Reviewerly/exHarmony huggingface dataset:
Implicit Diversity in Image Summarization

Studies have shown that the people depicted in image search results tend to be of majority groups with respect to socially salient attributes such as gender or race. This skew goes beyond that which already exists in the world - i.e., the search results for images of people are more imbalanced than the ground truth would suggest. For example, Kay et al. showed that although 28% of CEOs in the U.S. are women, only 10% of the top 100 results for "CEO" in Google Image Search are women. Similar observations abound across search terms and across socially salient attributes. Most existing approaches to correct for this kind of bias assume that the images of people include labels denoting the relevant socially salient attributes. These labels are explicitly used to either change the dataset, adjust the training of the algorithm, and/or in the execution of the method. However, such labels are often unknown. Further, using machine learning techniques to infer these labels may often not be possible within acceptable accuracy ranges and may not be desirable due to the additional biases this process could incur. As observed in prior work, alternate approaches consider the diversity of image features, which often do not translate to images of visibly diverse people. We develop a novel approach that takes as input a visibly diverse control set of images of people and uses this set as part of a procedure to select a set of images of people in response to a query. The goal is to have a resulting set that is more visibly diverse in a manner that emulates the diversity depicted in the control set. It accomplishes this by evaluating the similarity of the images selected by a black-box algorithm with the images in the diversity control set, and incorporating this "diversity score" into the final selection process. Importantly, this approach does not require images to be labelled at any point; effectively, it gives a way to implicitly diversify the set of images selected. We provide two variants of our approach: the first is a modification of the well known MMR algorithm to incorporate the diversity scores, and the second is a more efficient variant that does not consider within-list redundancy. We evaluate these approaches empirically on two image datasets: 1) a new dataset we collect which contains the top 100 Google Image results for 96 occupations, for which we evaluate gender and skin-tone diversity with respect to occupations and 2) the well-known CelebA dataset containing images of celebrities for which we can evaluate gender diversity with respect to facial features such as "smiling" or "glasses". Both of our approaches produce image sets that significantly improve the visible diversity of the results (i.e., include a larger fraction of anti-stereotypical images) with respect to current Google Image Search results and other state-of-the-art algorithms for diverse image summarization. Further, they seem to come at a minimal cost to accuracy. These empirical results demonstrate the effectiveness of simple label-independent interventions to diversify image search.
Chemicals in the Creek: designing a situated data physicalization of open government data with the community

Over the last decade growing amounts of government data have been made available in an attempt to increase transparency and civic participation, but it is unclear if this data serves non-expert communities due to gaps in access and the technical knowledge needed to interpret this “open” data. We conducted a two-year design study focused on the creation of a community-based data display using the United States Environmental Protection Agency data on water permit violations by oil storage facilities on the Chelsea Creek in Massachusetts to explore whether situated data physicalization and Participatory Action Research could support meaningful engagement with open data. We selected this data as it is of interest to local groups and available online, yet remains largely invisible and inaccessible to the Chelsea community. The resulting installation, Chemicals in the Creek, responds to the call for community-engaged visualization processes and provides an application of situated methods of data representation. It proposes event-centered and power-aware modes of engagement using contextual and embodied data representations. The design of Chemicals in the Creek is grounded in interactive workshops and we analyze it through event observation, interviews, and community outcomes. We reflect on the role of community engaged research in the Information Visualization community relative to recent conversations on new approaches to design studies and evaluation.
Multi-resource balance optimization for virtual machine placement in cloud data centers

Network virtualization is widely regarded as a key enabler to improve the desired flexibility and scalability of cloud computing services. With the rapid expansion of data centers, it remains a challenge to provide efficient management of resources for virtual machine placement. Due to multi-dimensional physical resources in servers, there maybe resource fragmentation causing by an imbalanced usage of multi-dimensional resources, when multiple virtual machines carrying diverse requests are deployed on the same server simultaneously. In this paper, we devote to how to balance multiple resources usage to alleviate resource fragmentation while maximizing the service rate for virtual machine placement, so that it can prevent waste of physical resources and undesirable performance. In order to solve such a bi-objective optimization problem, we present a joint bin-packing heuristic and genetic algorithm which achieves an approximate optimal solution with much lower time complexity.
Learning Markets: An AI Collaboration Framework Based on Blockchain and Smart Contracts

Artificial intelligence (AI) has been witnessed to provide valuable solutions to all walks of life. However, data island and computing resources limitations in the centralized AI architectures have increased their technical barriers, and thus distributed AI collaboration in data, models, and resources has attracted intensive research interests. Since the existing trust-based collaboration models are no longer applicable for the large-scale distributed collaboration among trustless machines in open and dynamic environments, this article proposes a novel decentralized AI collaboration framework, i.e., learning markets (LM), in which blockchain provides a trustless environment for collaboration and transaction, while smart contracts serve as software-defined agents to encapsulate and process scalable collaboration relationships and market mechanisms. LM can not only help those participants without mutual trust realize collaborative mining with dynamic and quantitative rewards but also build an AI market with natural auditability and traceability for trading trusted and verified models. We implement and comprehensively analyze LM based on the Ethereum interplenary file system platform (IPFS), and the results prove that it has advantages in collaboration fairness, transparency, security, decentralization and universality. Based on our collaboration framework, distributed AI contributors are expected to cooperate and complete those learning tasks that cannot be done previously due to lack of complete data, sufficient computing resources and state-of-the-art models.
Sparkle: User-Aware Viewport Prediction in 360-Degree Video Streaming

