Reviewerly/exHarmony huggingface dataset:
Robust Instruments Position Estimation using Improved Kernelized Correlation Filter for Substation Patrol Robots

Substation patrol robots (SIR) play an increasingly important role in ensuring the safe operation of substations. The robust and precise position estimating of the instruments to be inspected on the images are a prerequisite for accurately detecting the target states or obtaining the target readings under all-weather environment. In order to achieve high location accuracy of instrument, this study proposed an improved kernelized correlation filter (KCF) algorithm for achieving robust instrument location on images for SPR. Firstly, multiple templates are selected for training KCF classifier parameters. Then, reliable SURF matching-point determination method is designed, and the regions including reliable matching points are selected as the candidate regions, so that the searching range is narrowed. Finally, for KCF response surface of each candidate region, Single-Peak Constraint (SPC) is designed for locating target and reducing mismatching rate. Furthermore, experiments are performed for validating the effectiveness of the proposed algorithm, in which four instruments mainly including lightning arrester monitor and transformer thermometer are selected. The experimental results show that the proposed method has higher accuracy of target localization than traditional SURF-based position estimating method.
Human-Computer Integration (Dagstuhl Seminar 18322)

The rise of technology that supports a partnership between user and computer highlights an opportunity for a new era of contrasting the previously dominant paradigm of computers functioning as tools. However, most work around these technologies only focused on the instrumental perspective to achieve extrinsic performance objectives. However, phenomenology emphasizes that it is also important to support the experiential perspective, which indicates that technology should also help people pay attention to their lived experiences and personal growth in order to deepen their understanding of their own bodies.This seminar focuses on embodied integration, where a computer tightly integrates with the person's body. Although an increasing number of systems are emerging, a thorough understanding of how to design such systems is notably absent. The reason for this is the limited knowledge about how such embodied partnerships unfold, and what underlying theory could guide such developments. This seminar brought together leading experts from industry and academia, including those who are central to the development of products and ideas such as wearables, on-body robotics, and exertion systems. The goal was to address key questions around the design of embodied integration and to jump-start collaborations to pioneer new approaches for a human-computer integrated future.
A Simple Heuristic for Order-Preserving Matching

Order preserving matching refers to the problem of reporting substrings in the text which are order-isomorphic to the pattern. In this paper, we show a simple heuristic which runs in linear time on average, based on finding the largest elements in each substring and checking their locations against that of the pattern. It is easy to implement and experimental results showed that the running time grows linearly.
A Fast On-Chip Adaptive Genetic Algorithm Processor for Evolutionary FIR Filter Implementation Using Hardware–Software Co-Design

Recent studies show the impact of genetic algorithms (GA) in the design of evolutionary finite impulse response (FIR) filters. Studies have shown hardware and software method of GA implementation for design. Hardware method improves speed due to parallelism, pipelining and the absence of the function calls compared to software implementation. But area constraint was the main issue of hardware implementation. Therefore, this paper illustrates a hardware–software co-design concept to implement an Adaptive GA processor (AGAP) for FIR filter design. The architecture of AGAP uses adaptive crossover and mutation probabilities to speed up the convergence of the GA process. The AGAP architecture was implemented using Verilog Hardware Description Language (HDL) and instantiated as a custom intellectual property (IP) core to the soft-core MicroBlaze processor of Spartan 6 (XC6SLX45-3CSG324I) FPGA. The MicroBlaze processor controls the AGAP IP core and other interfaces using Embedded C programs. The experiment demonstrated a significant 134% improvement in speed over hardware implementation but with a marginal increase in area. The complete evaluation and evolution of the filter coefficients were executed on a single FPGA. The system on chip (SoC) concept enables a robust and flexible system.
Analysis of the Hybrid Global Path Planning Algorithm for Different Environments

Abstract To achieve the overall goal of realising an efficient and advantageous participation of autonomous ground vehicles in the transport system as fast as possible, a lot of work is being done in different and specific research fields. One of the most important research fields, which has a large impact on safe autonomous ground vehicle realisation, is the development of path planning algorithms. Therefore, this work describes in detail the development and application of a hybrid path planning algorithm. The described algorithm is based on classical and heuristic path planning approaches and can be applied in unstructured and structured environments. The efficiency of the algorithm was investigated by applying the algorithm and executing theoretical and experimental tests. The theoretical and experimental tests were executed while optimising different complexity paths. Results analysis demonstrated that the described algorithm can generate a smooth, dynamically feasible and collision-free path.
SoK - Security and Privacy in the Age of Drones: Threats, Challenges, Solution Mechanisms, and Scientific Gaps

