Reviewerly/exHarmony huggingface dataset:
On the Robustness of Random Walks for Fitness Landscape Analysis

Fitness landscape analysis is used to characterize search landscapes of optimization problems. The objective or fitness function of the optimization problem is extended into a search landscape. Random walk algorithms are the most common methods used to sample these search landscapes and various analyses are performed on the obtained samples in order to quantify the fitness landscape characteristics. Random walks are used since they are computationally inexpensive relative to optimization algorithms. However, since random walks are samples of the search landscape, it is important to have the measures that result from the random walks be reliable. This paper investigates the robustness of fitness landscape analysis measures and the effects of higher dimensions on the robustness. This paper suggests that certain random walk algorithms produce robust fitness landscape measures with shorter random walks than other random walk algorithms. Furthermore, this paper suggests that the fitness landscape measures that result from a particular random walk algorithm become robust at similar lengths of random walks across all the optimization functions analyzed.
Automatic Extraction of Personality from Text: Challenges and Opportunities

In this study we examined the possibility to extract personality traits from a text. We created an extensive dataset by having experts annotate personality traits in a large number of texts from multiple online sources. From these annotated texts we selected a sample and made further annotations ending up with a large low-reliability dataset and a small high-reliability dataset. We then used the two datasets to train and test several machine learning models to extract personality from text, including a language model. Finally, we evaluated our best models in the wild, on datasets from different domains. Our results show that the models based on the small high-reliability dataset performed better (in terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) than models based on large low-reliability dataset. Also, the language model based on the small high-reliability dataset performed better than the random baseline. Finally, and more importantly, the results showed our best model did not perform better than the random baseline when tested in the wild. Taken together, our results show that determining personality traits from a text remains a challenge and that no firm conclusions can be made on model performance before testing in the wild.
Spoken Language Identification Using Bidirectional LSTM Based LID Sequential Senones

The effectiveness of features used to represent speech utterances influences the performance of spoken language identification (LID) systems. Recent LID systems use bottleneck features (BNFs) obtained from deep neural networks (DNNs) to represent the utterances. These BNFs do not encode language-specific features. The recent advances in DNNs have led to the usage of effective language-sensitive features such as LID-senones, obtained using convolutional neural network (CNN) based architecture. In this work, we propose a novel approach to obtain LID-senones. The proposed approach combines BNF with bidirectional long short-term memory (BLSTM) networks to generate LID-senones. Since each LID-senones preserve sequence information, we term it as LID-sequential-senones (LID-seq-senones). The proposed LID-seq-senones are then used for LID in two ways. In the first approach, we propose to build an end-to-end structure with BLSTM as front end LID-seq-senones extractor followed by a fully connected classification layer. In the second approach, we consider each utterance as a sequence of LID-seq-senones and propose to use support vector machine (SVM) with sequence kernel (GMM-based segment level pyramid match kernel) to classify the utterance. The effectiveness of proposed representation is evaluated on Oregon graduate institute multi-language telephone speech corpus (OGI-TS) and IIT Madras Indian language corpus (IITM-IL).
Automatic Evaluation for Arabic Essays: A Rule-Based System

Essay writing is a common way to evaluate students' writing skills. Automated Essay Scoring (AES) systems are designed to facilitate the grading task for a large number of students in schools, universities, and testing companies. This study proposes a rule-based system to evaluate Arabic essays automatically, based on free textual essay analysis, whether or not there are predefined model essays. The evaluation criteria include surface-based and text processing requirements such as spelling, punctuation marks, essay structure, coherence, and essay style. To evaluate our system, we collected unrestricted Arabic essays from native Arabic writers with university-level proficiency. This dataset has been manually evaluated following our rubric based on the common criteria for good writing. The results obtained by our system show that, for essay's overall score around 73% were correctly evaluated. In addition, results show a variety in the system performance for each specific criterion. This system is designed as a baseline for more advanced AES system that uses complex features with machine learning techniques in future.
Phase space Heisenberg-limited estimation of the average phase shift in a Mach–Zehnder interferometer

We address the problem of distributed quantum metrology with a single squeezed-vacuum source by using the formalism of quantum mechanics in phase space. In particular, we demonstrate Heisenberg-limited sensitivity in the measurement of the average of two arbitrary phase shifts in the arms of a Mach–Zehnder interferometer. We obtain exact results for the measurement probability at the interferometer output for any value of the phases, which give us insight into the emergence of Heisenberg-limited sensitivity for periodical values of the phases.
Image Moment-Based Extended Object Tracking for Complex Motions

