Reviewerly/exHarmony huggingface dataset:
Orienteering-based informative path planning for environmental monitoring

The use of robotic mobile sensors for environmental monitoring applications has gained increasing attention in recent years. In this context, a common application is to determine the region of space where the analyzed phenomena is above or below a given threshold level — this problem is known as level set estimation. One example is the analysis of water in a lake, where the operators might want to determine where the dissolved oxygen level is above a critical threshold value. Recent research proposes to model the spatial phenomena of interest using Gaussian Processes, and then use an informative path planning procedure to determine where to gather data. In this paper, in contrast to previous works, we consider the case where a mobile platform with low computational power can continuously acquire measurements with a negligible energy cost. This scenario imposes a change in the perspective, since now efficiency is achieved by reducing the distance traveled by the mobile platform and the computation required by this path selection process. In this paper we propose two active learning algorithms aimed at facing this issue: specifically, (i) SBOLSE casts informative path planning into an orienteering problem and (ii) PULSE that exploits a less accurate but computationally faster path selection procedure. Evaluation of our algorithms, both on a real world and a synthetic dataset show that our approaches can compute informative paths that achieve a high quality classification, while significantly reducing the travel distance and the computation time.
Multi-Modal and Multi-Domain Embedding Learning for Fashion Retrieval and Analysis

Big data analytics has been revolutionizing the fashion industry in recent years. This is evidenced by the fact that popular fashion brands and designers have relied on big data analytics to trace fashion trends and predict market patterns. In this paper, we propose learning a common latent feature representation from heterogeneous fashion data. Specifically, we design a multi-modal and multi-domain embedding learning framework for fashion analysis and data retrieval. Unlike most of the existing multi-view embedding methods, which only consider the heterogeneous similarity constraint, our proposed framework jointly considers both the homogeneous and heterogeneous similarity constraints to capture cross-view similarity and preserve the similarity of the same view. The proposed framework is comprised of two projection steps. In the first projection, a quintuplet-based ranking loss is proposed for multi-domain fashion data to preserve the homogeneous similarity. In the second projection, a cross-view similarity ranking loss is designed for multi-modal fashion data to capture heterogeneous similarity. By utilizing the learned common latent feature representation, the distance between any vector pairs from same or different modalities can reflect its semantic similarity. Quantitative evaluation on a new large-scale dataset and a fashion analysis case study demonstrate the effectiveness of our proposed method.
Real-Time Robotic Manipulation of Cylindrical Objects in Dynamic Scenarios Through Elliptic Shape Primitives

Robotic manipulation employs the object detection in images to create a scene awareness and locate an object's pose. In dynamic scenarios, fast multiobject detection and tracking are crucial. Many objects commonly found in household and industrial environments are represented by cylindrical shapes. Thus, it is available for robots to manipulate them through the real-time detection of elliptic shape primitives formed by the circular tops of these objects. We devise an efficient algorithm of the detection of elliptic shape primitives, which in turn enables robust and real-time robotic manipulations of such objects. The proposed algorithm incorporates the information of elliptic edge curvature, splits complex curves into arcs, classifies the arcs into different quadrants of a candidate elliptic shape, determines the quality of arc selection for ellipse fitting, and then retrieves the corresponding elliptic shape primitive. Our algorithm provides either faster or more accurate ellipse detection results than the current state-of-the-art methods, irrespective of challenging scenarios such as occluded or overlapping ellipses. This is verified by performance comparison with six state-of-the-art elliptic shape detection algorithms on four public image datasets. The algorithm has been integrated on robots to demonstrate the ability to carry out accurate robotic manipulations (tracking, grasping, and stacking) of cylindrical objects in real time. We show that the robotic manipulator, empowered by the elliptic shape primitive algorithm, performs well in complex manipulation experiments as well as dynamic scenarios.
Magnetic and Radar Sensing for Multimodal Remote Health Monitoring

With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyze and verify the validation of fusion method for different classifiers, a support vector machine with a quadratic kernel, and an artificial neural network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained.
Particle swarm Optimized Density-based Clustering and Classification: Supervised and unsupervised learning approaches

