Reviewerly/exHarmony huggingface dataset:
Social Presence in Virtual Event Spaces

It is generally acknowledged that the virtual event platforms of today do not perform satisfactorily at what is arguably their most important function: providing attendees with a sense of social presence. Social presence is the "sense of being with another" and can include ways of knowing who is in the virtual space, how others are reacting to what is happening in the space, an awareness of others' activities and availability, and an idea of how to connect with them. Issues around presence and awareness have been perennial topics in the CHI and CSCW communities for decades. Nevertheless, the time feels ripe for a new effort with a special focus on larger-scale virtual events, given the accelerated pace of change in the socio-technological landscape and the tremendous potential impact that new insights could now have. The goal of this workshop is to bring together researchers and developers from academia and industry with a shared interest in improving the experience of virtual events to exchange insights and hopefully energize an ongoing community effort in this area.
Designing and Embedding a Tangible Public Interface in the COVID Era

Due to public concerns over touch-based disease transmission, tangible and embedded interfaces are perhaps the most unsuited technology during a pandemic. Even so, this case study documents the development and evaluation of such a system from early 2020 when people were told to avoid actions that might spread the virus (e.g., touch). Adding to the challenge, the Lookout was installed outside in a city centre for widespread public use. Despite these challenges, a COVID-safe touchable device was embedded and extensively used. This Case Study reports the co-creation of the device noting COVID restriction adaptations over a nine-month deployment. Our contributions are twofold: the study acts as a case-point of the impact of the unique COVID design context, with lessons for future pandemic scenarios; and, given we had over 10,000 users at a time when people were cautious about using shared devices or services, we surface some design characteristics that can promote the use of public technology.
Traffic classification algorithm using CNN and multi-head attention mechanism for representation learning

Abstract With the development of the times, the demand for privacy and security by people, enterprises and government units has become stronger and stronger, driven by this, encrypted traffic has shown a blowout growth, and the security of traffic has become an issue that cannot be ignored. To solve the traffic classification problem, this paper proposes a new traffic classification algorithm based on convolutional neural network and multi-head attention mechanism. In addition, this paper uses a feature engineering method based on representation learning and proposes a discard threshold to improve the quality of data sets obtained by feature engineering. The experimental results show that the algorithm model and the optimized feature engineering method proposed in this paper have good performance on the traffic classification task.
An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network

Abstract Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mechanism. Our experimental results show that the proposed algorithm effectively improves the aggregation efficiency of federated learning compared with FedAvg algorithm.
Research on the application of OpenPose in escalator safety systems

Abstract This paper describes the application of the OpenPose based human pose detection model in escalator safety systems. The YOLO target detection is used to obtain the position of the human body in the image, and then the human limb information is extracted by OpenPose, and the human joint connection algorithm connects the identified human limb information to obtain the human pose information in the image. The experimental results show that the algorithm can effectively identify the human body posture and can provide timely alarms for dangerous postures, thus improving the safety of escalator riders.
An efficient algorithm for the longest common palindromic subsequence problem

The longest common palindromic subsequence (LCPS) problem is a variant of the longest common subsequence (LCS) problem. Given two input sequences A and B, the LCPS problem is to find the common subsequence of A and B such that the answer is a palindrome with the maximal length. The LCPS problem was first proposed by Chowdhury et al. (2014) [9], who proposed a dynamic programming algorithm with O(m2n2) time, where m and n denote the lengths of A and B, respectively. This paper proposes a diagonal-based algorithm for solving the LCPS problem with O(L(m−L)Rlog⁡n) time and O(RL) space, where R denotes the number of match pairs between A and B, and L denotes the LCPS length. In our algorithm, the 3-dimensional minima finding algorithm is invoked to overcome the domination problem. As experimental results show, our algorithm is efficient practically compared with some previously published algorithms.
Augmented experiences in archeological sites: Presentation of Alexandria Troas Podium Temple to visitor experience

Set aside the issues concerning their excavation, documentation, and conservation, as far as their presentation to the public experience is concerned, Archaeological sites represent a special case of cultural heritage that come with distinctive set of conditions and demands, posing a problem situation deserving a special treatment. Problem is manifold: The presentation should be informative, entertaining, and educational, all accomplished through an active corporeal and mental participation where interactivity and immersion must be the key. The setting must provide a holistic, comprehensible experience by completing “missing parts and layers,” and contextualizing it, perhaps through a story, a theme, or a background. Any intervention must be non-invasive, reversible and updateable; alternatives and different layers must be presented, preferably, synchronously. Above all, final setting should be subordinate to the primacies of “conservation of cultural heritage,” while providing an intellectually and physically accessible and sustainable overall historical environment. This has been an age-old issue for the scholars, a genuine challenge due to the ill-defined nature of the problem situation itself. The present study departs from the proposition that, Augmented Reality(AR), by definition, has a potential to contribute to such a problem situation. AR is a combination of real and virtual worlds, where “virtual” could complement what was missing in the real and new objects and layers might be woven together, into one new reality where active bodily and mental participation and interaction is possible. Though it might seem implied in the definition, the proposition still needs a rigorous investigation since AR is a rapidly emerging but still quite a young field that has a long way to go; and since, research on AR’s specific adoption to presentation of archaeological sites, apart from few examples, is still an unbeaten path. The present multidisciplinary study aims to take a step towards such an investigation. Established upon a detailed investigation and analysis of examples, the present study involves development of an AR application of a selected case: “Alexandria Troas Podium Temple,” followed by a field study. In the present report, we share our experience and observations of the process and the implementation. In conclusion, we propose that AR is a serious candidate to be a considerable asset for the presentation of archaeological sites for the visitor experience, without compromising the universal norms of conservation of cultural heritage. We also argue that AR seems to have its own agenda, coming with unprecedented possibilities still to be appreciated and adopted, which in turn might help us to go beyond the conventional conceptions and modes of conservation of cultural heritage and presentation.
Portrait imaging relighting system based on a simplified photometric stereo method

