Reviewerly/exHarmony huggingface dataset:
Perceptions of the Agency and Responsibility of the NHS COVID-19 App on Twitter: Critical Discourse Analysis

Since September 2020, the National Health Service (NHS) COVID-19 contact-tracing app has been used to mitigate the spread of COVID-19 in the United Kingdom. Since its launch, this app has been a part of the discussion regarding the perceived social agency of decision-making algorithms. On the social media website Twitter, a plethora of views about the app have been found but only analyzed for sentiment and topic trajectories thus far, leaving the perceived social agency of the app underexplored.We aimed to examine the discussion of social agency in social media public discourse regarding algorithm-operated decisions, particularly when the artificial intelligence agency responsible for specific information systems is not openly disclosed in an example such as the COVID-19 contact-tracing app. To do this, we analyzed the presentation of the NHS COVID-19 App on Twitter, focusing on the portrayal of social agency and the impact of its deployment on society. We also aimed to discover what the presentation of social agents communicates about the perceived responsibility of the app.Using corpus linguistics and critical discourse analysis, underpinned by social actor representation, we used the link between grammatical and social agency and analyzed a corpus of 118,316 tweets from September 2020 to July 2021 to see whether the app was portrayed as a social actor.We found that active presentations of the app-seen mainly through personalization and agency metaphor-dominated the discourse. The app was presented as a social actor in 96% of the cases considered and grew in proportion to passive presentations over time. These active presentations showed the app to be a social actor in 5 main ways: informing, instructing, providing permission, disrupting, and functioning. We found a small number of occasions on which the app was presented passively through backgrounding and exclusion.Twitter users presented the NHS COVID-19 App as an active social actor with a clear sense of social agency. The study also revealed that Twitter users perceived the app as responsible for their welfare, particularly when it provided instructions or permission, and this perception remained consistent throughout the discourse, particularly during significant events. Overall, this study contributes to understanding how social agency is discussed in social media discourse related to algorithmic-operated decisions This research offers valuable insights into public perceptions of decision-making digital contact-tracing health care technologies and their perceptions on the web, which, even in a postpandemic world, may shed light on how the public might respond to forthcoming interventions.
Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)—overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.
10 Gb/s classical secure key distribution based on temporal steganography and private chaotic phase scrambling

Secure distribution of high-speed digital encryption/decryption keys over a classical fiber channel is strongly pursued for realizing perfect secrecy communication systems. However, it is still challenging to achieve a secret key rate in the order of tens of gigabits per second to be comparable with the bit rate of commercial fiber-optic systems. In this paper, we propose and experimentally demonstrate a novel solution for high-speed secure key distribution based on temporal steganography and private chaotic phase scrambling in the classical physical layer. The encryption key is temporally concealed into the background noise in the time domain and randomly phase scrambled bit-by-bit by a private chaotic signal, which provides two layers of enhanced security to guarantee the privacy of key distribution while providing a high secret key rate. We experimentally achieved a record classical secret key rate of 10 Gb/s with a bit error rate lower than the hard-decision forward error correction (HD-FEC) over a 40 km standard single mode fiber. The proposed solution holds great promise for achieving high-speed key distribution in the classical fiber channel by combining steganographic transmission and chaotic scrambling.
Grounding spatial relations in text-only language models

This paper shows that text-only Language Models (LM) can learn to ground spatial relations like left of or below if they are provided with explicit location information of objects and they are properly trained to leverage those locations. We perform experiments on a verbalized version of the Visual Spatial Reasoning (VSR) dataset, where images are coupled with textual statements which contain real or fake spatial relations between two objects of the image. We verbalize the images using an off-the-shelf object detector, adding location tokens to every object label to represent their bounding boxes in textual form. Given the small size of VSR, we do not observe any improvement when using locations, but pretraining the LM over a synthetic dataset automatically derived by us improves results significantly when using location tokens. We thus show that locations allow LMs to ground spatial relations, with our text-only LMs outperforming Vision-and-Language Models and setting the new state-of-the-art for the VSR dataset. Our analysis show that our text-only LMs can generalize beyond the relations seen in the synthetic dataset to some extent, learning also more useful information than that encoded in the spatial rules we used to create the synthetic dataset itself.
Non-Fluent Synthetic Target-Language Data Improve Neural Machine Translation

When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available. These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora. Furthermore, this improvement is independent of the size of the original training corpus, the resulting systems are much more robust against domain shift and produce less hallucinations.
Augmented Reality Registration System for Visualization of Skull Landmarks

