Reviewerly/exHarmony huggingface dataset:
Modeling geochemical anomalies of stream sediment data through a weighted drainage catchment basin method for detecting porphyry Cu-Au mineralization

Stream sediment surveying is a geochemical sampling method which is typically applied in the preliminary stages of mineral prospecting. Both continuous and discrete mapping approaches have been proposed to delineate geochemical anomalies at large scales using stream sediment samples. We aim to enhance the efficiency of a recent discrete mapping method called Weighted Drainage Catchment Basin (WDCB) which is independent of sampling sites and density, and has an advantage given that samples may not be uniformly distributed throughout the study area. The Enhanced WDCB (E-WDCB) method can be applied to calculate the weight of each catchment basin using different geochemical parameters such as factor scores. Our study focuses on the factor analysis of three datasets from the Bathurst region, New South Wales, Australia, with different numbers of samples. Different associations of the elements revealed by the factor analysis show that such analyses highly depend on the contributing samples and elements. We propose the Catchment Basin Score (CBS) index to delineate the suitability of sampling in each catchment basin. This index can also be applied to integrate the components resulting from several multivariate analyses on different datasets in a same area. The prediction-area plots show that our proposed method is able to predict a higher percentage of porphyry CuAu occurrences in a smaller area compared to other canonical methods, e.g. the median of factor scores in each catchment basin. We demonstrate that prospective areas for porphyry CuAu mineralization using the proposed discrete mapping method are also well correlated with volcanic and intrusive rocks.
Prediction of a complex system with few data: Evaluation of the effect of model structure and amount of data with dynamic bayesian network models

A major challenge in environmental modeling is to identify structural changes in the ecosystem across time, i.e., changes in the underlying process that generates the data. In this paper, we analyze the Baltic Sea food web in order to 1) examine potential unobserved processes that could affect the ecosystem and 2) make predictions on some variables of interest. To do so, dynamic Bayesian networks with different setups of hidden variables (HVs) were built and validated applying two techniques: rolling-origin and rolling-window. Moreover, two statistical inference approaches were compared at regime shift detection: fully Bayesian and Maximum Likelihood Estimation. Our results confirm that, from the predictive accuracy point of view, more data help to improve the predictions whereas the different setups of HVs did not make a critical difference in the predictions. Finally, the different HVs picked up patterns in the data, which revealed changes in different parts of the ecosystem.
A Context-Aware Augmentative and Alternative Communication System for School Children With Intellectual Disabilities

Children with intellectual disabilities (ID), especially those who fail to communicate with normal speech, suffer from various limitations in their social functioning. They often rely on alternate methods to supplement or replace verbal communication. There is a rapidly growing number of augmentative and alternative communication (AAC) mobile applications available over recent years. However, the operation of these applications remains a challenge for users with ID since they may not have the sufficient cognitive proficiency to cope with the application interface. In this paper, we present the design, implementation, and evaluation of a Bluetooth low energy (BLE) based context-aware AAC system. Our system facilitates daily communication for nonverbal school children with moderate ID in order to address their learning and communication needs. We have piloted and evaluated our system at a special education school in Hong Kong. Our technical contribution is twofold. First, we introduce BLE technology to the AAC field and show its effectiveness on users with ID. Second, by converting conventionally standalone AAC software into a service system and empowering it with Internet of Things functionality, we open up many new opportunities brought by the system-of-systems (SoS) paradigm for the intellectually disabled population.
Reducing energy consumption of wireless sensor networks using rules and extreme learning machine algorithm

Wireless sensor networks consist of a collection of sensors to monitor physical or environmental events. Nowadays, the sensor networks are used in important applications like military, health and civilian monitoring. Since it is a wireless medium, deployed in remote locations and resource-constrained nature, the sensor networks are easily vulnerable to attacks. The attack creates significant damages to the sensor networks. To avoid these problems, intrusion detection system (IDS) is implemented at the base station to filter any abnormal packets. In the proposed system, a survey is made on the attacks and rules to detect the attacks. Filtering the attacks using rule-based IDS at the sensor nodes would reduce the amount of packet transmission to the base station which, in turn, would reduce the energy consumption of the sensor network. Extreme learning machine (ELM) algorithm is implemented at the base station to detect the abnormal packets. The experimental result shows the performance of different classification techniques and cross-layer rules over the NSL-KDD and real-time datasets. The detection rate of the ELM algorithm is higher compared to other systems.
Constrained multimodal ensemble Kalman filter based on Kullback–Leibler (KL) divergence