This study proposes a portrait image relighting system based on a simplified photometric stereo method. The system, comprising a controllable digital single lens reflex camera and five polarized flashlights, can obtain a color shade-less image and synthesize a normal map from shaded images. When calibrating the photometric stereo, the normal map is taken as a linear combination of shaded images and clamped with respect to specific normal directions on a white-coated sphere. The relit images were generated through inverse rendering in a predefined virtual environment. To evaluate personal preference, 24 adult subjects were recruited to conduct subjective assessments comparing the deep portrait relighting method results. From experiments regarding different scenarios, we concluded that the proposed system based on a simplified photometric stereo performs acceptably for relighting portrait images.
Exploiting Morpheme and Cross-lingual Knowledge to Enhance Mongolian Named Entity Recognition

Mongolian named entity recognition (NER) is not only one of the most crucial and fundamental tasks in Mongolian natural language processing, but also an important step to improve the performance of downstream tasks such as information retrieval, machine translation, and dialog system. However, traditional Mongolian NER models heavily rely on the feature engineering. Even worse, the complex morphological structure of Mongolian words makes the data sparser. To alleviate the feature engineering and data sparsity in Mongolian named entity recognition, we propose a novel NER framework with Multi-Knowledge Enhancement (MKE-NER) . Specifically, we introduce both linguistic knowledge through Mongolian morpheme representation and cross-lingual knowledge from Mongolian-Chinese parallel corpus. Furthermore, we design two methods to exploit cross-lingual knowledge sufficiently, i.e., cross-lingual representation and cross-lingual annotation projection. Experimental results demonstrate the effectiveness of our MKE-NER model, which outperforms strong baselines and achieves the best performance (94.04% F1 score) on the traditional Mongolian benchmark. Particularly, extensive experiments with different data scales highlight the superiority of our method in low-resource scenarios.
Neural component search for single image super-resolution

Deep learning has become the mainstream method in the field of single image super-resolution (SISR), and the neural architecture search has been gradually applied to build SISR networks in a non-hand-crafted way. However, the existing methods can only search the structure of models and the searching speed is slow. To solve this problem, a neural component search (NCS) method is proposed. When searching for SISR networks, the color space and the composition of loss functions during training are also parts of the search space. Under a specific computational constraint, the peak signal noise ratio (PSNR) or structural similarity (SSIM) can be used as the reward to search out an optimal super-resolution network. In addition, a super graph is designed with the idea of parameter sharing to sample adaptive residual dense networks (ARDNs), thus the NCS can complete the search of SISR networks at faster speed compared to existing methods. Experimental results indicate that ARDNs searched by the NCS is competitive with the hand-crafted state-of-the-art networks, and ARDNs achieve favorable performance against state-of-the-art methods with similar computational consumption. • NCS takes the loss functions and color space as part of the search space. • ARDN is designed with the thought of parameter sharing to speed up the search time. • NCS can be utilized as a framework to search for new super-resolution networks.
Design Choices for Embodied Conversational Agents to Preserve Testimonies by Contemporary Witnesses

Conversations with contemporary witnesses, e.g., Holocaust survivors, are valuable opportunities to learn about impacts of historical events. They lead to a more thorough conveyance of history by complementing aggregated facts and numbers with emotional personal experiences and detailed accounts. Encounters and interviews with first-hand witnesses to a number of historical periods soon are a thing of the past due to the respective survivors’ advanced age. Embodied Conversational Agents using recordings of contemporary witnesses are an attempt at preserving interactive and personal testimonies for future generations of students and educators. Since the implementation of such Interactive Digital Testimonies (IDT) contains irreversible design decisions, an overview of available choices and their respective consequences are needed. While a number of IDTs have been created over the past years, there is little empirical research on available design choices and the resulting effects on users. In my dissertation I will identify the required HCI features of IDTs, as well as investigate and evaluate how these features can be implemented. This scientific basis will allow for informed decisions during the development of future IDTs.
Mutually unbiased special entangled bases with Schmidt number 2d in ℂ2d+1 ⊗ ℂ4d

