bluuebunny/crossref_metadata_2025 huggingface dataset:
User and Entity Behavior Analysis under Urban Big Data
Authors: Tian Zhihong , Luo Chaochao , Lu Hui , Su Shen , Sun Yanbin , Zhang Man
Published: July 2020

Recently, the urban network infrastructure has undergone a rapid expansion that is increasingly generating a large quantity of data and transforming our cities into smart cities. However, serious security problems arise with this development with more and more smart devices collecting private information under smart city scenario. In this article, we investigate the task of detecting insiders’ anomalous behaviors to prevent urban big data leakage. Specifically, we characterize a user's daily activities from four perspectives and use several deep learning algorithms (long short-term memory (LSTM) and convolutional LSTM (convLSTM)) to calculate deviations between realistic actions and normalcy of daily behaviors and use multilayer perceptron (MLP) to identify abnormal behaviors according to those deviations. To evaluate the proposed multimodel-based system (MBS), we conducted experiments on the CERT (United States Computer Emergency Readiness Team) dataset. The experimental results show that our proposed MBS has a remarkable ability to learn the normal pattern of users’ daily activities and detect anomalous behaviors.
Task Allocation in Hybrid Big Data Analytics for Urban IoT Applications
Authors: Ding Weilong , Zhao Zhuofeng , Wang Jianwu , Li Han
Published: July 2020

In urban Internet of Things (IoT) environments, data generated in real time could be processed by analytical applications in online or offline mode. In the management perspective of runtime environments, such modes can hardly be supported in a unified framework under multiple restrictions such as latency, utility, and QoS (quality of service). Meanwhile in the optimization perspective of specific applications, it is difficult for current infrastructure to efficiently allocate sufficient resources to tasks of an application, simultaneously considering multiple factors such as data size, velocity, and locality. In this article, two task allocation methods are proposed for batch and stream analytics to improve resource utility with auto-scaling guarantee when an analytical application is submitted or sudden workloads appear. Taking the highway domain as an example, the task allocation methods are implemented in a novel combined framework accordingly. Using both real-world and simulated data, extensive experiments show that our methods can improve utility efficiency with effective offload support.
Unsupervised Learning Techniques for Malware Characterization
Authors: Burton Renée
Published: July 2020

This article details data science research in the area of Cyber Threat Intelligence applied to a specific type of Distributed Denial of Service (DDoS) attack. We study a DDoS technique prevalent in the Domain Name System (DNS) for which little malware have been recovered. Using data from a globally distributed set of a passive collectors (pDNS), we create a statistical classifier to identify these attacks and then use unsupervised learning to investigate the attack events and the malware that generates them. The first known major study of this technique, this work demonstrates that current attacks have little resemblance to earlier published descriptions and identifies several features of the attacks. Through a combination of text and time-series features, we are able to characterize the dominant malware and demonstrate that the number of global-scale attack systems is relatively small.
Automated Model Learning for Accurate Detection of Malicious Digital Documents
Authors: Scofield Daniel , Miles Craig , Kuhn Stephen
Published: July 2020

Modern cyber attacks are often conducted by distributing digital documents that contain malware. The approach detailed herein, which consists of a classifier that uses features derived from dynamic analysis of a document viewer as it renders the document in question, is capable of classifying the disposition of digital documents with greater than 98% accuracy even when its model is trained on just small amounts of data. To keep the classification model itself small and thereby to provide scalability, we employ an entity resolution strategy that merges syntactically disparate features that are thought to be semantically equivalent but vary due to programmatic randomness. Entity resolution enables construction of a comprehensive model of benign functionality using relatively few training documents, and the model does not improve significantly with additional training data. In particular, we describe and quantitatively evaluate a fully automated, document format--agnostic approach for learning a classification model that provides efficacious malicious document detection.
Searching Activity Trajectories by Exemplar
Authors: Yang Zhong , Zheng Bolong , Li Guohui , Hung Nguyen Quoc Viet , Liu Guanfeng , Zheng Kai
Published: July 2020

