Reviewerly/exHarmony huggingface dataset:
Zippro: The Design and Implementation of An Interactive Zipper

Zippers are common in a wide variety of objects that we use daily. This work investigates how we can take advantage of such common daily activities to support seamless interaction with technology. We look beyond simple zipper-sliding interactions explored previously to determine how to weave foreground and background interactions into a vocabulary of natural usage patterns. We begin by conducting two user studies to understand how people typically interact with zippers. The findings identify several opportunities for zipper input and sensing, which inform the design of Zippro, a self-contained prototype zipper slider, which we evaluate with a standard jacket zipper. We conclude by demonstrating several applications that make use of the identified foreground and background input methods.
Anomaly Detection of Storage Battery Based on Isolation Forest and Hyperparameter Tuning

The safety of an uninterruptible power supply (UPS) unit is very important in the operation of a telecommunication room. It is necessary to identify and replace abnormal electrical batteries of the UPS to ensure the normal operation of the equipment. In this paper, a single-model method based on isolation forest and hyperparameter tuning is proposed for detecting abnormal batteries. Experimental results show that the proposed method is efficient in offline situations. A multi-model method is also proposed to deal with the online anomaly detection problem, which is found performing well.
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is important to interpret how such predictive models hidden behind APIs make their decisions. The biggest challenge of interpreting such predictions is that no access to model parameters or training data is available. Existing works interpret the predictions of a model hidden behind an API by heuristically probing the response of the API with perturbed input instances. However, these methods do not provide any guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named OpenAPI to compute exact and consistent interpretations for the family of Piecewise Linear Models (PLM), which includes many popular classification models. The major idea is to first construct a set of overdetermined linear equation systems with a small set of perturbed instances and the predictions made by the model on those instances. Then, we solve the equation systems to identify the decision features that are responsible for the prediction on an input instance. Our extensive experiments clearly demonstrate the exactness and consistency of our method.
Video Surveillance for the Crime Detection Using Features

This paper aims at extending the comparison between two images and locating the query image in the source image by matching the features in the videos by presenting a method for the recognition of a particular person or an object. The frames matching the feature (not feature its query) object in a given video will be the output. We describe a method to find unique feature points in an image or a frame using SIFT, i.e., scale-invariant feature transform method. SIFT is used for extracting distinctive feature points which are invariant to image scaling or rotation, presence of noise, changes in image lighting, etc. After the feature points are recognized in an image, the image is tracked for comparison with the feature points found in the frames. The feature points are compared using homography estimation search to find the required query image in the frame. In case the object is not present in the frame, then it will not present any output.
Quality of experience measurements of multipath TCP applications on iOS mobile devices

Multipath TCP (MPTCP) promises improvements in Quality of Service through connection bundling. This leads to the belief that it will inevitably improve the Quality of Experience (QoE), especially, for mobile applications running on top. The networking and transport layer improvements stem from bundling multiple paths, e.g., WiFi and LTE, as well as increasing the connection reliability through redundancy. For example, a smartphone running an application over WiFi may switch to the cellular network without service interruption upon user movement that gets the device out of the WiFi range, thus avoiding outage events. However, the impact of MPTCP on QoE for different applications has not yet been fully understood.
PDE Learning of Filtering and Propagation for Task-Aware Facial Intrinsic Image Analysis

Filtering and propagation are two basic operations in image analysis and rendering, and they are also widely used in computer graphics and machine learning. However, the models of filtering and propagation were based on diverse mathematical formulations, which have not been fully understood. This article aims to explore the properties of both filtering and propagation models from a partial differential equation (PDE) learning perspective. We propose a unified PDE learning framework based on nonlinear reaction-diffusion with a guided map, graph Laplacian, and reaction weight. It reveals that: 1) the guided map and reaction weight determines whether the PDE produces filtering or propagation diffusion and 2) the kernel of graph Laplacian controls the diffusion pattern. Based on the proposed PDE framework, we derive the mathematical relations between different models, including learning to diffusion (LTD) model, label propagation, edit propagation, and edge-aware filter. In practical verification, we apply the PDE framework to design diffusion operations with the adaptive kernel to tackle the ill-posed problem of facial intrinsic image analysis (FIIA). A flexible task-aware FIIA system is built to achieve various facial rendering effects, such as face image relighting and delighting, artistic illumination transfer, illumination-aware face swapping, or transfiguring. Qualitative and quantitative experiments show the effectiveness and flexibility of task-aware FIIA and provide new insights on PDE learning for visual analysis and rendering.
Big Data Forensics: Hadoop 3.2.0 Reconstruction