Augmented reality (AR) is an emerging technology in neurosurgery with the potential to become a strategic tool in the delivery of care and education for trainees. Advances in technology have demonstrated promising use for improving visualization and spatial awareness of critical neuroanatomic structures. In this report, we employ a novel AR registration system for the visualization and targeting of skull landmarks. A markerless AR system was used to register 3-dimensional reconstructions of suture lines onto the head via a head-mounted display. Participants were required to identify craniometric points with and without AR assistance. Targeting error was measured as the Euclidian distance between the user-defined location and the true craniometric point on the subjects' heads. All participants successfully registered 3-dimensional reconstructions onto the subjects' heads. Targeting accuracy was significantly improved with AR (3.59 ± 1.29 mm). Across all target points, AR increased accuracy by an average of 19.96 ± 3.80 mm. Posttest surveys revealed that participants felt the technology increased their confidence in identifying landmarks (4.6/5) and that the technology will be useful for clinical care (4.2/5). While several areas of improvement and innovation can further enhance the use of AR in neurosurgery, this report demonstrates the feasibility of a markerless headset-based AR system for visualizing craniometric points on the skull. As the technology continues to advance, AR is expected to play an increasingly significant role in neurosurgery, transforming how surgeries are performed and improving patient care.
Inequality-Constrained 3D Morphable Face Model Fitting

3D morphable model (3DMM) fitting on 2D data is traditionally done via unconstrained optimization with regularization terms to ensure that the result is a plausible face shape and is consistent with a set of 2D landmarks. This paper presents inequality-constrained 3DMM fitting as the first alternative to regularization in optimization-based 3DMM fitting. Inequality constraints on the 3DMM's shape coefficients ensure face-like shapes without modifying the objective function for smoothness, thus allowing for more flexibility to capture person-specific shape details. Moreover, inequality constraints on landmarks increase robustness in a way that does not require per-image tuning. We show that the proposed method stands out with its ability to estimate person-specific face shapes by jointly fitting a 3DMM to multiple frames of a person. Further, when used with a robust objective function, namely gradient correlation, the method can work "in-the-wild" even with a 3DMM constructed from controlled data. Lastly, we show how to use the log-barrier method to efficiently implement the method. To our knowledge, we present the first 3DMM fitting framework that requires no learning yet is accurate, robust, and efficient. The absence of learning enables a generic solution that allows flexibility in the input image size, interchangeable morphable models, and incorporation of camera matrix.
BLOCKSCRIPTS -- A BLOCKCHAIN SYSTEM FOR UNIVERSITY TRANSCRIPTS

Abstract The concept of blockchains started around 2008 with Bitcoin, which is a cryptocurrency. More recently, the application of Blockchains has been expanded well beyond cryptocurrency. As part of a capstone project for the Master of Science in Computer Science, a Blockchain that holds student transcript information and/or diplomas was created. The information is stored in a digital format that is immutable and can be retrieved easily by any authorized student, faculty, or interested third party. Having immutable records in a digital blockchain format allows students to have an official record that cannot be changed yet be publicly available for authorized viewers, satisfying the need for secure communication channels. This is beneficial for the student, the university, and any prospective employers. Creating and storing records in this way also ensures the digital transcripts and diplomas cannot be altered in any way. Additionally, Blockscripts provides non-repudiation and a more secure platform for storing transcripts due to the nature of decentralized data storage. As such, if one node in the blockchain network goes offline, other nodes in the network will persist and maintain accurate copies of the transcript ledger data. This also negates the risk associated with a data center, especially in regard to disaster recovery (i.e. fires or earthquakes) which could potentially have the capacity to destroy localized data. Another benefit to be realized from storing transcripts and diplomas on a blockchain is the processing time saved. Time is saved for the person requesting the transcript, the registrar's office producing it, and the third party receiving the transcript. In many universities, a transcript request can take up to three weeks to be fulfilled; by using blockchain technology it is likely that requesting parties can receive a requested transcript the same day. A user interface (UI) has been created that allows a student to request his/her transcript to be viewed by a particular entity. Blockscripts also enables agencies or universities to verify a degree or degrees that may be held by a particular student or graduate. The student’s transcripts/original data is owned and initially published to a blockchain by the university they attended. It is made viewable by encrypting with either the student’s public key or the public key of a third party; the student is able to decrypt his/her data with their private key and a third party is able to decrypt the transcript data with their private key. This ensures that the transcript will be publicly available, yet the contents will remain private and available only to authorized parties. If the student wants to send their transcripts to someone else, they include those parties’ public keys in the transcript request. Each transcript request can include a fixed fee for processing; the university administration can take a portion while the validating network node (miner) takes the remainder for providing the service of validation, data storage, and transmission costs.
Instilling Entrepreneurial Mindset by Vertical Integration of Engineering Projects

The goal of this research project is to instill an entrepreneurial mindset by vertically integrating a single design project among two consecutive freshmen engineering design classes.The paper describes the context and background of two vertically integrated classes.Example work from students show the interpretation of the entrepreneurial mindset.A survey measures the effectiveness of the entrepreneurial mindset among students that are taking part in the project.Next, there are recommendations based on the information gathered during the implementation, including specific recommendations about the types of projects, constraints and methodologies.Other vertically integrated classes and curricula can use this case-study as a starting point for introducing entrepreneurial mindset.Lastly, there is a current discussion of case studies of vertical integration among student groups in non-consecutive semesters of a program, and student groups from completely different programs.
Hierarchical image-to-image translation with nested distributions modeling