The rapid explosion of urban cities has modernized the residents’ lives and generated a large amount of data (e.g., human mobility data, traffic data, and geographical data), especially the activity trajectory data that contains spatial and temporal as well as activity information. With these data, urban computing enables to provide better services such as location-based applications for smart cities. Recently, a novel exemplar query paradigm becomes popular that considers a user query as an example of the data of interest, which plays an important role in dealing with the information deluge. In this article, we propose a novel query, called searching activity trajectory by exemplar, where, given an exemplar trajectory τ

q

, the goal is to find the top-
k
trajectories with the smallest distances to τ

q

. We first introduce an inverted-index-based algorithm (ILA) using threshold ranking strategy. To further improve the efficiency, we propose a gridtree threshold approach (GTA) to quickly locate candidates and prune unnecessary trajectories. In addition, we extend GTA to support parallel processing. Finally, extensive experiments verify the high efficiency and scalability of the proposed algorithms.
A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities
Authors: Xie Yiqun , Shekhar Shashi
Published: July 2020

Given
N
geo-located point instances (e.g., crime or disease cases) in a spatial domain, we aim to detect sub-regions (i.e., hotspots) that have a higher probability density of generating such instances than the others. Hotspot detection has been widely used in a variety of important urban applications, including public safety, public health, urban planning, and equity, among others. The problem is challenging because its societal applications often have low tolerance for false positives and require significance testing that is computationally intensive. In related work, the spatial scan statistic introduced a likelihood ratio--based framework for hotspot evaluation and significance testing. However, it fails to consider the effect of spatial non-determinism, causing many missing detections. Our previous work introduced a non-deterministic normalization--based scan statistic to mitigate this issue. However, its robustness against false positives is not stably controlled. To address these limitations, we propose a unified framework that can improve the completeness of results without incurring more false positives. We also propose a reduction algorithm to improve the computational efficiency. Experiment results confirm that the unified framework can greatly improve the recall of hotspot detection without increasing the number of false positives, and the reduction algorithm can greatly reduce execution time.
Privacy Norms and Preferences for Photos Posted Online
Authors: Hoyle Roberto , Stark Luke , Ismail Qatrunnada , Crandall David , Kapadia Apu , Anthony Denise
Published: July 2020

We are surrounded by digital images of personal lives posted online. Changes in information and communications technology have enabled widespread sharing of personal photos, increasing access to aspects of private life previously less observable. Most studies of privacy online explore differences in individual privacy preferences. Here we examine privacy perceptions of online photos considering both social norms, collectively—shared expectations of privacy and individual preferences. We conducted an online factorial vignette study on Amazon’s Mechanical Turk (
n
= 279). Our findings show that people share common expectations about the privacy of online images, and these privacy norms are socially contingent and multidimensional. Use of digital technologies to share personal photos is influenced by social context as well as individual preferences, while such sharing can affect the social meaning of privacy.
EmotionSense
Authors: Wang Zhu , Yu Zhiwen , Zhao Bobo , Guo Bin , Chen Chao , Yu Zhiyong
Published: July 2020

With the recent surge of smart wearable devices, it is possible to obtain the physiological and behavioral data of human beings in a more convenient and non-invasive manner. Based on such data, researchers have developed a variety of systems or applications to recognize and understand human behaviors, including both physical activities (e.g., gestures) and mental states (e.g., emotions). Specifically, it has been proved that different emotions can cause different changes in physiological parameters. However, other factors, such as activities, may also impact one’s physiological parameters. To accurately recognize emotions, we need not only explore the physiological data but also the behavioral data. To this end, we propose an adaptive emotion recognition system by exploring a sensor-enriched wearable smart watch. First, an activity identification method is developed to distinguish different activity scenes (e.g., sitting, walking, and running) by using the accelerometer sensor. Based on the identified activity scenes, an adaptive emotion recognition method is proposed by leveraging multi-mode sensory data (including blood volume pulse, electrodermal activity, and skin temperature). Specifically, we extract fine-grained features to characterize different emotions. Finally, the adaptive user emotion recognition model is constructed and verified by experiments. An accuracy of 74.3% for 30 participants demonstrates that the proposed system can recognize human emotions effectively.
Gaps and Opportunities in Situational Awareness for Cybersecurity
Authors: Gutzwiller Robert , Dykstra Josiah , Payne Bryan
Published: July 2020

