Reviewerly/exHarmony huggingface dataset:
Global event influence model: integrating crowd motion and social psychology for global anomaly detection in dense crowds

Crowd motions usually play a basic role of analyzing and understanding abnormal events. However, only using these visible features cannot fully describe the scenarios because the influence caused by an abnormal event is not considered from the social psychological point of view. Although the invisible influences cannot be directly observed through the video, they objectively exist and have precise definitions and specific analytical methods in sociology and psychology. Therefore, a mid-level representation named global event influence (GEI) for global anomaly detection in dense crowds is introduced. The proposed GEI integrates the crowd motions and social psychology attributes to improve the description of crowds. For this, low-level motion features are abstracted as crowd attributes of scale, velocity, and disorder. Then, the detailed definitions and mathematical expressions of GEI are presented through calculating the convolution of rise factor and decay factor. Based on GEI, a model for global anomaly detection is proposed. Compared with most previous methods, our proposed model is robust to detect not only the occurrence of anomalous events but also elimination time of the event influence. Accordingly, strategies for event occurrence detection and influence elimination detection are proposed, respectively. In addition, a dataset of dense crowds is introduced and used for evaluation. The experimental comparison on benchmark datasets shows that the performance of our GEI model has not only the competitive accuracy of event occurrence detection but also the claimed effectiveness of influence elimination, which is more advanced than others.
Requirements-driven evolution of sociotechnical systems via probabilistic reasoning and hill climbing

Sociotechnical systems (STSs) are defined by the interaction between technical systems, like software and machines, and social entities, like humans and organizations. The entities within an STS are autonomous, thus weakly controllable, and the environment where the STS operates is highly dynamic. As a result, the design artifacts that represent the requirements of an STS, such as requirements models, may end up being invalid when the system operates, for the autonomous entities do not comply with the requirements, or the environment changes. In this paper, we present a framework that uses runtime execution data to support the runtime validation of requirements models and to guide the evolution of an STS. We propose two types of evolution: (i) manual: the analyst uses Bayesian inference to discover which assumptions in a requirements model are invalid and manually adjusts the system or its model; and (ii) automated: requirements are iteratively revised by an hill climbing algorithm searching for requirements that maximize the achievement of the stakeholders’ objectives. We evaluate the effectiveness of different revision heuristics on a smart traffic simulation applied to an exemplar from the self-adaptive systems literature. The results show that our heuristics, informed by runtime execution data, outperform standard uninformed heuristics, in terms of convergence speed, solution quality, and stability. Moreover, the algorithms show good resilience to noise introduced into the execution data.
Privacy-preserved distinct content collection in human-assisted ubiquitous computing systems

Human beings with smart devices have dramatically facilitated the knowledge acquisition in ubiquitous computing systems. However, the collected contents also severely threat the security and privacy for individuals, as these contents achieve a fine-grained coverage of one’s behaviors and movements. Local differential privacy, as one of the widely accepted properties, enables individuals to contribute to data collection while remaining indistinguishable against adversaries. However, existing solutions and mechanisms fail to capture the contents with relatively low frequency, e.g., the temperature in local regions visited by very few people. Therefore, this work proposes a novel framework for distinct content estimation under local differential privacy. We propose a hash function and random response based framework for the estimation. The framework allows individuals in the ubiquitous computing system to participate with flexible bandwidth, and properly assigns the given bandwidth to improve estimated results. We prove the optimization problem of assignment in our framework to be NP-complete, and provides an effective heuristic algorithm. Our framework is also proved to guarantee an unbiased estimation for distinct contents, and achieves local differential privacy for users. Meanwhile, we also propose a randomized algorithm designed for more general cases, where the preposed knowledge for the first algorithm is unavailable. Finally, we evaluate both algorithms on real world datasets, and the results reveal that our algorithms outperform existing methods.
Programming by Demonstration in Augmented Reality for the Motion Planning of a Three-Axis CNC Dispenser

This study presents a programming by demonstration (PbD) interface in augmented reality for motion planning in a three-axis CNC glue dispenser. The interface assists a human planner to effectively determine dispenser motion in a planning task. An augmented reality application prompts the planner with instructive information in a video of real environment while guiding the machine in 3D space to dispense glue on a work part. The purpose involves facilitating the generation of the dispenser’s motion commands by enhancing individual spatial reasoning between the dispenser’s tip and the work part. CAD models are not required prior to the planning task, thus eliminating the use of motion planning software. Test results demonstrate the effectiveness of the proposed interface with respect to accelerating the process of the dispenser PbD. The study shows the practical value of augmented reality as a cost-effective interfacing technology for human–machine collaborations in smart manufacturing.
Modeling Fully and Partially Constrained Lasso Movements in a Grid of Icons