Two pattern recognition technologies in the field of machine learning, clustering and classification, have been applied in many domains. Density-based clustering is an essential clustering algorithm. The best known density-based clustering method is Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which can find arbitrary shaped clusters in datasets. DBSCAN has three drawbacks: firstly, the parameters for DBSCAN are hard to set; secondly, the number of clusters cannot be controlled by the users; and thirdly, DBSCAN cannot directly be used as a classifier. In this paper a novel Particle swarm Optimized Density-based Clustering and Classification (PODCC) is proposed, designed to offset the drawbacks of DBSCAN. Particle Swarm Optimization (PSO), a widely used Evolutionary and Swarm Algorithm (ESA), has been applied in optimization problems in different research domains including data analytics. In PODCC, a variant of PSO, SPSO-2011, is used to search the parameter space so as to identify the best parameters for density-based clustering and classification. PODCC can function in terms of both Supervised and Unsupervised Learnings by applying the appropriate fitness functions proposed in this paper. With the proposed fitness function, users can set the number of clusters as input for PODCC. The proposed method was evaluated by testing ten synthetic datasets and ten benchmarking datasets selected from various open sources. The experimental results indicate that the proposed PODCC can perform better than some established methods, especially with respect to imbalanced datasets.
Proxy-Free Privacy-Preserving Task Matching with Efficient Revocation in Crowdsourcing

Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including Server-Local Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the whole system. The proposed scheme is provably secure in the random oracle model under the Decisional q-Combined Bilinear Diffie-Hellman (q-DCDBH) assumption. Comprehensive theoretical analysis and detailed simulation results show that the proposed scheme outperforms the state-of-the-art work.
Protective Measures and Security Policy Non-Compliance Intention

Internal vulnerabilities and insider threats top the list of information security (InfoSec) incidents; prompting organizations to establish InfoSec policy (ISP). Yet, mitigating user's ISP non-compliance is still an arduous task. Hence, this study aims to minimize user's ISP non-compliance intention by investigating their perception and attitude toward ISP non-compliance. Specifically, protective measures drawing upon the protection motivation theory - perceived severity of ISP non-compliance, rewards and familiarity with ISP - analyze users' attitude toward ISP non-compliance. Further, the new construct, information technology (IT) vision conflict, is the mismatch between the values that users hold and those embedded in the ISP. The misalignment of the two conflicting values moderates the relationship between the protective measures and attitude toward ISP non-compliance. Findings show that IT vision conflict weakens the negative relationship between perceived severity of ISP non-compliance and attitude toward ISP non-compliance; indirectly affecting ISP non-compliance intention.
Distributed meter data aggregation framework based on Blockchain and homomorphic encryption

A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber-physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real-time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non-trivial task. This study proposes a distributed, privacy-preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.
System-on-a-Chip (SoC)-Based Hardware Acceleration for an Online Sequential Extreme Learning Machine (OS-ELM)

Machine learning algorithms such as those for object classification in images, video content analysis, and human action recognition are used to extract meaningful information from data recorded by image sensors and cameras. Among the existing machine learning algorithms for such purposes, extreme learning machines (ELMs) and online sequential ELMs (OS-ELMs) are well known for their computational efficiency and performance when processing large datasets. The latter approach was derived from the ELM approach and optimized for real-time application. However, OS-ELM classifiers are computationally demanding, and the existing state-of-the-art computing platforms are not efficient enough for embedded systems, especially for applications with strict requirements in terms of low power consumption, high throughput, and low latency. This paper presents the implementation of an ELM/OS-ELM in a customized system-on-a-chip field-programmable gate array-based architecture to ensure efficient hardware acceleration. The acceleration process comprises parallel extraction, deep pipelining, and efficient shared memory communication.
Enforcing public data archiving policies in academic publishing: A study of ecology journals

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. The effort to increase data sharing in the sciences is one part of a broader “data revolution” that has prompted discussion about a paradigm shift in scientific research. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.
Attack-Defense Differential Game Model for Network Defense Strategy Selection

The existing game-theoretic approaches for network security problems mostly use the static game or the multi-stage dynamic game. However, these researches can not meet the timeliness requirment to analyze the network attack and defense. It is better to regard the attack and defense as a dynamic and real-time process, in which way the rapidity and continuity of network confrontation can be described more precisely. Referring to the epidemic model SIR, we formulated the novel model NIRM to analyze the evolution of network security states. Based on the mentioned above, the attack-defense differential game model was constructed by introducing the differential game theory. Then we figured out the solution of saddle-point strategies in the game. By analyzing the game equilibrium, the algorithm of optimal defense strategies selection in the real-time confrontation was designed, which is more targeted and has greater timeliness. Finally by simulation experiments, we demonstrated the validity of the model and method proposed in this paper, and drew some instructive conclusions on network defense deployment.
Quantifying the resource content of quantum channels: An operational approach

We propose a general method to operationally quantify the ``resourcefulness'' of quantum channels via channel discrimination, an important information processing task. A main result is that the maximum success probability of distinguishing a given channel from the set of free channels by free probe states is exactly characterized by the resource generating power, i.e., the maximum amount of resource produced by the action of the channel, given by the trace distance to the set of free states. We apply this framework to the resource theory of quantum coherence, as an informative example. The general results can also be easily applied to other resource theories such as entanglement, magic states, and asymmetry.
Inviwo — A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities.
Making it tangible: hybrid card sorting within qualitative interviews

