Reviewerly/exHarmony huggingface dataset:
Fraction-Score: A New Support Measure for Co-location Pattern Mining

Co-location patterns are well-established on spatial objects with categorical labels, which capture the phenomenon that objects with certain labels are often located in close geographic proximity.Similar to frequent itemsets, co-location patterns are defined based on a support measure which quantifies the popularity (or prevalence) of a pattern candidate (a label set).Quite a few support measures exist for defining co-location patterns and they share an idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects carry all the labels in C and are located close to one another.Unfortunately, these measures suffer from various weaknesses, e.g., some fail to capture all possible instances while some others overlook the cases when multiple instances overlap.In this paper, we propose a new measure called Fraction-Score whose idea is to count instances fractionally if they overlap.Compared to existing measures, Fraction-Score not only captures all possible instances, but also handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and consistent with the desirable antimonotonicity property).To solve the co-location pattern mining problem based on Fraction-Score, we develop efficient algorithms which are significantly faster than a baseline that adapts the stateof-the-art.We conduct extensive experiments using both real and synthetic datasets, which verified the superiority of Fraction-Score and also the efficiency of our developed algorithms.
Blockchain Interoperable Digital Objects

The future of distributed ledger technology such as blockchain is dependent on its ability to interact and integrate with other systems. Therefore, interoperability has become a fundamental issue that needs to be addressed. The emerging category of crypto-assets are managed and understood using different frameworks. There is, therefore, a need for a unified classification of crypto-assets. This work aims to bring some clarity to and understanding on interoperable crypto-assets and their characteristics. This paper categorizes digital crypto-assets for the purpose of implementing interoperability. The categorization of crypto-assets is based on their functionalities and their purpose. An interoperability scenario has been given for the defined crypto-asset classes.
Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry

The paper presents a sensor fusion module based on Error State Kalman Filter (ESKF) for land vehicle localization using inertial MEMS and odometry. The module fuses inputs from the Inertial Measurement Unit (IMU), On-Board Diagnostics (OBD), and GNSS to provide a vehicle trajectory estimate in real-time. Based on multiple field tests the mean circular error was 30 meters after 16 minutes of drive without GNSS signal (or 7 centimeters after 30 seconds) with average speed of 11 meters per second, which was in agreement with a theoretical estimate based on the IMU with 1 °/hr bias instability.
AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning

The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit relates directly to the code generated by the front-end compiler. Choosing a good order-often referred to as the phase-ordering problem-is an NP-hard problem. In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. We implement a framework in the context of the LLVM compiler to optimize the ordering for HLS programs and compare the performance of deep reinforcement learning to state-of-the-art algorithms that address the phase-ordering problem. Overall, our framework runs one to two orders of magnitude faster than these algorithms, and achieves a 16% improvement in circuit performance over the -O3 compiler flag.
Adaptive Deep Reuse: Accelerating CNN Training on the Fly

This work proposes adaptive deep reuse, a method for accelerating CNN training by identifying and avoiding the unnecessary computations contained in each specific training on the fly. It makes two-fold major contributions. (1) It empirically proves the existence of a lot of similarities among neuron vectors in both forward and backward propagation of CNN. (2) It introduces the first adaptive strategy for translating the similarities into computation reuse in CNN training. The strategy adaptively adjusts the strength of reuse based on the different tolerance of precision relaxation in different CNN training stages. Experiments show that adaptive deep reuse saves 69% CNN training time with no accuracy loss.
Generalization of the Output of a Variational Quantum Eigensolver by Parameter Interpolation with a Low-depth Ansatz

Quantum computing hardware is now surpassing the 50-qubit level, which cannot be simulated with a classical computer. In practical use, generating the ground states of slightly different Hamiltonians ($e.g.$ induced by the different atomic coordinates in a molecule) is often required, but here the traditional form of the variational quantum eigensolver is inefficient. Thus the authors discuss ``training'' a quantum circuit with a small number of Hamiltonians and then generalizing the output by interpolation, and also propose a purpose-built quantum circuit, to greatly reduce the time needed to find ground states with a near-term quantum computer.
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

