Reviewerly/exHarmony huggingface dataset:
Hardware-accelerated Real-time Drift-awareness for Robust Deep Learning on Wireless RF Data

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.
Critical temperature for the existence of entanglement in the Bell-diagonal states

We study systems formed by two-qubits in the Bell-diagonal states interacting in equilibrium with a thermal reservoir. We report some geometric relations between the entanglement and the system temperature, and calculate the minimum temperature above which the entanglement of the two-qubit system vanishes.
The resilience of conjunctive queries with inequalities

The resilience of a query q (RES(q)) measures the minimum number of source tuples responsible for a specific query answer. If q is an IQ query, whose at most one attribute from a table has inequality relationships with attributions from other tables, the state-of-the-art work had shown that RES(q) is in PTIME. However, the state-of-the-art work is inefficient for computing the resilience of IQ queries. Moreover, the complexity of resilience for self-join free (sj-free) CQs with other kinds of inequalities has not been explored. Therefore, we provide a dichotomy for the complexity of resilience for sj-free CQs with inequalities q in this paper. We first introduce query condition graph, which includes a special query condition graph called query condition triad, and triangle vertex cover problem. Then we reduce the vertex cover problem to the triangle vertex cover problem. According to query condition graphs, our dichotomy is as follows: If q is a qc-triad query, which is not an IQ query but contains a query condition triad, then RES(q) is NP-complete. If q is a qc-linear query, which neither is an IQ query nor contains a query condition triad, then RES(q) is in PTIME. Finally, we introduce an algorithm called Rlin to compute the resilience of qc-linear queries, and an efficient algorithm named RIQ to efficiently compute the resilience of IQ queries. The experiments show that our RIQ has much better performance than other algorithms for computing the resilience of IQ queries.
(Re)discovering Laws of Music Theory Using Information Lattice Learning

Information lattice learning (ILL) is a novel framework for knowledge discovery based on group-theoretic and information-theoretic foundations, which can rediscover the rules of music as known in the canon of music theory and also discover new rules that have remained unexamined. Such probabilistic rules are further demonstrated to be human-interpretable. ILL itself is a rediscovery and generalization of Shannon's lattice theory of information, where probability measures are not given but are learned from training data. This article explains the basics of the ILL framework, including both how to construct a lattice-structured abstraction universe that specifies the structural possibilities of rules, and how to find the most informative rules by performing statistical learning through an iterative student-teacher algorithmic architecture that optimizes information functionals. The ILL framework is finally shown to support both pedagogy and novel patterns of music co-creativity.
Applying Activity System-Based Process Model in Augmented Reality-Based Learning for Natural Science Course in Elementary School

Since learning environments have the support of innovational technology, blended learning environments could integrate the physical learning methods with multimedia learning materials, which combines the use of technology with the traditional teaching methods and offers a potential for teachers and students to meet the requirements of learning flexibility and innovation. This study aims to redesign a user-centered Activity System-Based Process Model (ASPM) and integrate the Augmented Reality (AR) into blended learning to implement the Augmented Reality-Based Blended Learning (ARBL) system. There were 57 participants who learned with the ARBL while the control group was made up of 52 students who learned with the Traditional Blended Learning (TBL) approach. Analytical results indicate that the learning outcomes and learning attitude of the ARBL group were better than those of the TBL group. The activity system-based process model could provide a helpful structure in the ARBL to guide course designers, teachers, and researchers for designing the AR learning activity.
Impact Analysis of Foveation and Depth on the Perceptual Quality of Immersive Visual Content With Fixed Viewport

With the rapid development of devices for virtual reality, massive amounts of multimedia data have been produced to enrich the immersive experience. To construct vivid virtual reality, high-resolution content is required to achieve a good user experience. With the limitations of hardware capability, efficient data compression is one of the keys to addressing the current dilemma. In our opinion, visual perception is a promising strategy for addressing this problem. In this study, the impact of human visual perception in immersive environment is studied. Two hypotheses accounting for their impact on the quality of immersive videos at a fixed viewport are proposed and validated by subjective evaluation and analysis. A series of videos are created with various blur distortions based on the stimulus triggered by depth perception and the rapid falloff of acuity in the peripheral vision. Stereoscopic videos with imposed blur distortions arranged with different severities are evaluated subjectively in an immersive viewing environment. We then follow statistical approaches to examine the collected subjective quality distributions to discuss the feasibility of video compression based on processed videos. To improve encoding efficiency, a heuristic mechanism for immersive visual content compression is proposed. We examine a simple compression framework to demonstrate that the proposed hypotheses are applicable for practical video compression.
Self-Supervised Fully Automatic Learning Machine for Intelligent Retail Container

