Reviewerly/exHarmony huggingface dataset:
Sample-level sound synthesis with recurrent neural networks and conceptors

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined.
An improved probability hypothesis density filter for multi-target tracking

For the problem that the standard probability hypothesis density is unable to correctly estimate the states of targets and their number when tracking multiple targets in possible missed detection environments, an improved probability hypothesis density filter based multi-target tracking algorithm is proposed under the linear Gaussian conditions. Two assisted parameters, namely label and probability of existence, are introduced to expand the standard target state in the proposed algorithm which includes three robust schemes compared with the Gaussian mixture probability hypothesis density filter. Firstly, the extended component set of target states representing the target intensity can be correctly updated in the proposed target intensity update scheme. Secondly, by optimizing the component set that approximates the target posterior intensity, the excess and invalid components are effectively reduced in the improved component fusion scheme. Lastly, a new target state extraction scheme is able to accurately estimate the states of targets by comprehensively utilizing both the weight and existent probability of the target. Simulation results show that the proposed algorithm not only provides relatively accurate multi-target estimates, but also has a relatively low computational burden.
Depth prediction from a single image based on non-parametric learning in the gradient domain

Plausible depth prediction from a single monocular image is a challenging task in computer vision. This paper presents a new non-parametric learning-based depth recovery framework in the gradient domain. Specifically, the proposed method leverage a global matching-based depth gradient transfer process to generate meaningful depth gradients from training images. Then, a confidence measure-based depth gradient fusion scheme is designed, which allows us to measure the individual contribution of each pixel in the warped depth gradient maps. In addition, an edge-aware depth gradient refinement strategy is proposed to mitigate the depth gradient outliers and generate accurate depth gradient values. In the end, reliable depth maps can be reconstructed based on these refined depth gradients by using the Poisson solver. Qualitative and quantitative evaluation results on the outdoor and indoor datasets demonstrate that the proposed depth estimation algorithm outperforms other data-driven methods, and is very effective for inferring convincing depth maps.
Haze removal algorithm based on single-images with chromatic properties

Dense fog conditions formed by particles and water droplets in the atmosphere impact the road segmentation accuracy of Advanced Driver Assistance Systems (ADAS) by degrading the contrast and color fidelity of images. Existing road segmentation methods are not suitable for dense haze conditions; darkness, under-estimation, and over-enhancement problems occur after dehazing. This paper proposes a single-image haze removal technique based on hazy state detection and simple priors. Hazy state detection serves to identify the fog in an image by the linear relationship between brightness and saturation in HSV color space. Simple priors both in the background and hazy layers are based on a dichromatic reflection model (DRM) to accommodate different concentrations of fog. We find that according to color consistency and physical properties, fog and highlight appear to share the same chromatic properties. Based on this observation, the DRM can approximate the fog map. The maximum a posteriori formulation can also be utilized to robustly refine the fog map and remove black noise. We employ the fog map to compute atmospheric light and predict transmission rate in order to obtain haze-free images. Our results show that this method outperforms existing methods in regards to both efficiency and dehazing ability.
GM-PHD Filter for Searching and Tracking an Unknown Number of Targets With a Mobile Sensor With Limited FOV

We study the problem of searching for and tracking a collection of moving targets using a robot with a limited field-of-view (FOV) sensor. The actual number of targets present in the environment is not known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> . We propose a search and tracking framework based on the concept of Bayesian random finite sets (RFSs). Specifically, we generalize the Gaussian mixture probability hypothesis density (GM-PHD) filter which was previously applied for tracking problems to allow for simultaneous search and tracking with a limited FOV sensor. The proposed framework can extract individual target tracks as well as estimate the number and the spatial density of targets. We also show how to use the Gaussian process (GP) regression to extract and predict unknown target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations and a real data collected from an aerial robot. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by search-and-rescue operations where a robot with limited field-of-view (FOV) is used to search and track lost targets. This article presents an estimation and planning framework to estimate the position of targets and track them over time. The key feature of the proposed algorithm is that it can deal with an unknown and varying number of targets. The framework can also deal with an unknown motion model for targets which itself can be complex. The algorithm is shown to be robust to a poor initialization and can handle an initial belief which overestimates or underestimates the actual number of targets. The proposed scheme includes various user-defined parameters. It is recommended to tune these parameters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> using simulations for a better performance. Incorporating a multirobot approach into the proposed algorithm and finding a better planning strategy that minimizes the time are potential future works.
Graph-based supervised discrete image hashing

Learning based hashing have been widely adopted to the approximate nearest neighbour search in large-scale image retrieval. However, how to preserve the semantic information in hashing embedding is still a challenge problem. Moreover, most of the existing methods employ the relaxation strategy to solve discrete constraint problem, which may accumulate binary quantization error as the coding length increases. In this paper, we propose a graph-based supervised hashing framework to address these problems, where the semantic information is preserved from two aspects. On one hand, we employ a supervised learning model to keep the semantic consistency. On the other hand, the intrinsic manifold structure is captured by a graph-based model. In addition, to reduce the quantization error, we adopt a discrete optimization strategy to replace the relaxation one. Experiments conducted on three benchmark datasets to demonstrate the effectiveness of the proposed method.
Visual saliency based structural contrast quality index