Partial label learning is an emerging weakly-supervised learning framework where each training example is associated with multiple candidate labels among which only one is valid. Dimensionality reduction serves as an effective way to help improve the generalization ability of learning system, while the task of partial label dimensionality reduction is challenging due to the unknown ground-truth labeling information. In this paper, the first attempt towards partial label dimensionality reduction is investigated by endowing the popular linear discriminant analysis (LDA) techniques with the ability of dealing with partial label training examples. Specifically, a novel learning procedure named DELIN is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to kNN aggregation in the LDA-induced feature space. Extensive experiments on synthetic as well as real-world partial label data sets clearly validate the effectiveness of DELIN in improving the generalization ability of state-of-the-art partial label learning algorithms.
Motion-constrained tile set based 360-degree video streaming using saliency map prediction

In 360-degree video streaming, Most solutions are based on tile-based streaming that divides videos into tiles and streams the high-quality tiles corresponding to the user's viewport areas. However, these methods cannot transmit different combinations of tile coding efficiently. In this paper, we experimented with streaming 360-degree videos using a motion-constrained tile set (MCTS) technique that allows encoding with constraining motion vectors such that each tile can be decoded and transmitted independently. Moreover, we have used a tile-based approach using a saliency map that integrates the information of human visual attention with the contents to deliver high-quality tiles to the region of interest (ROI). We encoded the 360-degree videos at various quality representations with MCTS techniques and assigned a tile quality representation using a saliency map predicted by the existing convolutional neural network (CNN) model. We proposed a novel heuristic algorithm to assign appropriate quality to the tiles on the centerline. Consequently, mixed quality videos based on the saliency map enable efficient streaming in 360-degree videos. Using the Salient360! dataset, the proposed method shows an improvement in terms of bandwidth with little loss of viewport image quality.
A Statistical Fault Analysis Methodology for the Ascon Authenticated Cipher

Authenticated ciphers are trending in secret key cryptography, since they combine confidentiality, integrity, and authentication into one algorithm, and offer potential efficiencies over the use of separate block ciphers and keyed hashes. Current cryptographic contests and standardization efforts are evaluating authenticated ciphers for weaknesses, to include implementation vulnerabilities, such as fault attacks. In this paper, we analyze fault attacks against the Ascon authenticated cipher, which was selected by CAESAR as the first choice for the lightweight use case. We propose a fault attack technique based on statistical ineffective fault analysis (SIFA) using double-fault injection and key dividing. Faults are injected at two selected S-boxes for every encryption during the last round of permutation in the Ascon Finalization stage. The correct tag values, resulting from ineffective fault inductions, are then used to analyze key hypotheses. The complexity of our attack method is a trade-off between the size of key hypothesis search space and the number of double-fault injections. The sufficient number of correct tag values needed to recover a key subset depends on the bias of fault distributions. We perform experiments on a software implementation of Ascon to show that between 12.5 to 2500 correct tag values (i.e., ineffective faults) are enough for key recovery for highly biased to more uniform fault distributions, respectively.
Health Recommender Systems: A State-of-the-Art Review

The use of information technologies in the health domain started decades ago, nevertheless there are still many issues that need to be addressed. Health Recommender Systems (HRSs) constitute a promising alternative when it comes to providing tools to assist doctors in the diagnosis of diseases, as well as to help patients with recommendations in regards of how to keep their wellness. This work provides insights about methods and techniques used in the design and development of HRSs, focusing on the areas or types of the recommendations these systems provide and the data representations they employ to build their knowledge base. For this effect, a number of articles published between January 2006 and August 2018, from five different scientific databases, were retrieved. After an examination and selection of publications, 249 articles were categorized by means of a classification framework which was built upon related works and the content of the studies analyzed. Furthermore, the results of the classification are presented and major findings are outlined and discussed.
Two Hybrid Multiobjective Motion Planning Schemes Synthesized by Recurrent Neural Networks for Wheeled Mobile Robot Manipulators

To make manipulators fulfill end-effector maintaining tasks, such as writing or drawing tasks in a complex environment, two hybrid multiobjective motion planing schemes, i.e., end-effector posture-maintaining and obstacle avoidance (hybrid PM-OA) schemes are proposed and investigated for wheeled mobile redundant robot manipulators. Specifically, the end-effector posture maintaining, obstacle avoidance, and joint physical limits are considered in a quadratic programming (QP) problem. With these two hybrid PM-OA schemes, the wheeled mobile robot manipulators can maintain its end-effector posture, avoid the obstacle and joint physical limits during executing end-effector tasks. The hybrid PM-OA schemes are finally formulated into a piecewise-linear projection equations (PLPEs) and solved by a recurrent neural network (RNN). Computer simulations are given to substantiate the effectiveness, accuracy, safety, and practicability of the proposed hybrid PM-OA schemes. Comparisons with other schemes and simulations further show that the proposed hybrid PM-OA schemes are more suitable for applications.
Adversarial Matching of Dark Net Market Vendor Accounts