Lassoing objects is a basic function in illustration software and presentation tools. Yet, for many common object arrangements lassoing is sometimes time-consuming to perform and requires precise pen operation. In this work, we studied lassoing movements in a grid of objects similar to icons. We propose a quantitative model to predict the time to lasso such objects depending on the margins between icons, their sizes, and layout, which all affect the number of stopping and crossing movements. Results of two experiments showed that our models predict fully and partially constrained movements with high accuracy. We also analyzed the speed profiles and pen stroke trajectories and identified deeper insights into user behaviors, such as that an unconstrained area can induce higher movement speeds even in preceding path segments.
Relations are more than Bytes

Critical approaches to smart technologies have emerged in HCI that question the conditions necessary for smart technologies to benefit people. Smart services rely on a relation of trust and sense of security between people and technology requiring a more expansive definition of security. Using established design methods, we worked with two residents' groups to critically explore and rethink smart services in the home and city. From our data analysis, we derive insights about perceptions and understandings of trust, privacy and security of smart devices, and identify how technological security needs to work in concert with social and relational forms of security for smart services to be effective. We conclude with an orientation for HCI that focuses on designing services for and with smart people and things.
An efficient method for fault tolerance in cloud environment using encryption and classification

Cloud computing may be defined as management and provision of resources, software, application and information as services over the cloud which are dynamically scalable. Fault tolerance includes all the techniques necessary for robustness and dependability. The main advantages of using fault tolerance in cloud computing include failure recovery, lower costs and improved standards in performance. Even though the benefits are immeasurable, the element of risk on user applications due to failure remains a major drawback. So our suggested technique utilizes the effective fault tolerance method with the encryption algorithm. To improve the security of the recommended technique, triple-DES encryption algorithm is employed before the data transmission. For the transmission of encrypted data, the implemented method selects the minimum fault tolerance node. So the recommended technique utilizes the effective classification technique. Here, improved support vector machine (ISVM) classifier is used to classify the nodes based on its feature value and the content similarity each node. The proposed ISVM helps in predicting the faults if available, earlier before it occurs. The various parameters considered in our proposed system are accuracy, service reliability and availability. In the proposed method, the accuracy value of the fault tolerance is 79% which is better than in the existing method. The proposed method will be implemented in JAVA with CloudSim.
Towards the use of deep generative models for the characterization in size of aggregated TiO<sub>2</sub> nanoparticles measured by Scanning Electron Microscopy (SEM)

Recent advances in deep generative models based on convolutional neural networks (CNNs) are used to demonstrate the potential of these approaches for the estimation of particle size distribution on images of aggregated TiO2 particles obtained by Scanning Electron Microscopy (SEM). This very promising framework shall permit effective automation of SEM measurements analysis. Indeed, common image processing softwares bring the end-users with segmentation algorithms as well as measuring tools to estimate individual particle diameters. In the case of aggregated nanoparticles, most particles suffer missing contents and are not considered in the computations. In this paper, we use a recently developed method called 'context encoder's to predict missing parts of the nanoparticles. The approach is tested against simulated and real dropped image regions.
A fully convolutional network feature descriptor: Application to left ventricle motion estimation based on graph matching in short-axis MRI

Cardiac diseases cause abnormal motion dynamics over the cardiac cycle, and can, therefore, be diagnosed by analyzing the myocardial motion of the left ventricle (LV). In this paper, a feature point descriptor using fully convolutional neural networks (FCNs) is proposed and applied to cardiac motion estimation based on graph matching. A fully convolutional network is trained to predict endocardial contours and extract features of points from short-axis cine magnetic resonance (MR) images. An LV graph is constructed using the extracted point features, and a convex graph matching cost function is defined to estimate the point correspondence between images in two given phases. The sparsity and double stochastic constraints are introduced into the cost function, which is optimized iteratively by the alternating direction method of multipliers (ADMM). Finally, the transformation using compact supported radial basis functions with sparsity constraint is employed to estimate the dense displacement field between two cardiac images in two phases based on the correspondence relationship. The performance of the proposed method was evaluated on two public cardiac databases, and the experimental results show that the FCN feature descriptor outperforms traditional feature descriptors in estimating the correspondence between endocardial contours of the LV. For LV motion estimation, the proposed method provides more accurate motion fields than existing graph matching algorithms.
Visualization approaches for understanding uncertainty in flow diagrams