Intelligent retail containers are a representative application of object detection in open-world scenarios. However, their wide application is limited because of labor-intensive data labeling and frequently updated retail goods categories. To address the two challenges, we propose a self-supervised fully automatic learning machine (SFLM), which consists of a self-paced active learning (SPAL) human-machine cooperation module and clustering auto-learning (CAL) module. Based on active learning and self-paced learning, we use the SPAL module to handle the labor-intensive data labeling task, which speeds up data labeling and reduces its cost. We further use the CAL module to overcome the challenge of frequently updated goods categories in intelligent retail. Considering the lack of large-scale datasets in real retail container scenarios, we propose a new challenging intelligent retail container dataset. This dataset consists of 35, 000 images, with 370, 000 bounding boxes and instance segmentation masks, covering 200 common categories of goods for different data acquisition settings. Extensive experimental results on the proposed dataset show that the proposed SFLM greatly reduced the labeling cost and time consumption, and achieved superior performance in recognizing new categories. The dataset will be available at https://github.com/SunYM2020/IRC.
Worker Assignment for Multiple Masters to Speed up Coded Distributed Computing in Heterogeneous Clusters

In distributed computing systems, coding has played an important role to robustify the system against the effect of noise, e.g., stragglers, system failures and communication bottlenecks. Most of the existing work has focused on a simple master-worker model with one master and homogeneous workers. However, real-world systems are typically configured with heterogeneous workers distributed to computing nodes and serve multiple tasks in parallel. In this study, we consider the scenario in which multiple masters perform matrix multiplications using the workers having group heterogeneity. The group heterogeneity models that homogeneous workers are located in the same location and regarded as a group; the workers deployed in the different locations are potentially heterogeneous. We propose an asymptotically optimal worker assignment to multiple masters for coded distributed computing in the presence of heterogeneous groups of workers. Specifically, we present a lower bound for the expected latency in terms of the numbers of workers assigned to the masters and the amount of tasks allocated to workers. Adding the concentration constraints on the number of workers allocated to masters, we can obtain the minimum of the lower bound by taking the optimal worker assignment. We find the optimal worker assignment by converting the problem at hand into a linear programming problem. From both numerical simulations and experiments on Amazon EC2 clusters, we confirm that the effect of the proposed worker assignment is significant in various scenarios.
DReD–A Descriptive Relation Dataset for Expanding Relation Extraction

Relation extraction is a fundamental topic in document information extraction. Traditionally, datasets for relation extraction have been annotated with named entities and classified with a subset of relation categories. Models then predict either the entities and relations (end-to-end) or assume the entities are given and only classify the relations. However, current approaches are limited by datasets with a narrow definition of entities and relations. We seek to remedy this by introducing our Descriptive Relation Dataset (DReD), which contains 3286 annotations for descriptions of relations between more general noun phrases inspired by linguistic theory. We benchmark our dataset using several seq2seq models and find that T5 achieves the best results with a ROUGE-1 score of 75.5. We verify the usefulness of DreD by collecting feedback on 100 predictions and comparing human judgment to automated scoring methods. Finally, we verify that relations can be described accurately by transforming the CoNLL04 and Re-TACRED datasets and mapping sentence templates to relation categories. T5 achieves competitive accuracy on CoNLL-04 and Re-TACRED with an F1 score of 78.6 and 90.4, respectively. With this article, we prove that relations can be described, therefore overcoming the limitations set by previous datasets and approaches. We publicly provide our dataset and training code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/logan-markewich/DReD</uri> . Relation extraction is a powerful task, providing a method to extract labeled connections between words in a document. Existing datasets focus on relations between important named entities, with relations sourced from a list of predefined categories. These categories create limitations for trained models, missing important context that a category name cannot capture alone. Our new Descriptive Relation Dataset, DReD, overcomes these limitations by providing a dataset that allows models to learn how to describe relations in a sentence. DReD contains 3286 annotations of descriptions of relations between general noun phrases, removing the previously stated limitations and providing a way to uncover previously unseen relation types while providing meaningful context. Furthermore, any sequence-to-sequence model can be easily trained on DReD, allowing for flexible and future-proof applications.
GADAN: Generative Adversarial Domain Adaptation Network For Debris Detection Using Drone