A novel image moment-based model for shape estimation and tracking of an extended target moving with a complex trajectory is presented. The proposed extended object tracking algorithm is based on multiple noisy measurement points sampled from the target at each time step. The shape of the object, approximated by an ellipse, is estimated using a combination of image moments. Dynamic models of image moments for constant velocity and coordinated turn motions are mathematically derived. An unscented Kalman filter - interacting multiple model (UKF-IMM) method is used to track the object and estimate its shape. A likelihood function based on average log-likelihood is derived for the IMM filter. Simulation results of the proposed UKF-IMM algorithm with the image moment-based models are presented that show the estimation of the shape of the object moving in a complex trajectory. The intersection over union (IoU), and the root mean square errors (RMSEs) of the position and velocity of the centroid of the ellipse are used as metrics. The comparison results of the proposed algorithm with a benchmark algorithm from literature based on the IoU and RMSE metrics are presented.
Secure server-aided data sharing clique with attestation

In this paper, we consider the security issues in data sharing cliques via remote server. We present a public key re-encryption scheme with delegated equality test on ciphertexts (PRE-DET). The scheme allows users to share outsourced data on the server without performing decryption-then-encryption procedures, allows new users to dynamically join the clique, allows clique users to attest the message underlying a ciphertext, and enables the server to partition outsourced user data without any further help of users after being delegated. We introduce the PRE-DET framework, propose a concrete construction and formally prove its security against five types of adversaries regarding two security requirements on message confidentiality and unforgeability of attestation against the server. We also theoretically analyze and compare the proposed PRE-DET construction with related schemes in terms of ciphertext sizes and computation costs of encryption, decryption, ciphertext equality testing and re-encryption, which confirms the practicality of our construction.
Estimating the imageability of words by mining visual characteristics from crawled image data

Natural Language Processing and multi-modal analyses are key elements in many applications. However, the semantic gap is an everlasting problem, leading to unnatural results disconnected from the user’s perception. To understand semantics in multimedia applications, human perception needs to be taken into consideration. Imageability is an approach originating from Pyscholinguistics to quantize the human perception of words. Research shows a relationship between language usage and the imageability of words, making it useful for multimodal applications. However, the creation of imageability datasets is often manual and labor-intensive. In this paper, we propose a method using image data mining of a variety of visual features to estimate the imageability of words. The main assumption is a relationship between the imageability of concepts, human perception, and the contents of Web-crawled images. Using a set of low- and high-level visual features from Web-crawled images, a model is trained to predict imageability. The evaluations show that the imageability can be predicted with both a sufficiently low error, and a high correlation to the ground-truth annotations. The proposed method can be used to increase the corpus of imageability dictionaries.
On the Quality of Classification Models for Inferring ABAC Policies from Access Logs

The attribute-based access control (ABAC) model has been gaining popularity in recent years because of its advantages in granularity, flexibility, and usability. Few approaches based on association rules mining have been proposed for the automatic generation of ABAC policies from access logs. Their aim is the identification of policies that do not overfit over training data, are not too general and thus does to disclose sensitive resources to everyone, and are interpretable by humans. The large ABAC privilege space along with the sparsity and unbalance distribution of the available logs make the solution of this task particularly complex and current approaches have different limitations. In this paper we compare different symbolic and nonsymbolic machine learning (ML) techniques for inferring ABAC policies and discuss their pros and cons. Based on experimental results on a toy dataset and on a real dataset, we argue that which is the best technique depends on the characteristics of the considered data. When the data are highly separable according to PCA and t-SNE decomposition, the quality of the obtained ABAC policies is higher and also policies are easily interpretable. By contrast, when this property does not hold, the quality of the obtained policies is low; in this case, non-symbolic ML techniques show better results than the symbolic ones.
Temperature and deformation measurement for large-scale flat specimens based on image mosaic algorithms

Synchronous measurement of the temperature and deformation fields of large-scale flat specimens is challenging in engineering experiments, especially for high-temperature environment where the non-contact optical method is attempted. To overcome this difficulty of large-scale flat specimens tested at high temperature in the open arc wind tunnel environment, measurement principles and experiments of large-scale flat specimens based on a multi-camera system are proposed using digital image stitching as well as the improved two-color method for temperature measurement. First, the digital image mosaic method is used to process and evaluate the mosaic effect of multi-camera images, the optimal mosaic parameters are selected, and the calculation results are given. Second, a set of images for large-scale flat specimens are deduced based on an improved two-color method of temperature measurement and digital image mosaic algorithms. A stitching algorithm for full-field temperature measurement and calculation results are given. Finally, full-field displacement of the stitched images is calculated by the digital image correlation method. Synchronous measurement of temperature and deformation established in this paper provides guidance for measurement of large-scale flat specimens with high spatial resolution in engineering tests.
Fast adaptive neighbors clustering via embedded clustering

