Reviewerly/exHarmony huggingface dataset:
Class-Attentive Diffusion Network for Semi-Supervised Classification

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.
Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI

Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies.
An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection

In the past few decades, vibration-based structural damage detection (SDD) has attracted widespread attention. Using the response data of engineering structures, the researchers have developed many methods for damage localization and quantification. Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach. Various PSO variants have also been proposed for improving its performance in SDD, and they are generally based on the Global topology. However, in addition to the Global topology, other topologies are also developed in the related literature to enhance the performance of the PSO algorithm. The effects of PSO topologies depend significantly on the studied problems. Therefore, in this article, we conduct a performance investigation of eight PSO topologies in SDD. The success rate and mean iterations that are obtained from the numerical simulations are considered as the evaluation indexes. Furthermore, the average rank and Bonferroni-Dunn’s test are further utilized to perform the statistic analysis. From these analysis results, the Four Clusters are shown to be the more favorable PSO topologies in SDD.
Towards Benchmarking the Utility of Explanations for Model Debugging

Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision.But how useful are they for an end-user towards accomplishing a given task?In this vision paper, we argue the need for a benchmark to facilitate evaluations of the utility of post-hoc explanation methods.As a first step to this end, we enumerate desirable properties that such a benchmark should possess for the task of debugging text classifiers.Additionally, we highlight that such a benchmark facilitates not only assessing the effectiveness of explanations but also their efficiency.
Electricity Consumption Data Analysis Using Various Outlier Detection Methods

Nowadays, detecting abnormal power consumption behavior of householders has become a big concern in the smart energy field; overcoming this limitation will help in identifying efficient solutions to reduce power consumption. This paper proposes a new methodology for detecting abnormal energy consumption in residential buildings based on hourly readings of energy consumption and peak energy consumption. The proposition is implemented using three unsupervised outlier detection methods (isolation forest, one-class SVM, and k-means). The authors propose this solution to help residents in reducing operating costs by detecting consumption failures that cannot be detected easily. On the other hand, energy providers will have the access to detailed data about anomalies, faulty appliances, and houses with poor power control strategy in general, which will help in pinpointing overconsumption problems, thus enhancing human awareness and reducing energy consumption.
Blockchain and AI in Pharmaceutical Supply Chain

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Limitations of Autoregressive Models and Their Alternatives

Standard autoregressive language models perform only polynomial-time computation to compute the probability of the next symbol.While this is attractive, it means they cannot model distributions whose next-symbol probability is hard to compute.Indeed, they cannot even model them well enough to solve associated easy decision problems for which an engineer might want to consult a language model.These limitations apply no matter how much computation and data are used to train the model, unless the model is given access to oracle parameters that grow superpolynomially in sequence length.Thus, simply training larger autoregressive language models is not a panacea for NLP.Alternatives include energy-based models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string).Both are powerful enough to escape the above limitations.* Part of this work was done at Facebook AI.In this paper we use the shorthand x < 1 . . .-1 .Model family Compact parameters?Efficient scoring?Efficient sampling and normalization?Support can be . . .ELN/ELNCP: Autoregressive models ( §3.1) some but not all ∈ P EC/ECCP: Energy-based models ( §4.1) all ∈ P but no ∈ NPC Lightly marginalized ELNCP: Latent-variable autoregressive models ( §4.2) all ∈ NP Lookup models ( §4.3) anything
Research on the Development of Digital Currencies under the COVID-19 Epidemic

In recent years, with the rapid growth of global non-cash transactions, digital currencies have booming development and gained wide attention in the industry. The COVID-19 outbreak in 2020 has a positive impact on stimulating the rapid development of digital currencies, which magnifying the effect of monetary policy, increasing the vulnerability of the financial system, and posing challenges to the regulatory framework and cyber security. On the basis of literature review, this article analyzes the development history, operational logic and existing problems of Libra Coin, discusses the impact of COVID-19 epidemic on the development of digital currencies from the pros and cons, and puts forward some suggestions.
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning

We curated WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction.Although automatic annotation can lead to a high degree of label noise, it is an inexpensive process and can generate large volumes of annotated documents.We trained a BERT-based NER model with WikiPII and showed that with an adequately large training dataset, the model can significantly decrease the cost of manual information extraction, despite the high level of label noise.In a similar approach, organizations can leverage text mining techniques to create customized annotated datasets from their historical data without sharing the raw data for human annotation.Also, we explore collaborative training of NER models through federated learning when the annotation is noisy.Our results suggest that depending on the level of trust to the ML operator and the volume of the available data, distributed training can be an effective way of training a personal information identifier in a privacy-preserved manner.
FL-MSRE: A Few-Shot Learning based Approach to Multimodal Social Relation Extraction