The aim of this study is to incorporate inequality constraints in the state estimation problem for nonlinear systems. In particular, we consider the case where the posterior density is multimodal. To this end, we propose a Gaussian mixture model-based ensemble Kalman filter (GMM-EnKF) in which the probability density function of the state is approximated using a Gaussian mixture distribution. To handle inequality constraints in the recursive filtering framework for multimodal distributions, we propose to project the unconstrained GMM-EnKF into the constrained region. This can be accomplished by determining the constrained posterior density function such that the Kullback–Leibler divergence between the unconstrained and constrained GMM is minimized. Since the resulting optimization problem is non-convex, we propose to solve a two-step convex optimization problem in the update step of the state estimation problem. Two demonstrative case studies are presented to illustrate the effectiveness of the proposed constrained GMM-EnKF algorithm.
Analyzing Real and Fake users in Facebook Network based on Emotions

Fake profile detection is one of the critical problems in Online Social Networks (OSNs). So far, studies have mainly focused on profile-based, behavioral-based, network-based and content-based attributes. However, user sentiments have not been explored along this domain. In the present study, we proposed the fake profile detection model that incorporates sentiment-based attributes to differentiate real and fake OSN profiles. The study is grounded in the fact that the posts of real users reveal varied categories of emotions such asjoy, sad, angry, fear, etc. based on their life experiences. On the contrary, fake users share posts to accomplish a specific purpose, and therefore, it is highly likely that their post content will contain similar types of emotions. The experiments are conducted on the posts of Facebook users. The detection model is trained on 12 emotion-based attributes including Plutchik's eight basic emotions, positivity and negativity. Furthermore, a noise removal technique is presented to remove the outliers from the dataset. Finally, several machine learning techniques including Support Vector Machine (SVM), Naive Bayes, JRip and Random Forest have been used to train the detection model.
Public key encryption with equality test from generic assumptions in the random oracle model

Public key encryption with equality test (PKEET) is a variant of classical public key encryption (PKE) with the special functionality of an equality test, and can be used in many applications such as in keyword search on encrypted data and for efficient management by partitioning encrypted data in the cloud. Since the original proposal of Yang et al. (CT-RSA, 2010), several subsequent proposals to improve the efficiency or functionality of PKEET have been reported. We present a PKEET construction from generic assumptions in the random oracle model. In particular, whereas previous results require number-theoretic assumptions or strictly stronger generic assumptions such as the existence of secure hierarchical identity-based encryption, our proposal requires only the existence of cryptographic hash functions and secure PKE schemes satisfying a special property, called randomness extractability. Informally, randomness extractability means that one can recover the randomness used in a ciphertext when given a secret key corresponding to a public key for the ciphertext. We investigate the fact that PKE schemes satisfying this property can be designed by the Fujisaki-Okamoto (FO) transformation, which is the widely utilized method to obtain secure PKE schemes from basic cryptographic primitives in the random oracle model. As a result, in combination with the FO transformation, we obtain a PKEET construction in the random oracle model if there exist a one-way PKE scheme, a one-time secure symmetric key encryption scheme, collision-resistant and one-way hash functions, and a pseudorandom function. In this sense, we remark that our PKEET construction is derived from fundamental generic assumptions only.
American and russian sign language dactyl recognition

Sign languages are the main way for people from deaf community to communicate with other people. In this paper, we have compared several real-time sign language dactyl recognition systems using deep convolutional neural networks. Our system is able to recognize words from natural language gestured using signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. For ASL, we trained on dataset prepared by Massey University, Institute of Information and Mathematical Sciences, for RSL we collect our own dataset, which we aim to enlarge together with RSL community in Russia. The results showed 100% accuracy for ASL Massey dataset, while RSL recognition quality is behind sufficient quality due to much more complex nature of real-world RSL dataset.
Emoji Sequence Use in Enacting Personal Identity

