Reviewerly/exHarmony huggingface dataset:
Equivalent Continuous Formulation of General Hashing Problem

Hashing-based approximate nearest neighbors search has attracted broad research interest, due to its low computational cost and fast retrieval speed. The hashing technique maps the data points into binary codes and, meanwhile, preserves the similarity in the original space. Generally, we need to solve a discrete optimization problem to learn the binary codes and hash functions, which is NP-hard. In the literature, most hashing methods choose to solve a relaxed problem by discarding the discrete constraints. However, such a relaxation scheme will cause large quantization error, which makes the learned binary codes less effective. In this paper, we present an equivalent continuous formulation of the discrete hashing problem. Specifically, we show that the discrete hashing problem can be transformed into a continuous optimization problem without any relaxations, while the transformed continuous optimization problem has the same optimal solutions and the same optimal value as the original discrete hashing problem. After transformation, the continuous optimization methods can be applied. We devise the algorithms based on the idea of DC (difference of convex functions) programming to solve this problem. The proposed continuous hashing scheme can be easily applied to the existing hashing models, including both supervised and unsupervised hashing. We evaluate the proposed method on several benchmarks and the results show the superiority of the proposed method compared with the state-of-the-art hashing methods.
Blockchain Economic Theory: Digital Asset Contracting Reduces Debt and Risk

Disparate aspects of the emerging Blockchain Economics paradigm have been discussed, particularly cryptotokens and Initial Coin Offerings (ICOs), however, a comprehensive picture of the greater economic transformation unfolding with blockchain technology has not yet been articulated. This chapter proposes a Blockchain Economic Theory of Digital Asset Contracting as an explanatory model. The central argument is that blockchain-registered digital assets can be transacted instantaneously and pledged in new ways. This advance is leading to new modes of contracting (smart contracts) and new forms of money (cryptotokens), which in turn facilitate new structures of financial interaction. Distributed ledgers and blockchain-based structures might be applied to structural economic problems such as debt, systemic risk, technological job outsourcing, entitlements overhang, healthcare cost-outcome disconnects, and financial inclusion. A key innovation is Payment Channels, which enable the use of capital on a net rather than a gross basis, which might eventually lead to a restructuring of debt burdens.
Recommending Mobile Services with Trustworthy QoS and Dynamic User Preferences via FAHP and Ordinal Utility Function

Due to ubiquitous Internet connectivity, widely available cloud services, and popular mobile devices, mobile networks have become service delivery and consumption platforms for many industries worldwide. To recommend optimal mobile Web services with trustworthy Quality-of-Service (QoS) and dynamic user preferences, this paper proposes a novel service recommendation model based on Fuzzy Analytic Hierarchy Process (FAHP) and ordinal utility function. First, a Multi-QoS vector is defined, and to take into account the trustworthiness of QoS, the fidelity of QoS is modeled as one component of the Multi-QoS vector. Then, a fuzzy hierarchy including dual attributes of QoS (objective attribute and subjective evaluation) is established to fully consider the objective and subjective attributes' impact on optimal service recommendation. Furthermore, a FAHP-based weighting mode is developed, in which the resolution ratio of weight can be adjusted dynamically by decision-maker according to user preferences. Finally, the optimal service is obtained through the calculation of ordinal utility function of candidate service. Experimental results and method comparison illuminate the feasibility and efficiency of the proposed model.
Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Convolutional Neural Networks (CNN) are the leading models for human body landmark detection from RGB vision data. However, as such models require high computational load, an alternative is to rely on depth images which, due to their more simple nature, can allow the use of less complex CNNs and hence can lead to a faster detector. As learning CNNs from scratch requires large amounts of labeled data, which are not always available or expensive to obtain, we propose to rely on simulations and synthetic examples to build a large training dataset with precise labels. Nevertheless, the final performance on real data will suffer from the mismatch between the training and test data, also called domain shift between the source and target distributions. Thus in this paper, our main contribution is to investigate the use of unsupervised domain adaptation techniques to fill the gap in performance introduced by these distribution differences. The challenge lies in the important noise differences (not only gaussian noise, but many missing values around body limbs) between synthetic and real data, as well as the fact that we address a regression task rather than a classification one. In addition, we introduce a new public dataset of synthetically generated depth images to cover the cases of multi-person pose estimation. Our experiments show that domain adaptation provides some improvement, but that further network fine-tuning with real annotated data is worth including to supervise the adaptation process.
Learning Event Representations by Encoding the Temporal Context

