Reviewerly/exHarmony huggingface dataset:
Real-Time Facial Expression Recognition Based on CNN

In this paper, we propose a method for improving the robustness of real-time facial expression recognition. Although there are many ways to improve the accuracy of facial expression recognition, a revamp of the training framework and image preprocessing allow better results in applications. One existing problem is that when the camera is capturing images in high speed, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of the human facial expression. To solve this problem for smooth system operation and maintenance of recognition speed, we take changes in image characteristics at high speed capturing into account. The proposed method does not use the immediate output for reference, but refers to the previous image for averaging to facilitate recognition. In this way, we are able to reduce interference by the characteristics of the images. The experimental results show that after adopting this method, overall robustness and accuracy of facial expression recognition have been greatly improved compared to those obtained by only the convolution neural network (CNN).
A Context-aware Virtual Reality First Aid Training Application

With the spread of virtual reality (VR) applications in various domains (e.g. education and training, engineering or entertainment) targeting heterogeneous end-users, the aspect context-awareness started to play an important role. As tasks and environments in VR applications become more and more complex, it is important to adapt the interaction to the needs and the context in order to improve usability. In this paper, we study how to exploit context-awareness in VR applications in order to automatically adapt the interaction techniques in a VR application based on context parameter like user, platform, and environment. To show the benefit of our approach, we present a context-aware VR application for first aid training.
Combining text summarization and aspect-based sentiment analysis of users' reviews to justify recommendations

In this paper we present a methodology to justify recommendations that relies on the information extracted from users' reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques including aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts, and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as a justification of the received recommendation. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users.
Edge–texture feature-based image forgery detection with cross-dataset evaluation

A digital image is a rich medium of information. The development of user-friendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs discriminative robust local binary patterns for encoding tampering traces and a support vector machine for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.
Determination of the in-plane shear modulus of unidirectional carbon fiber-reinforced plastics using digital image correlation and finite-element analysis

Accurate in-plane shear properties are essential to simulate the mechanical behavior of fiber-reinforced laminated composites. Herein, a digital image correlation technique was used to analyze unidirectional carbon fiber-reinforced composites by three shear-test methods (±45° in-plane, Iosipescu, and V-notched rail shear tests), focusing on the uniformity of shear deformation during each test. The V-notched rail shear test imposed the most uniform shear deformation. This test, however, still imposed non-uniformity around the deformation regime for the in-plane shear modulus calculation, which needed to be accommodated. The non-uniformity was virtually characterized by a combination of various material properties using finite-element analysis. A universal correction factor of 0.925 resolved the non-uniformity involved in the V-notched rail shear test. This enabled the in-plane shear modulus of unidirectional carbon fiber- or glass fiber-reinforced composites to be accurately determined.
To Mask or Not to Mask?

Activity-oriented cameras are increasingly being used to provide visual confirmation of specific hand-related activities in real-world settings. However, recent studies have shown that bystander privacy concerns limit participant willingness to wear a camera. Researchers have investigated different image obfuscation methods as an approach to enhance bystander privacy; however, these methods may have varying effects on the visual confirmation utility of the image, which we define as the ability of a human viewer to interpret the activity of the wearer in the image. Visual confirmation utility is needed to annotate and validate hand-related activities for several behavioral-based applications, particularly in cases where a human in the loop method is needed to label (e.g., annotating gestures that cannot be automatically detected yet). We propose a new type of obfuscation, activity-oriented partial obfuscation, as a methodological contribution to researchers interested in obtaining visual confirmation of hand-related activities in the wild. We tested the effects of this approach by collecting ten diverse and realistic video scenarios that involved the wearer performing hand-related activities while bystanders performed activities that could be of concern if recorded. Then we conducted an online experiment with 367 participants to evaluate the effect of varying degrees of obfuscation on bystander privacy and visual confirmation utility. Our results show that activity-oriented partial obfuscation (1) maintains visual confirmation of the wearer's hand-related activity, especially when an object is present in the hand, and even when extreme filters are applied, while (2) significantly reducing bystander concerns and enhancing bystander privacy. Informed by our analysis, we further discuss the impact of the filter method used in activity-oriented partial obfuscation on bystander privacy and concerns.
Can a simple approach identify complex nurse care activity?