Many datasets feature seemingly disparate entries that actually refer to the same entity. Reconciling these entries, or "matching," is challenging, especially in situations where there are errors in the data. In certain contexts, the situation is even more complicated: an active adversary may have a vested interest in having the matching process fail. By leveraging eight years of data, we investigate one such adversarial context: matching different online anonymous marketplace vendor handles to unique sellers. Using a combination of random forest classifiers and hierarchical clustering on a set of features that would be hard for an adversary to forge or mimic, we manage to obtain reasonable performance (over 75% precision and recall on labels generated using heuristics), despite generally lacking any ground truth for training. Our algorithm performs particularly well for the top 30% of accounts by sales volume, and hints that 22,163 accounts with at least one confirmed sale map to 15,652 distinct sellers---of which 12,155 operate only one account, and the remainder between 2 and 11 different accounts. Case study analysis further confirms that our algorithm manages to identify non-trivial matches, as well as impersonation attempts.
The Optimal Linear Canonical Wigner Distribution of Noisy Linear Frequency-Modulated Signals

A qualitative analysis to the output signal-to-noise ratio (SNR) optimization modeling and solving of Wigner distribution in linear canonical transform (LCT) domains is introduced. Within the well-established output SNR optimization model analysis framework, the strategies on the determination of the optimal LCT free parameters for one-component linear frequency-modulated signal added with white noise are developed. Numerical experiments are also carried out to validate the correctness of the optimal LCT free parameters selection results and the feasibility of the output SNR optimization model analysis approach.
Gender recognition in smartphones using touchscreen gestures

This paper presents an approach for gender recognition in smartphones using touchscreen gestures performed by the user. The primary behavioral data comprising readings from the accelerometer, gyroscope, and orientation sensors are acquired while the user interacts with the touchscreen device. These measurements are further enriched by deriving a secondary set of gesture attributes such as swipe length and point curvature. The GIST descriptor-based features are then extracted from two-dimensional maps of the gesture attributes. Finally, a k-nearest neighbor (k-NN) classifier recognizes the user's gender based on a subset of features identified through feature selection. We have evaluated the performance of the proposed approach on two datasets, which consist of 2268 touch gestures from 126 subjects, collected using two different touchscreen devices. Our experiments show that the approach achieves higher gender classification accuracy compared to the existing method. In addition, the performance of our approach is consistent as it provides classification accuracy of 93.65% and 92.96% on the first and second datasets, respectively when multiple gestures are combined for gender recognition. Our study demonstrates that holistic image features considered in this work provide reliable information for smartphone-based gender classification.
Toward Becoming a Better Self

Adolescence is a challenging period, particularly for individuals with autism spectrum disorder (ASD), due to having little knowledge about themselves. Self-management is a strategy to enhance self-understanding through continuous self-monitoring, which can support adaptive transitions to adulthood. Meanwhile, the advent of digital self-tracking tools enables users to collect and reflect on data about themselves. In this work, we investigated how adolescents with ASD kept track of their everyday lives to better understand themselves using a custom self-tracking platform, OmniTrack, over a two-week period. Our findings indicate that personalized self-tracking experiences enable adolescents to monitor the detailed contexts, causes, and consequences of problematic situations; regulate negative emotion and anxiety while interacting with the tracker; and communicate through data with their caregivers, teachers, and therapists. Building on these findings, we suggest the design of a new form of flexible, scaffolded self-tracking technique that can inform both researchers for designing pervasive health technologies for adolescents with ASD and clinicians for guiding adolescents with ASD toward better self-management using such technologies.
Remote sensing data quality model: from data sources to lifecycle phases

The importance of data quality assessment has significantly increased with the boom of information technology and the growing demand for remote sensing (RS) data. The Remote Sensing Data Quality Working Group of the International Society for Photogrammetry and Remote Sensing aimed to conduct an investigation on the principles of data quality. Literature review revealed that most publications introduce data quality models for application specific processing chains and quality schemes are built case by case with particular domain indicators only. Yet no general concept independent from applications has been developed so far. This paper focuses on the formulation of a RS quality concept adopted from information technology domain describing a triangular RS data quality scheme that relates data sources, quality dimensions and lifecycle phases. Following the introduction it provides examples of international standards and fundamentals of theoretic quality modelling. After a short overview on platforms/sensors, definitions of different quality dimensions are presented with their metrics organised in clusters (like resolution or accuracy). The main achievement of the paper relates lifecycle phases to different quality dimensions of high relevance. The objective is not only to address experts of RS but to raise awareness of uncertainty for the general RS user community.
A comparative analysis of digital forensic readiness models using CFRaaS as a baseline