The evolution of drone technology in the past nine years since the first commercial drone was introduced at CES 2010 has caused many individuals and businesses to adopt drones for various purposes. We are currently living in an era in which drones are being used for pizza delivery, the shipment of goods, and filming, and they are likely to provide an alternative for transportation in the near future. However, drones also pose a significant challenge in terms of security and privacy within society (for both individuals and organizations), and many drone related incidents are reported on a daily basis. These incidents have called attention to the need to detect and disable drones used for malicious purposes and opened up a new area of research and development for academia and industry, with a market that is expected to reach $1.85 billion by 2024. While some of the knowledge used to detect UAVs has been adopted for drone detection, new methods have been suggested by industry and academia alike to deal with the challenges associated with detecting the very small and fast flying objects. In this paper, we describe new societal threats to security and privacy created by drones, and present academic and industrial methods used to detect and disable drones. We review methods targeted at areas that restrict drone flights and analyze their effectiveness with regard to various factors (e.g., weather, birds, ambient light, etc.). We present the challenges arising in areas that allow drone flights, introduce the methods that exist for dealing with these challenges, and discuss the scientific gaps that exist in this area. Finally, we review methods used to disable drones, analyze their effectiveness, and present their expected results. Finally, we suggest future research directions.
An MRF Optimisation Framework for Full 3D Helmholtz Stereopsis

Accurate 3D modelling of real world objects is essential in many applications such as digital film production and cultural heritage preservation. However, current modelling techniques rely on assumptions to constrain the problem, effectively limiting the categories of scenes that can be reconstructed. A common assumption is that the scene’s surface reflectance is Lambertian or known a priori. These constraints rarely hold true in practice and result in inaccurate reconstructions. Helmholtz Stereopsis (HS) addresses this limitation by introducing a reflectance agnostic modelling constraint, but prior work in this area has been predominantly limited to 2.5D reconstruction, providing only a partial model of the scene. In contrast, this paper introduces the first Markov Random Field (MRF) optimisation framework for full 3D HS. First, an initial reconstruction is obtained by performing 2.5D MRF optimisation with visibility constraints from multiple viewpoints and fusing the different outputs. Then, a refined 3D model is obtained through volumetric MRF optimisation using a tailored Iterative Conditional Modes (ICM) algorithm. The proposed approach is evaluated with both synthetic and real data. Results show that the proposed full 3D optimisation significantly increases both geometric and normal accuracy, being able to achieve sub-millimetre precision. Furthermore, the approach is shown to be robust to occlusions and noise.
Prospects For Using Big Data To Improve The Effectiveness Of An Education Organization

Today, teaching methods have changed significantly. With new innovations in the field of information technology, work in the classroom becomes virtual, and cloud knowledge. Although these new technologies have been adopted in the field of education, the informative organizations still face the challenge of improving the quality of education and reducing dropout rates. This article will examine the analysis of big data that can be used in education and how it affects the improvement of the quality of education.
Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning

Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning. More specifically, spatial consistency between neighbouring feature matches is used as supervisory fine-tuning signal. The adapted feature leads to more accurate style-invariant matching, and can be used with a standard discovery approach, based on geometric verification, to identify duplicate patterns in the dataset. The approach is evaluated on several different datasets and shows surprisingly good qualitative discovery results. For quantitative evaluation of the method, we annotated 273 near duplicate details in a dataset of 1587 artworks attributed to Jan Brueghel and his workshop. Beyond artwork, we also demonstrate improvement on localization on the Oxford5K photo dataset as well as on historical photograph localization on the Large Time Lags Location (LTLL) dataset.
Transfer Learning in Sentiment Classification with Deep Neural Networks