This work aims at learning image representations suitable for event segmentation, a largely unexplored problem in the computer vision literature. The proposed approach is a self-supervised neural network that captures patterns of temporal overlap by learning to predict the feature vector of neighbor frames, given the one of the current frame. The model is inspired to recent experimental findings in neuroscience, showing that stimuli associated with similar temporal contexts are grouped together in the representational space. Experiments performed on image sequences captured at regular intervals have shown that a representation able to encode the temporal context provides very promising results on the task of temporal segmentation.
A New Transformation of 3D Models Using Chaotic Encryption Based on Arnold Cat Map

In the emerging Virtual Reality era, 3D multimedia contents are popularized as images and videos. Encryption is a methodology that enhances the security by converting the original content into unintelligible content. The Arnold transform or Arnold cat map is a commonly used chaos-based encryption system that encrypts by shuffling the data. 3D models include vertices, faces and textures. An efficient secure symmetric chaotic cryptosystem is proposed for 3D mesh graphical models using 3D Arnold cat map. Arnold cat map is performed to encrypt the 3D mesh model using shuffling and substitution. The cryptosystem is proposed for vertices and faces separately and are composited together to form the final encrypted model. Each round introduces a good permutation and substitution through the confusion and diffusion generated by the 3D Arnold map. The chaotic function delivers more security for the 3D models through the shuffling and substitution. Simulation results show that the proposed scheme encrypts and decrypts the 3D mesh models and resists various attacks.
Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

The objective of the chapter is to show current trends in robot navigation systems related to indoor environments. Navigation systems depend on the level of abstraction of the environment representation. The three main techniques for representing the environment will be described: geometric, topological, and semantic. The geometric representation of the environment is closer to the sensor and actuator world and it is the best one to perform local navigation. Topological representation of the environment uses graphs to model the environment and it is used in large navigation tasks. The semantic representation is the most abstract representation model and adds concepts such as utilities or meanings of the environment elements in the map representation. In addition, regardless of the representation used for navigation, perception plays a significant role in terms of understanding and moving through the environment.
Empirical evaluation of compounds indexing for Turkish texts

In this article, we describe an empirical evaluation of compounds indexing for Turkish texts. We dive beyond the keyword indexing to propose a framework for Turkish compounds extraction and indexing. We identify twelve Turkish compounds pattern types that we classify in six categories. To extract Turkish compounds, we rely on a light natural language processing approach based on syntactic pattern recognition. We compare different compounds indexing strategies. We also investigate the effectiveness of using one compounds type and the effectiveness of combining different compound types. We conduct experiments over the Milliyet test dataset. The results of our experiments show that using compounds as index terms can improve retrieval performances. However, not all the compound types have a positive impact on the retrieval process.
Entropy Isolation Forest Based on Dimension Entropy for Anomaly Detection

Anomaly detection, as an important basic research task in the field of data mining, has been concerned by both industry and academia. Among many anomaly detection methods, iForest (isolation Forest) has low time complexity and good detection effect. It has better adaptability in the face of high-capacity and high-dimensional data. However, iForest is not suitable for the special high-dimensional data, is not stable enough, and is not so robust to the noise features. In view of these problems, this paper proposes an improved anomaly detection method E-iForest (entropy-isolation forest) based on dimension entropy. By introducing the dimension entropy as the basis for selecting the isolation attribute and the isolation point during the training process, the method uses three isolation strategies and adjust the path length calculation. The experiments show that the E-iForest has better detection effect, has better speed in high-capacity datasets, is more stable than iForest and is more robust to the noise features.
Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

“Estimation at completion” (EAC) is a manager's projection of a project's total cost at its completion. It is an important tool for monitoring a project's performance and risk. Executives usually make high-level decisions on a project, but they may have gaps in the technical knowledge which may cause errors in their decisions. In this current study, the authors implemented new coupled intelligence models, namely global harmony search (GHS) and brute force (BF) integrated with extreme learning machine (ELM) for modeling the project construction estimation at completion. GHS and BF were used to abstract the substantial influential attributes toward the EAC dependent variable, whereas the effectiveness of ELM as a novel predictive model for the investigated application was demonstrated. As a benchmark model, a classical artificial neural network (ANN) was developed to validate the new ELM model in terms of the prediction accuracy. The predictive models were applied using historical information related to construction projects gathered from the United Arab Emirates (UAE). The study investigated the application of the proposed coupled model in determining the EAC and calculated the tendency of a change in the forecast model monitor. The main goal of the investigated model was to produce a reliable trend of EAC estimates which can aid project managers in improving the effectiveness of project costs control. The results demonstrated a noticeable implementation of the GHS-ELM and BF-ELM over the classical and hybridized ANN models.
Single Space Based Sensor Bias Estimation Using a Single Target of Opportunity