For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple human activities, e.g., walking, eating, and running. However, many of our daily life activities are usually more complex than those. To instigate research in complex activity recognition, the "Nurse Care Activity Recognition Challenge" [1] is initiated where six nurse activities are to be identified based on location, air pressure, motion capture, and accelerometer data. Our team, "IITDU", investigates the use of simple methods for this purpose. We first extract features from the sensor data and use one of the simplest classifiers, namely K-Nearest Neighbors (KNN). Experiment using an ensemble of KNN classifiers demonstrates that it is possible to achieve approximately 87% accuracy on 10-fold cross-validation and 66% accuracy on leave-one-subject-out cross-validation.
Comparison of extreme learning machine models for gasoline octane number forecasting by near-infrared spectra analysis

The octane number is often used in the evaluation of the gasoline quality due to its antiknock property. The traditional ASTM-CFR internal combustion engine method costs an enormous amount of expenditure and time to determine its level of each product upgrade. The extreme learning machine (ELM) and its improved models are a new type of single-hidden layer feed-forward neural network (SLFN). This structure has advantages over the traditional neural network technology in regression tasks. Based on the response relationship between the near-infrared spectra and the octane number, 60 groups of collected gasoline data are divided into training and test sets. The ELM, online sequential extreme learning machine (OS-ELM), and self-adaptive evolutionary extreme learning machine (SaDE-ELM) models are applied to the prediction task of the octane number. The statistical parameters RMSE, CORR, and R2 and the execution time are used as the performance comparison criteria for the three models. The simulation results show that the OS-ELM and the SaDE-ELM improve the prediction accuracy, the generalization ability, the stability of the ELM in different levels.
Geostatistics in the presence of geological boundaries: Application to mineral resources modeling

This paper addresses the problem of simulating the copper grade and rock type in a Chilean porphyry copper deposit recognized by a set of exploration drill holes. The application of contact analysis tools (cross-to-direct variogram ratios, cross-correlograms, pseudo cross-variograms and lagged scatter plots) reveals the coexistence of hard and soft boundaries between rock type domains. A quadrivariate coregionalization model is developed to fit the spatial correlation structure of the rock type and grade, based on indicator and normal scores direct and cross-variograms. A two-stage approach is first used for simulating the intrusive porphyry and the copper grade in it, while the other two rock type domains and their copper grade are jointly simulated in the space not occupied by porphyry. Such a mixed approach is shown to reproduce the nature of the rock type boundaries and is validated by split-sample validation.
Understanding Visual-Haptic Integration of Avatar Hands Using a Fitts' Law Task in Virtual Reality

Virtual reality (VR) is becoming more and more ubiquitous to interact with digital content and often requires renderings of avatars as they enable improved spatial localization and high levels of presence. Previous work shows that visual-haptic integration of virtual avatars depends on body ownership and spatial localization in VR. However, there are different conclusions about how and which stimuli of the own appearance are integrated into the own body scheme. In this work, we investigate if systematic changes of model and texture of a users' avatar affect the input performance measured in a two-dimensional Fitts' law target selection task. Interestingly, we found that the throughput remained constant between our conditions and that neither model nor texture of the avatar significantly affected the average duration to complete the task even when participants felt different levels of presence and body ownership. In line with previous work, we found that the illusion of virtual limb-ownership does not necessarily correlate to the degree to which vision and haptics are integrated into the own body scheme. Our work supports findings indicating that body ownership and spatial localization are potentially independent mechanisms in visual-haptic integration.
Uplift-based evaluation and optimization of recommenders

