Reviewerly/exHarmony huggingface dataset:
A Stochastic Algorithm to Design Min-Entropy Tuning Controllers for True Random Number Generators

We discuss a stochastic algorithm to design tuning controllers for cryptographic True Random Number Generators, compliant to NIST recommendations, as an effective low-complexity solution to counteract entropy variability in integrated architectures implementing tunable entropy sources. Taking as a reference the min-entropy concept, we discussed the proposal from both the theoretical and hardware design points of view, validating claims with proofs and experiments. Depending on the target accuracy, the proposed architecture is scalable, and its profitable use in TRNG design strongly depends on the kind of core entropy sources taken into account. Furthermore, we show that the low-complexity entropy measurement techniques exploited in this proposal can be used to design a legitimate alternative to the Adaptive Proportion Health Test recommended in the NIST 800.90B publication.
Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.
Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality

In virtual reality (VR) applications, hand-controller interaction is largely limited by the biomechanical structure of the arm and its kinematical features. Earlier research revealed that different arm postures generate distinct arm fatigue levels in mid-air operational tasks; however, how they impact interaction performance, e.g., accuracy of target grasp and manipulation, has been less investigated. To fill this gap in knowledge, we conducted an empirical experiment in which thirty participants were recruited to complete a series of target acquisition tasks in a specifically designed VR application. Results show that (1) a bent arm posture resulted in a higher interaction accuracy than a stretched arm posture; (2) a downward arm posture interacted more accurately than an upraised arm posture; since two arms are bilaterally symmetric, (3) either selected arm interacted more accurately on the corresponding side than on the opposite side; and (4) the user-preferred or dominant arm interacted more persistently than the non-dominant one, though two arms generated little difference in interaction accuracy. Implications and suggestions are discussed for designing more efficient and user-satisfying interactive spaces in VR.
An End-to-end Heterogeneous Restraint Network for RGB-D Cross-modal Person Re-identification

The RGB-D cross-modal person re-identification (re-id) task aims to identify the person of interest across the RGB and depth image modes. The tremendous discrepancy between these two modalities makes this task difficult to tackle. Few researchers pay attention to this task, and the deep networks of existing methods still cannot be trained in an end-to-end manner. Therefore, this article proposes an end-to-end module for RGB-D cross-modal person re-id. This network introduces a cross-modal relational branch to narrow the gaps between two heterogeneous images. It models the abundant correlations between any cross-modal sample pairs, which are constrained by heterogeneous interactive learning. The proposed network also exploits a dual-modal local branch, which aims to capture the common spatial contexts in two modalities. This branch adopts shared attentive pooling and mutual contextual graph networks to extract the spatial attention within each local region and the spatial relations between distinct local parts, respectively. Experimental results on two public benchmark datasets, that is, the BIWI and RobotPKU datasets, demonstrate that our method is superior to the state-of-the-art. In addition, we perform thorough experiments to prove the effectiveness of each component in the proposed method.
Detection of Hate Speech Texts Using Machine Learning Algorithm

Identifying hate speech on social media has become increasingly crucial for society. It has been shown that cyberbul-lying significantly affects the social tranquillity of the Chadian population, mainly in places of conflict. This article aims to detect hate speech for texts written in “lingua franca”, a mix of the local Chadian and French languages. The dataset consists of 14,000 comments extracted from the most visited Facebook pages and annotated in four categories (hate, offence, insult and neutral) were used for this study. The data were cleaned by Natural Language Processing techniques (NLP) and applied to three word embedding methods such as Word2Vec, Doc2Vec, and Fasttext. Finally, four Machine Learning methods, namely Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbours (KNN), were computed to classify the different categories. The result showed that FastText features representation as input to SVM classifier was the best with 95.4% accuracy for predicting the comment contained insult statement followed by hate statement 93.9%. The result demonstrated our model could be used to detect the hate speech made by Chadians on social media texts.
Incorporating textual reviews in the learning of latent factors for recommender systems

In the field of recommender systems, the latent factor model is one of the state-of-the-art ones thanks to its strengths in accuracy and scalability. Its core is to learn latent factors for the representation of users and items using rating data collected through surveys after the users have experienced the items. However, on e-commerce applications, besides ratings, users can write reviews for items. A review generally indicates a user's experience with an item while a rating indicates his/her level of satisfaction with such an experience. Latent factors can be learned more accurately if supported by such reviews. This study is distinctive in interpreting a review as both a description of the user/item and a description of the surrounding elements affecting the user's experience with the item. It has proven to be more effective than those that only consider a review as a description of the user/item. Especially, the analysis of the experimental results shows that our model provides supportive recommendations for users with detailed reviews in spite of their few collected ratings.
How Frauds in Times of Crisis Target People