Sensor registration refers to the estimation/compensation of systematic (bias) errors, in contrast to the random errors from sensor noise. Various methods have been proposed for bias estimation of multiple optical sensors using common targets of opportunity. However, the proposed solutions required the use of multiple (two or more) optical sensors and the need to solve the data association problem arising from the fusion of measurements from multiple sensors. In order to remove these constraints, we provide in this paper a new methodology using a single exoatmospheric target of opportunity seen in a single satellite borne sensor's field of view to estimate the sensor's biases simultaneously with the state of the target. The satellite-based sensor sees the target from a changing direction as a function of its position, allowing the target in this nonlinear tracking system to be observable. The sensor provides the line of sight (LOS) measurements of azimuth and elevation to the target. Sensor pointing calibration is the key precondition for accurate tracking of a target in a space-based system. Statistical tests on the results of simulations, and the evaluation of the Cramér-Rao lower bound (CRLB) on the covariance of the bias estimates show that this method is statistically efficient.
Using continuous Hopfield neural network for solving a new optimization architecture model of probabilistic self organizing map

Probabilistic models have proven their strength to model many natural phenomena as close as possible to reality, in particular, the probabilistic self organizing map (PRSOM) that belongs to the unsupervised learning models. It allows to provide an estimation of the probability density function through the likelihood maximization. This latter function depends on several parameters given by the model architecture. In this context, the aim of this paper is to deal with the architecture choice problem that consists in determining the optimal number of components needed for a better performance. In this paper, we propose a new optimization model that describes the problem above. We present the architecture of PRSOM in a mathematical system of a non linear objective function with mixed variables under linear and quadratic constraints. Due to the complexity of the resolution, we suggest the continuous Hopfield neural network (CHN) that we support by a deep stability analysis. Performance of the proposed model is demonstrated through the dataset clustering.
Causation: A functional perspective

Causation is shrouded in mystery. Notwithstanding much work on causation in a number of different domains, it remains a challenge to build an adequate theory of causation for applied ontology. This paper offers a functional perspective on causation, thereby enabling domain experts to have a fairly expressive representation of various causal phenomena. As for its theoretical basis, causation is modeled upon the device ontology view of reality, assuming an intimate connection between causation and context; and a conceptual affinity between causation and function is discussed in detail, based on the recently developed theory of systemic function and the idea of ‘causally efficacious occurrents’, thereby introducing achieves and prevents as two basic causal relations. In addition, the idea of a state-centered approach to causation results in the definition of allows and disallows causal relations, in order to accommodate a wide range of examples based on the state-mediated type of causation. All this leads to three kinds of causal relata (events, processes, and states) and a fourfold distinction of causal relations (achieves, prevents, allows, and disallows). This paper also provides a preliminary formalization of these four causal relations and takes the first step towards a full-fledged functional theory of causation.
Decompositions of Edge-Colored Digraphs: A New Technique in the Construction of Constant-Weight Codes and Related Families

We demonstrate that certain Johnson-type bounds are asymptotically exact for a variety of classes of codes, namely, constant-composition codes, nonbinary constant-weight codes, group divisible codes, and multiply constant-weight codes.We achieve this via an application of the theory of decomposition of edge-colored digraphs. Key words. edge-colored digraphs
Efficient dummy generation for considering obstacles and protecting user location

Summary To receive location‐based services (LBS), users must disclose their locations and queries to the LBS server, which can expose the user's identity, location, and other information. Recently, techniques for protecting user privacy using dummies have been researched. However, many factors, such as the distance between the obstacles and the dummies, must be considered in order to create dummies. Therefore, this study proposes an efficient dummy creation technique to improve user privacy protection. Experimental results show that the proposed technique improves on other recent techniques.
QDF: A face database with varying quality