Recommender systems aim to increase user actions such as clicks and purchases. Typical evaluations of recommenders regard the purchase of a recommended item as a success. However, the item may have been purchased even without the recommendation. An uplift is defined as an increase in user actions caused by recommendations. Situations with and without a recommendation cannot both be observed for a specific user-item pair at a given time instance, making uplift-based evaluation and optimization challenging. This paper proposes new evaluation metrics and optimization methods for the uplift in a recommender system. We apply a causal inference framework to estimate the average uplift for the offline evaluation of recommenders. Our evaluation protocol leverages both purchase and recommendation logs under a currently deployed recommender system, to simulate the cases both with and without recommendations. This enables the offline evaluation of the uplift for newly generated recommendation lists. For optimization, we need to define positive and negative samples that are specific to an uplift-based approach. For this purpose, we deduce four classes of items by observing purchase and recommendation logs. We derive the relative priorities among these four classes in terms of the uplift and use them to construct both pointwise and pairwise sampling methods for uplift optimization. Through dedicated experiments with three public datasets, we demonstrate the effectiveness of our optimization methods in improving the uplift.
Survey of incremental learning

Incremental learning has become a new research hotspot in the field of machine learning. Compared with traditional machine learning, incremental learning can continuously learn new knowledge from new samples and preserve most of the knowledge that has been learned before. This paper is an overview of the existing incremental learning, and introduces the current popular incremental learning methods from incremental supervisory learning and incremental unsupervised learning, and shows the potential in application. Finally, online incremental research is prospected in this paper.
Focal elements of neural information retrieval models. An outlook through a reproducibility study

This paper analyzes two state-of-the-art Neural Information Retrieval (NeuIR) models: the Deep Relevance Matching Model (DRMM) and the Neural Vector Space Model (NVSM). Our contributions include: (i) a reproducibility study of two state-of-the-art supervised and unsupervised NeuIR models, where we present the issues we encountered during their reproducibility; (ii) a performance comparison with other lexical, semantic and state-of-the-art models, showing that traditional lexical models are still highly competitive with DRMM and NVSM; (iii) an application of DRMM and NVSM on collections from heterogeneous search domains and in different languages, which helped us to analyze the cases where DRMM and NVSM can be recommended; (iv) an evaluation of the impact of varying word embedding models on DRMM, showing how relevance-based representations generally outperform semantic-based ones; (v) a topic-by-topic evaluation of the selected NeuIR approaches, comparing their performance to the well-known BM25 lexical model, where we perform an in-depth analysis of the different cases where DRMM and NVSM outperform the BM25 model or fail to do so. We run an extensive experimental evaluation to check if the improvements of NeuIR models, if any, over the selected baselines are statistically significant.
Reversible Data Hiding by Using Adaptive Pixel Value Prediction and Adaptive Embedding Bin Selection

In this letter, a reversible data hiding (RDH) scheme by using adaptive pixel value prediction and adaptive embedding bin selection based on pixel-based pixel value ordering (PPVO) is proposed. Different from the previous PPVO based methods, each to-be-embedded pixel is predicted by its neighbor pixels, which are selected adaptively according to the complexity of its neighbor pixel values. Moreover, the value range of embedding bin is not constrained in our method. Especially, when the value of embedding bin is less than 0, its value is updated adaptively during the process of embedding and extracting secret bits. Our maximum embedding capacity (EC) is improved significantly due to unconstrained embedding bin. In addition, the optimal embedding bins can be selected to achieve highest visual quality under a given EC. Experimental results show that the proposed method outperforms some state-of-the-art PPVO based RDH methods.
Noise Robust Face Hallucination via Outlier Regularized Least Square and Neighbor Representation

