Reviewerly/exHarmony huggingface dataset:
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents

In this article we study the problem of training intelligent agents using Reinforcement Learning for the purpose of game development. Unlike systems built to replace human players and to achieve super-human performance, our agents aim to produce meaningful interactions with the player, and at the same time demonstrate behavioral traits as desired by game designers. We show how to combine distinct behavioral policies to obtain a meaningful “fusion” policy which comprises all these behaviors. To this end, we propose four different policy fusion methods for combining pre-trained policies. We further demonstrate how these methods can be used in combination with Inverse Reinforcement Learning in order to create intelligent agents with specific behavioral styles as chosen by game designers, without having to define many and possibly poorly-designed reward functions. Experiments on two different environments indicate that entropy-weighted policy fusion significantly outperforms all others. We provide several practical examples and use-cases for how these methods are indeed useful for video game production and designers.
Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure

The aim of this paper is to introduce a novel similarity measure using fractional-order derivative for patch comparison in low-rank image denoising approach. Recently, several outstanding low-rank image denoising algorithms have been proposed. However, these methods have limitations in the sense that certain irrelevant patches can be selected during patch comparison. These undesired patches affect singular values shrinkage and aggregation phases of these approaches. Thus, the fine details and edges of denoised image may not be well preserved. To address this issue, a novel method is proposed in which gradient information is injected in patch comparison using discretized fractional-order derivatives. The advantages of proposed approach are twofold: firstly, the patch comparison becomes more reliable by combining intensity and gradient information; secondly, the fractional-order gradient provides an additional degree of freedom to quantify the gradient information for patch comparison in an efficient way. In addition, the proposed algorithm estimates noise level using geometric details encoded in the image patches. The noise estimation strategy may help in terminating the iterative low-rank approximation. Experimental results on test images reveal that the proposed method performs better than several outstanding algorithms, specifically, in the presence of severe noise levels.
Facial expression analysis using local directional stigma mean patterns and convolutional neural networks

This paper represents automatic facial expression analysis method named Local Directional Stigma Mean Patterns (LDSMP) for automatic facial expression analysis and image retrieval using content based facial expression image retrieval and CNN. The traditional local patterns such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) are applied for face recognition and expression analysis, calculated using relationship between the center pixel and neighboring pixels. The proposed method calculates the eight directional difference values then divided into the three ranges based on threshold values. Thus, the values are substituted with basic three positive values (+3, +2, +1) and three negative values (-3, -2, -1) to get more sensitive information from an image rather than aforementioned methods. The threshold can be select either static which is selected by user or dynamic is evaluated from image itself and supports to improve the efficiency. The performance of the proposed method is further improved by giving this patterns as input to the Convolutional Neural Networks (CNN) and compared with the existing methods LBP, LTP, and Directional Binary Code (DBC) in terms of Average Precision (AP), Average Recall (AR), and Average Retrieval Rate (ARR) using standard databases COREL 10K (DB1) and JAFFE (The Japanese Female Facial Expression) (DB2) and Extended Cohn-Kanade (CK +) (DB3) dataset.
Euclidean Division Method for the Homomorphic Scheme CKKS

The use of cloud computing can reduce the economic costs of maintaining IT infrastructure, but at the same time, the likelihood of confidential data theft increases. To reduce the likelihood of it, cloud computing uses homomorphic encryption. However, a homomorphic cipher only allows adding and multiplying encrypted numbers; in some cases, a division operation is required to implement algorithms. To implement the division operation with encrypted numbers it is necessary to implement the encrypted number comparison operation. Considering that the operation of comparing encrypted numbers is carried out using numerical methods, it is necessary to adapt the existing algorithms for Euclidean division. In this article, we propose a two-stage algorithm for Euclidean division of numbers encrypted using the CKKS scheme and investigate its properties.
A privacy-preserving scheme for JPEG image retrieval based on deep learning

Abstract With the great increase of digital images, content-based image retrieval (CBIR) has been proposed to facilitate the usage of images. Local users need to outsource CBIR to cloud services, which leads to privacy leakage. Based on deep learning, this paper proposes a retrieval scheme of encrypted JPEG images. In this scheme, the image owner sends the corresponding encrypted image to the cloud server. The cloud server extracts the Variable-length Integer (VLI) code length from the encrypted image and uses Convolution Neural Network (CNN) to learn a value of fixed dimensions as the feature vector. We have shown through experiments that our scheme is safe, with good performance and retrieval accuracy.
ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition

This paper proposes an architecture based on the improved local binary pattern (LBP) shallow deep convolution neural network, which integrates hand-crafted feature pre-processing and the advantage of character learning in the supervised high-level function of CNN, in order to enhance its performance. This study introduced the information of scale space into the LBP to reduce the sensitivity to noise, and applied feature maps with two features, the maximum selection feature map (MLBP) and the first selection feature map (FLBP). The former selected the edge with the strongest intensity to reduce the influence of noise points, while the latter measured local binary features through the scale detection of an effective edge. In the network architecture design, according to the differences of input features, networks of different depths were used for learning, and the features learned by the two networks were adopted for classification. The experimental results show that, the ILBPSDNet proposed had certain recognition abilities in many character data sets, and the network parameters and computation were also reduced. Therefore, it has a significant effect in realizing the application of real-time character recognition. Finally, compared with other latest networks, its network performance could be maintained at a certain level.
Composite Code Sparse Autoencoders for First Stage Retrieval

We present a Composite Code Sparse Autoencoder (CCSA) approach for Approximate Nearest Neighbor (ANN) search of document representations based on Siamese-BERT models. In Information Retrieval (IR), the ranking pipeline is generally decomposed in two stages: the first stage focuses on retrieving a candidate set from the whole collection. The second stage re-ranks the candidates by relying on more complex models. Recently, Siamese-BERT models have been used as first stage rankers to replace or complement the traditional bag-of-words models. However, indexing and searching a large document collection requires efficient similarity search on dense vectors and this is why ANN techniques come into play. Since composite codes are naturally sparse, we show how CCSA can learn efficient parallel inverted index thanks to an uniformity regularizer. Our experiments on MS MARCO reveal that for the same quantization budget and [email protected] targets, CCSA is able to outperform IVF (inverted-index file) with product quantization on both
Improving the Retrieval of Arabic Web Search Results Using Enhanced k-Means Clustering Algorithm

Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The modern writing style of Arabic excludes the diacritical marking, without which Arabic words become ambiguous. For a search query, the user has to skim over the document to infer if the word has the same meaning they are after, which is a time-consuming task. It is hoped that clustering the retrieved documents will collate documents into clear and meaningful groups. In this paper, we use an enhanced k-means clustering algorithm, which yields a faster clustering time than the regular k-means. The algorithm uses the distance calculated from previous iterations to minimize the number of distance calculations. We propose a system to cluster Arabic search results using the enhanced k-means algorithm, labeling each cluster with the most frequent word in the cluster. This system will help Arabic web users identify each cluster’s topic and go directly to the required cluster. Experimentally, the enhanced k-means algorithm reduced the execution time by 60% for the stemmed dataset and 47% for the non-stemmed dataset when compared to the regular k-means, while slightly improving the purity.
Bayesian estimation of a decreasing density

Suppose X1,…,Xn is a random sample from a bounded and decreasing density f0 on [0,∞). We are interested in estimating such f0, with special interest in f0(0). This problem is encountered in various statistical applications and has gained quite some attention in the statistical literature. It is well known that the maximum likelihood estimator is inconsistent at zero. This has led several authors to propose alternative estimators which are consistent. As any decreasing density can be represented as a scale mixture of uniform densities, a Bayesian estimator is obtained by endowing the mixture distribution with the Dirichlet process prior. Assuming this prior, we derive contraction rates of the posterior density at zero by carefully revising arguments presented in Salomond (Electronic Journal of Statistics 8 (2014) 1380–1404). Several choices of base measure are numerically evaluated and compared. In a simulation various frequentist methods and a Bayesian estimator are compared. Finally, the Bayesian procedure is applied to current durations data described in Slama et al. (Human Reproduction 27 (2012) 1489–1498).
Automated Usage Characterization of Mining Vehicles For Life Time Prediction

The life of a vehicle is heavily influenced by how it is used, and usage information is critical to predict the future condition of the machine. In this work we present a method to categorize what task an earthmoving vehicle is performing, based on a data driven model and a single standalone accelerometer. By training a convolutional neural network using a couple of weeks of labeled data, we show that a three axis accelerometer is sufficient to correctly classify between 5 different classes with an accuracy over 96% for a balanced dataset with no manual feature generation. The results are also compared against some other machine learning techniques, showing that the convolutional neural network has the highest performance, although other techniques are not far behind. An important conclusion is that methods and ideas from the area of Human Activity Recognition (HAR) are applicable also for vehicles.
Design of Electric Wheelchair with Joystick Controller as Personal Mobility for Disabled Person