Recently, spectral clustering (SC) has been gaining more and more attention due to its excellent performance in unsupervised learning. However, the computational complexity of the SC is high. Also, the adjacency graph matrix of the SC is ofen constructed by the Gaussian kernel, so the clustering result is sensitive to the kernel parameter σ. Since most large-scale datasets are high-dimensional and sparse, it is a great challenge to apply the SC to these data. Therefore, a fast adaptive neighbor clustering method based on the embedded clustering (FANCEC) is proposed. First, m anchors are selected from raw data. Next, a bipartite graph matrix Z connecting the raw data and anchors is constructed in a parameter-free manner. Then, the graph embedded data are obtained from raw data by the singular value decomposition (SVD) method. The graph embedded data extracts and combines valid information from raw data while discarding the redundant information. After that, m anchors are selected from graph embedded data, and the adjacency matrix S is initialized. Finally, the adaptive neighbor strategy is used to update matrix S until optimal function convergences. The clustering result of the FANCEC can be obtained directly without the post-processing that is required in the k-means method. The experimental results show that the proposed FANCEC can reduce time-consumption for large-scale data and obtain a good comprehensive clustering effect compared with the traditional SC methods.
Metal fractionation in the soils around a refined petroleum products depot

Elemental composition and heavy metal speciation of the soils of Ejigbo oil depot, Lagos state, Nigeria were determined to assess the contamination status of the study area. Oil-impacted and control soils were collected from the oil depot and uncontaminated areas respectively. Pb, Cu, Zn, Cd, Cr, Ni, As, and Fe concentrations in the soils were determined using Atomic absorption spectrometry. Contamination factor (CF) and geo-accumulation index were used to determine the contamination/pollution status of the study area. Geo-accumulation index results indicated that the test soils fall into the unpolluted category (<0) for Cd, Fe, Pb, As, and Zn, while there was unpolluted to moderate pollution (between 0 and 1) for Cu, Ni, and Cr. CF results suggested that the oil depot was contaminated with all the analyzed metals as the CFs were >1. Inter-element clustering equally existed between the analyzed metals. Speciation analysis results showed relatively high bioavailability and mobility potential for Cd and Zn. The study concluded that the soil of the oil depot, whose elemental concentrations exceeded those of the control soils, was impacted with the analyzed metals. Remediation schemes should be developed to clean up the metal in the oil depot soil.
Long-Short term Memory based Channel Prediction for SISO System

Learning the characteristics of a wireless channel is one of the most fundamental and challenging issues in the wireless communication. Many conventional signal processing techniques have been developed so far to estimate the channel state information. In this paper, we present a novel approach based on deep learning using long short term memory (LSTM) neural network to predict the fast-varying Rayleigh fading channel. Having known application of LSTM neural network in the field of time-series prediction problems, we have applied LSTM to predict the future state of the channel by providing the past channel state values. The predicted values of channel is used to recover the transmitted symbol from noisy signal received at the receiver end. We have optimized the configuration of LSTM network by correctly tuning the hyper-parameters so that we get a correct prediction of channel which is in turn used for reconstruction of signal at receiver end. The simulated Bit Error Rate vs. Signal-to-Noise ratio plot proves that LSTM based channel predictor works better than currently used interpolation based channel predictor.
A Joint Model of Clinical Domain Classification and Slot Filling Based on RCNN and BiGRU-CRF

The task of the Intent Classification & Slot Filling serves as a key joint task in the voice assistant, which also plays the role of the pre-work in the construction of the medical consultation assistant system. How to distribute a doctor-patient conversation into a formatted electronic medical record to an accurate department (Intent Classification) to extract the key named entities or mentions (Slot Filling) through a specialized domain knowledge recognizer is one of the key steps of the entire system. In real cases, the medical vocabulary and clinical entities in different departments of the hospital often differ to some extent. Therefore, we propose a comprehensive model based on CMed-BERT, RCNN and BiGRU-CRF for a joint task of department identification and slot filling of the specific domain. Experimental results confirmed the competitiveness of our model.
TVENet: Temporal variance embedding network for fine-grained action representation