In surveillance scenario the captured face may be of small size, low-quality, low-resolution and noisy. Noise introduces outliers in the captured face images which cause problems in similarity matching, an essential component in attaining the face reconstruction constraints. The situation becomes even more complicated when face images are corrupted by mixed Gaussian-Impulse noise (MIXGIN). To address this problem, a novel outlier regularized least square and neighbor representation (ORLSNR) based face hallucination method is proposed here. The proposed method starts with the detection of the outliers in an input face and performs outlier regularization to reduce the impact of outliers on the reconstruction produce. This assists in achieving the sparsity and locality simultaneously by allowing the selection of the most relevant patches for reconstruction of the high-resolution face. Experimental results performed on public FEI, CMU+MIT face databases, and surveillance videos reflect that the proposed method is computationally efficient and demonstrate better performance than the existing state-of-the-art face hallucination methods.
Prediction of Upstaged Ductal Carcinoma <i>In Situ</i> Using Forced Labeling and Domain Adaptation

The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our clinical task is to use mammographic features to predict whether ductal carcinoma in situ (DCIS) identified at needle core biopsy will be later upstaged or shown to contain invasive breast cancer.To improve the prediction of pure DCIS (negative) versus upstaged DCIS (positive) cases, this study considers the adjunctive roles of two related classes: atypical ductal hyperplasia (ADH), a non-cancer type of breast abnormity, and invasive ductal carcinoma (IDC), with 113 computer vision based mammographic features extracted from each case. To improve the baseline Model A's classification of pure vs. upstaged DCIS, we designed three different strategies (Models B, C, D) with different ways of embedding features or inputs.Based on ROC analysis, the baseline Model A performed with AUC of 0.614 (95% CI, 0.496-0.733). All three new models performed better than the baseline, with domain adaptation (Model D) performing the best with an AUC of 0.697 (95% CI, 0.595-0.797).We improved the prediction performance of DCIS upstaging by embedding two related pathology classes in different training phases.The three new strategies of embedding related class data all outperformed the baseline model, thus demonstrating not only feature similarities among these different classes, but also the potential for improving classification by using other related classes.
Interventions for long‐term software security: Creating a lightweight program of assurance techniques for developers

Summary Though some software development teams are highly effective at delivering security, others either do not care or do not have access to security experts to teach them how. Unfortunately, these latter teams are still responsible for the security of the systems they build: systems that are ever more important to ever more people. We propose that a series of lightweight interventions, six hours of facilitated workshops delivered over three months, can improve a team's motivation to consider security and awareness of assurance techniques, changing its security culture even when no security experts are involved. The interventions were developed after an Appreciative Inquiry and Grounded Theory survey of security professionals to find out what approaches work best. We tested the interventions in a participatory action research field study where we delivered the workshops to three software development organizations and evaluated their effectiveness through interviews beforehand, immediately afterwards, and after twelve months. We found that the interventions can be effective with teams with limited or no security experience and that improvement is long‐lasting. This approach and the learning points arising from the work here have the potential to be applied in many development teams, improving the security of software worldwide.
The Adaptive Multi-Level Phase Coding Method in Audio Steganography

Audio steganography allows and inspires many researchers to design methods for secure communication. Based on the evaluation on the existing methods, it was found that most methods focused on one or two requirements while disregarding others, causing imbalanced performance. Moreover, most methods lack adaptivity and dynamic allocation. Therefore, in this research, a method called Adaptive Multi-level Phase Coding (AMPC) was proposed to optimize the above issues. The reverse logic of the main tradeoffs was used to empirically design several embedding levels that that simultaneously attained good performance for all aspects as much as possible. Then, an adaptive component was added by selecting the embedding level that provided the best performance for each embedding process. Moreover, the error spreading factor was introduced to achieve a fair payload distribution. The performance balance objective requires a new formulation that will enable the accurate selection of the degree of modification, multiple-bit embedding per modification, and reduced retrieval errors. As a result, the interval centering quantization (ICQ) was formulated and implemented in the proposed method. The experimental results show that AMPC successfully fulfilled the research objectives. Also, AMPC surpassed other phase coding methods in all aspects while time-domain methods achieved the highest transparency and capacity with the lowest robustness. Moreover, experiments show that the implementation of adaptive multi-level concept is able to improve the existing method's performance significantly. In summary, AMPC was able to achieve a stable embedding rate of 33 Kbps at 35 dB of SNR, which is higher than the recorded embedding rate of other phase coding methods.