Reviewerly/exHarmony huggingface dataset:
Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs Dataset

Abstract We offer methods to analyze the “differentially private” Facebook URLs Dataset which, at over 40 trillion cell values, is one of the largest social science research datasets ever constructed. The version of differential privacy used in the URLs dataset has specially calibrated random noise added, which provides mathematical guarantees for the privacy of individual research subjects while still making it possible to learn about aggregate patterns of interest to social scientists. Unfortunately, random noise creates measurement error which induces statistical bias—including attenuation, exaggeration, switched signs, or incorrect uncertainty estimates. We adapt methods developed to correct for naturally occurring measurement error, with special attention to computational efficiency for large datasets. The result is statistically valid linear regression estimates and descriptive statistics that can be interpreted as ordinary analyses of nonconfidential data but with appropriately larger standard errors.
A fuzzy-based ensemble model for improving malicious web domain identification

Accurate identification of malicious web domains is crucial for protecting users from the risks of theft of private information, malware attack, and monetary loss. Various methods, including blacklists and machine learning-based models, have been proposed to identify malicious web domains effectively. However, maintaining an up-to-date blacklist is difficult, and standard machine learning-based models are typically sensitive to noise in data. In this paper, we propose an ensemble model based on the fuzzy-weighted Least Squares Support Vector Machine (EFW-LS-SVM) for improving malicious web domain identification. Given the fact that different data samples may have varying importance, we introduce a fuzzy-weighted operation by applying it to each data sample. This is the first time the fuzzy-weighted operation has been incorporated into an ensemble approach for malicious web domain identification. Our proposed EFW-LS-SVM delivers excellent results for identifying malicious web domains; it outperformed the compared machine learning models in terms of the F-measure score, as well as provided the best or very competitive accuracy of up to 94.50% for all datasets included in our experiments. Further, considering the imbalanced nature of benign and malicious web domain data, where malicious web domains tend to be the minority, we used the Synthetic Minority Over-sampling Technique (SMOTE) to further improve the performance of all models tested. Our experimental results confirm that SMOTE re-sampling can improve the performance of all the models, including our proposed EFW-LS-SVM—the F-measure score of EFW-LS-SVM was improved by up to 3.29%.
A Keylogging Inference Attack on Air-Tapping Keyboards in Virtual Environments

Enabling users to push the physical world&#x2019;s limits, augmented and virtual reality platforms opened a new chapter in perception. Novel immersive experiences resulted in the emergence of new interaction methods for virtual environments, which came with unprecedented security and privacy risks. This paper presents a keylogging inference attack to infer user inputs typed with in-air tapping keyboards. We observe that hands follow specific patterns when typing in the air and exploit this observation to carry out our attack. Starting with three plausible attack scenarios where the adversary obtains the hand trace patterns of the victim, we build a pipeline to reconstruct the user input. Our attack pipeline takes the hand traces of the victim as an input and outputs a set of input inferences ordered from the best to worst. Through various experiments, we showed that our inference attack achieves a pinpoint accuracy ranging from 40% to 87% within at most the top-500 candidate reconstructions. Finally, we discuss countermeasures, while the results presented provide a cautionary tale of the security and privacy risk of the immersive mobile technology.
A nonlinear modeling of fractional order based variational model in optical flow estimation

In this paper, a nonlinear fractional order variational (NFOV) model is introduced to estimate the optical flow. In particular, the presented model can be scaled to the existing integer order variational models. The proposed variational functional is designed by combining a non-quadratic Charbonnier norm based data term and Marchaud fractional derivative based regularization term. This non-quadratic penalty is robust against outliers, whereas the non-local character of Marchaud fractional derivative leads the model to deal with the discontinuous information about texture and edges, and yields a dense flow field. The numerical discretization of the Marchaud fractional derivative is carried out with the help of Grünwald–Letnikov fractional derivative. The discretized nonlinear system is further reduced to a linear system of equations by employing an outer fixed point iteration scheme, and finally solved with an efficient iteration technique. The experiments are performed over a variety of datasets. The performance of the model is tested using different error measures (AAE, AEE, AENG, WE) depending upon the availability of the ground truth (GT) flow. The robustness of the NFOV model is shown under different amounts of noise along with the variation of smoothing parameter. A detailed comparison with the recently published works is presented to demonstrate the efficiency and accuracy of the proposed model.
How did you feel during the navigation? Influence of emotions on browsing time and interaction frequency in immersive virtual environments