Conducting digital forensic investigations in a big data distributed file system environment presents significant challenges to an investigator given the high volume of physical data storage space. Presented is an approach from which the Hadoop Distributed File System logical file space is mapped to the physical data location. This approach uses metadata collection and analysis to reconstruct events in a finite time series.
Advances in Cryptography and Secure Hardware for Data Outsourcing

Despite extensive research, secure outsourcing remains an open challenge. This tutorial focuses on recent advances in secure cloud-based data outsourcing based on cryptographic (encryption, secret-sharing, and multi-party computation (MPC)) and hardware-based approaches. We highlight the strengths and weaknesses of state-of-the-art techniques, and conclude that, while no single approach is likely to emerge as a silver bullet. Thus, the key is to merge different hardware and software techniques to work in conjunction using partitioned computing wherein a computation is split across different cryptographic techniques carefully, so as not to compromise security. We highlight some recent work in that direction.
Mobile Application Development and Testing for Work Zone Activity Real-Time Data Collection

Work zones are prevalent in the United States as the infrastructure is increasingly in need of maintenance. Lack of reliable data is one of the main obstacles in work zone research. Reliability suffers because of underreporting of crashes and inclusion in the analysis of irrelevant activities that are not attributable to work zones. In addition, the work zone environment is very dynamic, resulting in differing reasons for crashes. These are barriers to gaining an accurate understanding of safety in work zones. The objective of this paper is to design, develop, and deploy a mobile application (app) for real-time work zone data collection to address these issues. The development process consisted of the following steps. First, a user interface was designed to enable users to collect various work zone activity information. Second, taking advantage of recent advances in cloud computing, a real-time database was designed for efficient storage and instantaneous communication of work zone activity data. Field tests were then conducted at 13 work zone sites in Columbia, Missouri. Finally, the performance of the app was evaluated based on scalability, precision, and user friendliness. The app was able to respond to queries at real-time speeds even as the size of the database and the number of users increased. The precision of sensors was within appreciable accuracy for the geolocation. The app’s user friendliness was acknowledged by the users. The successful deployment of this mobile app would lead to accurate work zone data which is very useful for work zone management, traveler information, contract monitoring, safety analysis, and project coordination.
A Complexity Analysis of User Interaction with Hotel Robots

Service robots have been introduced to hotel industry in the past decade and received various feedback on their performance. To provide better service, one needs to understand how the hotel customers look at the service robots. Understanding their interests, motivation, and behaviors in human-robot interaction is the key to develop high-quality services and improve robot’s performance. This is the first work to study human-robot interaction in hotels in China. Frequent pattern mining and social network analysis techniques are used in this work to find out useful suggestions to both hotel management and robot manufactory. Turning on and off lights, TV, curtain, and window screens are popular services that most of the hotel customers preferred during their stays. Service robots are also found to entertain customers to carry out repeated commands for fun or to kill time. Customers also showed various motivations to stay in hotel rooms by calling different commands.
Workload merging potential in SAP Hybris

OLTP DBMSs in enterprise scenarios are often facing the challenge to deal with workload peaks resulting from events such as Cyber Monday or Black Friday. The traditional solution to prevent running out of resources and thus coping with such workload peaks is to use a significant over-provisioning of the underlying infrastructure. Another direction to cope with such peak scenarios is to apply resource sharing. In a recent work, we showed that merging read statements in OLTP scenarios offers the opportunity to maintain low latency for systems under heavy load without over-provisioning.
Adding Data to Curry