In this paper, we construct mutually unbiased special entangled bases with Schmidt number [Formula: see text] in [Formula: see text]. Precisely, we first provide a necessary and sufficient condition for two special entangled bases with Schmidt number [Formula: see text] are mutually unbiased, and then use the condition to construct two mutually unbiased special entangled bases with Schmidt number 2 in [Formula: see text].
Video behavior recognition algorithm based on two-stream heterogeneous convolutional neural network

Abstract In video behavior recognition, making full use of the spatio-temporal information contained in video frame is the critical point to further improve the recognition accuracy. In our study, we propose a video behavior recognition algorithm based on two-stream heterogeneous network, so that we can further extract the spatio-temporal feature information in the video frame, and finally take full advantage of the spatio-temporal features in the video sequence. In view of the characteristics of RGB and optical flow images, this paper uses DenseNet121 and Inception-V4 to structure a two-stream network, to fully extract the spatio-temporal feature information in the video. In this paper, UCF101 dataset is used to evaluate the algorithm, and the recognition accuracy is 91.7%, which verified the reliability of the proposed algorithm.
The Role of Audio in the Diegesis of Virtual Reality Games

While static media makes the distinction between diegetic and non-diegetic audio, the use of these concepts in interactive media, such as games, presents problems of classification. This is especially true for virtual reality where players embody virtual characters through their own bodily movements. This PhD research focuses on the diegetic aspect of audio in virtual reality and the effect that different approaches might have on players’ sense of embodiment and willingness to suspend disbelief.
SocialStools: A Playful, Socio-Spatial Interface for Fostering Togetherness Across Strangers

Leveraging the human propensity for embodied interaction, SocialStools is a socio-spatial interface that facilitates playful social interactions across strangers in a physical space, fostering togetherness. Three stools on caster wheels generate sound and image around them in response to sitting on them, and moving and rotating relative to each other. In this paper, we identify the challenges of stranger interactions, introduce our cyber-physical system, and describe and demonstrate three interaction situations: sitting, moving closer and away from each other, and rotating to face or turn away from each other. By translating these interactions into visual and auditory effects, we explore the possibilities of merging the socio-physical world with a digital system to create unique social affordances for interpersonal interactions that foster togetherness. Our demonstration transforms three strangers into a trio of sound-and-image makers interacting through creative, embodied play.
Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct “nearest neighbor” and the target user, and the similarity between the indirect “nearest neighbor” and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data.
Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application

Abstract To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.
Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos

In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificially intelligent agents would make them better social learners by allowing them to evaluate human action under a teleological lens. To validate this ability of deep learning models to perform this task, we curate the W-Oops dataset, built upon the Oops dataset [11]. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations. Due to the expensive segment annotation procedure, we propose a weakly supervised algorithm for localizing the goal-directed as well as unintentional temporal regions in the video leveraging solely video-level labels. In particular, we employ an attention mechanism based strategy that predicts the temporal regions which contributes the most to a classification task. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method. We further conduct a video captioning experiment which demonstrates that the proposed localization module does indeed assist teleological action understanding. Project website can be found at: https://asu-apg.github.io/TragedyPlusTime.
CrossGAN-Detection: A generative adversarial network with directly controllable fusion for target detection

Most of the deep learning object detection methods based on multi-modal information fusion cannot directly control the quality of the fused images at present, because the fusion only depends on the detection results. The indirectness of control is not conducive to the target detection of the network in principle. For the sake of the problem, we propose a multimodal information cross-fusion detection method based on a generative adversarial network (CrossGAN-Detection), which is composed of GAN and a target detection network. And the target detection network acts as the second discriminator of GAN during training. Through the content loss function and dual discriminator, directly controllable guidance is provided for the generator, which is designed to learn the relationship between different modes adaptively through cross fusion. We conduct abundant experiments on the KITTI dataset, which is the prevalent dataset in the fusion-detection field. The experimental results show that the AP of the novel method for vehicle detection achieves 96.66%, 87.15%, and 78.46% in easy, moderate, and hard categories respectively, which is improved about 7% compared to the state-of-art methods.
Grand Challenges for Personal Informatics and AI

Increasing availability of personal data opened new possibilities for technologies that support individuals’ reflection, increase their self-awareness, and inform their future choices. Personal informatics, chiefly concerned with investigating individuals’ engagement with personal data, has become an area of active research within Human-Computer Interaction. However, more recent research has argued that personal informatics solutions often place high demands on individuals and require knowledge, skills, and time for engaging with personal data. New advances in Machine Learning (ML) and Artificial Intelligence (AI) can help to reduce the cognitive burden of personal informatics and identify meaningful trends using analytical engines. Furthermore, introducing ML and AI can enable systems that provide more direct support for action, for example through predictions and recommendations. However, there are many open questions as to the design of personal informatics technologies that incorporate ML and AI. In this workshop, we will bring together an interdisciplinary group of researchers in personal informatics, ML, and AI to outline the design space for intelligent personal informatics solutions and develop an agenda for future research in this area.