Demand is present among security practitioners for improving cyber situational awareness (SA), but capability and assessment have not risen to match. SA is an integral component of cybersecurity for everyone from individuals to business to response teams and threat exchanges. In this Field Note, we highlight existing research and our field observations, a recent review of cyber SA research literature, and call upon the research community to help address three research problems in situational awareness for cybersecurity. The gaps suggest the need to (1) understand what cyber SA is from the human operators’ perspectives, then (2) measure it so that (3) the community can learn whether SA makes a difference in meaningful ways to cybersecurity, and whether methods, technology, or other solutions would improve SA and thus, improve those outcomes.
Making Appearances
Authors: Joosse Michiel , Lohse Manja , van Berkel Niels , Sardar Aziez , Evers Vanessa
Published: July 2020

To prepare for a future in which robots are more commonplace, it is important to know what robot behaviors people find socially normative. Previous work suggests that for robots to be accepted by people, the robot should adhere to the prevalent social norms, such as those related to approaching people. However, we do not expect that socially normative approach behaviors for robots can be translated on a one-on-one basis from people to robots, because currently robots have unique and different features to humans, including (but not limited to) wheels, sounds, and shapes. The two studies presented in this article go beyond the state-of-the-art and focus on socially normative approach behaviors for robots. In the first study, we compared people’s responses to violations of personal space done by robots compared to people. In the second study, we explored what features (sound, size, speed) of a robot approaching people have an effect on acceptance. Findings indicate that people are more lenient toward violations of a social norm by a robot as compared to a person. Also, we found that robots can use their unique features to mitigate the negative effects of norm violations by communicating intent.
Interval-based Queries over Lossy IoT Event Streams
Authors: Busany Nimrod , Aa Han Van Der , Senderovich Arik , Gal Avigdor , Weidlich Matthias
Published: July 2020

Recognising patterns that correlate multiple events over time becomes increasingly important in applications that exploit the Internet of Things, reaching from urban transportation through surveillance monitoring to business workflows. In many real-world scenarios, however, timestamps of events may be erroneously recorded, and events may be dropped from a stream due to network failures or load shedding policies. In this work, we present SimpMatch, a novel simplex-based algorithm for probabilistic evaluation of event queries using constraints over event orderings in a stream. Our approach avoids learning probability distributions for time-points or occurrence intervals. Instead, we employ the abstraction of segmented intervals and compute the probability of a sequence of such segments using the notion of order statistics. The algorithm runs in linear time to the number of lost events and shows high accuracy, yielding exact results if event generation is based on a Poisson process and providing a good approximation otherwise. We demonstrate empirically that SimpMatch enables efficient and effective reasoning over event streams, outperforming state-of-the-art methods for probabilistic evaluation of event queries by up to two orders of magnitude.
Open Workflows for Polychromatic Reconstruction of Historical Sculptural Monuments in 3D
Authors: Mccarthy J. , Sebo E. , Wilkinson B. , Sheehan F.
Published: July 2020

Many historical monuments were originally vividly painted (polychromatic), and researchers have been able to reach consensus on this even for monument classes in which only indirect evidence of painting survives. However, academic caution has led to an understandable reluctance to pass this knowledge to the public through use of reconstructions showing the colour schemes of these monuments. As a result, use of polychromatic reconstruction has been very limited and has made a significant impact on public perception for the classical period, with insignificant progress for many other important monument classes. We argue that large-scale projects to create digital reconstructions are inevitable but that adoption of open workflows is vitally important to achieve the objectives of the London Charter. We demonstrate a practical workflow with two medieval stone crosses.
Spiffy
Authors: Sun Kuei , Fryer Daniel , Wang Russell , Patel Sagar , Chu Joseph , Lakier Matthew , Brown Angela Demke , Goel Ashvin
Published: July 2020

Many file-system applications such as defragmentation tools, file-system checkers, or data recovery tools, operate at the storage layer. Today, developers of these file-system aware storage applications require detailed knowledge of the file-system format, which requires significant time to learn, often by trial and error, due to insufficient documentation or specification of the format. Furthermore, these applications perform ad-hoc processing of the file-system metadata, leading to bugs and vulnerabilities.

