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"Topic": models, code, and papers

An Open Access Dataset of Tweets related to Exoskeletons and 100 Research Questions

Nov 04, 2021
Nirmalya Thakur, Chia Y. Han

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a comprehensive dataset is necessary. The Internet of Everything (IOE) era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by the mining of relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. To address this research challenge by utilizing the potential of the IOE style of living, this paper makes multiple scientific contributions to this field. First, it presents a novel approach of mining tweets that is not bound by any restrictions on the number of days during which the tweets can be mined. Second, by application of this approach, it presents an open-access dataset of approximately 20,000 tweets related to exoskeletons, that were posted over a period of 231 days. Finally, based on an exploratory review of 108 emerging works in this field and its interrelated disciplines, the paper discusses multiple interdisciplinary applications of this dataset and presents 100 research questions for researchers to study, analyze, evaluate, and investigate.

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Uniquely Decodable Multi-Amplitude Sequence for Massive Grant-free Multiple-Access Adder Channels

Oct 22, 2021
Q. Y. Yu, K. X. Song

Massive grant-free multiple-access is a valuable research topic for next generation multiple-access, since it significantly reduces the control signaling overhead and transmission latency. This paper constructs a novel uniquely-decodable multi-amplitude sequence (UDAS) set for grant-free multiple-access systems, which can provide high spectrum efficiency (SE) without additional redundancy and realize low-complexity active user detection (AUD). We firstly propose an UDAS-based multi-dimensional bit interleaving coded modulation (MD-BICM) transmitter. Then, this paper presents the detailed definition of UDAS, and provides three conditions for constructing a UDAS set. Following, two kinds of UDAS sets are constructed based on cyclic and quasi-cyclic matrix modes; and some important features of the cyclic/quasi-cyclic UDAS sets are deduced. Besides, we present a statistic of UDAS feature based AUD algorithm (SoF-AUD), and a joint multiuser detection and improved message passing iterative decoding algorithm for the proposed system. Finally, the active user error rate (AUER) and Shannon limits of the proposed system are deduced in details. Simulation results show that the AUER of our proposed system can reach an extremely low value $10^{-6}$, when $E_b/N_0$ is 0 dB and the length of transmit block is larger than a given value (e.g., 576). Meanwhile, the SE of our proposed system can compare with the designed non-orthogonal multiple-access (NOMA) codebooks, verifying the valid and flexible.

* 14 pages, 8 figures 

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Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

Oct 19, 2021
Yu Song, Jianxun Lian, Shuai Sun, Hong Huang, Yu Li, Hai Jin, Xing Xie

User interest exploration is an important and challenging topic in recommender systems, which alleviates the closed-loop effects between recommendation models and user-item interactions. Contextual bandit (CB) algorithms strive to make a good trade-off between exploration and exploitation so that users' potential interests have chances to expose. However, classical CB algorithms can only be applied to a small, sampled item set (usually hundreds), which forces the typical applications in recommender systems limited to candidate post-ranking, homepage top item ranking, ad creative selection, or online model selection (A/B test). In this paper, we introduce two simple but effective hierarchical CB algorithms to make a classical CB model (such as LinUCB and Thompson Sampling) capable to explore users' interest in the entire item space without limiting it to a small item set. We first construct a hierarchy item tree via a bottom-up clustering algorithm to organize items in a coarse-to-fine manner. Then we propose a hierarchical CB (HCB) algorithm to explore users' interest in the hierarchy tree. HCB takes the exploration problem as a series of decision-making processes, where the goal is to find a path from the root to a leaf node, and the feedback will be back-propagated to all the nodes in the path. We further propose a progressive hierarchical CB (pHCB) algorithm, which progressively extends visible nodes which reach a confidence level for exploration, to avoid misleading actions on upper-level nodes in the sequential decision-making process. Extensive experiments on two public recommendation datasets demonstrate the effectiveness and flexibility of our methods.

* WSDM 2022 

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Recommender systems based on graph embedding techniques: A comprehensive review

Sep 20, 2021
Yue Deng

Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Sep 18, 2021
Jinli Suo, Weihang Zhang, Jin Gong, Xin Yuan, David J. Brady, Qionghai Dai

Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision.Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.

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COfEE: A Comprehensive Ontology for Event Extraction from text, with an online annotation tool

Aug 01, 2021
Ali Balali, Masoud Asadpour, Seyed Hossein Jafari

Data is published on the web over time in great volumes, but majority of the data is unstructured, making it hard to understand and difficult to interpret. Information Extraction (IE) methods extract structured information from unstructured data. One of the challenging IE tasks is Event Extraction (EE) which seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval, summarization and online monitoring systems. In the past decades, some event ontologies like ACE, CAMEO and ICEWS were developed to define event forms, actors and dimensions of events observed in the text. These event ontologies still have some shortcomings such as covering only a few topics like political events, having inflexible structure in defining argument roles, lack of analytical dimensions, and complexity in choosing event sub-types. To address these concerns, we propose an event ontology, namely COfEE, that incorporates both expert domain knowledge, previous ontologies and a data-driven approach for identifying events from text. COfEE consists of two hierarchy levels (event types and event sub-types) that include new categories relating to environmental issues, cyberspace, criminal activity and natural disasters which need to be monitored instantly. Also, dynamic roles according to each event sub-type are defined to capture various dimensions of events. In a follow-up experiment, the proposed ontology is evaluated on Wikipedia events, and it is shown to be general and comprehensive. Moreover, in order to facilitate the preparation of gold-standard data for event extraction, a language-independent online tool is presented based on COfEE.

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CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1

Jun 17, 2021
Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart

Neural networks are increasingly used in environmental science applications. Furthermore, neural network models are trained by minimizing a loss function, and it is crucial to choose the loss function very carefully for environmental science applications, as it determines what exactly is being optimized. Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification. However, there are very few resources available that cover the basics of custom loss function development comprehensively, and to the best of our knowledge none that focus on the needs of environmental scientists. This document seeks to fill this gap by providing a guide on how to write custom loss functions targeted toward environmental science applications. Topics include the basics of writing custom loss functions, common pitfalls, functions to use in loss functions, examples such as fractions skill score as loss function, how to incorporate physical constraints, discrete and soft discretization, and concepts such as focal, robust, and adaptive loss. While examples are currently provided in this guide for Python with Keras and the TensorFlow backend, the basic concepts also apply to other environments, such as Python with PyTorch. Similarly, while the sample loss functions provided here are from meteorology, these are just examples of how to create custom loss functions. Other fields in the environmental sciences have very similar needs for custom loss functions, e.g., for evaluating spatial forecasts effectively, and the concepts discussed here can be applied there as well. All code samples are provided in a GitHub repository.

* 37 pages 

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Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset

May 02, 2021
W. Y. Ayele

Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

* 13 pages, 14 figures. International Journal of Advanced Computer Science and Applications, 2020 

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Countering Malicious DeepFakes: Survey, Battleground, and Horizon

Feb 27, 2021
Felix Juefei-Xu, Run Wang, Yihao Huang, Qing Guo, Lei Ma, Yang Liu

The creation and the manipulation of facial appearance via deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications. The evil side of this new technique poses another popular study, i.e., DeepFake detection aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of the battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 191 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and directions in the field of DeepFake generation and detection. We also elaborately design interactive diagrams ( to allow researchers to explore their own interests on popular DeepFake generators or detectors.

* 34 pages, 14 figures 

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