In maritime, coastal, and riverine environments debris has become an abundant pollutant, posing a significant threat to aquatic life. Existing debris detection systems are mostly designed to detect debris for specific type of environment. Therefore, we propose GADAN, an autonomous debris detection model fusing RGB and thermal images, and experiment on both over land and aquatic environment. We collected data using the Parrot Anafi Thermal drone from different water streams, and construction sites near the aquatic environment. We employ a generative domain adaptation based architecture to generate the thermal image compatible with the RGB image. We postulate a two-stream network on these thermal and RGB pairs of images separately, and pass the concatenated features to object detecting YOLO model. We also demonstrated the performance of debris detection using only RGB and only thermal images. Our proposed GADAN network incorporating RGB and thermal images outperforms (with a mean average precision of 0.97) RCNN and single modality based models.
3D Object Detection for Aerial Platforms via Edge Computing: An Experimental Evaluation

LiDAR is rapidly emerging as a central sensor in many applications involving autonomous navigation. However, the execution of state-of-the-art neural models for the analysis of point clouds produced by LiDARs necessitates considerable computing power, energy and memory. As a consequence, real-time analysis – e.g., 3D object detection – on resource-constrained mobile platforms such as Unmanned Aerial Vehicles (UAV) is often impractical. In this paper, we evaluate the feasibility of real-time LiDAR-based object detection for UAVs using measures and data obtained from a real-world deployment. First, we demonstrate that state-of-the-art neural models for 3D object detection cannot be executed even in relatively powerful embedded computers suitable for airborne drones, such as the NVIDIA Jetson Nano. Then, we focus our attention on edge computing, where the UAV offloads the execution of the analysis model to a compute-capable device (an edge server) positioned at the network edge. The key challenge is that point clouds generated by LiDARs have a large size (1.2MB per point cloud frame). We evaluate the overall capture-to-output delay of a remote analysis loop experimentally for WiFi and using expected data rate for cellular LTE environments. Finally, we evaluate the performance of 3D object detection on available datasets for autonomous vehicles and emphasize the challenges posed by the ability of UAVs to move in the 3D space. With our LiDAR-UAV system, we achieved detections with averages of 86% accuracy, 71.6% precision, and 55.14% recall outputted with an average end-to-end delay of 1920ms.
Network Economics-based Crowdsourcing in UAV-assisted Smart Cities Environments

In this paper, a novel crowdsourcing mechanism is introduced in Unmanned Aerial Vehicles (UAVs)-assisted smart cities environments based on the principles of Contract Theory and Reinforcement Learning. Initially, a contract-theoretic mechanism is proposed to enable the UAVs to incentivize the Internet of Things (IoT) nodes to report their collected data (i.e., effort) via providing personalized rewards to them. This novel mechanism exploits the IoT nodes’ physical and social characteristics in order to design optimal personalized contracts, i.e., pairs of {effort, reward}, while jointly optimizing the benefits of the UAVs and the IoT nodes from the crowdsourcing process. Additionally, a reinforcement learning-based mechanism is designed to enable the IoT nodes to select a UAV to report their data, while considering the received rewards in the crowdsourcing process. A set of detailed numerical and comparative results are presented to demonstrate the operational characteristics of the proposed crowdsourcing framework, as well as its drawbacks and benefits compared to the state of the art.
Exploring the User Experience Design of Commercially Available Cybersecurity Products for Personal Mobile Devices

