Reviewerly/exHarmony huggingface dataset:
Reliability seeking virtual organizations: Challenges for high reliability organizations and resilience engineering

High reliability organizing is an important approach to crisis prevention and mitigation. Resilience is a term that has been used to describe an organization's ability to respond or 'bounce back' from untoward, surprising or disruptive events. A growing number of organizations have increased mandates for maintaining safety, reliability and resilience; some of these are also virtual or semi virtual organizations—geographically distributed organizations whose members are bound by a long-term common interest or goal, and who largely communicate and coordinate their work through information technology. The objective of this paper is to explicate the challenges that reliability-seeking virtual organizations (RSVOs) pose for high reliability and resilient organizations, based on what is known about high reliability organizations (HROs) and resilient organizations, and what can be surmised about RSVOs. We begin by exploring a test case for RSVOs, distributed offshore oil and gas systems, using the example of the challenges presented during the Deepwater Horizon event. Since a common characteristic of resilient and reliable organizations is fluidity in organizational structures, we examine requirements for fluidity in and across RSVO organizational structures and the forms that fluidity can assume, leaning on our understanding of the role of fluidity in HROs and resilient organizations, and suggesting challenges posed by these different forms of interconnectedness. We then follow with a discussion of when a virtual organization is not highly reliable or resilient. We describe future RSVO challenges in remote settings with little infrastructure, such as Arctic energy exploration and development and IT-enabled healthcare systems in third world countries, and conclude with an agenda for future research.
EE-SPFAL: A Novel Energy-Efficient Secure Positive Feedback Adiabatic Logic for DPA Resistant RFID and Smart Card

The emergence of Internet of Things (IoT) have increased the need of Radio Frequency Identification (RFID) and smart cards that are energy-efficient and secure against Differential Power Analysis (DPA) attacks. Adiabatic logic is one of the circuit design techniques that can be used to design energy-efficient and secure hardware. However, the existing DPA resistant adiabatic logic families suffer from non-adiabatic energy loss. Therefore, this work presents a novel adiabatic logic family called Energy-Efficient Secure Positive Feedback Adiabatic Logic (EE-SPFAL) family that reduces the non-adiabatic energy loss and also is secure against DPA attacks. The proposed EE-SPFAL is used to design logic gates such as buffers, XOR, and NAND. Further, the logic gates based on EE-SPFAL are used to implement a Positive Polarity Reed Muller (PPRM) architecture based S-box circuit. SPICE simulations at 12.5 MHz show that EE-SPFAL based S-box circuit saves up to 65 percent of energy and 90 percent of energy per cycle as compared to the S-box circuit implemented using existing Secured Quasi-Adiabatic Logic (SQAL) and conventional CMOS logic, respectively. Further, the security of EE-SPFAL based S-box circuit has been evaluated by performing the DPA attack through SPICE simulations. We proved that the EE-SPFAL based S-box circuit is resistant to a DPA attack through a developed DPA attack flow applicable to SPICE simulations. Further, we have implemented the one round of Advanced Standard Encryption (AES) algorithm and we found that one round of EE-SPFAL logic based AES consumes uniform current with different input plain texts. Low energy consumption and security against DPA attacks makes EE-SPFAL logic a suitable candidate to implement in IoT devices such as RFID and smart cards.
A Johnson's-Rule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing

One of the keys to making cloud data-centers (CDCs) proliferate impressively is the implementation of efficient task scheduling. Since all the resources of CDCs, even including operating systems (OSes) and application programs, can be stored and managed on remote data-centers, this study first analyzed the task scheduling problem for CDCs and established a mathematical model of the scheduling of two-stage tasks. The Johnson's rule was combined with the genetic algorithm to create a Johnson's-rule-based genetic algorithm (JRGA), which takes into account the characteristics of multiprocessor scheduling in CDCs. New crossover and mutation operations were devised to make the algorithm converge more quickly. In the decoding process, the Johnson's rule is used to optimize the makespan for each machine. Simulations were used to compare the performance of the JRGA with that of the list scheduling algorithm and an improved list scheduling algorithm. The results demonstrate the validity of the JRGA.
DimReader: Axis lines that explain non-linear projections

Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.
Machine learning for optimal parameter prediction in quantum key distribution