We propose Spiffy, an annotation language for specifying the on-disk format of a file system. File-system developers annotate the data structures of a file system, and we use these annotations to generate a library that allows identifying, parsing, and traversing file-system metadata, providing support for both offline and online storage applications. This approach simplifies the development of storage applications that work across different file systems because it reduces the amount of file-system--specific code that needs to be written.

We have written annotations for the Linux Ext4, Btrfs, and F2FS file systems, and developed several applications for these file systems, including a type-specific metadata corruptor, a file-system converter, an online storage layer cache that preferentially caches files for certain users, and a runtime file-system checker. Our experiments show that applications built with the Spiffy library for accessing file-system metadata can achieve good performance and are robust against file-system corruption errors.
Chinese Short Text Classification with Mutual-Attention Convolutional Neural Networks
Authors: Hao Ming , Xu Bo , Liang Jing-Yi , Zhang Bo-Wen , Yin Xu-Cheng
Published: July 2020

The methods based on the combination of word-level and character-level features can effectively boost performance on Chinese short text classification. A lot of works concatenate two-level features with little processing, which leads to losing feature information. In this work, we propose a novel framework called Mutual-Attention Convolutional Neural Networks, which integrates word and character-level features without losing too much feature information. We first generate two matrices with aligned information of two-level features by multiplying word and character features with a trainable matrix. Then, we stack them as a three-dimensional tensor. Finally, we generate the integrated features using a convolutional neural network. Extensive experiments on six public datasets demonstrate improved performance of our new framework over current methods.
Commanding and Re-Dictation
Authors: Ghosh Debjyoti , Liu Can , Zhao Shengdong , Hara Kotaro
Published: July 2020

Existing voice-based interfaces have limited support for text editing, especially when
seeing
the text is difficult, e.g., while walking or cooking. This research develops voice interaction techniques for eyes-free text editing. First, with a Wizard-of-Oz study, we identified two primary user strategies: using commands, e.g., “replace
go
with
goes
” and re-dictating over an erroneous portion, e.g., correcting “he go there” by saying “he goes there.” To support these user strategies with an actual system implementation, we developed two eyes-free voice interaction techniques,
Commanding
and
Re-dictation
, and evaluated them with a controlled experiment. Results showed that while Re-dictation performs significantly better for more semantically complex edits, Commanding is more suitable for making one-word edits, especially deletions. We developed
VoiceRev
to combine both the techniques in the same interface and evaluated it with realistic tasks. Results showed improved usability of the combined techniques over either of the two techniques used individually.
Serendipity-based Points-of-Interest Navigation
Authors: Ge Xiaoyu , Chrysanthis Panos K. , Pelechrinis Konstantinos , Zeinalipour-Yazti Demetrios , Sharaf Mohamed A.
Published: July 2020

Traditional venue and tour recommendation systems do not necessarily provide a
diverse
set of recommendations and leave little room for
serendipity
. In this article, we design MPG, a Mobile Personal Guide that recommends: (i) a set of diverse yet surprisingly interesting venues that are aligned to user preferences and (ii) a set of routes, constructed from the recommended venues. We also introduce EPUI, an Experimental Platform for Urban Informatics. Our comparison with the state-of-the-art schemes indicates that MPG is capable of providing high-quality venues and route recommendations while incorporating seamlessly both the notion of diversity and that of serendipity.
PANDORA
Authors: Stitt Greg , Campbell David
Published: July 2020

In this article, we introduce a
p
arallelizing
a
pproximatio
n
-
d
isc
o
very f
ra
mework, PANDORA, for automatically discovering application- and architecture-specialized approximations of provided code. PANDORA complements existing compilers and runtime optimizers by generating approximations with a range of Pareto-optimal tradeoffs between performance and error, which enables adaptation to different inputs, different user preferences, and different runtime conditions (e.g., battery life). We demonstrate that PANDORA can create parallel approximations of inherently sequential code by discovering alternative implementations that eliminate loop-carried dependencies. For a variety of functions with loop-carried dependencies, PANDORA generates approximations that achieve speedups ranging from 2.3x to 81x, with acceptable error for many usage scenarios. We also demonstrate PANDORA’s architecture-specialized approximations via FPGA experiments, and highlight PANDORA’s discovery capabilities by removing loop-carried dependencies from a recurrence relation with no known closed-form solution.
Applying Text Analytics to the Mind-section Literature of the Tibetan Tradition of the Great Perfection
Authors: Krishna Ravi , Mu Norman , Keutzer Kurt
Published: July 2020