Unpaired image-to-image translation among category domains has achieved remarkable success in past decades. Recent studies mainly focus on two challenges. For one thing, such translation is inherently multi-modal (i.e. many-to-many mapping) due to variations of domain-specific information (e.g., the domain of house cat contains multiple sub-modes), which is usually addressed by predefined distribution sampling. For another, most existing multi-modal approaches have limits in handling more than two domains with one model, i.e. they have to independently build two distributions to capture variations for every pair of domains. To address these problems, we propose a Hierarchical Image-to-image Translation (HIT) method which jointly formulates the multi-domain and multi-modal problem in a semantic hierarchy structure by modeling a common and nested distribution space. Specifically, domains have inclusion relationships under a particular hierarchy structure. With the assumption of Gaussian prior for domains, distributions of domains at lower levels capture the local variations of their ancestors at higher levels, leading to the so-called nested distributions. To this end, we propose a nested distribution loss in light of the distribution divergence measurement and information entropy theory to characterize the aforementioned inclusion relations among domain distributions. Experiments on ImageNet, ShapeNet, and CelebA datasets validate the promising results of our HIT against state-of-the-arts, and as additional benefits of nested modeling, one can even control the uncertainty of multi-modal translations at different hierarchy levels.
The Effects of Online Course Design on Student Course Satisfaction

Her research interests revolve around the investigations of how learning analytics informs instructional design to foster learning behaviors and strategies associ-
Iterative Improvement in Flipped Classroom Teaching of Lower Division Engineering Courses

Abstract Lower division engineering courses are important yet hard to teach as many students find these highly abstracted material hard to comprehend. Recent studies have suggested that flipped classroom teaching has potential to improve the teaching and learning of lower division engineering courses. While some educators are optimistic about the potential impact that flipped classroom teaching might provide, there are still many challenges to overcome during implementation. One of the major challenges faced by course developers, especially first-time developers, is to manage the heavy workload needed to design an effective practice that is suitable for their unique student body. This paper presents an iterative framework to help ease the adoption and continuous improvement of flipped classroom teaching, so that students' learning experience can be optimized over time. The framework is composed of two phases – the initial design phase and the iterative tuning phase. In the initial design phase, a strategy that allows an instructor to quickly convert the existing course materials used for other lecturing methods into those suitable for flipped classroom teaching is introduced. This process involves designing a course structure according to its objectives, rearranging course content, and patching additional resources. Then, the iterative tuning for improvement will be explained, with a focus on identifying and addressing the most critical areas for improvement. Since the course instructor only focuses on resolving the most critical issues every semester, the workload becomes manageable. The flipped classroom teaching of a lower division mechanical engineering course "ME 2040 – Circuit Analysis for Mechanical Engineers" is used as example to explain details about the utilization of the proposed course development and implementation process. Towards the end, the effectiveness of the development process in improving students' learning outcomes is discussed.
Students are improving their spatial skills in introductory engineering class through projects utilizing 3D printers and laser cutters

Abstract A growing body of research indicates that well-developed spatial skills are important to student success in many STEM disciplines, including many engineering majors. This research has further found that spatial skills are malleable and can be improved with targeted training activities. This presentation will share strategies and successes with incorporating spatial visualization skills training in a freshman-level introduction to engineering course. This course is a general introduction that also includes orientation to the engineering profession, academic success strategies, problem solving skills, and introductory math and science applications to engineering. The course prerequisite is college algebra. Much of the spatial skills training content is combined with introductory design and teamwork activities and in total makes up approximately twenty percent of the course. Students engage in two team projects which are supported by a series of spatial skills practice worksheets. The worksheets prompt students to sketch blocks of varying height in both elevation and isometric views and perform mental rotations about one and two axes. The team projects utilizing the OnShape CAD system, a cloud-based parametric solid modeling system that is available at no cost to educators and students. The first project has teams of three students creating a Soma puzzle; a 3×3×3 cube assembled from seven pieces made out of unit cubes that has 240 unique solutions. The class follows a detailed explanation of how to create one of the seven pieces, then each student is required to create and share two of the remaining six pieces. Each student is then required to generate an assembly with two unique solutions. Groups then modify and 3D print their parts. There will be 3D printed models available at the presentation for participants to find their own solutions. The second project has students working first as individual designers and then in pairs to designing a desktop organizer which will be created out of 3mm plywood on the laser cutter. After their initial sketches, individual students create a 3D model with card stock and tape. Students then pair up and negotiate for a final design that emphasizes their own ideas. Together students create a base CAD model, from which, they generate their own versions. Finally, they use the laser cutter and assembly their finished product. There will be models available at the presentation. Students’ pre- and post- test scores on the Purdue Spatial Visualization Test: Rotations (PSVT:R) assessment showed statistically significant improvement with mean scores increasing by 6% (p &lt; 0.0002, N = 76).