Social relation extraction (SRE for short), which aims to infer the social relation between two people in daily life, has been demonstrated to be of great value in reality. Existing methods for SRE consider extracting social relation only from unimodal information such as text or image, ignoring the high coupling of multimodal information. Moreover, previous studies overlook the serious unbalance distribution on social relations. To address these issues, this paper proposes FL-MSRE, a few-shot learning based approach to extracting social relations from both texts and face images. Considering the lack of multimodal social relation datasets, this paper also presents three multimodal datasets annotated from four classical masterpieces and corresponding TV series. Inspired by the success of BERT, we propose a strong BERT based baseline to extract social relation from text only. FL-MSRE is empirically shown to outperform the baseline significantly. This demonstrates that using face images benefits text-based SRE. Further experiments also show that using two faces from different images achieves similar performance as from the same image. This means that FL-MSRE is suitable for a wide range of SRE applications where the faces of two people can only be collected from different images.
A Novel Browser-based No-code Machine Learning Application Development Tool

Machine learning (ML) application development constitutes a complex process that requires developers to have considerable amount of programming and specialized technical skills. There is still a substantial barrier for researchers who do not possess the programming and technical skills to develop ML models that meets the needs of their specific fields of study. The typical ML model development workflow entails the installation of a set of software elements and a certain amount of code development. This paper introduces a browser-based ML application development tool that is geared towards the needs of researchers who have limited knowledge in programming. The tool allows the users to create a customized image classifier model based on the image sets that they provide. The tool provides a no-code workflow that enables users to create and test their ML models on a browser without downloading any software modules.
An Unsupervised Temporal Consistency (TC) Loss to Improve the Performance of Semantic Segmentation Networks

Deep neural networks (DNNs) for highly automated driving are often trained on a large and diverse dataset, and evaluation metrics are reported usually on a per-frame basis. However, when evaluated on video sequences, the predictions are often unstable between consecutive frames. As such unstable predictions over time can lead to severe safety consequences, there is a growing need to understand, evaluate, and improve the temporal consistency of DNNs. In this paper, we explore such a temporal characteristic and propose a novel unsupervised temporal consistency (TC) loss that penalizes unstable semantic segmentation predictions. This loss function is used in a two-stage training scheme to jointly optimize for both, accuracy of semantic segmentation predictions, and its temporal consistency based on video sequences. We demonstrate that our training strategy helps in improving the temporal consistency of two state-of-the-art semantic segmentation networks on two different road-scenes datasets. We report an absolute 4.25% improvement in the mean temporal consistency (mTC) of the HRNetV2 network and an absolute 2.78% improvement on the DeepLabv3+ network, both evaluated on the Cityscapes dataset, with only a slight decrease in accuracy. When evaluating on the same video sequences using a synthetic dataset Sim KI-A, we show absolute improvements in both, accuracy (2.19% mIoU) and temporal consistency (0.21% mTC) for the DeepLabv3+ network. We confirm similar improvements for the HRNetV2 network.
A Study and Overview of the Mobile App Development Industry

Background/Purpose: We are in an era where a lot of disruptive technologies that change the entire perspective of the market are arriving very often. One such is the emergence of Mobile App Development, which completely changed the way users utilized software. Even though software development generally meant a stand-alone or Web application in the past, now it has enormous space given to Mobile App Development. Standalone technologies demanded the user to have a Personal Computer at least, with them to utilize their applications. However, since the platform on which an app can be deployed is just a mobile phone, the target audience for this type of application development becomes manifold. App development that targets handheld devices has now become a standard by which services reach the target audience faster since almost everyone has access to a mobile device. The App Development industry has only grown in leaps and bounds ever since its inception. This paper discusses in detail the Mobile App Development Industry and its predominance in India. Objectives: To analyse and understand the Mobile App Development Industry worldwide and in India, to understand the preference, usage, revenue, cost, and scope of App Development in India and to perform a SWOC Analysis for the Mobile App Development Industry. Design/Methodology/Approach: A detailed analysis of the Mobile App Development Industry is done by various secondary data sources such as websites and blogs. Some Scholarly research articles are also referred to understand the challenges in the industry. Findings/Result: Based on the SWOC Analysis of the Mobile App Development Industry, we can say that this industry is going to be one of the most sought-after industries especially in a pandemic-stricken world. Originality/Value: This paper, analyses various aspects of the Mobile App Development Industry. Based on the analysis, the current and future state of the industry is presented. Paper Type: A research case study paper on the Mobile App Development Industry.
Towards fully-fledged archiving for RDF datasets