The movement aids for people with motor system disability, especially the legs, has been currently very essential, because they also surely want to move freely from a place to another without bothering their family or closest friends. Therefore, this electric wheelchair research was made to facilitate the movement of people with disabilities within their activities, such as going to certain places or doing something. The design of the electric wheelchair was arranged in such ways to assist the body movements using joystick control connected to microcontroller Arduino. If the joystick lever was directed forward, backward, left and right, then the joystick command would be processed by the Arduino, so that it provided voltage to the motor driver, and the wheelchair could move forward, backward, turn left, and turn right. From 25 joystick command tests, it was shown the forward accuracy was 88%, the backward was 100%, the left turn was 100%, and the right turn was also 100%. Based on the test that have been conducted, the accuracy values represented that this electric wheelchair using joystick controller can be implemented as its function.
The Subconscious Director: Dynamically Personalizing Videos Using Gaze Data

Watching TV has become a side event rather than a deliberate pastime. Movie directors thus struggle to find new ways to sustain the attention of their audience. Interactive movies usually require the viewer to actively decide how the plot progresses, creating an experience more akin to video games than film. In this paper, we propose a system that analyses gaze data to personalize the plot of a video without the viewer’s active intervention. User preferences are inferred from their gaze allocation to different elements in a scene. The subsequent scene is then dynamically tailored towards the user’s predicted preference. In a user study (N = 175), we evaluate the effectiveness of the system with regard to user engagement. Our findings show that personalized videos have a positive effect on focused attention and involvement, whereas novelty perception is not significantly affected.
A Hex Itoh-Tsujii inversion algorithm for FPGA platforms

The Itoh-Tsujii inversion algorithm forms a major contribution in finding the inverse in cryptographic applications such as Elliptic Curve Cryptography. In this paper, a new Hex Itoh-Tsujii inversion algorithm is proposed to compute the multiplicative inverse efficiently on Field-Programmable Gate-arrays (FPGA) platforms for binary fields generated by NIST recommended irreducible trinomials. The Hex Itoh Tsujii inversion Algorithm based proposed architecture is constructed with hex circuits and quad addition chain. This combination improves the resource utilization. The experimental results shows that the proposed work has better Area-Time Performance compared with the existing implementation.
A New Approach for Dehazing and Enhancement of Infrared Images

This paper proposes the implementation of an algorithm that dehazes and enhances outdoor infrared (IR) images. Dehazing is carried out through dark channel prior (DCP) and enhancement is achieved by multi-scale retinex (MSR) technique. The atmospheric light present in the haze concentrated image is approximated and a transmission map is estimated which separates out the concentration of haze. Enhancement of dehazed images is done by augmenting the image contrast directly instead of unraveling the physical model of image degradation. The reflectance component of image is acquired by approximating the atmospheric brightness of scene. Resulting images are then assimilated in optical attenuation model to recover the final output image. The proposed method is implemented using MATLAB and is validated by assessing the images according to their average gradient, information entropy, and root mean square error. Experimental results show that the proposed method can remove the haze concentration in images successfully and enhance them significantly.
Challenging the traceability of natural gold by combining geochemical methods: French Guiana example

Considering the high economic importance and worldwide distribution of gold, the ability to identify its provenance is critical and challenging to ensuring a responsible supply chain from deposit to consumer, especially within conflict-affected areas. Here we present an innovative approach to trace the provenance of natural gold from French Guiana through a combination of geochemical and statistical methods. This approach is divided in three steps and allows the identification of illicit gold, the certification of the declared gold origin coming from legal operators and, in some cases, the identification of unknown gold. French Guiana was chosen as a demonstration case of our approach because it is a well-known conflict-affected area where illegal mining is widespread. In the first step, we showed that the use of illegal Hg amalgamation can be easily revealed by looking the gold grain morphology with an optical microscope or by detecting the Hg with laser-induced breakdown spectroscopy that allow direct detection on the field. In the second and principal step, we demonstrated that a declared provenance of gold can be certify with a high degree of confidence by measuring the Ag content of a gold grain population and by checking it against a database with the Kolmogorov-Smirnov statistic. In the final step that allows the identification of the origin of unknown gold, we used (i) the content of minor elements (Cu and Hg), (ii) the identification of mineral inclusions and their relative proportions within samples and (iii) the trace element composition of natural gold grains determined by LA-ICP-MS and coupled with a permutational multivariate analysis of variance and a similarity percentage analysis. This method allows the identification of the provenance of 69% of the gold samples; the provenance of other gold populations (31%) cannot be identified because of their geological similarity with other groups. The traceability of natural gold can be guaranteeing with our innovative approach, in particular by certifying declared gold population provenance. Further challenges to be addressed will be the implementation of such approach in others conflict-affected regions to support the global supply chain of gold.
Sentimental Visualization: Semantic Analysis of Online Product Reviews Using Python and Tableau

