Reviewerly/exHarmony huggingface dataset:
Encounter Aware Flight Planning in the Unmanned Airspace

Ensuring safe and efficient operations for unmanned aircraft is vital to their integration into the civil airspace. This paper proposes a set of encounter-based metrics that can be used to estimate the complexity and thus the capacity of an unmanned airspace. We show that these metrics are able to provide a more detailed view of safety failure characteristics in the airspace than traditional metrics. The encounter-based metrics are used to formulate a novel path planning algorithm that combines 4D trajectory optimization with a flight scheduling heuristic in order to reduce the number of difficult to solve encounters in the airspace. The proposed algorithm is distributed in nature and can be applied to an airspace managed by multiple service suppliers that are capable of generating flight plans. Through simulation, we show that this approach is able to outperform other distributed flight planning methods and can perform nearly as well as a centralized approach that jointly optimizes flight plans simultaneously. We also demonstrate that this approach can scale to high operational volumes and is able to efficiently handle spikes in airspace usage.
Experimental Validation of Inheritance Metrics’ Impact on Software Fault Prediction

Software faults can cause trivial annoyance to catastrophic failures. Recent work in software fault prediction (SFP) advocates the need for predicting faults before deployment to aid testing process. Object-oriented programming is complex while comparing it with procedural languages having multiple dimensions wherein inheritance is an important aspect. In this paper, we aim to investigate how much inheritance metrics assist in predicting software fault proneness. We first select the Chidamber and Kemerer (CK) metrics, most accepted metric suite for predicting software faults and inheritance metrics. We use 65 publicly available base datasets having CK metrics and some other inheritance metrics to evaluate the impact of inheritance on SFP. We split each dataset into further two datasets: inheritance with CK and CK without inheritance for comparison of results. An artificial neural network (ANN) is used for model building, and accuracy, recall, precision, F1 measures, and true negative rate (TNR) are used for measuring performance. Comparison is made and the results show an acceptable contribution of inheritance metrics in SFP. The testing community can safely use inheritance metrics in predicting software faults. Moreover, high inheritance is not desirable, as this can potentially lead to software faults.
A User-Centered Framework for Designing Midair Gesture Interfaces

Due to the recent advances in technologies for gesture recognition, midair gestures can be considered the interface of the future in a large number of applications. However, designing effective interfaces with midair gestures is not an easy task because the design is application dependent and it must fulfill many requirements at the same time. Despite the availability of general guidelines in the literature, clear and well-established procedures for the optimal design of midair gesture-based interfaces are, to date, not available and remain an open issue. The main contribution of this paper is a user-centered modular framework, which integrates existing and novel methods. It supports the designer considering multiple aspects including ergonomics, memorability, and specific user requirements tailored to the application scenario. The framework involves three design steps and a final validation step, also supported by dedicated software. We tested with success the proposed framework in an industrial case study, where technicians must easily access technical information by browsing digital manuals during maintenance operations.
Participatory Design and the Future of Rural LGBTQ Communities

This paper describes The LGBTQ Futures Project, a collaborative and community-based research project that uses participatory design to understand how lesbian, gay, bisexual, transgender, and queer (LGBTQ) people in rural places experience community and technology. We highlight the resource needs among our participants, how they differ between rural students and non-students, and how rural LGBTQ people envision the future of social technology that is designed explicitly for their needs.
Quantum vision representations and multi-dimensional quantum transforms

Quantum vision representation (QVR) is the foundation of quantum vision information processing, which is a possible solution to store and process massive visual data efficiently. In this paper, firstly, quantum image representations are divided into three categories based on different methods of color information storage. Secondly, in order to systematize quantum image representation, we propose five new methods. Thirdly, we develop models of QVR by extending three categories of quantum image representations into corresponding QVRs. Next, we design and implement 1D, 2D, and 3D quantum transforms based on QVR for the first time. Simulation experiments demonstrate that proposed multi-dimensional quantum transforms are effective. In conclusion, this paper develops a model of QVR and provides a feasible scheme for multi-dimensional quantum transforms to be applied in quantum vision information processing.
Continuous-time quantum search and time-dependent two-level quantum systems