Focusing on a Chinese social media platform, this study adopts computer-mediated discourse analysis to examine how users employ emoji sequences to construct their personal identity through the expression of stance and engagement. Seven types of linguistic elements were identified by conducting stance and engagement analysis on emoji sequences in posts by social media influencers. Stance was more frequent than engagement. Attitude markers were the most common element used to convey stance, whereas directive was the most prevalent element used to express engagement. In addition, emoji sequences that did not convey stance and engagement were coded as n/a. This study also observed creative usages in the composition of emoji sequences that compensate for the lack of a prescribed emoji sequence grammar. Based on these findings, it advances recommendations for the design of emoji and of social media platforms grounded in linguistic principles.
Double-ant Colony Based UAV Path Planning Algorithm

Path planning plays an important role in the applications of Unmanned Aerial Vehicles (UAVs). It allows the UAV to autonomously compute an optimal path from the initial point to the end by checking some specific control points or fulfill some mission specific constraints (e.g., obstacle avoidance, fuel consumption, etc.). While ant colony optimization (ACO) algorithm has attracted a great deal of attention due to the fact that ants can work cooperatively to find an optimal path. However, ACO converges slowly in finding an optimal path, particularly for the case when the problem domain is large. To solve this problem, a double-ant colony based algorithm is proposed in this paper. More specifically, in the early stage we exploit genetic algorithm to generate pheromones, thus accelerating the convergence of the algorithm. Numerical results validate the effectiveness of the proposed algorithm.
Speech Recognition of Moroccan Dialect Using Hidden Markov Models

This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC's are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC's of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.
Discovering Product Defects and Solutions from Online User Generated Contents

The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about products and services. Often, these posts contain complaints that the consumers purchasing the products and services have. However, discovering and summarizing product defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the product aspects and their corresponding opinions, are not sufficient to discover the product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering product defects from online data.
Manifold regularized stacked denoising autoencoders with feature selection

This paper proposes a new stacked denoising autoencoders (SDAE), called manifold regularized SDAE (MRSDAE) based on particle swarm optimization (PSO), where manifold regularization and feature selection are embedded in the deep network. This study concentrates on using PSO to simultaneously optimize structure and parameters of SDAEs through a specific particle representation and learning method. MRSDAE aims to generate discriminant features from the data based on the integration of these effective techniques, i.e., structure and parameter optimization, manifold regularization and feature selection. The experimental results on a number of benchmark classification datasets demonstrate that MRSDAE can construct compact SDAEs with high generalization performance. Finding from this study can be used as effective guideline in learning both the structure and parameters of deep neural networks (DNNs) with manifold regularization and feature selection techniques.
Combining Depth Fusion and Photometric Stereo for Fine-Detailed 3D Models

In recent years, great progress has been made on the problem of 3D scene reconstruction using depth sensors. On a large scale, these reconstructions look impressive, but often many fine details are lacking due to limitations in the sensor resolution. In this paper we combine two well-known principles for recovery of 3D models, namely fusion of depth images with photometric stereo to enhance the details of the reconstructions. We derive a simple and transparent objective functional that takes both the observed intensity images and depth information into account. The experimental results show that many details are captured that are not present in the input depth images. Moreover, we provide a quantitative evaluation that confirms the improvement of the resulting 3D reconstruction using a 3D printed model.
Advancing science and practice using immersive virtual reality: what behavioral medicine has to offer

Interest in immersive virtual reality (VR) technologies is burgeoning as the hardware becomes less costly and more accessible to users, including researchers and practitioners. This commentary outlines the field of immersive VR and highlights applications of its use relevant to translational behavioral medicine. We describe the challenges facing VR applications for health and medicine, and how the core strengths of behavioral medicine can advance theory, research, and practice using VR. By highlighting potential uses of immersive VR alongside the challenges facing the field, we hope to inspire researchers to apply robust theories, methods, and frameworks to generate stronger evidence about the feasibility, acceptability, efficacy, and effectiveness of using this technology in translational behavioral medicine.
RFTC