For a practical quantum key distribution (QKD) system, parameter optimization, the choice of intensities and the probabilities of sending them, is a crucial step in gaining optimal performance, especially when one realistically considers a finite communication time. With the increasing interest in the field to implement QKD over free space on moving platforms, such as drones, handheld systems, and even satellites, one needs to perform parameter optimization with low latency and with very limited computing power. Moreover, with the advent of the internet of things, a highly attractive direction of QKD could be a quantum network with multiple devices and numerous connections, which provides a huge computational challenge for the controller that optimizes parameters for a large-scale network. Traditionally, such an optimization relies on brute-force search or local search algorithms, which are computationally intensive, and will be slow on low-power platforms (which increases latency in the system) or infeasible for even moderately large networks. In this work we present a method that uses a neural network to directly predict the optimal parameters for QKD systems. We test our machine learning algorithm on hardware devices including a Raspberry Pi 3 single-board computer (similar devices are commonly used on drones) and a mobile phone, both of which have a power consumption of less than 5 W, and we find a speedup of up to two to four orders of magnitude when compared to standard local search algorithms. The predicted parameters are highly accurate and can preserve, e.g., over 95%--99% of the optimal secure key rate for a given protocol. Moreover, our approach is highly general and can be applied effectively to various kinds of common QKD protocols.
SafeChain: Securing Trigger-Action Programming From Attack Chains

The proliferation of the Internet of Things (IoT) is reshaping our lifestyle. With IoT sensors and devices communicating with each other via the Internet, people can customize automation rules to meet their needs. Unless carefully defined, however, such rules can easily become points of security failure as the number of devices and complexity of rules increase. Device owners may end up unintentionally providing access or revealing private information to unauthorized entities due to complex chain reactions among devices. Prior work on trigger-action programming either focuses on conflict resolution or usability issues or fails to accurately and efficiently detect such attack chains. This paper explores the security vulnerabilities when users have the freedom to customize automation rules using trigger-action programming. We define two broad classes of attack-privilege escalation and privacy leakage -and present a practical model-checking-based system called SafeChain that detects hidden attack chains exploiting the combination of rules. Built upon existing model-checking techniques, SafeChain identifies attack chains by modeling the IoT ecosystem as a finite-state machine. To improve practicability, SafeChain avoids the need to accurately model an environment by frequently rechecking the automation rules given the current states and employs rule-aware optimizations to further reduce overhead. Our comparative analysis shows that SafeChain can efficiently and accurately identify attack chains, and our prototype implementation of SafeChain can verify 100 rules in less than 1 s with no false positives.
Improved Viola-Jones face detection algorithm based on HoloLens

The current face detection in Microsoft HoloLens can only be achieved by remote call of face detection interface algorithm which is, however, restricted by network, resulting in slow detection and failing to meet real-time detection demand. This paper proposes an improved Viola-Jones algorithm of face detection based on HoloLens upgrading classical Viola-Jones face detection algorithm relying on Haar-like rectangle feature expansion to enhance detection efficiency, and accelerating detection building on two-dimensional convolution separation and image re-sampling technique. The detection efficiency of improved face detection algorithm is 12% on average higher than that of existing face detection interface algorithm, and its detection speed is four-folded. Moreover, HoloLens depth camera enables 3D face detection and location, and its unique gaze, voice, and gesture interacting techniques free the hands, thereby realizing easier and less-burdened man-computer interaction. HoloLens furnished with real-time video face detection algorithm as detailed in this paper can be applied in such fields as social contact, public security, and business management.
Anatomical context improves deep learning on the brain age estimation task

Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4–96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1–97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.
Properties of the Geometry of Solutions and Capacity of Multilayer Neural Networks with Rectified Linear Unit Activations

Rectified linear units (ReLUs) have become the main model for the neural units in current deep learning systems. This choice was originally suggested as a way to compensate for the so-called vanishing gradient problem which can undercut stochastic gradient descent learning in networks composed of multiple layers. Here we provide analytical results on the effects of ReLUs on the capacity and on the geometrical landscape of the solution space in two-layer neural networks with either binary or real-valued weights. We study the problem of storing an extensive number of random patterns and find that, quite unexpectedly, the capacity of the network remains finite as the number of neurons in the hidden layer increases, at odds with the case of threshold units in which the capacity diverges. Possibly more important, a large deviation approach allows us to find that the geometrical landscape of the solution space has a peculiar structure: While the majority of solutions are close in distance but still isolated, there exist rare regions of solutions which are much more dense than the similar ones in the case of threshold units. These solutions are robust to perturbations of the weights and can tolerate large perturbations of the inputs. The analytical results are corroborated by numerical findings.
When Does Diversity Help Generalization in Classification Ensembles?

Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element-"diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, we investigate three issues on diversity, that is, the measurement of diversity, the relationship between the proposed diversity and the generalization error, and the utilization of this relationship for ensemble pruning. In the diversity measurement, we measure diversity by error decomposition inspired by regression ensembles, which decompose the error of classification ensembles into accuracy and diversity. Then, we formulate the relationship between the measured diversity and ensemble performance through the theorem of margin and generalization and observe that the generalization error is reduced effectively only when the measured diversity is increased in a few specific ranges, while in other ranges, larger diversity is less beneficial to increasing the generalization of an ensemble. Besides, we propose two pruning methods based on diversity management to utilize this relationship, which could increase diversity appropriately and shrink the size of the ensemble without much-decreasing performance. The empirical results validate the reasonableness of the proposed relationship between diversity and ensemble generalization error and the effectiveness of the proposed pruning methods.
Query-Biased Self-Attentive Network for Query-Focused Video Summarization