Immersive virtual environments (IVEs) represent virtual settings that simulate the physical world. Users interacting in such virtual venues commonly display behaviours like the ones that would occur in the physical world. However, little is known about how the affective states experienced while browsing IVEs may in turn, influence user’s interactive behaviour. The present research investigates how the affect in terms of arousal and valence generated by IVEs influences browsing time and interaction frequency. Three studies analyse various facets of affect in IVEs. Study 1 investigates the cognitive responses and shows that browsing time is positively affected by self-reported arousal. Study 2 analyses neurophysiological responses and demonstrates consistent results with Study 1. It further shows that physiological correlates of arousal and valence positively influence interaction frequency. Study 3 delves into personal interest, a recurring factor emerged in the two previous studies, and investigates its interaction with arousal. Findings show that in a high arousal IVE, highly interested users are more likely to browse longer than low-interested users. Overall, the results show that behavioural realism evoked in IVEs involves both users’ actions and affective states. Implications in terms of design guidelines to foster positive affect in IVEs are drawn.
Multilayer extreme learning machines and their modeling performance on dynamical systems

In this paper, two novel Multilayer Extreme Learning Machine (ML-ELM) networks are presented. We call them Improved Multilayer Extreme Learning Machines (IML-ELM). The proposed network architectures use neuron activations both during and after the training. In the first IML-ELM (IML-ELM1) network, each layer has connection weights assigned randomly as orthonormal. On the other hand, the second IML-ELM (IML-ELM2) has connection weights assigned randomly as orthonormal only in the first layer. Its following layers’ connection weights are taken from the previous layer’s output weight matrix. This assignment strategy made in the IML-ELM2 decreases the computation time even more. The networks’ modeling performances on seven benchmark dynamic systems are investigated and it is shown that the proposed IML-ELM1 and IML-ELM2 perform better modeling than the ML-ELM. They have better modeling performance of more than 70% for both training and test data sets compared to ML-ELM for some systems studied. For instance, using 100 nodes, ML-ELM, IML-ELM1 and IML-ELM2 gave average testing root mean square error results of 0.627977, 0.104272 (83%) and 0.092683 (85%) respectively for BDS 7. In addition, it has been experimentally determined that the developed networks provide improvements in terms of average training time, and this improvement exceeds 60% in some cases. These achievements clearly prove that the proposed improved multilayer extreme learning machines are efficient tools for system modeling applications.
A survey of visual and procedural handwriting analysis for neuropsychological assessment

Abstract To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.
Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search

Moving Objects Segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, content based video search, traffic monitoring, surveillance, and security. MOS becomes challenging due to abrupt illumination variations, dynamic backgrounds, camouflage and scenes with bootstrapping. To address these challenges we propose a MOS algorithm exploiting multiple adversarial regularizations including conventional as well as least squares losses. More specifically, our model is trained on scene background images with the help of cross-entropy loss, least squares adversarial loss and ℓ1 loss in image space working jointly to learn the dynamic background changes. During testing, our proposed method aims to generate test image background scenes by searching optimal noise samples using joint minimization of ℓ1 loss in image space, ℓ1 loss in feature space, and discriminator least squares loss. These loss functions force the generator to synthesize dynamic backgrounds similar to the test sequences which upon subtraction results in moving objects segmentation. Experimental evaluations on five benchmark datasets have shown excellent performance of the proposed algorithm compared to the twenty one existing state-of-the-art methods.
Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm

In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot.
Isolation Forests and Deep Autoencoders for Industrial Screw Tightening Anomaly Detection

Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a key industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised detection approaches. In particular, we assume a computationally light low-dimensional problem formulation based on angle–torque pairs. Our work is focused on two unsupervised machine learning (ML) algorithms: isolation forest (IForest) and a deep learning autoencoder (AE). Several computational experiments were held by assuming distinct datasets and a realistic rolling window evaluation procedure. First, we compared the two ML algorithms with two other methods, a local outlier factor method and a supervised Random Forest, on older data related with two production days collected in November 2020. Since competitive results were obtained, during a second stage, we further compared the AE and IForest methods by adopting a more recent and larger dataset (from February to March 2021, totaling 26.9 million observations and related to three distinct assembled products). Both anomaly detection methods obtained an excellent quality class discrimination (higher than 90%) under a realistic rolling window with several training and testing updates. Turning to the computational effort, the AE is much lighter than the IForest for training (around 2.7 times faster) and inference (requiring 3.0 times less computation). This AE property is valuable within this industrial domain since it tends to generate big data. Finally, using the anomaly detection estimates, we developed an interactive visualization tool that provides explainable artificial intelligence (XAI) knowledge for the human operators, helping them to better identify the angle–torque regions associated with screw tightening failures.
Combining feature importance and neighbor node interactions for cold start recommendation