The Internet has become a tool used to disseminate opinions in the form of product reviews, which significantly impact decisions to buy the product. Most previous studies have been done in this field using K-means algorithm, this study employs Python and Tableau to perform the sentiment analysis of the text as well as semantically classify the keywords into meaningful insights. Data were collected from Amazon website reviews ( n=3,413) of the product with the most reviews (Trigger Point Grid Foam Roller) in five different colors (black, camo, pink, orange, and lime) from 2018-2019. The results show that the black one has the most positive sentiment compared with the others. Moreover, SpaCy's named entity recognition revealed clusters of people, locations and purposes, or events, for the item. These results can help managers to develop the areas of a product where customers have expressed negative sentiments; moreover, this semantic classification results would help the manager to more effectively market the product.
Obfuscating encrypted threshold signature algorithm and its applications in cloud computing

Current cloud computing causes serious restrictions to safeguarding users’ data privacy. Since users’ sensitive data is submitted in unencrypted forms to remote machines possessed and operated by untrusted service providers, users’ sensitive data may be leaked by service providers. Program obfuscation shows the unique advantages that it can provide for cloud computing. In this paper, we construct an encrypted threshold signature functionality, which can outsource the threshold signing rights of users to cloud server securely by applying obfuscation, while revealing no more sensitive information. The obfuscator is proven to satisfy the average case virtual black box property and existentially unforgeable under the decisional linear (DLIN) assumption and computational Diffie-Hellman (CDH) assumption in the standard model. Moreover, we implement our scheme using the Java pairing-based cryptography library on a laptop.
A survey of gamified Augmented Reality systems for procedural tasks in industrial settings

Gamification is the adoption of game design elements and mechanisms into non-game contexts. As gamification has been a growing approach to increase people's performance in multiple disciplines such as education, service and marketing, it is necessary to question if gamification is also applicable in the industrial setting. In this paper, we discuss the specific requirements of a gamifed Augmented Reality system in an industrial production setting, its applications as well as challenges.
Multi-target identity management for unknown and time-varying number of targets in clutter

A novel multi-target identity management algorithm, called IM-GMPHD (Identity management with Gaussian mixture probability hypothesis density) algorithm, is proposed to simultaneously track an unknown and time-varying number of targets in clutter and manage the targets’ identities effectively and efficiently over time. The targets’ identities are managed by recursively computing the target identity beliefs using the target-measurement correlation. An augmented GMPHD filter is also developed in this paper to calculate the target-measurement correlation. In addition, the target identity beliefs can be updated to a lower uncertainty level whenever there is available target identity information. The proposed multi-target identity management algorithm is demonstrated through various illustrative simulation scenarios.
Evolutionary multitasking network reconstruction from time series with online parameter estimation

Reconstructing the structure of complex networks from time series is beneficial to understanding and controlling the collective dynamics of networked systems. Existing network reconstruction algorithms can only deal with one network reconstruction problem at one time. However, real-world applications typically have multiple network reconstruction tasks and these tasks often relate to each other to certain extent. For the purpose of exploring similar network structure patterns in different tasks, we establish an evolutionary multitasking framework to simultaneously optimize double network reconstruction tasks. In the proposed method, each task is modeled as a single objective problem containing the reconstruction error and the l0-norm of the weight matrix, which takes both the reconstruction error and the structure error into consideration and deals with the NP-hard problem directly. Online parameter learning scheme is employed to learn the parameter automatically controlling the amount of genetic material to exchange, thus avoiding the negative transfer while allowing the useful information pass between tasks. In addition, the least absolute shrinkage and selection operator (LASSO) initialization is employed to further enhance the performance. We apply the evolutionary multitasking framework to reconstruct both synthetic and real networks of evolutionary game, resistor networks, and communication network dynamic models. The experimental results demonstrate that the proposal exhibits competitive performance against state-of-the-arts in terms of all evaluation measures.