Studies on human visual systems (HVS) recommend that assessing the structural information change can provide a good approximation of the perceived image quality. Moreover, psychovisual studies have suggested that the HVS is more sensitive to the extraction of visual information in the salient regions rather than minor regions. Based on the above observations, this Letter proposes a novel full-reference image quality assessment metric called the visual saliency based structural contrast-quality index by the modification and adjustment of an image signature to incorporate into the structural contrast index (SCI) features, which can well depict the visual quality by considering the local and global visual quality perceptions for different types of structural distortions on the salient regions. The proposed method was tested on four world recognised databases and from the experimental result, it became apparent that the authors proposed method strongly correlates the subjective mean opinion scores and performs better than the existing state-of-the-art image quality assessment methods.
Distributed classification based on distances between probability distributions in feature space

We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning algorithm based on combining with different weights the outputs of classifiers trained at each node is proposed. The weights depend on the distributional distance between each node and the test set in the feature space. Two different weighting approaches are introduced, which are referred to as per-Node Weighting (pNW) and per-Instance Weighting (pIW). While pNW assigns the same weight to all test instances at each node, pIW allows distinct weights for test instances differently represented at the node. By construction, our approach is particularly useful to deal with unbalanced nodes. Our methods require no communication between nodes, allowing for data privacy, independence of the kind of trained classifier at each node and maximum training speedup. In fact, our methods do not require retraining of the node’s classifiers if available. Although a range of different combination rules are considered to ensemble the single classifiers, theoretical support for the optimality of using the sum rule is provided. Our experiments illustrate all of these properties and show that pIW produces the highest classification accuracies compared with pNW and the standard unweighted approaches.
Rosser Provability and Normal Modal Logics

In this paper, we investigate Rosser provability predicates whose provability logics are normal modal logics. First, we prove that there exists a Rosser provability predicate whose provability logic is exactly the normal modal logic $$\mathsf{KD}$$. Secondly, we introduce a new normal modal logic $$\mathsf{KDR}$$ which is a proper extension of $$\mathsf{KD}$$, and prove that there exists a Rosser provability predicate whose provability logic includes $$\mathsf{KDR}$$.
Gallery based k-reciprocal-like re-ranking for heavy cross-camera discrepancy in person re-identification

Recently more and more attention is paid to the re-ranking step for person re-identification in computer vision community, especially those fully automatic, unsupervised solutions. Among them, k-reciprocal re-ranking (KR) method achieved big success. However, when the heavy cross-camera discrepancy exists between query and gallery datasets, it may degrade the performance. To alleviate the heavy cross-camera discrepancy between query and gallery datasets, we propose a gallery based k-reciprocal-like re-ranking (GKR) method. GKR adopts graph matching to construct the matching correspondence between query and gallery datasets. Then the proposed k-reciprocal-like neighbors are computed only on gallery dataset instead of on the union of query and gallery datasets like KR does. Moreover, to perform unsupervised video-based person re-identification, we incorporate our proposed GKR method into the dynamic label graph matching (DGM) framework, which can improve the cross-camera labels estimating in training step but also can improve the re-identification accuracy by re-ranking in testing step. Experimental results by supervised and unsupervised solutions on some benchmarks, demonstrate the effectiveness of our GKR method to handle the cross-camera discrepancy problem for person re-identification.
SmartExchange: Decentralised Trustless Cryptocurrency Exchange

Trading cryptocurrency on current digital exchange platforms is a trust-based process, where the parties involved in the exchange have to fully trust the service provider. As it has been proven several times, this could lead to funds being stolen, either due to malicious service providers that simply disappear or due to hacks that these platforms might suffer. In this work, we propose and develop a decentralised exchange solution based on smart contracts running on the Ethereum network that is open, verifiable, and does not require trust. The platform enables two parties to trade different currencies, limited to Ethereum and Bitcoin in the current status of the system. A smart contract, deployed on the Ethereum blockchain, functions as an escrow, which holds a user’s funds until a verified transaction has been made by the other party. To make the smart contract able to detect a Bitcoin transfer, we implement our solution by utilising an oracle. We define the system architecture and implement a working platform, which we test in a model scenario, successfully exchanging Bitcoin and Ether on the blockchain test networks. We conclude the paper identifying possible challenges and threats to such a system.
Elite Loss for scene text detection

Many scene text detection approaches generate foreground segmentation maps to detect the text instances. In these methods, usually all the pixels within the bounding box regions of the text are equally treated as foreground during the training process. However, different from the general object segmentation problem, we argue that not all the pixels across the text bounding box region contribute equally for locating the text instance. Specifically, some in-box not-on-stroke pixels even degrade the detection performance. Moreover, for the segmentation based methods with a regression step applied to predict the corresponding bounding box on each pixel, not all the pixels need to be fully trained to predict foreground texts. Therefore, in this paper, we propose Elite Loss, which is intended to down-weight the contributions of the in-box not-on-stoke pixels while paying more attention to the on-stoke pixels. Furthermore, we design a segmentation-based method to validate the effectiveness of the proposed Elite Loss. Extensive experiments demonstrate that our methods achieve the state-of-the-art results on all three challenging datasets, with the F-score of 0.855 on ICDAR2015, 0.425 on COCO-Text, and 0.819 on MSRA-TD500.
Towards Reducing the Gap between Cryptography and Its Usage