Over the past decade, through a mixture of optical character recognition and manual input, there is now a growing corpus of Tibetan literature available as e-texts in Unicode format. With the creation of such a corpus, the techniques of text analytics that have been applied in the analysis of English and other modern languages may now be applied to Tibetan. In this work, we narrow our focus to examine a modest portion of that literature, the Mind-section portion of the literature of the Tibetan tradition of the Great Perfection. Here, we will use the lens of text analytics tools based on machine learning techniques to investigate a number of questions of interest to scholars of this and related traditions of the Great Perfection. It has been necessary for us to participate in all portions of this process: corpora identification and text edition selection, rendering the text as e-texts in Unicode using both Optical Character Recognition and manual entry, data cleaning and transformation, implementation of software for text analysis, and interpretation of results. For this reason, we hope this study can serve as a model for other low-resource languages that are just beginning to approach the problem of providing text analytics for their language.
A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier
Authors: Muthu Balaanand , Cb Sivaparthipan , Kumar Priyan Malarvizhi , Kadry Seifedine Nimer , Hsu Ching-Hsien , Sanjuan Oscar , Crespo Ruben Gonzalez
Published: July 2020

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.
Efficient Online Classification and Tracking on Resource-constrained IoT Devices
Authors: Aftab Muhammad , Chau Sid Chi-Kin , Shenoy Prashant
Published: July 2020

Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area, and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this article,* we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.
Design and Implementation of BCI-based Intelligent Upper Limb Rehabilitation Robot System
Authors: Kim Tae-Yeun , Kim Sung-Hwan , Ko Hoon
Published: July 2020

The present study aimed to use the proposed system to measure and analyze brain waves of users to allow intelligent upper limb rehabilitation and to optimize the system using a genetic algorithm. The study used EPOC Neuroheadset for Emotiv with EEG electrodes attached as a non-invasive method for measuring brain waves. The brain waves were measured according to the EEG 10-20 standard electrode layout, which allows measurement of signals from each spot where electrodes are attached based on EEG characteristics. The measured data were added in a database. In the intelligent neuro-fuzzy model, wave transform was used for extracting brain wave characteristics according to user intentions and to eliminate noise from the signals in an effort to increase reliability. Moreover, to construct the option rules of the neuro-fuzzy system, FCM technique and optimal cluster evaluation method were used. Furthermore, the asymmetric Gaussian membership function was used to improve performance, whereas SD and WF divided into left and right sides were used to express the chromosomes. Optimal EEG electrode locations were found, and comparative analysis was performed on the differences based on membership function, number of clusters, and number of learning generations, learning algorithm, and wavelet settings. The performance evaluation results showed that the optimal EEG electrode locations were F7, F8, FC5, and FC6, whereas the accuracy of learning and test data of user-intention recognition was found to be 94.2% and 92.3%, respectively, which suggests that the proposed system can be used to recognize user intention for specific behavior. The system proposed in the present study can allow continued rehabilitation exercise in everyday living according to user intentions, which is expected to help improve the user's willingness to participate in rehabilitation and his or her quality of life.
The Future of False Information Detection on Social Media
Authors: Guo Bin , Ding Yasan , Yao Lina , Liang Yunji , Yu Zhiwen
Published: July 2020

The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.
Cut-n-Reveal
Authors: Muralidhar Nikhil , Tabassum Anika , Chen Liangzhe , Chinthavali Supriya , Ramakrishnan Naren , Prakash B. Aditya
Published: July 2020

Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights theculprittime series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results.
Robot-Assisted Tower Construction—A Method to Study the Impact of a Robot’s Allocation Behavior on Interpersonal Dynamics and Collaboration in Groups
Authors: Jung Malte F. , Difranzo Dominic , Shen Solace , Stoll Brett , Claure Houston , Lawrence Austin
Published: July 2020

Research on human-robot collaboration or human-robot teaming, has focused predominantly on understanding and enabling collaboration between a single robot and a single human. Extending human-robot collaboration research beyond the dyad, raises novel questions about how a robot should allocate resources among group members and about what the consequences of such allocation are for a group’s social dynamics and outcomes. Methodological advances are needed to answer these questions allow researchers to collect data about a robot’s impact not only on interactions with the robot but also on interactions of people with each other. This paper presents Robot Assisted Tower Construction, a novel task that allows researchers to examine the impact of a robot’s allocation behavior on the dynamics of a group or team collaborating on a task. By focusing on the question of whether and how a robot’s allocation of resources (wooden blocks required for a building task) affects collaboration dynamics and outcomes, a case is provided of how this task can be applied in a laboratory study with 124 participants to collect data about human robot collaboration that involves a group of people. We highlight the kinds of insights the task can yield and how it can be adapted to various human robot collaboration contexts.
O&M Indoor Virtual Environments for People Who Are Blind
Authors: Façanha Agebson Rocha , Darin Ticianne , Viana Windson , Sánchez Jaime
Published: July 2020

BACKGROUND:
Knowing their current position in the surroundings constitutes one of the biggest challenges faced by people with visual disabilities when they move around. For them, it is difficult to be aware of the direction in which they are going, and the location of nearby objects and obstacles. In this context, obtaining relevant spatial information is always very significant to these individuals. Hence, the research in the development of assistive technologies for needs and perspectives of people who are blind has been a promising area in terms of the orientation and mobility (O&M) challenges.

OBJECTIVE:
The purpose of this study is to systematically examine the literature on &M virtual environments designed to support indoor navigation to identify techniques for both developing and evaluating the usability and cognitive impact of these applications.

METHODS:
A systematic literature review (SLR) was performed, considering population, intervention, outcomes, and study design as eligibility criteria. After a filtering process from 987 works retrieved from six databases, we extracted data from 51 papers, which meet the study selection criteria.

RESULTS:
The analysis of the 51 papers describing 31 O&M indoor virtual environments, indicated that O&M virtual environments to support indoor navigation are usually designed for desktop, adopt spatial audio as way to support orientation, and use joystick as primary interaction device. Regarding evaluation techniques, questionnaires, interviews, user observation, and performance logs are commonly used to evaluate usability in this context. In tests involving users, the participants are usually adults aged 21–59 years, who individually spend about 90 minutes split in usually two evaluation sessions. Most papers do not report any strategies to evaluate the cognitive impact of O&M virtual environments on users’ navigational and wayfinding skills. Thirteen papers (25.49%) reported the conduction of experiments or quasi-experiments and demonstrated pieces of evidence associated with a positive cognitive impact resultant from O&M indoor virtual environments usage. Finally, only four papers (7.84%) reported the development of indoor maps editors for O&M virtual environments.

CONCLUSION:
Our SLR summarizes the characteristics of 32 O&M virtual environments. It compiles state-of-the-art for indoor simulations in this domain and highlights their challenges and impacts in O&M training. Also, the absence of clear guidelines to design and evaluate O&M virtual environments and the few available computer editors of indoor maps appear as research opportunities.
An Evaluation of the 6TiSCH Distributed Resource Management Mode
Authors: Righetti Francesca , Vallati Carlo , Das Sajal K. , Anastasi Giuseppe
Published: July 2020

The IETF is currently defining the 6TiSCH architecture for the Industrial Internet of Things to ensure reliable and timely communication. 6TiSCH relies on the IEEE TSCH MAC protocol and defines different scheduling approaches for managing TSCH cells, including a distributed (
neighbor-to-neighbor
) scheduling scheme, where cells are allocated by nodes in a cooperative way. Each node leverages a
Scheduling Function (SF)
to compute the required number of cells, and the 6top (6P) protocol to negotiate them with neighbors. Currently, the
Minimal Scheduling Function (MSF)
is under consideration for standardization. However, multiple SFs are expected to be used in real deployments, in order to accommodate the requirements of different use cases. In this article, we carry out a comprehensive analysis of 6TiSCH distributed scheduling to assess its performance under realistic conditions. Firstly, we derive an analytical model to assess the 6P protocol, and we show that 6P transactions take a long time to complete and may also fail. Then, we evaluate the performance of MSF and other distributed SFs through simulations and real experiments. The results show that their performance is affected by the failure of 6P transactions and the instability of the routing protocol, which may lead to congestion from which the network is unable to recover. Finally, we propose a new SF (E-OTF) and show, through simulations and real experiments, that it can effectively improve the overall performance, by allowing nodes to quickly recover from congestion.
A New Error-Modeling of Hardy’s Paradox for Superconducting Qubits and Its Experimental Verification
Authors: Das Soumya , Paul Goutam
Published: July 2020