Face Recognition is one of the well-researched areas of biometrics. Although many researchers have shown considerable interest, the problems still persist because of unpredictable environmental factors affecting the acquisition of real-life face images. One of the major factors that causes poor recognition performance of the most face recognition algorithms is due to the unavailability of a proper training dataset which reflects real-life scenarios. In this paper, we propose a face dataset, of about 100 subjects, with varying degree of quality in terms of distance from the camera, ambient illumination, pose variations and natural occlusions. This database can be used to train systems with real-life face images. The face quality of this dataset has been quantified with popular Face Quality Assessment (FQA) algorithms. We have also tested this database with standard face recognition, super-resolution image processing and fiducial point estimation algorithms. Database is available to research community through https://sites.google.com/view/quality-based-distance-face-da/.
Deep rectified system for high-speed tracking in images

Deep Rectified System for High-speed Tracking in Images (DRSHTI) is a unified open-source web portal developed for object detection in images. It aims to be a platform for the end user, where he/she can perform object detection on images without going through the hassles of debugging countless lin es of code or setting up the right environment to perform computer vision tasks. By making the platform open-source, this work targets beginners in computer vision to form a basic understanding of object detection as an artificial intelligence task. This is made possible by releasing source codes, tools and tutorials on its usage via GitHub. This open-source portal offers two detection pipelines based on Faster-RCNN – a model to detect ground vehicles in aerial images and a model to detect everyday objects in 37 different classes in normal images. The former model is trained on VEDAI dataset, which gave 98.6% accuracy during testing and is offered as proof-of-concept that showcases the models ability to perform small target detections, but the latter model is trained on the PASCAL VOC dataset. Making the project open-source also aims at bringing in more development and tweaking to the existing vehicle detection module. The web portal can be accessed via https://drshti.github.io, where user can upload images and get annotations on objects present in it. Tutorials and source codes can be found at https://github.com/vyzboy92/Object-Detection-Net.
Completeness and soundness guarantees for conjunctive SPARQL queries over RDF data sources with completeness statements1

RDF generally follows the open-world assumption: information is incomplete by default. Consequently, SPARQL queries cannot retrieve with certainty complete answers, and even worse, when they involve negation, it is unclear whether they produce sound answers. Nevertheless, there is hope to lift this limitation. On many specific topics (e.g., children of Trump, Apollo 11 crew, EU founders), RDF data sources contain complete information, a fact that can be made explicit through completeness statements. In this work, we leverage completeness statements over RDF data sources to provide guarantees of completeness and soundness for conjunctive SPARQL queries. We develop a technique to check whether query completeness can be guaranteed by taking into account also the specifics of the queried graph, and analyze the complexity of such checking. For queries with negation, we approach the problem of query soundness checking, and distinguish between answer soundness (i.e., is an answer of a query sound?) and pattern soundness (i.e., is a query as a whole sound?). We provide a formalization and characterize the soundness problem via a reduction to the completeness problem. We further develop heuristic techniques for completeness checking, and conduct experimental evaluations based on Wikidata, a prominent, real-world knowledge base, to demonstrate the feasibility of our approach.
Smart Speed Imaging in Digital Image Correlation: Application to Seismotectonic Scale Modeling

Analogue models of earthquakes and seismic cycles are characterized by strong variations in strain rate: from slow interseismic loading to fast coseismic release of elastic energy. Deformation rates vary accordingly from micrometer per second (e.g. plate tectonic motion) to meter per second (e.g. rupture propagation). Deformation values are very small over one seismic cycle, in the order of a few tens of micrometer, because of the scaled nature of such models. This cross-scale behaviour poses a major challenge to effectively monitor the experiments by means of digital image correlation techniques, i.e. at high resolution ($>$100 Hz) during the coseismic period but without dramatically oversampling the interseismic period. We developed a smart speed imaging tool which allows on-the-fly scaling of imaging frequency with strain rate, based on an external trigger signal and a buffer. The external trigger signal comes from a force sensor that independently detects stress drops associated with analogue earthquakes which triggers storage of a short high frequency image sequence from the buffer. After the event has passed, the system returns to a low speed mode in which image data is downsampled until the next event trigger. Here we introduce the concept of smart speed imaging and document the necessary hard- and software. We test the algorithms in generic and real applications. A new analogue earthquake setup based on a modification of the Schulze ring-shear tester is used to verify the technique and discuss alternative trigger systems.