Purpose Qualitative researchers and information practitioners often investigate questions that probe the underlying mental models, nuanced perspectives, emotions and experiences of their target populations. The in-depth qualitative interview is a dominant method for such investigations and the purpose of this paper is to demonstrate how incorporating hybrid card-sorting activities into interviews can enable deeper participant reflections and generate rich data sets to increase understanding. Design/methodology/approach Following a review of relevant literature, the case illustration presented is a grounded theory study into the student-researcher information experience with personal academic information management. This study uses hybrid card sorting within in-depth, semi-structured interviews, a unique adaptation that extends multi-disciplinary awareness of the benefits of card-sort exercises for qualitative research. Findings Emerging from diverse fields, ranging from computer science, engineering, psychology and human–computer interaction, card sorting seeks to illuminate how participants understand and organise concepts. The case illustration draws largely on methods used in interaction design and information architecture. Using either open or fixed designs, or hybrid variations, card-sort activities can make abstract concepts more tangible for participants, offering investigators a new approach to interview questions with the aid of this interactive, object-based technique. Originality/value Opening with a comprehensive review of card-sort studies, the authors present an information experience case illustration that demonstrates the rich data generated by hybrid card sorting within qualitative interviews, or interactive interviews. This is followed by discussion of the types of research questions that may benefit from this original method.
User Preferences and Persona Design for an mHealth Intervention to Support Adherence to Cardiovascular Disease Medication in Singapore: A Multi-Method Study

The use of mobile health (mHealth) has gained popularity globally, including for its use in a variety of health interventions, particularly through short message service (SMS) text messaging. However, there are challenges to the use of mHealth, particularly among older users who have a large heterogeneity in usability and accessibility barriers when using technology.In order to better understand and conceptualize the diversity of users and give insight into their particular needs, we turned to persona creation. Personas are user archetypes created through data generated from multi-method inquiry with actual target users. Personas are an appropriate yet largely underutilized component of current mHealth research.Leveraging data from a multi-method study conducted in Singapore with an ethnically diverse population including Chinese, Malay, and Indian participants, we used a proforma to analyze data from the qualitative component (ie, 20 in-depth interviews) and quantitative component (ie, 100 interviewer-guided surveys). We then identified key characteristics, including technology use and preferences as well as adherence factors, to synthesize five personas reflective of persons over the age of 40 years in Singapore with atherosclerotic cardiovascular disease (ASCVD) or ASCVD risk factors, such as hypertension.We present five personas typologized as (1) The Quiet Analog, (2) The Busy Grandparent, (3) The Socializer, (4) The Newly Diagnosed, and (5) The Hard-to-Reach. We report on four key characteristics: health care access, medication adherence, mobile phone technology usage (ie, ownership, access, and utilization), and interest in mHealth. Finally, we provide insights into how these personas may be used in the design and implementation of an mHealth intervention. Our work demonstrates how multi-method data can create biopsychosocial personas that can be used to explore and address the diversity in behaviors, preferences, and needs in user groups.With wider adoption of mHealth, it is important that we consider user-centered design techniques and design thinking in order to create meaningful, patient-centered interventions for adherence to medications. Future research in this area should include greater exploration of how these five personas can be used to better understand how and when is best to deliver mHealth interventions in Singapore and beyond.
Impertinent mobiles - Effects of politeness and impoliteness in human-smartphone interaction

This study aims to reveal first insights into human-smartphone interaction by focusing on the effects of smartphones either “speaking” politely or impolitely. Following the idea of media equation and the corresponding paradigm “computers as social actors” (CASA), smartphones are conceptualized as social agents suggested to elicit social responses in their human users (Nass, Steuer, & Tauber, 1994). In a laboratory experiment, (n = 85) participants interacted with a talking phone, which replied to them either politely or impolitely. Participants evaluated this phone twice, before and after they had received the phone's feedback. ANOVA revealed polite phones to be evaluated significantly better than impolite phones. Comparing evaluations before and after the feedback showed that polite phones were revaluated regarding friendliness but not regarding competence. In contrast, the second evaluation of impolite phones deteriorated on both dimensions: friendliness and competence. Furthermore, results were not affected by ownership (subject's own vs. not subject's own phone). However, the gender of the phone (female vs. male voice) impacted the evaluation: impolite male phones were evaluated less positively regarding their competence, impolite female phones were not. Transferring the CASA paradigm to “talking smartphones” is considered as a heuristically fruitful approach to further analyze humans interacting with phones as well as with speech assistants in general. Results are discussed as an empirical contribution of conceptualizing “smartphones as social actors” (SASA), activating social norms originally exclusive for human-human interactions.