The dynamicity of RDF data has motivated the development of solutions for archiving, i.e., the task of storing and querying previous versions of an RDF dataset. Querying the history of a dataset finds applications in data maintenance and analytics. Notwithstanding the value of RDF archiving, the state of the art in this field is under-developed: (i) most existing systems are neither scalable nor easy to use, (ii) there is no standard way to query RDF archives, and (iii) solutions do not exploit the evolution patterns of real RDF data. On these grounds, this paper surveys the existing works in RDF archiving in order to characterize the gap between the state of the art and a fully-fledged solution. It also provides RDFev, a framework to study the dynamicity of RDF data. We use RDFev to study the evolution of YAGO, DBpedia, and Wikidata, three dynamic and prominent datasets on the Semantic Web. These insights set the ground for the sketch of a fully-fledged archiving solution for RDF data.
Mixed Best Members Based Optimizer for Solving Various Optimization Problems

Numerous designed optimization problems in different disciplines of science should be solved using appropriate techniques.population based optimization algorithms are one of the powerful tools in solving optimization problems.The innovation of this paper is to present a new optimization algorithm called Mixed Best Members Based Optimizer (MBMBO) that can be used to solve various optimization problems.The main idea in designing the proposed MBMBO algorithm is to create a mixed member of several top members of the population in order to guide and update the algorithm population.The main feature and advantage of the MBMBO is the lack of control parameters.Therefore, the proposed MBMBO does not need to adjust the parameter.The various steps of the MBMBO are described and then mathematically modeled for implementation in solving optimization problems.The performance of the MBMBO in solving optimization problems is evaluated on a set of twenty-three standard objective functions.These objective functions are of three different types, including seven unimodal objective functions, six high dimensional multi-model objective functions, and ten fixed dimensional multi-model objective functions.The results of evaluation of single-model objective functions indicate the high exploitation power and also the results of evaluation of multi-model objective functions indicate the high exploration power of the proposed MBMBO algorithm.Also, the results obtained from the simulation of the MBMBO are compared with the results of eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO).The results of optimizing the objective functions of unimodal and multi-modal types using MBMBO show the acceptable ability of the proposed algorithm to provide suitable solutions.Comparison of the simulation results shows that the proposed MBMBO is much more competitive than the other eight optimization algorithms.
An Online Transfer Learning Framework for Time-Varying Distribution Data Prediction

Data-driven methods, especially those based on deep learning, have been successfully applied to various tasks. However, training and testing data may have different distributions in many real cases, which lead to unreliable results. Transfer learning can address this issue by learning domain-invariant features. Online transfer learning (OTL), as one type of transfer learning, deals with the situation where the data of the target domain arrive in a sequential manner. The limitations of the existing OTL methods are that they cannot deal with the online data generated by time-varying distribution because of varying operational conditions, for example, besides, they mainly focus on classification tasks. In this article, we propose a novel OTL framework for a regression task, which includes an offline stage and an online stage. In the former, a basic model is built to learn domain-invariant features. In the latter, an updating strategy is proposed to adapt the prediction model to the new data. Besides, an ensemble approach is further developed to avoid over- or underfitting and also fully utilize the knowledge from the source domain for the target domain. The effectiveness of the proposed framework is verified by a real dataset regarding the diesel hydrofining process collected from a petrochemical workshop.
Portalware: Exploring Free-Hand AR Drawing with a Dual-Display Smartphone-Wearable Paradigm

Free-hand interaction enables users to directly create artistic augmented reality content using a smartphone, but lacks natural spatial depth information due to the small 2D display's limited visual feedback. Through an autobiographical design process, three authors explored free-hand drawing over a total of 14 weeks. During this process, they expanded the design space from a single-display smartphone format to a dual-display smartphone-wearable format (Portalware). This new configuration extends the virtual content from a smartphone to a wearable display and enables multi-display free-hand interactions. The authors documented experiences where 1) the display extends the smartphone's canvas perceptually, allowing the authors to work beyond the smartphone screen view; 2) the additional perspective mitigates the difficulties of depth perception and improves the usability of direct free-hand manipulation; 3) the wearable use cases depend on the nature of the drawing, such as: replicating physical objects, "in-situ" mixed reality pieces, and multi-planar drawings.
Exploring the Design Space of InterActive Urban Environments

Promoting healthy lifestyles is an essential endeavor for public health. The design of active urban environments can be an effective medium to nudge people into moving. With technology increasingly integrated into our daily lives, designers have access to more data than ever. In this pictorial, we explore the design space of interActive environments (contraction of 'interactive' and 'active'); places designed to increase the physical activity of users or passers-by through the use of interactive technology. Through sketches, a benchmark of existing concepts and an analysis of designed artefacts, we map the different intervention levels, interaction modalities, behavior change strategies and technological opportunities to design such interActive environments. With this work, we invite the community to consider how digital technology can help understand and shape human behavior in urban environments, and provide inspiration to designers and practitioners.