Reviewerly/exHarmony huggingface dataset:
Concept Craft Cards

This is a demonstration of how to use a deck of Concept Craft Cards that represent the first iteration of a toolkit for Animal-Computer Interaction (ACI) developers. The cards will be available to share, discuss and deploy online in relation to a series of non-human client briefs. At the top conceptual level, we offer suggestions for ACI developers embarking on a new design journey; the next conceptual level considers user experience and offers our recommendations for interaction design that values cognitive and sensory enrichment; at a practical level, we provide a set of topic cards, to be used during development to support the realization of design concepts through crafting and tinkering.
Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?

This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants’ run submissions.
Research with a Solidarity Clinic: Design Implications for CSCW Healthcare Service Design

Abstract This paper reports on a long-term collaboration with a self-organised social clinic, within solidarity movements in Greece. The collaboration focused on the co-creation of an oral history group within the social clinic, aiming to record and make sense of a collection of digital oral histories from its volunteers and volunteers-doctors. The process aimed to support reflection and shape the future of the clinic’s ongoing social innovation and to transform institutional public health services. Positioning the work of solidarity movement as designing social innovation, the work contributes to CSCW and ‘infrastructuring’ in Participatory Design aspiring to support social activism and social transformation processes. More specifically, through our empirical insights on the process of infrastructuring an oral history group within a social movement; and related insights about their ongoing participatory health service provision—we provide implications for CSCW concerned with its role in institutional healthcare service transformation.
Submodular Span, with Applications to Conditional Data Summarization

As an extension to the matroid span problem, we propose the submodular span problem that involves finding a large set of elements with small gain relative to a given query set. We then propose a two-stage Submodular Span Summarization (S3) framework to achieve a form of conditional or query-focused data summarization. The first stage encourages the summary to be relevant to a given query set, and the second stage encourages the final summary to be diverse, thus achieving two important necessities for a good query-focused summary. Unlike previous methods, our framework uses only a single submodular function defined over both data and query. We analyze theoretical properties in the context of both matroids and polymatroids that elucidate when our methods should work well. We find that a scalable approximation algorithm to the polymatroid submodular span problem has good theoretical and empirical properties. We provide empirical and qualitative results on three real-world tasks: conditional multi-document summarization on the DUC 2005-2007 datasets, conditional video summarization on the UT-Egocentric dataset, and conditional image corpus summarization on the ImageNet dataset. We use deep neural networks, specifically a BERT model for text, AlexNet for video frames, and Bi-directional Generative Adversarial Networks (BiGAN) for ImageNet images to help instantiate the submodular functions. The result is a minimally supervised form of conditional summarization that matches or improves over the previous state-of-the-art.
A Theoretical and Experimental Comparison of Large-Scale Join Algorithms in Spark

Currently, the estimated amount of data created daily have reached the threshold of petabytes or even zettabytes globally. It is no wonder that traditional data processing technologies cannot process and manage extremely large volumes of such data. However, these massive and various data can be used to deal with business problems that we would not have been able to tackle before. To discover their value, it is necessary to effectively perform query operations in a parallel and distributed manner. One of the standard and common query operations is an expensive join operation. This research systematically presents a theoretical and experimental comparison of the prominent join algorithms in the Spark environment. At first, this study shows the details of important strategies of two-way joins and recursive joins. Then, it exposes the advantages and disadvantages of each approach. Especially, the work provides mathematical cost models to make a more convince comparison of the joins before verifying by experiments. The results show that the comparison using the cost models is consistent with that using the experiments. Generally, the two-way and recursive joins using filters are the best choices while performing in the Spark environment.
IRONMAN: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning.

Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes conceals optimization opportunities; 2) existing HLS tools do not provide flexible trade-off (Pareto) solutions among different objectives and constraints; 3) the actual quality of the resulting RTL designs is hard to predict. To address these challenges, we propose an end-to-end framework, namelyIronMan. The primary goal is to enable a flexible and automated design space exploration (DSE), to provide either optimal solutions under user-specified constraints, or various trade-offs among different objectives (such as different types of resources, area, and latency). Such DSE either requires tedious manual efforts or is not achievable to attain these goals through existing HLS tools. There are three components in IronMan: 1) GPP, a highly accurate graph-neural-network-based performance and resource predictor; 2) RLMD, a reinforcement-learning-based multi-objective DSE engine that explores the optimal resource allocation strategy, to provide Pareto solutions between different objectives; 3) CT, a code transformer to assist RLMD and GPP, which extracts the data flow graph from original HLS C/C++ and automatically generates synthesizable code with HLS directives. The experimental results show that: 1) GPP achieves high prediction accuracy, reducing prediction errors of HLS tools by 10.9x in resource utilization and 5.7x in timing; 2) RLMD obtains optimal or Pareto solutions that outperform the genetic algorithm and simulated annealing by 12.7% and 12.9%, respectively; 3) IronMan is able to find optimized solutions perfectly matching various DSP constraints, with 2.54x fewer DSPs and up to 6x shorter latency than those of HLS tools while being up to 400x faster than the heuristic algorithms and HLS tools.
Multi-Task Recurrent Modular Networks

We consider the models of deep multi-task learning with recurrent architectures that exploit regularities across tasks to improve the performance of multiple sequence processing tasks jointly. Most existing architectures are painstakingly customized to learn task relationships for different problems, which is not flexible enough to model the dynamic task relationships and lacks generalization abilities to novel test-time scenarios. We propose multi-task recurrent modular networks (MT-RMN) that can be incorporated in any multi-task recurrent models to address the above drawbacks. MT-RMN consists of a shared encoder and multiple task-specific decoders, and recurrently operates over time. For better flexibility, it modularizes the encoder into multiple layers of sub-networks and dynamically controls the connection between these sub-networks and the decoders at different time steps, which provides the recurrent networks with varying degrees of parameter sharing for tasks with dynamic relatedness. For the generalization ability, MT-RMN aims to discover a set of generalizable sub-networks in the encoder that are assembled in different ways for different tasks. The policy networks augmented with the differentiable routers are utilized to make the binary connection decisions between the sub-networks. The experimental results on three multi-task sequence processing datasets consistently demonstrate the effectiveness of MT-RMN.
General density-peaks-clustering algorithm

Density-peaks-clustering (DPC) algorithm plays an important role in clustering analysis with the advantages of easy realization and comprehensiveness whereas without the requirement of any iteration or optimization. However, the DPC accuracy depends on two user-specified parameters, and each of them can greatly affect clustering results. To solve this problem, we extend DPC to a general and hierarchical form. Without the need of any parameters, the proposed E-DPC algorithm can effectively cluster points in any dataset with various characteristics. The results of experiments show that the proposed algorithm is more accurate and general in comparison with two mostly used algorithms.
Two-sequential conclusive discrimination between binary coherent states via indirect measurements

A general scenario for an $N$-sequential conclusive state discrimination introduced recently in Loubenets and Namkung [arXiv:2102.04747] can provide a multipartite quantum communication realizable in the presence of a noise. In the present article, we propose a new experimental scheme for the implementation of a sequential conclusive discrimination between binary coherent states via indirect measurements within the Jaynes-Cummings interaction model. We find that if the mean photon number is less than 1.6, then, for our two-sequential state discrimination scheme, the optimal success probability is larger than the one presented in Fields, Varga, and Bergou [2020, IEEE Int. Conf. Quant. Eng. Comp.]. We also show that, if the mean photon number is almost equal to 1.2, then the optimal success probability nearly approaches the Helstrom bound.
Research on Intelligent Question Answering System Based on College Enrollment

Abstract Due to the increasing number of educated students and the continuous improvement of teaching level, China has formed the largest higher education system in the world, and the information about college enrollment has become the focus of the attention of Chinese students and parents. The intelligent question answering system for college enrollment can understand the user’s intention, provide students and parents with the enrollment information of the school more quickly and efficiently, make up for the shortcomings in the enrollment work, and greatly improve the efficiency of enrollment.
GEM: A General Evaluation Benchmark for Multimodal Tasks