While there are no new frauds, internet technology provides new opportunities for fraudsters by facilitating volumes of attacks that law enforcement then struggles to address. Moreover, since context can affect how potential victims respond to frauds, crisis context influences how fraudsters design frauds. This article assesses fraudsters’ fraud design strategies during two external crisis events that impacted Australia: The Black Summer Bushfires that occurred from September 2019 to March 2020 and the onset and first year of the COVID-19 pandemic that occurred from January 2020 through January 2021. Targets, during these crises, were more likely to be vulnerable according to Steinmetz’s model victim for social engineering framework. This study shows that, in both crises, fraudsters deployed the social engineering techniques of “authority” and “scarcity,” techniques that are more likely to be successful based solely on initial contact. Fraudsters designed their requests to be easily actioned and crafted their scams to reference very recent events as the external crisis events evolved. Thus, they targeted broad audiences with minimal personal involvement. Furthermore, this study shows that fraudsters, when disseminating their scams via social media outlets, attempted to build “social proof” to expand their potential victim pool to include the marks’ social circles.
Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications

This work presents a new method to estimate atmospheric turbidity with improved accuracy in estimating clear-sky irradiance. The turbidity is estimated by machine learning algorithms using commonly measured meteorological data including ambient air temperature, relative humidity, wind speed and atmospheric pressure. The estimated turbidity is then served as the Linke Turbidity input to the Ineichen-Perez clear-sky model to estimate clear-sky global horizontal irradiance (GHI) and direct normal irradiance (DNI). When compared with the original Ineichen-Perez model which uses interpolated turbidity from the monthly climatological means, our turbidity estimation better captures its daily, seasonal, and annual variations. When using the improved turbidity estimation in the Ineichen-Perez model, the root mean square error (RMSE) of clear-sky GHI is reduced from 24.02 W m−2 to 9.94 W m−2. The RMSE of clear-sky DNI is deceased from 76.40 W m−2 to 29.96 W m−2. The presented method is also capable to estimate turbidity in partially cloudy days with improved accuracy, evidenced by that the corresponding estimated clear-sky irradiance has smaller deviation from measured irradiance in the cloudless time instants. In sum, the proposed method brings new insights about turbidity estimation in both clear and partially cloudy days, providing support to solar resourcing and forecasting.
Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.
Orthographic-Perspective Epipolar Geometry

In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many applications. They can e.g. model 2D maps (such as floor plans), aerial/satellite photography and even approximate narrow field-of-view cameras (e.g. from telephoto lenses). In our experiments we highlight various applications of the developed theory and solvers, including Radar-Camera calibration and aligning Structure-from-Motion models to aerial or satellite images.
Auto-Perceiving Correlation Filter for UAV Tracking

Discriminative correlation filter (DCF)-based methods have demonstrated superior performance in UAV tracking via fusing multiple types of features and updating models online. However, most DCF-based trackers simply cascade different features, failing to fully take advantage of their complementary strength. In addition, online update strategies are limited to using a single and fixed learning rate, which often leads to model degradation when suffering tracking challenges. In this paper, we present an Auto-Perceiving Correlation Filter (APCF) which explicitly models the target and context with a novel Target State and Background Perception (TSBP) feature. Concretely, we first propose a simple yet effective State Evaluation Metric (SEM) to estimate target states by analyzing the spatial distribution of responses. Based on SEM, we extract TSBP features by adaptively selecting effective features depending on the current target state. Accordingly, a new online model update strategy is also introduced to avoid model degradation. Moreover, we further introduce a perception regularization term to make the extracted feature emphasis more on the target rather than background. Extensive experiments on four widely-used UAV benchmarks have well demonstrated the superiority of the proposed method compared with both DCF and deep learning based trackers while running at a high speed of 76.7 FPS on a single CPU. In addition, APCF with deep features also performs favorably against state-of-the-art trackers.
Addressing Data Sparsity with GANs for Multi-fault Diagnosing in Emerging Cellular Networks

Data-driven machine learning is considered a means to address the paramount challenge of timely fault diagnosis in modern and futuristic ultra-dense and highly complex mobile networks. Whereas diagnosing multiple faults in the network at the same time remains an open challenge. In this context, the data sparsity is hindering the potential of machine learning to address such issues. In this work, we have proposed a data augmentation scheme comprising Pix2Pix Generative Adversarial Network (GAN) and a customized loss function never used before, to address the data sparsity challenge in Minimization of Drive Tests (MDT) data. Our proposed unique augmentation scheme generates images of MDT coverage maps with Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values of 25 and 0.97 respectively, which are significantly higher than those achieved without our customized loss function. The performance of data augmentation scheme used is further evaluated with a Convolutional Neural Network (CNN) model for simultaneously detecting most commonly occurring network faults, such as antenna up-tilt, antenna down-tilt, transmission power degradation, and cell outage. The CNN applied on the data generated from the 1% of the MDT data with the proposed augmentation scheme has lead to a gain of 550% in the detection of all classes, including the four faults and cell with normal behavior, as compared to when it is applied on the data generated without our customized loss function.
Towards automated content analysis of rhetorical structure of written essays using sequential content-independent features in Portuguese