Cross-domain sentiment classifiers aim to predict the polarity (i.e. sentiment orientation) of target text documents, by reusing a knowledge model learnt from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Deep neural networks have recently reached the state-of-the-art in many NLP tasks, including in-domain sentiment classification, but few of them involve transfer learning and cross-domain sentiment solutions. This paper moves forward the investigation started in a previous work [1], where an unsupervised deep approach for text mining, called Paragraph Vector (PV), achieved cross-domain accuracy equivalent to a method based on Markov Chain (MC), developed ad hoc for cross-domain sentiment classification. In this work, Gated Recurrent Unit (GRU) is included into the previous investigation, showing that memory units are beneficial for cross-domain when enough training data are available. Moreover, the knowledge models learnt from the source domain are tuned on small samples of target instances to foster transfer learning. PV is almost unaffected by fine-tuning, because it is already able to capture word semantics without supervision. On the other hand, fine-tuning boosts the cross-domain performance of GRU. The smaller is the training set used, the greater is the improvement of accuracy.
Spatio-temporal Video Autoencoder for Human Action Recognition

The demand for automatic systems for action recognition has increased significantly due to the development of surveillance cameras with high sampling rates, low cost, small size and high resolution. These systems can effectively support human operators to detect events of interest in video sequences, reducing failures and improving recognition results. In this work, we develop and analyze a method to learn two-dimensional (2D) representations from videos through an autoencoder framework. A multi-stream network is used to incorporate spatial and temporal information for action recognition purposes. Experiments conducted on the challenging UCF101 and HMDB51 data sets indicate that our representation is capable of achieving competitive accuracy rates compared to the literature approaches.
DETERMINATION OF RELEVANCE OF VISUAL OBJECT IMAGES BY APPLICATION OF STATISTICAL ANALYSIS OF REGARDING FRAGMENT REPRESENTATION OF THEIR DESCRIPTIONS

The problem of visual object recognition in the space features of descriptors of special image points is solved with the application of statistical distributions for a system of fragments of image description. The efficiency of application of the distribution variants for single- and multi-bit sizes of the fragments of binary descriptors in the calculation of the level of relevance of descriptions in the process of object recognition is analyzed. Using software simulation, an experimental evaluation of the computation time of the relevance value in the constructed feature space is carried out compared to the traditional voting approach of the set of descriptors. The efficiency of the proposed approach to data representation in terms of a significant increase in the performance rate with ensuring a sufficient level of recognition quality is confirmed.
Quantitative Affine Feature Detector Comparison based on Real-World Images Taken by a Quadcopter

Feature detectors are frequently used in computer vision. Recently, detectors which can extract the affine transformation between the features have become popular. With affine transformations, it is possible to estimate the properties of the camera motion and the 3D scene from significantly fewer feature correspondences. This paper quantitatively compares the affine feature detectors on real-world images captured by a quadcopter. The ground truth (GT) data are calculated from the constrained motion of the cameras. Accurate and very realistic testing data are generated for both the feature locations and the corresponding affine transformations. Based on the generated GT data, many popular affine feature detectors are quantitatively compared.
Nuclear Norm Regularized Structural Orthogonal Procrustes Regression for Face Hallucination with Pose

In real applications, the observed low-resolution (LR) face images usually have pose variations. Conventional learning based methods ignore these variations, thus the learned representations are not beneficial for the following reconstruction. In this paper, we propose a nuclear norm regularized structural orthogonal Procrustes regression (N2SOPR) method to learn pose-robust feature representations for efficient face hallucination. The orthogonal Procrustes regression (OPR) seeks an optimal transformation between two images to correct the pose from one to the other. Additionally, our N2SOPR uses the nuclear norm constraint on the error term to keep image’s structural information. A low-rank constraint on the representation coefficients is imposed to adaptively select the training samples that belong to the same subspace as the inputs. Moreover, a locality constraint is also enforced to preserve the locality and the sparsity simultaneously. Experimental results on standard face hallucination databases indicate that our proposed method can produce more reasonable near frontal face images for recognition purpose.
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations

Graph classification is a significant problem in many scientific domains. It addresses tasks such as the classification of proteins and chemical compounds into categories according to their functions, or chemical and structural properties. In a supervised setting, this problem can be framed as learning the structure, features and relationships between features within a set of labelled graphs and being able to correctly predict the labels or categories of unseen graphs. A significant difficulty in this task arises when attempting to apply established classification algorithms due to the requirement for fixed size matrix or tensor representations of the graphs which may vary greatly in their numbers of nodes and edges. Building on prior work combining explicit tensor representations with a standard image-based classifier, we propose a model to perform graph classification by extracting fixed size tensorial information from each graph in a given set, and using a Capsule Network to perform classification. The graphs we consider here are undirected and with categorical features on the nodes. Using standard benchmarking chemical and protein datasets, we demonstrate that our graph Capsule Network classification model using an explicit tensorial representation of the graphs is competitive with current state of the art graph kernels and graph neural network models despite only limited hyper-parameter searching.
Using Behavior Data to Predict User Success in Ontology Class Mapping - An Application of Machine Learning in Interaction Analysis

Ontology visualization has played an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability evaluations of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavior data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates how user behavior data may offer real time indications as to how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may inform the adaptation of the given visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis and demonstrate such predictions can be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance.
Performance Evaluation of Real-time and Scale-invariant LoG Operators for Text Detection

This paper presents a state-of-the-art and a performance evaluation of real-time text detection methods, having particular focus on the family of Laplacian of Gaussian (LoG) operators with scale-invariance. The computational complexity of operators is discussed and an adaptation to text detection is obtained through the scale-space representation. In addition, a groundtruthing process and a characterization protocol are proposed, performance evaluation is driven with repeatability and processing time. The evaluation highlights a near-exact approximation with real-time operators at one to two orders of magnitude of execution time. The real-time operators are adapted to recent camera devices to process high resolution images. Perspectives are provided for operator robustness, optimization and characterization of the detection strategy.
Wave pattern simulation around the breakwater configuration in Bantan coast, Bengkalis

Bantan Coast in Bengkalis Island that suffered worst abrasion due to wave attack. Protection of abrasion area against the wave can be done by proper lay-out of breakwater with certain configuration. To get the optimal configuration of breakwater, wave pattern around the breakwater were analyzed quantitatively. It was conducted to obtain the optimum condition of wave height reduction produced. Evaluation of breakwater lay-out effect to wave pattern can be done by numerical model approached. Wave pattern simulation that occurred due to the breakwater was done by Surface water Modelling System (SMS) - CGWAVE software module. This module is a 2-D depth average numerical model using finite element model. To get accurate simulation, a deviation of the numerical result and the measurement result at several locations was used as the indicator parameter in the calibration process. The results showed that from the result of calibration process was used 1.3 meters wave height and 5.4 seconds wave period. Three breakwaters 20 meters slit configuration produced a maximum value of wave height reduction and wave velocity reduction (in comparison wave simulation without breakwater). This configuration produces 60% height reduction and 14% velocity reduction.
Optimal bounds on state transfer under quantum channels with application to spin system engineering

Modern applications of quantum control in quantum information science and technology require the precise characterization of quantum states and quantum channels. In particular, high-performance quantum state engineering often demands that quantum states are transferred with optimal efficiency via realizable controlled evolution, the latter often modeled by quantum channels. When an appropriate quantum control model for an interested system is constructed, the exploration of optimal bounds on state transfer for the underlying quantum channel is then an important task. In this work, we analyze the state transfer efficiency problem for different classes of quantum channels, including unitary, mixed unitary, and Markovian. We then apply the theory to nuclear magnetic resonance (NMR) experiments. We show that two most commonly used control techniques in NMR, namely gradient field control and phase cycling, can be described by mixed unitary channels. Then we show that employing mixed unitary channels does not extend the unitarily accessible region of states. Also, we present a strategy of optimal experiment design, which incorporates coherent radio-frequency field control, gradient field control, and phase cycling, aiming at maximizing state transfer efficiency and meanwhile minimizing the number of experiments required. Finally, we perform pseudopure state preparation experiments on two- and three-spin systems, in order to test the bound theory and to demonstrate the usefulness of nonunitary control means.
FinalGen revisited: new discoveries

Romero’s FINALGEN of 2012 creates designer endgame tables for specific chess positions that feature no more than one non-pawn piece per side. Larger hard discs and faster solid-state discs have extended the reach of this software and encouraged its greater use. Some new discoveries illustrate here what is now feasible and how FINALGEN may be combined with other tools to reach definitive and likely truths.