In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus on natural language tasks, GEM is a large-scale vision-language benchmark, which consists of GEM-I for image-language tasks and GEM-V for video-language tasks. Comparing with existing multimodal datasets such as MSCOCO and Flicker30K for image-language tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the same time, but also labeled in multiple languages. We also provide two baseline models for this benchmark. We will release the dataset, code and baseline models, aiming to advance the development of multilingual multimodal research.
Performance Comparison of Multiple Containers Running Artificial Intelligence Applications

Abstract Due to the performance overhead of virtual machines, traditional virtualization solutions are generally not suitable for artificial intelligence applications. Container-based virtualization technology represented by Docker can provide a customized environment, good portability, good compatibility and low overhead. In this article, we give a performance comparison among Docker, Singularity, and Charliecloud containers. In particular, CPU and GPU are used in each container to run a real application of artificial intelligence for the performance comparison. Finally, the experimental results are given and discussed.
REVIEW ON PHOTOGRAMMETRIC SURFACE INSPECTION IN AUTOMOTIVE PRODUCTION

Abstract. When purchasing a premium car for a substantial sum, first impressions count. Key to that first impression is a flawless exterior appearance, something self-explanatory for the customer, but a far greater challenge for production than one might initially assume. Fortunately, photogrammetric technologies and evaluation methods are enabling an ever greater degree of oversight in the form of comprehensive quality data at different automotive production stages, namely stamping, welding, painting and finishing. A drawback lies in the challenging production environment, which complicates inline integratability of certain technologies. In recent years, machine vision and deep learning have been applied to photogrammetric surface inspection with ever increasing success. Given comprehensive surface quality information throughout the entire production chain, production parameters can be dialed in ever tighter in a data-driven fashion, leading to a sustainable increase in quality. This paper provides a review of current and potential contributions of photogrammetry to this end, discussing several recent advances in research along the way. Particular emphasis will be placed on early production stages, as well as the application of machine vision and deep learning to this challenging task. An outline for further research conducted by the authors will conclude this paper.
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks.

Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan. We propose a graph neural network architecture for predicting object importance in a single inference pass, thus incurring little overhead while greatly reducing the number of objects that must be considered by the planner. Our approach treats the planner and transition model as black boxes, and can be used with any off-the-shelf planner. Empirically, across classical planning, probabilistic planning, and robotic task and motion planning, we find that our method results in planning that is significantly faster than several baselines, including other partial grounding strategies and lifted planners. We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances. Video: https://youtu.be/FWsVJc2fvCE Code: https://git.io/JIsqX
Car Damage Identification and Categorization Using Various Transfer Learning Models

Cars play a critical role in today's modern world, and the ability to automatically classify car damages is of specific importance to the auto insurance industry. On a daily basis, car insurance companies cope with car inspection and testing. These inspections are labour-intensive, time-consuming, and sometimes inaccurate processes. Clients and insurance firms alike face costs and discomforts as a result of certain processes. Term strategy to assist, boost, or improve such strict monitoring processes may be feasible with current technology, even if entire modification is still a long way off. This research work analyzes the problem of automatic car damage detection and classification - this is an issue of importance to insurance companies in handling auto insurance claims quickly. In the classification of images, object recognition and image segmentation, developments in computer vision algorithms that employ deep learning have generated promising results. Convolutional Neural Networks (CNN) can be used for detection, analysis and estimation of various types of damage in different parts of the car. This research work has used the transfer learning-based models, Inception V3,Xception, VGG16, VGG19, ResNet50, and MobileNet in the Kera's library to train our model to predict the damage and to compare the efficacy of these models. The proposed dataset is trained with these pre-trained models in order to obtain the maximum accuracy and speed with negligible loss so that the model could be employed in real-life to predict the claim. Our analysis indicates that MobileNet is more accurate and the training speed is also less when compared to other models. An accuracy of 97.28% in predicting damage and classifying it into different types was achieved - this is substantially better than results achieved in the past in a similar test set.
Development of a system for adaptive control of the components of an intelligent educational environment