This paper addresses the task of query-focused video summarization, which takes user queries and long videos as inputs and generates query-focused video summaries. Compared to video summarization, which mainly concentrates on finding the most diverse and representative visual contents as a summary, the task of query-focused video summarization considers the user's intent and the semantic meaning of generated summary. In this paper, we propose a method, named query-biased self-attentive network (QSAN) to tackle this challenge. Our key idea is to utilize the semantic information from video descriptions to generate a generic summary and then to combine the information from the query to generate a query-focused summary. Specifically, we first propose a hierarchical self-attentive network to model the relative relationship at three levels, which are different frames from a segment, different segments of the same video, textual information of video description and its related visual contents. We train the model on video caption dataset and employ a reinforced caption generator to generate a video description, which can help us locate important frames or shots. Then we build a query-aware scoring module to compute the query-relevant score for each shot and generate the query-focused summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance of our approach compared to some methods.
Tilt Map: Interactive Transitions Between Choropleth Map, Prism Map and Bar Chart in Immersive Environments

We introduce Tilt Map, a novel interaction technique for intuitively transitioning between 2D and 3D map visualisations in immersive environments. Our focus is visualising data associated with areal features on maps, for example, population density by state. Tilt Map transitions from 2D choropleth maps to 3D prism maps to 2D bar charts to overcome the limitations of each. Our article includes two user studies. The first study compares subjects' task performance interpreting population density data using 2D choropleth maps and 3D prism maps in virtual reality (VR). We observed greater task accuracy with prism maps, but faster response times with choropleth maps. The complementarity of these views inspired our hybrid Tilt Map design. Our second study compares Tilt Map to: a side-by-side arrangement of the various views; and interactive toggling between views. The results indicate benefits for Tilt Map in user preference; and accuracy (versus side-by-side) and time (versus toggle).
AutoSpeech: Neural Architecture Search for Speaker Recognition

Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet. However, these backbones were originally proposed for image classification, and therefore may not be naturally fit for speaker recognition. Due to the prohibitive complexity of manually exploring the design space, we propose the first neural architecture search approach approach for the speaker recognition tasks, named as AutoSpeech. Our algorithm first identifies the optimal operation combination in a neural cell and then derives a CNN model by stacking the neural cell for multiple times. The final speaker recognition model can be obtained by training the derived CNN model through the standard scheme. To evaluate the proposed approach, we conduct experiments on both speaker identification and speaker verification tasks using the VoxCeleb1 dataset. Results demonstrate that the derived CNN architectures from the proposed approach significantly outperform current speaker recognition systems based on VGG-M, ResNet-18, and ResNet-34 back-bones, while enjoying lower model complexity.
Evaluating the noise resilience of variational quantum algorithms

We simulate the effects of different types of noise in state preparation circuits of variational quantum algorithms. We first use a variational quantum eigensolver to find the ground state of a Hamiltonian in presence of noise, and adopt two quality measures in addition to the energy, namely fidelity and concurrence. We then extend the task to the one of constructing, with a layered quantum circuit ansatz, a set of general random target states. We determine the optimal circuit depth for different types and levels of noise, and observe that the variational algorithms mitigate the effects of noise by adapting the optimised parameters. We find that the inclusion of redundant parameterised gates makes the quantum circuits more resilient to noise. For such overparameterised circuits different sets of parameters can result in the same final state in the noiseless case, which we denote as parameter degeneracy. Numerically, we show that this degeneracy can be lifted in the presence of noise, with some states being significantly more resilient to noise than others. We also show that the average deviation from the target state is linear in the noise level, as long as this is small compared to a circuit-dependent threshold. In this region the deviation is well described by a stochastic model. Above the threshold, the optimisation can converge to states with largely different physical properties from the true target state, so that for practical applications it is critical to ensure that noise levels are below this threshold.
On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows, balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google Street View (GSV) image dataset show the potential of large-scale localization entirely on a typical mobile device.
In the Eye of the Beholder: Gaze and Actions in First Person Video

We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera. To facilitate our research, we first introduce the EGTEA Gaze+ dataset. Our dataset comes with videos, gaze tracking data, hand masks and action annotations, thereby providing the most comprehensive benchmark for First Person Vision (FPV). Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV. Our method describes the participant's gaze as a probabilistic variable and models its distribution using stochastic units in a deep network. We further sample from these stochastic units, generating an attention map to guide the aggregation of visual features for action recognition. Our method is evaluated on our EGTEA Gaze+ dataset and achieves a performance level that exceeds the state-of-the-art by a significant margin. More importantly, we demonstrate that our model can be applied to larger scale FPV dataset-EPIC-Kitchens even without using gaze, offering new state-of-the-art results on FPV action recognition.