Cold start recommendation usually views preference embedding as a missing problem because there is not any historical interaction. Existing approaches on graph neural networks for cold start users/items build the attribute embedding of each node through simply concatenating multiple features equally, and then reconstruct node preference embedding from its attribute embedding through a mapping function which is learned from warm users/items. However, these approaches do not consider the different contributions of features for building the attribute embedding. In addition, they assume the neighbors of a target node are independent and ignore interactions between the neighbor nodes when building the mapping function between the attribute embedding and the preference embedding. These two limitations reduce the effectiveness of their performance. To overcome these limitations, we propose a novel framework called Feature Importance and Neighbor node Interactions graph neural network (FINI) that exploits feature weights and interactions between neighbor nodes. The core ideas of the proposed method are as follows. First, it designs a global–local contexts attention mechanism in the attribute encoding layer, which can dynamically learn the importance of the attributes of each node and improve the expression of the feature embeddings. Second, it proposes a mixed interaction mechanism to augment the weighted information of neighbor node interactions in the neighbor interaction layer, which can strengthen the expressive capability of the user/item embeddings and further improve the quality of the mapping function for cold start users/items. Additionally, we also combine the rating prediction loss and mimic loss as the total loss to train the network in the prediction layer for model training. To assess the performance of the FINI, both cold start users and cold start items recommendation are considered. The results demonstrate FINI outperforms the state-of-the-art approaches for cold start recommendation and gains significant improvements in terms of metric evaluations.
CAE‐WANN: A novel anomaly detection method for gas turbines via search space extension

Abstract Anomaly detection of gas turbines is a typical binary classification problem under small sample size. In essence, the goal of classification is to discover the mapping that can map samples in different categories to disjoint codomains. The neural network has strong nonlinear mapping ability thus it has great potentialities in classification tasks. However, how to design the architecture of the neural network is still a challenging problem. The inappropriate network architecture might cause either overfitting or underfitting problems. To alleviate the problem, this paper presents an effective anomaly detection model for gas turbines by combining the convolutional auto‐encoder (CAE) and weight agnostic neural network (WANN) search. More specifically, the CAE is used for the search space extension by obtaining high‐dimensional latent representations of the raw data. Then, the WANN search finds a suitable neural network architecture for anomaly detection in the extended search space. Moreover, a novel method based on maximum likelihood estimation is proposed for threshold selection, which is also effective in the case of unbalanced datasets. In the end, the real‐life monitoring data of gas turbines validate the effectiveness of the presented anomaly detection model.
Effective active learning in digital pathology: A case study in tumor infiltrating lymphocytes

Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they require a large amount of annotated training data from expert pathologists. The aim of this study is to minimize the data annotation need in these analyses.Active learning (AL) is an iterative approach to training deep learning models. It was used in our context with a Tumor Infiltrating Lymphocytes (TIL) classification task to minimize annotation. State-of-the-art AL methods were evaluated with the TIL application and we have proposed and evaluated a more efficient and effective AL acquisition method. The proposed method uses data grouping based on imaging features and model prediction uncertainty to select meaningful training samples (image patches).An experimental evaluation with a collection of cancer tissue images shows that: (i) Our approach reduces the number of patches required to attain a given AUC as compared to other approaches, and (ii) our optimization (subpooling) leads to AL execution time improvement of about 2.12×.This strategy enabled TIL based deep learning analyses using smaller annotation demand. We expect this approach may be used to build other analyses in digital pathology with fewer training samples.
Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation

We consider neural sign language translation: machine translation from signed to written languages using encoder–decoder neural networks. Translating sign language videos to written language text is especially complex because of the difference in modality between source and target language and, consequently, the required video processing. At the same time, sign languages are low-resource languages, their datasets dwarfed by those available for written languages. Recent advances in written language processing and success stories of transfer learning raise the question of how pretrained written language models can be leveraged to improve sign language translation. We apply the Frozen Pretrained Transformer (FPT) technique to initialize the encoder, decoder, or both, of a sign language translation model with parts of a pretrained written language model. We observe that the attention patterns transfer in zero-shot to the different modality and, in some experiments, we obtain higher scores (from 18.85 to 21.39 BLEU-4). Especially when gloss annotations are unavailable, FPTs can increase performance on unseen data. However, current models appear to be limited primarily by data quality and only then by data quantity, limiting potential gains with FPTs. Therefore, in further research, we will focus on improving the representations used as inputs to translation models.
An Effective Gaze-Based Authentication Method with the Spatiotemporal Feature of Eye Movement

Eye movement has become a new behavioral feature for biometric authentication. In the eye movement-based authentication methods that use temporal features and artificial design features, the required duration of eye movement recordings are too long to be applied. Therefore, this study aims at using eye movement recordings with shorter duration to realize authentication. And we give out a reasonable eye movement recording duration that should be less than 12 s, referring to the changing pattern of the deviation degree between the gaze point and the stimulus point on the screen. In this study, the temporal motion features of the gaze points and the spatial distribution features of the saccade are using to represent the personal identity. Two datasets are constructed for the experiments, including 5 s and 12 s of eye movement recordings. On the datasets constructed in this paper, the open-set authentication results show that the Equal Error Rate of our proposed methods can reach 10.62% when recording duration is 12 s and 12.48% when recording duration is 5 s. The closed-set authentication results show that the Equal Error Rate of our proposed methods can reach 5.25% when recording duration is 12 s and 7.82% when recording duration is 5 s. It demonstrates that the proposed method provides a reference for the eye movements data-based identity authentication.