Research shows that when software is difficult to use, the users will either not use it or find ways to shortcut the software. In the case of cybersecurity applications, shortcutting exposes individuals and their organizations to potential threats. While most research in cybersecurity has focused on designing, implementing, and testing enterprise-scale systems, little research exists on cybersecurity for individual devices and especially not the user experience of those applications. Additionally, most literature on cybersecurity systems focuses on the technical aspects of the systems with little regard for the preferences of actual users. The purpose of this study, therefore, was to examine the end user experience of consumer software for securing mobile devices from cyberattacks. Results generated from a three-part method of survey, heuristic analysis, and sentiment analysis suggest that user experience is not a significant obstacle in the adoption of mobile cybersecurity applications. The study, therefore, indicates that individuals’ choice not to protect their mobile devices is behavioral and not a user experience problem Future research should seek to better understand the attitudinal opposition to using mobile cybersecurity applications.
Undergraduates’ Perceptions on Data Visualization Consumption versus Production: It’s Communication, but Is It “Writing”?

Is producing data visualizations (charts, graphs, tables) considered to be ’writing’? We know university students are fairly comfortable critiquing data visualizations and are exposed to data visualization interpretations as early as elementary school, but where does the onus of data visualization production fall within university curriculum? This exploratory study describes the results of a pilot test of undergraduate students’ experiences with and confidence of producing data visualization artifacts from raw data. The purpose of the paper is to provide a starting point for collecting information about how confident students feel by the time they reach college to produce graphs based on raw data. Most students were neutral/slightly confident in their current abilities but “hoped/planned to be so by graduation.” 65% of students were somewhat confident or confident in their ability to produce charts. The paper asks where we are teaching these skills, and is the humanities, rather than business, the right place for teaching data visualization production skills? The pilot survey is under review for revision to include additional questions posed by audience members for the conference session, and from members of SIGDOC, PROCOMM, Association of Teachers of Technical Writing (ATTW) and Council of Programs of Technical & Scientific Communication (CPTSC). The presentation will serve as a sounding board for when and where we teach the production of data visualization in our curriculums.
Extended Abstract: UX in/as political renegotiation

This panel extends the scope of user experience (UX) to be more inclusive of social and environmental justice aims by providing a series of diverse case studies that cover topics of community engagement, transnational research, technical communication pedagogy, content management systems, and environmental resilience and accentuating aspects of justice, diversity, and inclusion that underlie each. The case studies are held together by a commitment to offer researchers and practitioners tangible, inclusive UX methods.
Data Visualizations: An Integrative Literature Review of Empirical Studies Across Disciplines

This paper addresses the increasing importance of information literacy, especially visual communication and data visualizations. We use visualizations here to mean the visual depictions of data via charts, graphs, and other forms. We focus on data visualizations because they are typically the first thing that draws the reader’s attention.Our study updates the results of a previous literature review on data visualizations [6] that reported on 25 empirical studies that asked participants about data visualizations in health and medical information contexts. By updating this integrative literature review with data from the last five years (2017-2021), our goal is to examine changes in empirical research on data visualizations across disciplines. This analysis of 32 empirical studies found that pictographs, icon arrays, and bar charts remain effective choices for data visualizations with diverse users. However, the studies also point to an ongoing need to conduct research with more contextualized research questions and a focus on interactive displays, issues of numeracy, and a closer look at risk. More so than scholars in other disciplines, TPC scholars are uniquely positioned to focus on context with an eye toward generalizable approaches, which are much needed in research communicating complex health information through data visualizations.
Cultural Differences in Web Design on Chinese and Western Websites: A Literature Review

Understanding cultural differences in web design is essential to facilitate cross-cultural communication and guide website localization. A literature review has been done with 17 studies that compare design elements of Chinese and Western websites. We found most results of the studies were difficult to compare, since different design elements or different attributes of the same elements were investigated. Only a few consistent findings could be drawn from the reviewed studies: (1) Chinese websites have more items and active elements, while Western websites have more footers; (2) Chinese websites use more red and blue colors, and links opening in new windows. By contrast, Western websites use more white color and underlined links; (3) Chinese websites have more images related to leaders or ceremonies, while Western websites have more images related to daily life or nature. We also found that most studies relate the differences to various dimensions of Hofstede’s or Hall’s cultural models, but the explanations sometimes raise questions. Based on the findings of this review, a discussion on future directions of cross-cultural design studies is presented.