It was recently emphasized by Byrnes, Forster and Tessler [Phys. Rev. Lett. 120 (2018) 060501] that the continuous-time formulation of Grover’s quantum search algorithm can be intuitively understood in terms of Rabi oscillations between the source and the target subspaces. In this work, motivated by this insightful remark and starting from the consideration of a time-independent generalized quantum search Hamiltonian as originally introduced by Bae and Kwon [Phys. Rev. A 66 (2002) 012314], we present a detailed investigation concerning the physical connection between quantum search Hamiltonians and exactly solvable time-dependent two-level quantum systems. Specifically, we compute in an exact analytical manner the transition probabilities from a source state to a target state in a number of physical scenarios specified by a spin-[Formula: see text] particle immersed in an external time-dependent magnetic field. In particular, we analyze both the periodic oscillatory as well as the monotonic temporal behaviors of such transition probabilities and, moreover, explore their analogy with characteristic features of Grover-like and fixed-point quantum search algorithms, respectively. Finally, we discuss from a physics standpoint the connection between the schedule of a search algorithm, in both adiabatic and nonadiabatic quantum mechanical evolutions, and the control fields in a time-dependent driving Hamiltonian.
Advances in exploration geochemistry, 2007 to 2017 and beyond

Mineral exploration under relatively young, exotic cover still presents a major challenge to discovery. Advances and future developments can be categorized in four key areas, (1) understanding metal mobility and mechanisms, (2) rapid geochemical analyses, (3) data access, integration and interoperability and (4) innovation in laboratory-based methods. Application of ‘regolith-style9 surface mapping in covered terrains outside of conventional lateritic terrains is achieving success in terms of reducing background noise and improving geochemical contrasts. However, process models for anomaly generation are still uncertain and require further research. The interaction between the surface environment, microbes, hydrocarbons and chemistry is receiving greater attention. While significant progress has been achieved in understanding the role of vegetation, interaction with the water table and cycling of metals in the near surface environment in Australia, other regions of the world, for example, the till-covered terrains in the northern hemisphere and arid colluvium-covered areas of South America, have seen less progress. In addition to vegetation, the influence of bacteria, fungi and invertebrates is not as well studied with respect to metal mobilization in cover. Field portable XRF has become a standard field technique, though more often used in a camp setting. Apart from a tweaking of analytical quality, instruments have probably reached their peak of analytical development with add-ons, such as cameras, beam-limiters, wireless transmission and GPS as the main differences between instrument suppliers. Their future rests in automated application in unconventional configurations, for example, core scanning and better integration of analytical data with other information such as spectral analyses. Pattern drilling that persists in industry, however, has benefited from innovative application of field-portable tools along with rock and mineral chemistry to provide near real-time results and assist in a shift toward more flexible and targeted drilling in greenfields settings. Innovation in the laboratory continues to progress. More selective geochemical analysis, imaging of fine particle size fractions and resistate mineral phases and isotope analysis are faster and more accessible than ever before. The application of genomics (and data analysis) as mineral exploration tools is on the horizon. A continuing problem in geoscience, the supply to industry of suitably trained geochemists, persists although some needs, particularly at junior level, will be met by recent initiatives at various universities at graduate level. Unfortunately, the current economic climate has had a significant impact on R&D and retention of geochemistry skills by the industry. Whilst the future is positive, significant investment is required to develop the next generation of geochemical exploration tools and concepts. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17
Avalon

Existing techniques for improving datacenter utilization while guaranteeing the QoS are based on the assumption that queries have similar behaviors. However, user queries in emerging compute demanding services demonstrate significantly diverse behavior and require adaptive parallelism. Our study shows that the end-to-end latency of the compute demanding query is determined together by the system-wide load, its workload, its parallelism, contention on shared cache, and memory bandwidth. When hosting such new services, the current cross-query resource allocation results in either severe QoS violation or significant resource under-utilization.
Biometric Mirror

Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people's facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology's possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.
Securing AES against Localized EM Attacks through Spatial Randomization of Dataflow

A localized electromagnetic (EM) attack is a potent threat to security of embedded cryptographic implementations. The attack utilizes high resolution EM probes to localize and exploit information leakage in sub-circuits of a system, providing information not available in traditional EM and power attacks. In this paper, we propose a countermeasure based on randomizing the assignment of sensitive data to parallel datapath components in a high-performance implementation of AES. In contrast to a conventional design where each state register byte is routed to a fixed S-box, a permutation network, controlled by a transient random value, creates a dynamic random mapping between the state registers and the set of S-boxes. This randomization results in a significant reduction of exploitable leakage.We demonstrate the countermeasure's effectiveness under two attack scenarios: a more powerful attack that assumes a fully controlled access to an attacked implementation for building a priori EM-profiles, and a generic attack based on the black-box model. Spatial randomization leads to a 150× increase of the minimum traces to disclosure (MTD) for the profiled attack and a 3.25× increase of MTD for the black-box model attack.
Real-Time Deep Tracking via Corrective Domain Adaptation

Visual tracking is one of the fundamental problems in computer vision. Recently, some deep-learning-based tracking algorithms have been illustrating record-breaking performances. However, due to the high complexity of neural networks, most deep trackers suffer from low tracking speed and are, thus, impractical in many real-world applications. Some recently proposed deep trackers with smaller network structure achieve high efficiency while at the cost of significant decrease in precision. In this paper, we propose to transfer the deep feature, which is learned originally for image classification to the visual tracking domain. The domain adaptation is achieved via some “grafted” auxiliary networks, which are trained by regressing the object location in tracking frames. This adaptation improves the tracking performance significantly both on accuracy and efficiency. The yielded deep tracker is real time and also illustrates the state-of-the-art accuracies in the experiment involving two well-adopted benchmarks with more than 100 test videos. Furthermore, the adaptation is also naturally used for introducing the objectness concept into visual tracking. This removes a long-standing target ambiguity in visual tracking tasks, and we illustrate the empirical superiority of the more well-defined task.
Intelligent Tutoring System for Negotiation Skills Training

Intelligent tutoring systems have proven very effective at teaching hard skills such as math and science, but less research has examined how to teach “soft” skills such as negotiation. In this paper, we introduce an effective approach to teaching negotiation tactics. Prior work showed that students can improve through practice with intelligent negotiation agents. We extend this work by proposing general methods of assessment and feedback that could be applied to a variety of such agents. We evaluate these techniques through a human subject study. Our study demonstrates that personalized feedback improves students’ use of several foundational tactics.
The Internet Access and Use in Public Libraries in Limpopo Province, South Africa

Citizens, including those living in poor and developing countries, need equitable access to information daily to satisfy their diverse and multiple information needs. In this information age and information economy era, easy and instant access to information and knowledge, which are inevitabilities for socio-economic development, cannot be overemphasised. With rolling out of public libraries even in disadvantaged rural communities due to the library conditional grant in South Africa, the study is aimed at investigating factors influencing the Internet access and use in public libraries in Limpopo. The study adopted a qualitative research approach. The twenty four users of three rural, semi-rural and urban public libraries were selected through convenience sampling. A well-placed government official of the Department of Sport, Arts and Culture was purposively selected. All informants were interviewed face to face to collect data. The study found that, although public libraries studied have networked computers for the Internet access and use, there are many challenges. The officials of the relevant department need to study the recommendations of this study to ensure that the people have digital access in public libraries to mitigate the diverse socio-economic challenges they are facing to improve their lives.
Locating Latent Design Information in Developer Discussions: A Study on Pull Requests