Random execution time-based countermeasures against power analysis attacks have reduced resource overheads when compared to balancing power dissipation and masking countermeasures. The previous countermeasures on randomization use either a small number of clock frequencies or delays to randomize the execution. This paper presents a novel random frequency countermeasure (referred to as RFTC) using the dynamic reconfiguration ability of clock managers of Field-Programmable Gate Arrays -- FPGAs (such as Xilinx Mixed-Mode Clock Manager -- MMCM) which can change the frequency of operation at runtime. We show for the first time how Advanced Encryption Standard (AES) block cipher algorithm can be executed using randomly selected clock frequencies (amongst thousands of frequencies carefully chosen) generated within the FPGA to mitigate power analysis attack vulnerabilities. To test the effectiveness of the proposed clock randomization, Correlation Power analysis (CPA) attacks are performed on the collected power traces. Preprocessing methods, such as Dynamic Time Warping (DTW), Principal Component Analysis (PCA) and Fast Fourier Transform (FFT), based power analysis attacks are performed on the collected traces to test the effective removal of random execution. Compared to the state of the art, where there were 83 distinct finishing times for each encryption, the method described in this paper can have more than 60,000 distinct finishing times for each encryption, making it resistant against power analysis attacks when preprocessed and demonstrated to be secure up to four million traces.
A multi-encoder neural conversation model

With the development of deep neural networks, Sequence-to-sequence (Seq2Seq) models become a popular technique of conversation models. Current Seq2Seq models with single encoder-decoder structures tend to generate responses which contain high frequency patterns on datasets. However, these patterns are always generic and meaningless. Generic and meaningless responses will lead the conversation between computer and human to an end quickly. According to our observations, human conversations are always topic related. If the conversation data can be divided into different clusters according to their topics, high frequency patterns will be topic related rather than generic. We consider that a model trained in different clusters can generate more topic related and meaningful responses. Inspired by this idea, we propose a Multi-Encoder Neural Conversation (MENC) model. MENC can make use of topic information by its multi-encoder structure. To the best of our knowledge, it is the first work which applies multi-encoder structures into conversation models. We conduct our experiments on two daily conversation datasets. Our experiments show that MENC gets a better performance than other mainstream models on both subject and object evaluation metrics.
Context- and Data-driven Satisfaction Analysis of User Interface Adaptations Based on Instant User Feedback

Modern User Interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context (platform, user, and environment) changes at runtime. Adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. However, evaluating end-user satisfaction of adaptive UIs is a challenging task, because the UI and the context-of-use are both constantly changing. Thus, an acceptance analysis of UI adaptation features should consider the context-of-use when adaptations are triggered. Classical usability evaluation methods like usability tests mostly focus on a posteriori analysis techniques and do not fully exploit the potential of collecting implicit and explicit user feedback at runtime. To address this challenge, we present an on-the-fly usability testing solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. The solution was applied to a mobile Android mail application, which served as basis for a usability study with 23 participants. A data-driven end-user satisfaction analysis based on the collected context information and user feedback was conducted. The main results show that most of the triggered UI adaptation features were positively rated.
Good Slang or Bad Slang? Embedding Internet Slang in Persuasive Advertising

Internet slang is a new language with innovative and novel characteristics, and its use can be considered a form of creative advertising. Embedding internet slang into advertisements can thus enhance their creative quality and increase the attention paid to them. In this study, we examined the effect of the characteristics of internet slang on attention to advertisements, brand awareness, product evaluation, and attitudes toward advertising by conducting two empirical studies, one utilizing eye-tracking experiments and the other utilizing questionnaires. We found that using internet slang in advertising significantly increased audience attention compared with standard language but did not necessarily improve product evaluation and brand awareness for various types of goods. We discovered code-switching effects of psycholinguistics existed in standard language and its variant (internet slang). Our findings can guide advertisers in selecting the embedded language that can be effective in achieving their desired advertising effect. Our findings also indicate that the excessive use of internet slang may have a negative effect on brand and product evaluation.