Functional logic languages can solve equations over user-defined data and functions. Thus, the definition of an appropriate meaning of equality has a long history in these languages, ranging from reflexive equality in early equational logic languages to strict equality in contemporary functional logic languages like Curry. With the introduction of type classes, where the equality operation “==” is overloaded and user-defined, the meaning became more complex. Moreover, logic variables appearing in equations require a different typing than pattern variables, since the latter might be instantiated with functional values or non-terminating operations. In this paper, we present a solution to these problems by introducing a new type class Data which is associated with specific algebraic data types, logic variables, and strict equality. We discuss the ideas of this class and its implications on various concepts of Curry, like unification, functional patterns, and program optimization.
Lawful Users: Copyright Circumvention and Legal Constraints on Technology Use

The study of human-computer interaction requires consideration of aspects of interactions with technology that may be outside of the control of both user and designer. One example of when a user's question of "can I do this?" may have an answer beyond technological affordances is that of legal constraints. This paper considers an example of this phenomenon: section 1201 of the Digital Millennium Copyright Act (DMCA) in the United States, which criminalizes circumventing copyright protection such as digital rights management (DRM). The DMCA also includes a triennial policymaking process that considers exemptions to the law to protect "lawful users" from adverse effects. Through an analysis of public comments of support for exemptions, this paper explores the ways in which users see the law as a hindrance to desired uses of technology. This analysis sheds light on users' expectations for rights of use, how these expectations clash with policy, and what this might mean for technology designers. Drawing lessons from the infrastructure problem in HCI, this paper concludes with laying out solutions that can both work within policy constraints, and more importantly, work to change them.
Improving Reliability of Virtual Collision Responses

In virtual reality (VR), a user's virtual avatar can interact with a virtual object by colliding with it. If collision responses do not occur in the direction that the user expects, the user experiences degradation of accuracy and precision in applications such as VR sports games. In determining the response of a virtual collision, existing physics engines have not considered the direction in which the user perceived and estimated the collision. Based on the cue integration theory, this study presents a statistical model explaining how users estimate the direction of a virtual collision from their body's orientation and velocity vectors. The accuracy and precision of virtual collisions can be improved by 8.77% and 30.29%, respectively, by setting the virtual collision response in the direction that users perceive.
Development of an Augmented Reality Mobile Application to Enhance the Pedagogical Approach in Teaching History

Abstract The rapid advancement of technology nowadays opens new opportunities for schools and universities worldwide to improve the quality of teaching and learning experience using Augmented Reality can help make classes more interactive and allow learners to focus more on practice instead of just theories. The researcher identified the problem that pedagogy has not been learner-cantered in most approaches in teaching and that teachers need to adapt to the new method of teaching. The goal of the study is to develop an Augmented Reality (AR) mobile application to enhance the pedagogical approach in teaching. This will use a marker-based method for transmitting virtual objects into the realworld. Blippar was used in the development of AR. This resulted in the form of instructional material and technology that can be used in teaching. During the testing of the material Students’ interest was aroused and it even makes them more eager to learn the subject. End-users and experts also deem that the AR mobile application as usable and acceptable with a rating of 78% using the System Usability Scale (SUS). This is an excellent contribution to the educational domain through augmented reality and improving the pedagogical approach in teaching.
An Improved Self-Labeled Algorithm for Cancer Prediction

Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models from different perspectives. The former attempts to achieve strong generalization by using multiple learners, while the latter attempts to achieve strong generalization by exploiting unlabeled data. In this work, we propose an improved semi-supervised self-labeled algorithm for cancer prediction, based on ensemble methodologies. Our preliminary numerical experiments illustrate the efficacy and efficiency of the proposed algorithm, proving that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.
Using machine learning to deal with Phishing and Spam Detection

Cybersecurity is a growing field that requires a lot of attention due to the remarkable progress made in social networks, cloud and web technologies, online banking, mobile environment, smart networks, etc. Various approaches have been developed to solve many computer security problems, including those based on machine learning. This paper examines and highlights the various works using machine learning in network security. Two types of detection are discussed. Phishing detection and Spam detection. For each type, related work is presented and some proposed methods in the literature are compared taking into account their accuracy and other characteristics.
Social networks for health communication in rural Uganda: A mixed-method analysis of Dekabusa Trading Centre, Luwero County