Hardy’s paradox (equivalently, Hardy’s non-locality or Hardy’s test) [Phys. Rev. Lett. 68, 2981 (1992)] is used to show non-locality without inequalities, and it has been tested several times using optical circuits. We, for the first time, experimentally test Hardy’s paradox of non-locality in superconducting qubits. For practical verification of Hardy’s paradox, we argue that the error-modeling used in optical circuits is not useful for superconducting qubits. So, we propose a new error-modeling for Hardy’s paradox and a new method to estimate the lower bound on Hardy’s probability (i.e., the probability of a specific event in Hardy’s test) for superconducting qubits. Our results confirmed the theory that any non-maximally entangled state of two qubits violates Hardy’s equations; whereas, any maximally entangled state and product state of two qubits do not exhibit Hardy’s non-locality. Further, we point out the difficulties associated with the practical implementation of quantum protocols based on Hardy’s paradox and propose possible remedies. We also propose two performance measures for any two qubits of any quantum computer based on superconducting qubits.
The Menstruating Entrepreneur Kickstarting a New Politics of Women's Health
Authors: Ng Sarah , Bardzell Shaowen , Bardzell Jeffrey
Published: July 2020

This article addresses itself to two developments in recent HCI research. One is the rising emphasis on women's health, a topic that is often seen as at least partly political. The other development in HCI research is the ongoing interest in supporting democracy and political activism. We present the case of a menstrual cup design project in Taiwan, called the Formoonsa Cup, whose product development led to the change in the legal status of menstrual cups and forcefully challenged a traditional value of hymen maintenance as an expression of “pure” and morally upright womanhood. We argue that this project is significant to HCI research for the following two reasons: first, because the design project is a successful, if complicated, case of political activism, and second, because the design and legalization processes were in part mediated by platform technologies, including social networking, crowdfunding, and direct democracy platforms.

Using philosopher Michel Foucault's notion of “subjugated knowledge,” we analyze the case to improve understandings of how design can engage in emancipatory politics in the domain of women's health in HCI. In this case study, we begin with the idea that women's freedom of self-care is the knowledge that is subjugated, though by the end we suggest that subjugated knowledge is a more complex, and troubling, category than this initial evaluation suggests. We also argue that design can critique and intervene when it materializes previously subjugated knowledges and renders them both socially intelligible and politically efficacious.
Taking the Long, Holistic, and Intersectional View to Women’s Wellbeing
Authors: Kumar Neha , Karusala Naveena , Ismail Azra , Tuli Anupriya
Published: July 2020

In this article, we present 6 cases (contained in 13 studies) variously connected with women’s health in a range of Indian contexts. Analyzing these cases, we highlight that “women’s health” is inextricably linked with extrinsic factors that also need addressing, to propose a broadened focus of “women’s wellbeing,” as defined through the lens of Martha Nussbaum’s
central human capabilities
. Drawing again on our cases, we discuss the importance of taking a long, holistic, and intersectional view to women’s wellbeing. Consolidating lessons learned across studies, we emphasize the potential of framing challenges around women’s health as learning problems, rather than problems of information access alone. Leveraging this perspective, we propose the use of
design-based implementation research
as a potential approach in identified learning ecologies, given its emphasis on long-term engagement with multiple stakeholders in the learning process. Although the empirical research we draw from took place in various Indian contexts, we conclude by arguing that key contextual characteristics may translate to other cultures and geographies as well.
Incentive-Driven Computation Offloading in Blockchain-Enabled E-Commerce
Authors: Deng Shuiguang , Cheng Guanjie , Zhao Hailiang , Gao Honghao , Yin Jianwei
Published: July 2020

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.
Improving Collaborative Filtering with Social Influence over Heterogeneous Information Networks
Authors: Zhou Yang , Liu Ling , Lee Kisung , Palanisamy Balaji , Zhang Qi
Published: July 2020