With the breakthroughs in general action understanding, it has become an inevitable trend to analyze the actions in finer granularity. However, related researches have been largely hindered by the lack of fine-grained datasets and the difficulty of capturing subtle differences between fine-grained actions that are highly similar overall. In this paper, we address the above challenges by constructing a fine-grained action dataset, i.e., Figure Skating, which can be used for end-to-end network training and presenting a framework for the joint optimization of classification and similarity constraints. We propose to incorporate the triplet loss into the training of Convolutional Neural Network, which learns a mapping from fine-grained actions to a compact Euclidean space where distances directly correspond to a measure of action similarity. Triplet loss compels actions of distinct classes to have larger distances than actions of the same class. Besides, to boost the discrimination of the fine-grained actions, we further propose a temporal variance embedding network (TVENet) embedding temporal context variances into the feature embeddings during the joint network training. The experimental results on Figure Skating dataset, HMDB51 dataset as well as UCF101 dataset demonstrate the effectiveness of TVENet representation for fine-grained action search.
A Variational Model Dedicated to Joint Segmentation, Registration, and Atlas Generation for Shape Analysis

In medical image analysis, constructing an atlas, i.e. a mean representative of an ensemble of images, is a critical task for practitioners to estimate variability of shapes inside a population, and to characterise and understand how structural shape changes have an impact on health.This involves identifying significant shape constituents of a set of images, a process called segmentation, and mapping this group of images to an unknown mean image, a task called registration, making a statistical analysis of the image population possible.To achieve this goal, we propose treating these operations jointly to leverage their positive mutual influence, in a hyperelasticity setting, by viewing the shapes to be matched as Ogden materials.The approach is complemented by novel hard constraints on the L ∞ norm of both the Jacobian and its inverse, ensuring that the deformation is a bi-Lipschitz homeomorphism.Segmentation is based on the Potts model, which allows for a partition into more than two regions, i.e. more than one shape.The connection to the registration problem is ensured by the dissimilarity measure that aims to align the segmented shapes.A representation of the deformation field in a linear space equipped with a scalar product is then computed in order to perform a geometry-driven Principal Component Analysis (PCA) and to extract the main modes of variations inside the image population.Theoretical results emphasizing the mathematical soundness of the model are provided, among which existence of minimisers, analysis of a numerical method, asymptotic results and a PCA analysis, as well as numerical simulations demonstrating the ability of the model to produce an atlas exhibiting sharp edges, high contrast and a consistent shape.
Finding Archetypal Spaces Using Neural Networks

Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of ”pure types” or archetypes. These archetypes represent extrema of a data space in which there is a trade-off between features, such as in biology where different combinations of traits provide optimal fitness for different environments. Existing methods for archetypal analysis work well when a linear relationship exists between the feature space and the archetypal space. However, such methods are not applicable to systems where the feature space is generated non-linearly from the combination of archetypes, such as in biological systems or image transformations. Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space. To solve this problem, we introduce Archetypal Analysis network (AAnet), which is a deep neural network framework for learning and generating from a latent archetypal representation of data. We demonstrate state-of-the-art recovery of ground-truth archetypes in non-linear data domains, show AAnet can generate from data geometry rather than from data density, and use AAnet to identify biologically meaningful archetypes in single-cell gene expression data.
Classifying Genuine Face images from Disguised Face Images

Detecting fake or disguised face images become much more challenging due to the significant advancements made in machine learning, computer vision, and image processing techniques. In addition, due to the rise of various DeepFakes, fake images can be maliciously used to attack individuals and deter true information. Therefore, it is crucial to building a classifier that accurately distinguishes an individual from different or similar persons. In this preliminary work, we aim to detect a target person's face from different similar individuals, Doppelgangers, leveraging the dataset from Disguised Faces in the Wild (DFW) 2018. We use well-known off-the-shelf face detection classifiers, such as ShallowNet, VGG-16, and Xception to evaluate the classification performance. In order to further improve the detection performance, we apply data augmentation. Our preliminary result shows that the Xception model can classify one from different individuals with a 62% accuracy.
GPU-accelerated registration of hyperspectral images using KAZE features

Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the images is HSI–KAZE, which is based on feature detection and detects keypoints using nonlinear diffusion filtering. The algorithm is oriented toward extreme situations in which the images are very different in terms of scale, rotation and displacement. In this paper, an efficient implementation of the HSI–KAZE algorithm on GPU using CUDA is proposed. A detailed analysis of the implementation as well as a performance comparison to an OpenMP multicore implementation is also presented. The resulting algorithm is suitable for on-board processing of high-resolution images.