Business Intelligence applications often handle data sets that contain uncertain values. In this contribution, we focus on product costing, which deals with the average costs of product components – that vary significantly based on many factors such as inflation, exchange rates, and commodity prices. After experts estimate the uncertainty information for single items, decision makers need to quickly ascertain the cost uncertainties within the hierarchical data structure of the complete product. We propose that only a holistic visualization containing both data and uncertainty can provide this kind of quick overview. Such a visualization must be able to visualize tree data structures associated with value attributes. After conducting interviews with product costing experts, we focused on Flow diagrams, which fulfill this basic requirement. However, they need to be extended in order to directly incorporate uncertainty information. We investigated three visualization techniques applicable to the ribbons of Flow diagrams to convey uncertainty information: Color-code, Gradient, and Margin. Moreover, we designed five visual approaches to show the uncertainty on nodes of Flow diagrams that we evaluated with visualization experts. The approaches add different geometries to the nodes such as triangles, blocks, or forks. The preferred solutions for the nodes was adding forks or filled blocks. With regards to the ribbons, we contribute a user study involving the solution of different product costing tasks using the three different visualizations. Although Gradient was considered an intuitive choice to show uncertainty, it yielded the highest error rates. In contrast, Color-code and Margin were superior depending on the performed task. Based on these findings and the subjective feedback, we designed an integrated approach that combines elements from all three distinct techniques and applied it to Sankey diagrams and Parallel sets.
A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance System

Automatic moving vehicle detection and recognition are the crucial steps in traffic surveillance applications. Frame extraction is the prior step, which is followed by box filter based background estimation and removal. Box filter based background estimation is used to smoothen the rapid variations, due to the movement of vehicles. Moving vehicles are then detected by analyzing the pixel wise variations between estimated background and input frames. Vehicle detection phase is then followed by recognition phase to classify variant vehicle classes. The deep learning framework Region based Convolutional Neural Network(RCNN) is implemented for the recognition of vehicles with region proposals. Due to the existence of region proposal in RCNN, computational multiplicity is reduced. Metrices like accuracy, sensitivity, specificity and precision values are evaluated to characterize the proficiency of the proposed methodology for vehicle.
"Collective Wisdom"

The home has been a major focus of the HCI community for over two decades. Despite this body of research, nascent works have argued that HCI's characterization of 'the home' remains narrow and requires more diverse accounts of domestic configurations. Our work contributes to this area through a four-month ethnography of three collective homes in Vancouver, Canada. Collective homes represent an alternative housing model that offers agency to individual members and the collective group by sharing values, resources, labour, space and memory. Our paper offers two contributions. First, we offer an in-depth design ethnography of three collective homes, attending to the values, ownership models, practices, and everyday interactions observed in the ongoing making of these domestic settings. Second, we interpret and synthesize our findings to provide new opportunities for expanding the way we conceptualize and design for 'the home' in HCI.
Multi-hop Path Queries over Knowledge Graphs with Neural Memory Networks

There has been increasing research interest in inferring missing information from existing knowledge graphs (KGs) due to the emergence of a wide range of knowledge graph downstream applications such as question answering systems and search engines. Reasoning over knowledge graphs, which queries the correct entities only through a path consisting of multiple consecutive relations/hops from the starting entity, is an effective approach to do this task, but this topic has been rarely studied. As an attempt, the compositional training method equally treats the constructed multi-hop paths and one-hop relations to build training data, and then trains conventional knowledge graph completion models such as TransE in a compositional manner on the training data. However, it does not incorporate additional information along the paths during training, such as the intermediate entities and their types, which can be helpful to guide the reasoning towards the correct destination answers. Moreover, compositional training can only extend some existing models that can be composable, which significantly limits its applicability. Therefore, we design a novel model based on the recently proposed neural memory networks, which have large external memories and flexible writing/reading schemes, to address these problems. Specifically, we first introduce a single network layer, which is then used as the building block for a multi-layer neural network called TravNM, and a flexible memory updating method is developed to facilitate writing intermediate entity information during the multi-hop reasoning into memories. Finally, we conducted extensive experiments on large datasets, and the experimental results show the superiority of our proposed TravNM for reasoning over knowledge graphs with multiple hops.
A Multiple-Layer Machine Learning Architecture for Improved Accuracy in Sentiment Analysis