To reach the most vulnerable individuals in under-resourced countries, health communication interventions increasingly move towards the community level. However, little is known about how health information spreads through local social networks. This paper maps the health information network of a rural trading centre in Uganda. As part of a five-year ethnographic study of sustainable community health resources, ego networks were obtained for 231 village residents in March 2014. Using both ethnographic and social network data, we analyze how the village social network is structured, and how this structure may influence the transmission of health information. Results show a network with low average proximity, with a small number of individuals, notably key administrative officials, much closer connected to many other community members than average. However, because of social partitioning in the village network, a number of people are outside the social clusters in which the top influencers are located.
A Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning

An optimal kinodynamic motion planning algorithm is presented and described as a generalized label correcting (GLC) method. Advantages of the technique include a simple implementation, convergence to the optimal cost with increasing resolution, and no requirement for a point-to-point local planning subroutine. The principal contribution of this paper is the construction and analysis of the GLC conditions which are the basis of the proposed algorithm.
Supporting Database Constraints in Synthetic Data Generation based on Generative Adversarial Networks

With unprecedented development in machine learning algorithms, it is crucial to have available large amount of data to verify the correctness and efficiency of these algorithms. Due to privacy concerns, we may not always have enough real data to use. In our research, we focus on data synthesization for relational databases where the database constraints of the original data must be imposed to the generated data. To the best of our knowledge, no study has been conducted on supporting database constraints in synthetic data generation. We offer solutions by designing extensions to Tabular Generative Adversarial Network algorithm. We implemented a prototype for our approach, and compared the performance of different extensions by experiments. Related work on synthetic data generation includes classical statistical methods and neural network approaches. Synthetic Data Vault is developed using classical statistical methods. It uses Kolmogorov-Smirnov test to select the best statistical distribution to describe columnar data. TableGAN and Tabular GAN use neural networks to minimize cross entropy or Kullback-Leibler divergence on marginal distributions. The main challenges to our research problem are: Classical statistical distributions cannot describe complex and mixed distributions in relational databases. Database constraints are non-differentiable. Neural networks require loss functions to be differentiable.
A Differentially Private Kernel Two-Sample Test

Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those distributions. However, raw data samples can expose sensitive information about individuals who participate in scientific studies, which makes the current tests vulnerable to privacy breaches. Hence, we design a new framework for kernel two-sample testing conforming to differential privacy constraints, in order to guarantee the privacy of subjects in the data. Unlike existing differentially private parametric tests that simply add noise to data, kernel-based testing imposes a challenge due to a complex dependence of test statistics on the raw data, as these statistics correspond to estimators of distances between representations of probability measures in Hilbert spaces. Our approach considers finite dimensional approximations to those representations. As a result, a simple chi-squared test is obtained, where a test statistic depends on a mean and covariance of empirical differences between the samples, which we perturb for a privacy guarantee. We investigate the utility of our framework in two realistic settings and conclude that our method requires only a relatively modest increase in sample size to achieve a similar level of power to the non-private tests in both settings.
A coverless steganography method based on generative adversarial network

The traditional information hiding is realized by embedding the secret information into the multimedia, but it will inevitably leave the modification mark in the carrier. This paper proposed a new method of coverless information hiding. First, the improved Wasserstein GAN (WGAN-GP) model is constructed, and the model is trained with disguised images and secret images. Then, after the model is stable, a disguised image is passed to the generator. Finally, the generator generates the image that is visually the same as the secret image, thereby achieving the same effect as transmitting the secret image. Experimental results show that this method not only has a good effect on the security of secret information transmission, but also increases the capacity of information hiding.
Join.Love: A Sociotechnical Genealogy of the Legalization of Same-Sex Marriage

HCI researchers interested in enhancing democracy have introduced methods and technologies that support democratic political processes, such as voting, and more broadly on empowering people to more fully participate in an increasingly technologized world. The aspiration for technologies to support meaningful democratic outcomes is not misplaced. In 2019, headlines around the world announced that Taiwan had become the first Asian country to legalize same-sex marriage, an impressive political achievement. But it was also an impressive technical achievement, the outcome of a concerted effort to develop responsive and impactful direct democracy platforms. We offer a sociotechnical genealogy of the process, informed by theory of deliberative democracy. We identify three opportunities for future HCI contributions: supporting less visible consensus-es, developing civic journeys, and engaging in deliberative experience design.