A software system's design determines many of its properties, such as maintainability and performance. An understanding of design is needed to maintain system properties as changes to the system occur. Unfortunately, many systems do not have up-to-date design documentation and approaches that have been developed to recover design often focus on how a system works by extracting structural and behaviour information rather than information about the desired design properties, such as robustness or performance. In this paper, we explore whether it is possible to automatically locate where design is discussed in on-line developer discussions. We investigate and introduce a classifier that can locate paragraphs in pull request discussions that pertain to design with an average AUC score of 0.87. We show that this classifier, when applied to projects on which it was not trained, agrees with the identification of design points by humans with an average AUC score of 0.79. We describe how this classifier could be used as the basis of tools to improve such tasks as reviewing code and implementing new features.
The sources of mineralizing fluids of orogenic gold deposits of the Baikal-Patom and Muya areas, Siberia: Constraints from the C and N stable isotope compositions of fluid inclusions

The compositions of fluid inclusions in quartz and sphalerite from five gold deposits of the Baikal-Patom (Sukhoi Log, Verninsk and Dogaldyn) and Baikal-Muya (Uryakh and Irokinda) foldbelts in the northern margin of the Central Asian Orogenic Belt, along its boundary with the Siberian craton, indicate that the gold mineralization was formed predominantly from heterogeneous CO2-H2O orogenic fluid. All deposits are structurally controlled and composed of veinlet-disseminated and vein to stockwork types of ores that are hosted by rocks that underwent regional metamorphism to the greenschist facies in Baikal-Patom and up to the granulite facies in Baikal-Muya. The gold-quartz veins of all the Baikal-Patom deposits and the Uryakh were formed by fluids of similar composition with salinity of 1.4–9.5 wt% NaCl equiv. and CO2 content of 1.4–8.6 mol/kg of solution, under comparable physical conditions, at temperatures of 128–385 °C and pressures of 570–3290 bar. Fluids of the granulate-hosted Irokinda deposit are distinguished for the highest temperatures and pressures and involve relics of medium- to high-temperature (179–453 °C) brines with salinity of 4.6–46.3 wt% NaCl equiv. The temperature and pressure of the mineralizing fluid increase roughly from north to south, which correlates with the increase in the metamorphic grade of the host rocks. The increase is accompanied by the simultaneous systematic shift in the C isotope composition of CO2 (released from the quartz by the chrush-leach method) from −1.9 to −5.5‰ δ13CCO2 in the Baikal-Patom and Uryakh from −0.6 to +0.7‰ δ13CCO2 at Irokinda. The C and N isotope compositions of CO2 suggest that Irokinda fluids were partly derived from decarbonated marine limestone, whereas the orogenic gold-bearing fluids of the other deposits are interpreted to have been composed mostly of fluid from a crustal magmatic (granitic?) source.
LifeSeeker

In this paper we present a first version of the LifeSeeker interactive lifelog retrieval engine that is under development at Dublin City University. This retrieval engine has been designed as a platform onto which future lifelog annotation and retrieval engines will be built. The first implementation of LifeSeeker has been designed for the LSC'19 comparative benchmarking challenge and it takes the form of a faceted search and browsing interface with the addition of query expansion to help solve the lexical-gap between novice users and the concept annotation tools employed for annotating the collection.
How Effective Are Social Influence Strategies in Persuasive Apps for Promoting Physical Activity?

The use of behavior change systems and persuasive technologies to promote desirable behavior is increasingly gaining attention. Most existing Persuasive Technologies (PTs) are targeted at promoting Physical Activity (PA) using three common socially-oriented persuasive strategies: competition, social comparison, and cooperation. This paper provides an empirical review of 19 years (54 papers) of literature on persuasive technology for physical activity promotion. The review aims to (1.) evaluate the effectiveness of PTs employing social influence strategies to promote PA; (2.) summarize and highlight trends in the outcomes and employed technological platforms; (3.) reveal some weaknesses of existing PTs for promoting PA; and finally, (4.) offer suggestions for improvements, and opportunities for future research in this area.
Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study

To determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.Full-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.Pre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.Pre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection.