received before these changes.Other benefits of this system include a marked decrease in the amount of time spent grading and a straightforward way to identify which subjects need to be re-thought and which are being learned to the intended depth.The system does come with an increase in the amount of contact time spent with students which is both a positive in that you develop a closer relationship with the students and a negative in that it is a large amount of contact time.Contributions include the structural changes and teaching artifacts needed to implement teaching for mastery in a course.The structural changes include flipping the classroom and replacing traditional sit-down exams with short one-topic take-when-ready exams.The teaching artifacts produced include a concept map.Students leave the course with a clear list of which topics they mastered and which they are still working on.This model still permits room for traditional laboratory and project components.
Identifying Collaborative Problem-Solving Behaviors Using Sequential Pattern Mining

Abstract A vast body of research demonstrated the learning benefits and importance of collaborative learning (i.e., pedagogical approaches involving students learning collectively by working on collaborative assessments that require joint intellectual effort). Such benefits include increasing student retention, higher-level thinking skills, improved communication skills, responsibility for students' own learning, and more. Acknowledging these benefits, more instructors are integrating collaborative learning into their curriculum (Järvenoja et al., 2020). With this increased adoption of collaborative learning approaches, instructors must understand their students' problem-solving approaches during collaborative learning activities to better design their class activities. Among the multiple ways to reveal collaborative problem-solving processes, temporal submission patterns is one that is more scalable and generalizable in Computer Science education. In this paper, we provide a temporal analysis of a large dataset of students' submissions to collaborative learning assignments in an upper-level database course offered at a large public university. This research uses sequential pattern mining techniques to identify temporal patterns that capture students' problem-solving strategies when working on collaborative assignments. We study the log data collected from an online assessment and learning system, which contains the timestamp data of each student's submissions to a problem on the collaborative assignment. Each submission was labeled as quick (Q), medium (M), or slow (S) based on its duration and whether it was shorter or longer than the 25th and 75th percentile. Our analysis revealed seven submission patterns after applying two compacting rules: Q, Q-LNG, M, M-LNG, QM-LNG, S, and S-LNG, where "LNG" dictates that the compacting rule was applied. The compacting rule indicates that there were at least three consecutive submissions of the same pattern (i.e., Q-LNG is a compaction of Q-Q-Q). We then investigated how submission patterns link to become discriminating sequences by computing the transition probabilities with L* metrics. We identified transition pairs that include the same submission patterns in different transition positions (i.e., transit from and transit to) to construct more extended transition patterns by combining transition pairs. For example, the transition pairs M-LNG to S-LNG and S-LNG to Q have a correlation of 0.782 (p<0.001), so we can generate a three-pattern transition: M-LNG to S-LNG to Q. With these transition patterns, we can then better pinpoint collaborative learning problem-solving behaviors among students.
Identifying the Static and Dynamic Nature of Course Content: Focus on Construction

Abstract In the US, there is a variety of programs that produce students ready to be employed in the construction industry. These programs are generally accredited by ABET or ACCE. ABET accredited programs are reviewed by a variety of commissions, such as the Engineering Accreditation Commission, the Engineering Technology Accreditation Commission, and the Applied and Natural Science Accreditation Commission, and have descriptions such as "Construction Management", "Construction Engineering", "Construction Engineering Technology", etc. ACCE accredited programs are more streamlined, and their descriptions are in general "Construction Management". In any case, these programs have curricula that are similar in various ways, and this research aims to identify these similarities. Research presented in this paper shows results from a survey of faculty from these programs that aimed to identify: 1) courses/topics with content that does not dramatically change over time and does not need to be constantly updated, 2) courses/topics that need constant updating and for what reasons, 3) courses/topics that are novel and represent material that is in the forefront of construction engineering/management technology, and 4) topics that would be appearing in curricula in the near future. The results of this investigation will help construction programs plan their future curricula and help faculty ensure an even distribution of their workload.
Central bank digital currencies and the future of monetary sovereignty

Initiatives to develop central bank digital currencies (CBDCs) have accelerated dramatically across the world in the last few years. What is their significance for longstanding scholarly debates about the fate of monetary sovereignty in the digital age? Public officials themselves state that a central reason for these initiatives is to defend monetary sovereignty against threats emanating from the growth of private digital currencies, foreign CBDCs, and the displacement of state issued cash by private digital payments instruments. They (and others) highlight how CBDCs could even strengthen monetary sovereignty by bolstering financial inclusion as well as enhancing the state's capacity to monitor and control monetary transactions and conduct monetary policy. In these ways, CBDC initiatives cast doubt on arguments that suggest the digital currency revolution necessarily challenges monetary sovereignty. However, critics of that line of argument also need to be cautious because CBDCs are already attracting much political opposition. Even if that opposition is overcome, CBDCs may be implemented in constrained ways or be unsuccessful in meeting their goals for other reasons. In short, CBDCs may hold the potential to defend and even strengthen monetary sovereignty, but it is far from clear whether this potential will be realised.
Smarter and resilient smart contracts applications for smart cities environment using blockchain technology

Our daily activities revolve around various technologies, the smart city technologies and the services they offer have all influenced modern living conditions. The purpose of a smart city is to enhance people's quality of life and provide possibilities to address social and environmental issues. As a developing technology, blockchain is beneficial for enhancing smart city services including food tracking, supply-demand matching, the security of connected cars, and regulatory compliance. We propose a new framework for describing how blockchain technology is used in smart contracts to improve security, dependability, and many other positive outcomes in a smart city environment. We propose smarter and resilient smart contracts using blockchain technology to manage real estate information. We propose a framework with tamperproof functionality to store the data and retrieve the data. Smart contract mathematical computations like overhead rate, execution time, mean computational cost, standard deviation, throughput, and resource utilization are evaluated and the results are compared. This paper focuses on real-world rental file management scenarios to demonstrate the benefits of blockchain technology and how it is used to address the issues that currently exist in developing smart contracts and services while exchanging real land and other properties in a smart city environment.
Community-Driven, Participatory Engineering Design Frameworks to Shape Just, Liberatory Health Futures

Abstract Engineering education regularly overlooks people it is supposed to serve, especially those historically and systemically marginalized by technology. To address this, we must teach students of engineering to engage critically with who they are designing and work directly with communities living closest to systemic problems. This can be done through community-driven, participatory design where community-based lived experiences inform the development of technical solutions. This work is vital for identifying what problems need technical solutions, and the limitations of technical solutions in addressing systemic challenges. I will investigate what this means in my field of medical technology, rife with racism, sexism, ableism, due to most technologies being developed by those in power, that being white, cis, able-bodied men. I argue that systemically marginalized populations receive worse medical care because of technology and how it was designed. Embedding myself in the narrative, I detail my own experiences living in a chronically ill body as I experience the severe limitations of current technologies, and how that impacts my own health. Tracing legacies of resistance to dominant systems of power within biomedicine, I uncover the stories of lay experts challenging the existing "politics of knowledge" to democratize biomedical innovation for their own benefits. Weaving together theories of black feminism, queer liberation, disability justice, and embodiment with design justice and participatory design, I outline principles for engineering liberation through health innovation. These five principles include 1) understand the system shaping inequity 2) realize your positioning and power, at the intersections of race, gender, sexual orientation, class, and (dis)ability 3) establish relationships with those closest to health disparities to root out root causes and stay accountable to potential harms 4) build technologies that create value for all parties while remaining "safe to fail" and 5) connect the innovation to a greater political strategy for achieving equity and liberation. This work in progress paper ends with a call to action for engineers to choose a side: do we serve as architects of the visions of the powerful, or the visions of the public? As architects of medical technology, our decisions shape who lives and thrives and who suffers and dies.
Using VR (Virtual Reality) Technology to Teach Fall Safety Topics to Students: Simulation Outcomes and Student Learnings

VR (Virtual Reality) which is a new technology, has been used to simulate training that is too expensive and time-consuming to practice in real life.This study aims to find students' learning improvement on Fall related safety by using low-cost VR technology.This will also help practicing how to survive Fall hazard techniques.In the current study, a well-known VR software was used to introduce participants to Fall safety scenarios.Rather than students being passive viewers where the instructor/trainer only demonstrates, the students were given full control to navigate through the virtual environment and to interact with virtual objects using a headset and a handheld controller.Students identified a simple climbing and Fall hazard example before going to more rigorous training in the virtual environment.Results indicated that students were more engaged by the new game-like learning environment and that they reported that they found the experience fun, interesting, and thought-provoking.Their enhanced enthusiasm for what is relatively standard Fall-safety training demonstrates the promise of using game-based virtual environments for vital but otherwise tedious Fall-safety skills training for students.
Using Deep Learning and Augmented Reality to Improve Accessibility: Inclusive Conversations Using Diarization, Captions, and Visualization

Abstract The problem of diarization - identifying different speakers in a conversation stream - has not been sufficiently addressed for deaf and hard-of-hearing students in learning communities such as student design teams in engineering and related STEM disciplines. Though the accuracy of the latest automated real-time speech-to-text systems is now approaching usable low word error rates, the generated text output is an incomplete representation of a multi-party conversation; In short, it solves the "what" but not the "who." This creates barriers to our ideal of an inclusive and equitable learning community. Thus students who are deaf or hard of hearing are further marginalized and excluded from multi-party peer discussions with non-deaf participants because it is hard to visually follow who is speaking. To address these communication barriers, we utilized the Human Centered Engineering Design framework to identify a set of features that overcomes the above barriers. This paper explores computerized diarization techniques that utilize a wide set of algorithms and audio metrics to assist in speaker identification. These techniques include mel-frequency cepstrum coefficients (MFCC), volume, fundamental frequency identification, and deep learning of voice prints. For the goals described in this paper, a subset of existing algorithms that respected privacy and legal constraints was selected and evaluated for the purposes of identifying speakers using a live audio stream. Several visualization methods were also designed and evaluated. These included visualization of embedding mel-frequency cepstrum, speaker identifier, pitch, volume, and other voice characteristics into a live caption stream. Both diarization and visualization were integrated into a live captioning tool, ScribeAR, previously introduced in ASEE regional proceedings, and rendered using a lightweight Augmented Reality display. In order to facilitate captioning services in areas with limited network connectivity, whisper.cpp, a derivative of OpenAI's Whisper project, was also incorporated into the application. Links to the open source project are included so that other educators may adopt this inclusive practice. Some accessibility-related opportunities that could be used as motivating design projects for engineering students are described.