The advent of social networks and activity networks affords us an opportunity of utilizing explicit social information and activity information to improve the quality of recommendation in the presence of data sparsity. In this article, we present a social-influence-based collaborative filtering (SICF) framework over heterogeneous information networks with three unique features. First, we integrate different types of entities, links, attributes, and activities from rating networks, social networks, and activity networks into a unified social-influence-based collaborative filtering model through the intra-network and inter-network social influence. Second, we propose three social-influence propagation models to capture three kinds of information propagation within heterogeneous information networks: user-based influence propagation on user rating networks, item-based influence propagation on user-rating activity networks, and term-based influence propagation on user-review activity networks, respectively. We compute three kinds of social-influence-based user similarity scores based on three social-influence propagation models, respectively. Third, a unified social-influence-based CF prediction model is proposed to infer rating tastes by incorporating three kinds of social-influence-based similarity measures with different weighting factors. We design a weight-learning algorithm, SICF, to refine the prediction result by quantifying the contribution of each kind of information propagation to make a good balance between prediction accuracy and data sparsity. Extensive evaluation on real datasets demonstrates that SICF outperforms existing representative collaborative filtering methods.
Packing R-trees with Space-filling Curves
Authors: Qi Jianzhong , Tao Yufei , Chang Yanchuan , Zhang Rui
Published: July 2020

The massive amount of data and large variety of data distributions in the big data era call for access methods that are efficient in both query processing and index management, and over both practical and worst-case workloads. To address this need, we revisit two classic multidimensional access methods—the R-tree and the space-filling curve. We propose a novel R-tree packing strategy based on space-filling curves. This strategy produces R-trees with an asymptotically optimal I/O complexity for window queries in the worst case. Experiments show that our R-trees are highly efficient in querying both real and synthetic data of different distributions. The proposed strategy is also simple to parallelize, since it relies only on sorting. We propose a parallel algorithm for R-tree bulk-loading based on the proposed packing strategy and analyze its performance under the massively parallel communication model. To handle dynamic data updates, we further propose index update algorithms that process data insertions and deletions without compromising the optimal query I/O complexity. Experimental results confirm the effectiveness and efficiency of the proposed R-tree bulk-loading and updating algorithms over large data sets.
Toward a Type-based Analysis of Road Networks
Authors: Babu Sarath , Manoj B. S.
Published: July 2020

Road networks are major influential factors in the development of any nation. Due to different factors involved in their evolution, road networks exhibit complex structures, which result in problems such as inefficient traffic patterns, congestion, and environmental pollution. The existing metrics of road network analysis are found to be inadequate to reveal the complexity of networks that contributes to such problems. Therefore, we propose a new dimension for the road network analysis using
road-types
to understand such problems and analyze 57 cities from eight regions around the world: (i) India, (ii) North America, (iii) South America, (iv) Europe, (v) Asia, (vi) China, (vii) Japan, and (viii) Oceania. We define four new metrics: (i)
Link Type Distribution
, (ii)
Link Type Demand
, (iii)
Preference Cost
, and (iv)
Type Closeness
, for measuring the influence of road-types in the cities concerned. Link type distribution computes the share of each road-type in shortest paths while link type demand measures the type distribution considering the users’ preference of road-types. The metric preference cost is defined to measure the additional cost users incur on choosing higher-quality roads and type closeness computes the minimum distance a user needs to travel to reach the preferred road-type. Our analysis shows that, in cities of India, Europe, Asia, China, and Oceania, the primary, secondary, and tertiary roads play a crucial role in the shortest paths in terms of link type distribution as well as link type demand. Also, the involvement of secondary road-type is an important feature among cities from all regions except Japan, where the distribution and demand concentrated in tertiary as well as unclassified types. Cities in North and South American regions provide more importance to residential roads besides the secondary type. In addition to distribution and demand, our analysis shows that the primary, secondary, and tertiary road-types with higher demands suffer from link type deficits while the least demanded types are offered with link type surpluses, which explains one of the reasons for inefficient traffic patterns in congested cities. As far as the additional cost incurred for higher-quality roads is concerned, cities in India and USA provide least preference cost while Chinese and South American cities incur high costs. Among the 57 road networks concerned, Japan shows uniqueness in terms of the proposed metrics due to their inclusion of tertiary and unclassified roads.