In 360-degree video streaming, users commonly watch a video scene within a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Field of View (FoV)</i> . Such observation provides an opportunity to save bandwidth consumption by predicting and then prefetching video tiles within the FoV. However, existing FoV prediction methods seldom consider the diversity among user behaviors and the impact of different video genres. Thus, previous one-size-fits-all models cannot make accurate prediction for users with different behavior patterns. In this paper, we propose a user-aware viewport prediction algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sparkle</i> , which is a practical whitebox approach for FoV prediction. Instead of training a single learning model to predict the behaviors for all users, our proposed algorithm is tailored to fit each individual user. In particular, unlike other learning models, our prediction model is completely explainable and all the parameters have their physical meanings. We first conduct a measurement study to analyze real user behaviors and observe that there exists sharp fluctuation of view orientation and user posture has significant impact on the viewport movement of users. Moreover, cross-user similarity is diverse across different video genres. Inspired by these insights, we further design a user-aware viewport prediction algorithm by mimicking a user's viewport movement on the tile map, and determine how a user will change the viewport angle based on his (or her) trajectory and other similar users’ behaviors in the past time window. Extensive evaluations with real datasets demonstrate that, our proposed algorithm significantly outperforms the state-of-the-art benchmark methods (e.g., LSTM-based methods) by over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{5}\%$</tex-math></inline-formula> , and the prediction accuracy is much more stable on various types of 360-degree videos than previous methods.
Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyperparametric configurations with improved performance for a given task, to the optimization of the model's parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: (a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, (b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and (c) challenges and new directions of research (What can be done, and what for?). In summary, three axes – optimization and taxonomy, critical analysis, and challenges – which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.
Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station

Automatic flying target detection and tracking in video sequences acquired from a camera mounted on another Unmanned Aerial Vehicle (UAV) is a challenging task due to the presence of non-stationary cameras in the system, dynamic motion of the moving target, and high-cost computation for real-time applications.In this paper, our aim is to automatically detect and track moving UAV by another one while simultaneously flying in the air.In order to provide efficiently in real-time applications, we develop a vision-based low-cost hardware system integrated with an independent ground control station.We initially created a new public dataset called ATAUAV that includes different types of UAV images obtained from videos recording in our experiments and searches on Google Images for the training process.Deep learning-based YOLOv3-Tiny (You Only Look Once) is used for target detection with the highest accuracy and fastest results.Kernelized Correlation Filter (KCF) adapted with YOLO, which runs on low-cost hardware, is used for real-time detected target tracking.We compared the performance of the proposed approach with different tracking algorithms.Experimental results show that the proposed approach provides the highest accuracy rate as 82.7% and a mean fps speed as 29.6 on CPU.
Designing Digital Safe Spaces for Peer Support and Connectivity in Patriarchal Contexts

This paper explores the opportunities and challenges in designing peer-support mechanisms for low-income,low-literate women in Pakistan, a patriarchal and religious context where women's movements, social relationsand access to digital technologies are restricted. Through a qualitative, empirical study with 21 participantswe examine the cultural and patriarchal framework where shame and fear of defamation restrict the seekingof support for personal narratives around taboo subjects like abortion, sexual harassment, rape and domesticabuse. Based on our qualitative data we also conduct a second qualitative study using a technology probe with15 low-income, low-literate women to explore the specific design of peer-support technologies for supportseeking and the sharing of sensitive and taboo narratives in a deeply patriarchal society. The design concernsraised by our participants regarding privacy, anonymity and safety provide CSCW researchers with valuableguidelines about designing for social connections and support for vulnerable populations within a particularcontext.
“This Is What We Don't Know”: Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment

ABSTRACT Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2021;17:221–232. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology &amp; Chemistry (SETAC)
A new similarity measure for vector space models in text classification and information retrieval

There are various models, methodologies and algorithms that can be used today for document classification, information retrieval and other text mining applications and systems. One of them is the vector space–based models, where distance metrics or similarity measures lie at the core of such models. Vector space–based model is one of the fast and simple alternatives for the processing of textual data; however, its accuracy, precision and reliability still need significant improvements. In this study, a new similarity measure is proposed, which can be effectively used for vector space models and related algorithms such as k-nearest neighbours ( k-NN) and Rocchio as well as some clustering algorithms such as K-means. The proposed similarity measure is tested with some universal benchmark data sets in Turkish and English, and the results are compared with some other standard metrics such as Euclidean distance, Manhattan distance, Chebyshev distance, Canberra distance, Bray–Curtis dissimilarity, Pearson correlation coefficient and Cosine similarity. Some successful and promising results have been obtained, which show that this proposed similarity measure could be alternatively used within all suitable algorithms and models for information retrieval, document clustering and text classification.
Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model