ICT development progresses, and many cryptographic algorithms are used. The most of cryptographic algorithms require assumptions to guarantee their security, but it is sometimes not clearly written. This causes many problems. This paper shows previous cases, and suggests to concede cryptographers and system developer each other from an industrial cryptographers viewpoint.
A Human-Computer Interaction System for Agricultural Tools Museum Based on Virtual Reality Technology

Traditional museums and most digital museums use window display to exhibit their collections. However, the agricultural tools are distinctive for their use value and wisdom contained. Therefore, this paper first proposes a method of virtual interactive display for agricultural tools based on virtual reality technology, which combines static display and dynamic use of agricultural tools vividly showing the agricultural tools. To address the problems of rigid interaction and terrible experience in the process of human-computer interaction, four human-computer interaction technologies are proposed to design and construct a more humanized system including intelligent scenes switching technology, multichannel introduction technology, interactive virtual roaming technology, and task-based interactive technology. The evaluation results demonstrate that the system proposed achieves good performance in fluency, instructiveness, amusement, and practicability. This human-computer interaction system can not only show the wisdom of Chinese traditional agricultural tools to the experiencer all over the world but also put forward a new method of digital museum design.
Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer

Background: Breast cancer is one of the most common forms of cancers among women and the leading cause of death among them. Countries like United States, England and Canada have reported a high number of breast cancer patients every year and this number is continuously increasing due to detection at later stages. Hence, it is very important to create awareness among women and develop such algorithms which help to detect malignant cancer. Several research studies have been conducted to analyze the breast cancer data. Objective: This paper presents an effective method in predicting breast cancer and its stage and will also analyze the performance of different supervised learning algorithms such as Random Classifier, Chi2 Square test used in order to predict. The paper focuses on the three important aspects such as the feature selection, the corresponding data visualisation and finally making a prediction call on different machine learning models. Methods: The dataset used for this work is breast cancer Wisconsin data taken from UCI library. The dataset has been used to show the different 32 features which are all important and how it can be achieved using data visualisation. Secondly, after the feature selection, different machine learning models have been applied. Conclusion: The machine learning models involved are namely Support Vector Machine (SVM), KNearest Neighbour (KNN), Random Forest, Principal Component Analysis (PCA), Neural Network using Perceptron (NNP). This has been done to check which type of model is better under what conditions. At different stages several charts have been plotted and eliminated based on relative comparison. Results have shown that Random Tree classifier along with Chi2 Square proves to be an efficient one.
Challenge of Tacit Knowledge in Acquiring Information in Cognitive Mimetics

Intelligent technologies are rising. This is why methods for designing them are important. One approach is to study how people process information in carrying out intelligence demanding tasks and use this information in designing new technology solutions. This approach can be called cognitive mimetics. A problem in mimetics is to explicate tacit or subconscious knowledge. Here, we study a combination of thinking aloud in ship simulator driving and focus group commenting the solutions of subjects. On the ground of these early experiments, a multiple method combination seems to be the best way forward to solve problems of tacit or subconscious knowledge.
Clustering Earthquake Data

Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.
Some Improved Constructions for Nonbinary Quantum BCH Codes

Maximal designed distances for nonbinary narrow-sense quantum Bose-Chaudhuri-Hocquenghem (BCH) codes of length $n=\frac{q^4-1}{r}$ and new constructions for them are given, where q is an odd prime power. These constructions are capable of designing quantum BCH codes with new parameters. Furthermore, some codes obtained here have better parameters than those constructed by other known constructions.
Multimodal activity recognition with local block CNN and attention-based spatial weighted CNN

Deep learning based human activity recognition approach combines spatial and temporal information to complete the recognition task. The temporal information is extracted by optical flow, which is always compensated by the warping method in order to achieve better performance. However, these methods usually take the global feature as the starting point, only consider global information of video frames, and ignore local information that reflects the changes of human behavior, causing the algorithm to be sensitive to the external environment such as occlusion, illumination change. In view of the above problems, this paper fuses the local spatial features of video frames, global spatial features and temporal features to recognize different actions, and further extracts the visual attention weight to make constraint on the global spatial features. Experiments show that the algorithm proposed in this paper has better accuracy compared with the existing methods.
Ontology-Based Opinion Mining

E-commerce business is very popular as a large amount of data is available on the internet in the form of unstructured data. To find new market trends and insight, it is very important for an organization to track the customers' opinions/reviews on a regular basis. Reviews available on the internet are very scattered and heterogeneous (i.e., structured as well as unstructured form of data). A good decision is always based on the quality of information within a specified period of time. Ontology is an explicit detailed study of concepts. The word ontology is borrowed from philosophy. It can also be defined as systematic maintenance of information about the things which already exist. In computer science, it could be said that it is a formal representation of knowledge with the help of a fixed set of believed concepts and the relationship between those concepts.