Brazilian universities have included essay writing assignments in the entrance examination procedure to select prospective students. The essay scorers manually look for the presence of required Rhetorical Structure Theory (RST) categories and evaluate essay coherence. However, identifying RST categories is a time-consuming task. The literature reported several attempts to automate the identification of RST categories in essays with machine learning. Still, previous studies have focused on using machine learning algorithms trained on content-dependent features that can diminish classification performance, leading to over-fitting and hindering model generalisability. Therefore, this paper proposes: (i) the analysis of state-of-the-art classifiers and content-independent features to the task of RST rhetorical moves; (ii) a new approach that considers the sequence of the text to extract features – i.e. sequential content-independent features; (iii) an empirical study about the generalisability of the machine learning models and sequential content-independent features for this context; (iv) the identification of the most predictive features for automated identification of RST categories in essays written in Portuguese. The best performing classifier, XGBoost, based on sequential content-independent features, outperformed the classifiers used in the literature and are based on traditional content-dependent features. The XGBoost classifier based on sequential content-independent features also reached promising accuracy when tested for generalisability.
Multiclass Imbalanced Handling using ADASYN Oversampling and Stacking Algorithm

Class imbalance conditions in datasets are common in real-world problems. Class imbalance is a condition where the number of classes in the dataset used in the classification process has a significant difference in number. In theory, most single classifiers have a weakness against class imbalance conditions in datasets, especially those with multiclass types, so their performance cannot be maximized. This study proposes two approaches to overcome the problem of multiclass imbalanced, namely the use of ADASYN (Adaptive Synthetic) Sampling and the Stacking Algorithm. As confirmed by testing on five multiclass datasets, the proposed method outperforms other methods in terms of accuracy values, sensitivity, specificity, and geometric mean values. As a result, the method proposed in this study can solve class imbalance problems in multiclass-type datasets. However, this study has limitations. Namely, the dataset used is a multiclass category with a maximum number of six classes. For this reason, further research will suggest testing using imbalanced class datasets in the category of multiclass datasets with more than six classes.
Adaptive Label Allocation for Unsupervised Person Re-Identification

Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task.
Multi-task joint training model for machine reading comprehension

Machine reading comprehension is an important topic in natural language processing. However, the redundancy of text and the diversity of answer types are neglected in existing machine reading comprehension. To tackle these issues, a multi-task joint training scheme is proposed for the multi-type answer machine reading comprehension task. In this scheme, a feature-based paragraph extraction mechanism is first designed in the training stage to extract various text features from answers and sentences. For efficiency, the redundancy of the text is greatly reduced, while the effective information of the text is retained. And then, a reading comprehension module is optimized by enhancing the representation of the pre-trained language model, as well as an answer type classification module is optimized through the capsule network. Finally, a multi-task joint training model is designed to simultaneous obtaining answer text and answer type. Experiments are conducted to evaluate the proposed approach with two open datasets, CJRC and Natural Questions, and demonstrate that our model is efficient and promising, in terms of both the reading comprehension and answer classification tasks. In particular, our model won the best method that is winner of the first prize in the CAIL-2020 machine reading comprehension contest.
Gender-Adversarial Networks for Face Privacy Preserving

Privacy concerns over face recognition systems have attracted extensive attention in various fields. For gender privacy-preserving work, there are two key challenges: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privacy</i> , i.e., confusing gender classifiers and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">utility</i> , i.e., maintaining its face verification performance. To address both issues, this article develops a novel gender-adversarial network, referred to as Gender-AN, to impart gender privacy to face images. Gender-AN employs an attribute-independent encoder–decoder GAN-based network to perturb the input face image, training with the assistance of the proper facial attributes. The perturbed image is then able to obfuscate gender classifiers while maintaining identity discriminability. To optimize the generator, a multitask-based loss function is utilized, which includes attribute manipulation loss, face matcher loss, adversarial loss, and reconstruction loss functions. This optimization facilitates our model to achieve the generalization, verification preserve, and natural appearance, simultaneously. Extensive experiments confirm the effectiveness of the proposed model in enhancing gender privacy and preserving face verification utility.