Abstract Digital forensic readiness (DFR) aims at maximizing the potential of conducting a digital forensic investigation while minimizing the cost of conducting postevent processes when a potential security incident is detected. Conducting digital forensic investigation (DFI) process and changing the functionality of software architectures and/or infrastructures while conducting these processes is a costly exercise; however, the availability of DFR processes can shorten and save the cost of these processes. A comparative analysis of the DFR process models is given that makes a strict comparison with the cloud forensic readiness as a service (CFRaaS) model. The main reason the CFRaaS model has been used as a basis for comparison is because it has been constructed by modifying the functionality of initially considered malicious botnets to allow the removal of potential digital evidence from the cloud without changing the architecture or the infrastructure of the cloud while conducting digital forensic processes. It is worth to note that the CFRaaS processes have been carefully developed based on the guidelines of ISO/IEC 27043:2015 international standards for information technology, security techniques, incident investigation principles and processes. Nevertheless, additional postevent response processes have also been incorporated in the CFRaaS like the reconstruction of the events and the Incident Response Procedures processes. The outcome of the comparison has shown promising results worth exploring. This article is categorized under: Digital and Multimedia Science &gt; Cloud Forensics Digital and Multimedia Science &gt; Cyber Threat Intelligence MoDigital and Multimedia Science &gt; Forensic Visualization
A GP Hyper-Heuristic Approach for Generating TSP Heuristics

A wide range of heuristics has been developed over the last decades as a way to obtain good quality solutions in reasonable time on large scale optimisation problems. However, heuristics are problem specific, i.e. lack of generalisation potential, while requiring time to design. Hyper-heuristics have been proposed to address these limitations by directly searching in the heuristics' space. This work more precisely focuses on a heuristic generation method, as opposed to heuristic selection, for the travelling salesman problem (TSP). Learning is achieved with a genetic programming (GP) approach, for which novel specific terminals are introduced. The performance of the proposed GP hyper-heuristic is evaluated on a large set of TSP instances and compared to state-of-the-art heuristics. Experiments demonstrate that the generated heuristics are outperforming existing ones while having similar or lower complexity.
Towards long-distance quantum networks with superconducting processors and optical links

We design a quantum repeater architecture, necessary for long distance quantum networks, using the recently proposed microwave cat state qubits, formed and manipulated via interaction between a superconducting nonlinear element and a microwave cavity. These qubits are especially attractive for repeaters because in addition to serving as excellent computational units with deterministic gate operations, they also have coherence times long enough to deal with the unavoidable propagation delays. Since microwave photons are too low in energy to be able to carry quantum information over long distances, as an intermediate step, we expand on a recently proposed microwave to optical transduction protocol using excited states of a rare-earth ion ($\mathrm{Er^{3+}}$) doped crystal. To enhance the entanglement distribution rate, we propose to use spectral multiplexing by employing an array of cavities at each node. We compare our achievable rates with direct transmission and with two other promising repeater approaches, and show that ours could be higher in appropriate regimes, even in the presence of realistic imperfections and noise, while maintaining reasonably high fidelities of the final state. Thus, in the short term, our work could be directly useful for secure quantum communication, whereas in the long term, we can envision a large scale distributed quantum computing network built on our architecture.
Comparison of Cubic SVM with Gaussian SVM: Classification of Infarction for detecting Ischemic Stroke

Abstract Ischemic Stroke is a condition whereby the blood supply to the brain is disrupted or reduced due to a blockage and if it is not treated immediately will cause the death of the brain. A decrease in blood flow resulting in dead brain tissue can be called an infarction. The classifications of infarction help the health sector in detecting ischemic stroke in patients. In medicine, CT scans can be used to identify Infarctions and for detecting Ischemic Stroke in patients. Therefore, studying the CT scans is crucial in helping doctors obtain functional information about the surrounding brain tissues which will be used for detecting infarction in the brain. Since it is important to pay more attention at the time of choosing the best method that gives the best results, therefore this study proposes to compare between two types of methods, Gaussian Support Vector Machine (Gaussian SVM) and Cubic Support Vector Machine (Cubic SVM). The Cubic SVM could be an efficient method for infarction classification with accurate performances as high as 80%.