Abstract Twitter is an online micro-blogging platform through which one can explore the hidden valuable and delightful information about the current context at any point of time, which also serves as a data source to carry out sentiment analysis. In this paper, the sentiments of large amount of tweets generated from Twitter in the form of big data have been analyzed using machine learning algorithms. A multi-tier architecture for sentiment classification is proposed in this paper, which includes modules such as tokenization, data cleaning, preprocessing, stemming, updated lexicon, stopwords and emoticon dictionaries, feature selection and machine learning classifier. Unigram and bigrams have been used as feature extractors together with χ2 (Chi-squared) and Singular Value Decomposition for dimensionality reduction together with two model types (Binary and Reg), with four types of scaling methods (No scaling, Standard, Signed and Unsigned) and represented them in three different vector formats (TF-IDF, Binary and Int). Accuracy is considered as the evaluation standard for random forest and bagged trees classification methods. Sentiments were analyzed through tokenization and having several stages of pre-processing and several combinations of feature vectors and classification methods. Through which it was possible to achieve an accuracy of 84.14%. Obtained results conclude that, the proposed scheme gives a better accuracy when compared with existing schemes in the literature.
Parallel Metropolis–Hastings Coupler

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular in signal processing over the last years. In this letter, we present a novel interacting parallel MCMC scheme, called parallel Metropolis-Hastings coupler (PMHC), where the information provided by different parallel MCMC chains is properly combined by the use of another advanced MCMC method, called normal kernel coupler (NKC). The NKC employs a mixture of densities as proposal density, which is updated according to a population of states. The PMHC is particularly efficient in multimodal scenarios, since it obtains a faster exploration of the state space with respect to other benchmark techniques. Several numerical simulations are provided showing the efficiency and robustness of the proposed method.
Domain adaptation with SBADA-GAN and Mean Teacher

Unsupervised domain adaptation has received much attention in the past decade, and various methods have been developed for this problem, e.g., SBADA-GAN (Symmetric Bidirectional ADAptive Generative Adversarial Network) and Mean Teacher, which achieved state-of-the-art results. However, their performance is not perfect when the source and target distributions are not well matched. By combining SBADA-GAN with Mean Teacher, we propose a powerful model for unsupervised domain adaptation, where we use Mean Teacher to replace the target classifier of SBADA-GAN and develop a bidirectional class cycle-consistency strategy to preserve the class identity of the transformed images. Furthermore, we apply gradient penalty and spectral normalization to improve the stability of the training process. The proposed method was demonstrated on six unsupervised domain adaptation benchmarks with significant and consistent improvements over the state-of-the-art methods.
Transfer Learning in 2.5D Face Image for Occlusion Presence and Gender Classification

Face depth image has been used for occlusion presence and gender prediction by transfer learning. This chapter discusses about the overfitting problem and how augmentation helps overcoming it. Pre-processing of the dataset includes converting a 3D object image into depth image for further processing. Five state-of-the-art 2D deep learning models (e.g., AlexNet, VGG16, DenseNet121, ResNet18, and SqueezeNet) have been discussed along with their architecture. The effect of increasing the number of epochs on the top-1 error rate has been presented in the experimental section. The result section consists of error rates in comparison of with and without augmentation on the datasets.
Avoiding the weaknesses of a penetration test

A penetration test is a traditional solution for evaluating and improving the robustness of an ICT system. Such tests can be comprehensive, but problems can arise when deciding how to use its results to select the countermeasures against a successful penetration. These problems may explain the successful attacks against systems that previously passed such tests. So, it's useful to look at some theoretical explanations of the weaknesses of a penetration test and suggest some alternatives.
Prediction of Default Probability by using Statistical Models for rare Events

Summary Prediction models in credit scoring usually involve the use of data sets with highly imbalanced distributions of the event of interest (default). Logistic regression, which is widely used to estimate the probability of default, PD, often suffers from the problem of separation when the event of interest is rare and consequently poor predictive performance of the minority class in small samples. A common solution is to discard majority class examples, to duplicate minority class examples or to use a combination of both to balance the data. These methods may overfit data. It is unclear how penalized regression models such as Firth’s estimator, which reduces bias and mean-square error relative to classical logistic regression, performs in modelling PD. We review some methods for class imbalanced data and compare them in a simulation study using the Taiwan credit card data. We emphasize the effect of events per variable for developing an accurate model—an often neglected concept in PD-modelling. The data balancing techniques that are considered are the random oversampling examples and synthetic minority oversampling technique methods. The results indicate that the synthetic minority oversampling technique improved predictive accuracy of PD regardless of sample size. Among the penalized regression models that are analysed, the log-F prior and ridge regression methods are preferred.