It is hoped that the robot could interact with the human when the robots help us in our daily lives. And understanding humans’ specific intention is the first crucial task for human-robot interaction. In this paper, we firstly develop a multi-task model for recognizing humans’ intention, which is composed of two sub-tasks: human action recognition and hand-held object identification. For the front subtask, an effective ST-GCN-LSTM model is proposed by fusing the Spatial Temporal Graph Convolutional Networks and Long Short Term Memory Networks. And for the second subtask, the YOLO v3 model is adopted for the hand-held object identification. Then, we build a framework for robot interacting with the human. Finally, these proposed models and the interacting framework are verified on several datasets and the testing results show the effectiveness of the proposed models and the framework.
Blockchain Based Framework for Modeling and Evaluating 5G Spectrum Sharing

The fifth-generation (5G) cellular technology aims at providing network services at high speed with reliable Quality of Service (QoS). To enable this, 5G deploys Massive Multi-Input Multi-Out-put (MIMO) to increase the capacity of a Base Station (BS) and the efficiency of the network. Provisioning guaranteed and reliable services to support MIMO requires effective resource management. Blockchain is a highly promising solution to enable multi-dimensional management of various resources such as spectrum allocation and user association. It can potentially mitigate spectrum under-utilization and can help in scaling up the deployment of different 5G services. In this article, we present a model for Blockchain-based multi-operator service provisioning for 5G users with Intra and Inter spectrum management among multiple telecom operators. In particular, we present a Blockchain-based implementation model for spectrum sharing between the operators to minimize spectrum under-utilization.
Solving the last mile problem in logistics: A mobile edge computing and blockchain‐based unmanned aerial vehicle delivery system

Summary The “last mile” problem in logistics is challenging due to its low efficiency and high cost. To address this problem, Unmanned Aerial Vehicle (UAV) delivery such as drone delivery has been proposed and widely accepted as a promising solution. However, currently most of the existing UAV delivery systems are based on Cloud Computing which cannot efficiently meet the requirements of many real‐time services in UAV delivery systems. Meanwhile, the security issues in UAV delivery systems also raise critical concerns due to the existence of multiple participants (such as the sender, middler, and receiver) who may not maintain a mutual trust relationship among them. How to secure the UAV delivery process in such an untrusted environment is still a challenging issue. In this paper, we propose a Mobile Edge Computing (MEC) and blockchain‐based UAV delivery system to resolve the “last mile” problem in logistics. Specifically, based on the MEC architecture, the blockchain nodes are deployed on the edge nodes to facilitate and secure the UAV delivery process. To verify the effectiveness of our proposed solution, a MEC‐based UAV delivery system prototype with a private blockchain on the Ethereum platform is implemented. Through the security analysis and performance evaluation, it is proven that our proposed solution can effectively solve the “last mile” problem and address the security issues in UAV delivery systems.
Automatic Text Summarization Using Gensim Word2Vec and K-Means Clustering Algorithm

The significance of text summarization in the Natural Language Processing (NLP) community has now expanded because of the staggering increase in virtual textual materials. Text summary is the process created from one or multiple texts which convey important insight in a little form of the main text. Multiple text summarization technique assists to pick indispensable points of the original texts reducing time and effort require reading the whole document. The question was approached from a different point of view, in a different domain by using different concepts. Extractive and abstractive are the two main methods of summing up text. Though extractive summary is primarily concerned with what summary content the frequency of words, phrases, and sentences from the original document should be used. This research proposes a sentence based clustering algorithm (K-Means) for a single document. For feature extraction, we have used Gensim word2vec which is intended to automatically extract semantic topics from documents in the most efficient way possible.
Combining Task- and Data-Level Parallelism for High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs

Nowadays Convolutional Neural Networks (CNNs) are widely used to perform various tasks in areas such as computer vision or natural language processing. Some of the CNN applications require high-throughput execution of the CNN inference, on embedded devices, and many modern embedded devices are based on CPUs-GPUs multi-processor systems-on-chip (MPSoCs). Ensuring high-throughput execution of the CNN inference on embedded CPUs-GPUs MPSoCs is a complex task, which requires efficient utilization of both task-level (pipeline) and data-level parallelism, available in a CNN. However, the existing Deep Learning frameworks utilize only task-level (pipeline) or only data-level parallelism, available in a CNN, and do not take full advantage of all embedded MPSoC computational resources. Therefore, in this paper, we propose a novel methodology for efficient execution of the CNN inference on embedded CPUs-GPUs MPSoCs. In our methodology, we ensure efficient utilization of both task-level (pipeline) and data-level parallelism, available in a CNN, to achieve high-throughput execution of the CNN inference on embedded CPUs-GPUs MPSoCs.