Reviewerly/exHarmony huggingface dataset:
10 Gb/s classical secure key distribution based on temporal steganography and private chaotic phase scrambling

Authors:
raw_author_name: Zhensen Gao
raw_affiliation_string: Ministry of Education; Pengcheng Laboratory
raw_author_name: Lihong Zhang
raw_author_name: Xulin Gao
raw_author_name: Yuehua An
raw_affiliation_string: Guangdong Polytechnic Normal University
raw_author_name: Anbang Wang
raw_author_name: Songnian Fu
raw_affiliation_string: Ministry of Education; Pengcheng Laboratory
raw_author_name: Zhaohui Li
raw_affiliation_string: Pengcheng Laboratory; Sun Yat-sen University
raw_author_name: Yuncai Wang
raw_affiliation_string: Ministry of Education; Pengcheng Laboratory
raw_author_name: Yuwen Qin

Abstract: 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

Authors:
raw_author_name: Gorka Azkune
raw_affiliation_string: HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), M. Lardizabal 1, Donostia 20018, Basque Country, Spain
raw_author_name: Ander Salaberria
raw_affiliation_string: HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), M. Lardizabal 1, Donostia 20018, Basque Country, Spain
raw_author_name: Eneko Agirre
raw_affiliation_string: HiTZ Basque Center for Language Technologies - Ixa NLP Group, University of the Basque Country (UPV/EHU), M. Lardizabal 1, Donostia 20018, Basque Country, Spain

Abstract: 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

Authors:
raw_author_name: Víctor M. Sánchez-Cartagena
raw_affiliation_string: Dep. de Llenguatges i Sistemes Inform&#x00E0;tics, Universitat d&#x0027;Alacant, Spain
raw_author_name: Miquel Esplà-Gomis
raw_affiliation_string: Dep. de Llenguatges i Sistemes Inform&#x00E0;tics, Universitat d&#x0027;Alacant, Spain
raw_author_name: Juan Antonio Pérez-Ortiz
raw_affiliation_string: Dep. de Llenguatges i Sistemes Inform&#x00E0;tics, Universitat d&#x0027;Alacant, Spain
raw_author_name: Felipe Sánchez-Martínez
raw_affiliation_string: Dep. de Llenguatges i Sistemes Inform&#x00E0;tics, Universitat d&#x0027;Alacant, Spain

Abstract: 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

Authors:
raw_author_name: Jonathan Cohen
raw_affiliation_string: Hoth Intelligence Inc., Philadelphia, Pennsylvania, USA

Abstract: 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

Authors:
raw_author_name: Evangelos Sariyanidi
raw_affiliation_string: Center for Autism Research, Children&#x0027;s Hospital of Philadelphia, Philadelphia, PA, USA
raw_author_name: Casey J. Zampella
raw_affiliation_string: Center for Autism Research, Children&#x0027;s Hospital of Philadelphia, Philadelphia, PA, USA
raw_author_name: Robert T. Schultz
raw_affiliation_string: Center for Autism Research, Children&#x0027;s Hospital of Philadelphia, Philadelphia, PA, USA
raw_author_name: Birkan Tunç
raw_affiliation_string: Center for Autism Research, Children&#x0027;s Hospital of Philadelphia, Philadelphia, PA, USA

Abstract: 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.
Hierarchical image-to-image translation with nested distributions modeling

Authors:
raw_author_name: Shishi Qiao
raw_affiliation_string: College of Information Science and Engineering, Ocean University of China, QingDao, 266100, China; Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Ruiping Wang
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Shiguang Shan
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Xilin Chen
raw_affiliation_string: Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: 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

Authors:
raw_author_name: Pauline Salim Muljana
raw_affiliation_string: Department of STEM Education and Professional Studies: Instructional Design and Technology -Old Dominion University, Norfolk, Virginia

Abstract: 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

Authors:
raw_author_name: Jianyu Dong
raw_affiliation_string: California State University, Los Angeles
raw_author_name: Ni Li
raw_affiliation_string: California State University, Los Angeles

Abstract: 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

Authors:
raw_author_name: Eric Davishahl
raw_affiliation_string: Whatcom Community College

Abstract: 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).
WORKSHOP: Spatial Vis™: A sketching app with automatic grading to teach spatial visualization skills to introductory engineering students.

Authors:
raw_author_name: Lelli Van Den Einde
raw_affiliation_string: University of California, San Diego
raw_author_name: Nathan Delson
raw_author_name: Elizabeth Rose Cowan

Abstract: Abstract Objectives: Spatial visualization (SV) refers to the ability to manipulate geometric shapes in one’s mind and is important in many STEM fields. Studies have shown that SV skills are learnable, and SV training can increase GPAs and graduate rates in engineering. Additionally, sketching has been shown to be an important part of increasing 3D visualization skills, which is key for creativity and team communication. However, few engineering curricula include SV training, and adding new content to an already full program can be difficult. The Spatial Vis™ App, developed by eGrove Education, was designed with the potential to teach SV training outside of scheduled class time. It allows for freehand sketches of isometric and orthographic assignments to be graded automatically and for hints to be provided when needed. The app has been tested in a number of trials at universities across the country and has been shown to be an engaging and effective tool in improving SV skills. The app is currently available on Android and Apple smartphones and tablets, as well as advanced touchscreen Chromebooks. Features of the app include automatic grading, personalized hints for students when stuck, teacher data for tracking student progress, gradual increase in difficulty of assignments, a gamification start system to encourage persistence, and several test questions the end of each lesson where the Hint and Peek questions have been disabled. Results from trials using the technology in a number of university classroom settings, which have demonstrated the efficacy of the app based on a pre- and post- standardized spatial visualization test, will be discussed. Targeted audience: First year engineering or engineering graphics college or community college instructors; K-12 pre-engineering teachers and administrators. Activities Planned: This workshop will provide an overview of the benefits and best practices of using the Spatial Vis app in your classroom. Participants will be given a license to the app to use on their own smartphone devices to engage in interactive spatial visualization (SV) activities. Learning Outcomes: Instructors will learn 1) spatial visualization skills such as 2D rotations, 3D isometric views, and 2D orthographic projections; 2) how to use the app including the algorithm that automatically grades students’ sketches and provides guidance when requested; and 3) best practices for how to implement the SV training in their curriculum. You won’t want to miss this interactive workshop! Approximate length: 2 hours Required supplies: Android or Apple touchscreen device such as a smartphone or tablet.
Visual Relationship Detection for Workplace Safety Applications

Authors:
raw_author_name: Thomas Truong
raw_affiliation_string: Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
raw_author_name: Svetlana Yanushkevich
raw_affiliation_string: Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada

Abstract: Applications of object and visual relationship detection for safety and security applications are in its infancy. State-of-the-art computer vision research is largely focused on improving mean Average Precision and mean Average Recall performance on standard, general datasets such as the Verbs in COmmon objects in COntext and the Visual Genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the back-end model for a decision support system. We use a naive Bayesian Network to determine scenarios where our proposed object and visual relationship detection network is error-prone. We also release a graphical user interface which demonstrates how our back-end neural network and naive Bayesian Network can be used for hazardous workplace safety and security applications. All models and source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/th-truong/rd-gui</uri> for future research and applications of object and visual relationship detection models in the safety and security sector.