The article discusses the issues of creating software and tools for managing the processes of training specialists in an intellectual educational environment. The complex of tools is being developed as part of a hyper-converged computing ecosystem to support open personalized learning technologies and is designed to customize and adaptively update educational programs and content, taking into account the requirements of federal standards and regional labor markets. At the first stage, the tools solve the problems of searching, collecting, consolidating and intelligent analysis of requirements for specialists extracted from open sources on the Internet, such as sections with employers’ vacancies on the websites of enterprises, recruitment agencies, labor exchanges, message boards, forums, chats, groups of social networks and messengers. At the next stage, the process of adaptive adjustment and synchronization of educational programs is implemented, taking into account the consolidated information and predicted data on the required competencies in the short and medium term in a given region. Setting up the educational process in an open information environment occurs during the evolutionary transition to a convergent learning model, continuous updating of educational programs and content, personalization of training trajectories. The convergent model determines the convergence of educational programs and content for different specialties in accordance with the digitalization processes of all spheres of human life, which is reflected in the requirements for competencies in professional and educational standards, as well as on the part of employers. The processes of actualization and personalization make it possible to increase the efficiency and quality of training specialists by reducing the risks of obtaining a low-quality and morally obsolete education. The architecture of the adaptive management system of the educational environment includes the following components: a) Learning Management System (LMS), b) Education Content Management System (ECMS), c) Learning Activity Management System (LAMS), d) tools for searching, collecting and analyzing employers’ requirements, e) cloud storage of educational content.
Symbolic Search for Optimal Total-Order HTN Planning

Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning.
Multi-Camera Tracking By Candidate Intersection Ratio Tracklet Matching

Multi-camera vehicle tracking at the city scale is an essential task in traffic management for smart cities. Large-scale video analytics is challenging due to the vehicle variabilities, view variations, frequent occlusions, degraded pixel quality, and appearance differences. In this work, we develop a multi-target multi-camera (MTMC) vehicle tracking system based on a newly proposed Candidates Intersection Ratio (CIR) metric that can effectively evaluate vehicle tracklets for matching across views. Our system consists of four modules: (1) Faster-RCNN vehicle detection, (2) detection association based on re-identification feature matching, (3) single-camera tracking (SCT) to produce initial tracklets, (4) multi-camera vehicle tracklet matching and re-identification that creates longer, consistent tracklets across the city scale. Based on popular DNN object detection and SCT modules, we focus on the development of tracklet creation, association, and linking in SCT and MTMC. Specifically, SCT filters are proposed to effectively eliminate unreliable tracklets. The CIR metric improves robust vehicle tracklet linking across visually distinct views. Our system obtains IDF1 score of 0.1343 on the AI City 2021 Challenge Track 3 public leaderboard.
Efficient Realistic Data Generation Framework Leveraging Deep Learning-Based Human Digitization

The performance of supervised deep learning algorithms depends significantly on the scale, quality and diversity of the data used for their training. Collecting and manually annotating large amount of data can be both time-consuming and costly tasks to perform. In the case of tasks related to visual human-centric perception, the collection and distribution of such data may also face restrictions due to legislation regarding privacy. In addition, the design and testing of complex systems, e.g., robots, which often employ deep learning-based perception models, may face severe difficulties as even state-of-the-art methods trained on real and large-scale datasets cannot always perform adequately as they have not adapted to the visual differences between the virtual and the real world data. As an attempt to tackle and mitigate the effect of these issues, we present a method that automatically generates realistic synthetic data with annotations for a) person detection, b) face recognition, and c) human pose estimation. The proposed method takes as input real background images and populates them with human figures in various poses. Instead of using hand-made 3D human models, we propose the use of models generated through deep learning methods, further reducing the dataset creation costs, while maintaining a high level of realism. In addition, we provide open-source and easy to use tools that implement the proposed pipeline, allowing for generating highly-realistic synthetic datasets for a variety of tasks. A benchmarking and evaluation in the corresponding tasks shows that synthetic data can be effectively used as a supplement to real data.