Reviewerly/exHarmony huggingface dataset:
Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously.
Prototyping and testing a virtual reality counterterrorism serious game for active shooting

An active shooting emergency requires an effective strategy to increase the chances of survival of the attacked population. Educational environments are one of the most common locations of such events. To reduce the impact of these disasters, several emergency plans have been developed and put in place. Traditionally, these emergency plans are taught to occupants using non-interactive videos, drills, etc. However, these conventional methods present many limitations regarding trainees' knowledge acquisition, engagement and self-efficacy. To overcome them, this paper presents the prototype of an immersive Virtual Reality (VR) Serious Game (SG) for counterterrorism training. A user-centred evaluation of the proposed training SG was conducted with 32 participants. The experiment aimed to investigate the impact of the proposed tool on participants' knowledge regarding the safest actions to take in case of an active shooter attack. Participants' intrinsic motivation and self-efficacy levels were also assessed before and after the VR-based training. Findings indicate that participants' knowledge, intrinsic motivation, and self-efficacy significantly increased immediately after the training.
Bots and Gender Detection on Twitter Using Stylistic Features

This paper describes our proposed method for the author profiling task at PAN 2019. The aim of this task is to identify the type of a Twitter user (i.e. bot or human). Then, in case of a human, determine its gender (i.e. male or female). Our approach uses a set of language-independent features and it applies machine learning algorithms. After an in-depth experimental study, conducted on English and Spanish datasets, we show that by using a simple set of stylistic information, we can surpass other existing methods that mainly depend on the content of the tweets. For the English dataset, accuracies of 93.06% and 90.04% are obtained for bot an gender classification tasks respectively. Using Spanish tweets, accuracies of 90.53% and 89.11% are achieved for bot and gender detection task respectively.
Deep Convolutional Neural Network for Arabic Speech Recognition

Deep neural networks (DNNs) have made remarkable achievements in acoustic modeling for speech recognition. In this paper, we compare the performance of two proposed models based on Convolutional neural network (CNN). In the first model, CNN is used for features extraction and Long Short- Term Memory (LSTM) is used for recognition. In the second model, CNN with deep architecture is mainly used to execute feature learning and recognition process. This work is focused on single word Arabic automatic speech recognition. We explore the optimal network structure and training strategy for the proposed models. All experiments are conducted using the Arabic Isolated Words Corpus (ASD) database. The results demonstrate the performance and advantages of using the deep CNN for both features extraction and recognition steps.
Scalable Linear Shallow Autoencoder for Collaborative Filtering

Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.
Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules

In this paper, we present a knowledge-aware recommendation framework based on neuro-symbolic graph embeddings that encode first-order logical (FOL) rules. In particular, our workflow starts from a knowledge graph (KG) encoding user preferences (based on explicit ratings [13]) and item properties. Next, knowledge-aware recommendation are obtained through the combination of three modules: (i) a rule learner, that extracts FOL rules from the KG; (ii) a graph embedding module, that learns the embeddings of users and items based on the triples of the KG and the FOL rules previously extracted; (iii) a recommendation module that uses the embeddings to feed a deep learning architecture. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and the results show that the combination of KG embeddings and FOL rules led to an improvement in the accuracy and in the novelty of the recommendations.
A probabilistic public key encryption switching scheme for secure cloud storage

Abstract The high demand for user-centric applications such as secure cloud storage laid the foundation for the development of user-centric security schemes with multiple security features in recent years. But, the current state-of-art techniques primarily emphasized only one type of security feature i.e., either homomorphism or non-malleability. To fill this gap and provide a common platform for both homomorphic and non-malleable cloud applications, we have introduced a new public key-based probabilistic encryption switching method. The proposed scheme generates $${\mathcal {O}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>O</mml:mi> </mml:math> ( m +2 log N ) bits of ciphertext where $$m\in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> and $$m&lt;n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , $$n\in {\mathbb {N}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>∈</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> is the plaintext size, N is the RSA composite. The superior performance of the proposed scheme has been tested in comparison to existing methods and is reported in this paper.
Integration of deep adaptation transfer learning and online sequential extreme learning machine for cross-person and cross-position activity recognition

• Deep adaptation transfer learning combined with OS-ELM to handle cross domain HAR. • GAP and SE structure in CNN facilitate feature extraction and adapt to input data. • DANN and DDC have different effects on cross-person and cross-position transfer. • OS-ELM classifier improves HAR accuracy with a few annotated data in target domain. Deep learning (DL) has been evolving to a prevalent method in human activity recognition (HAR). However, the performance of wearable sensor based HAR models decline significantly when training data come from different persons or sensor positions, and a time-consuming data annotation is indispensible to cater for the big-data driven DL models. In this paper we proposed a fast and robust hybrid model to handle the transfer issues of wearable sensor based HAR between different persons (cross-person) and different positions (cross-position) with just a few annotated data in target domain. The model consists of three parts: (1) A convolutional neural network (CNN) with global average pooling layer to facilitate the extraction of advanced common features in source domain and target domain; (2) A domain adaptive neural network with a gradient reversal layer (DANN) and deep domain confusion network with an adaptive layer (DDC) to reduce domain shift caused by the change of persons and sensor positions; (3) An adaptive classifier based on online sequential extreme learning machine (OS-ELM) to achieve fast and accurate classification with a few annotated data in target domain. Experimental results on four public datasets verified the superiority of the proposed hybrid model over standard CNN and deep transfer learning models in adapting the classifier to new sensor locations and subjects quickly, where the HAR accuracy can be improved by at least 12% for cross-person transfer and 20% for cross-position transfer, respectively.
Contrastive deep embedded clustering

Deep embedded clustering is a popular unsupervised learning method owing to its outstanding performance in data-mining applications. However, existing methods ignore the difficulty in learning discriminative features via clustering due to the lack of supervision, which can be easily obtained in classification tasks. To alleviate this problem, we build a contrastive learning based deep embedded clustering method, i.e., CDEC. Specifically, our model adopts deep auto-encoders to learn a latent discriminative embedded clustering structure. To overcome the problem of lacking label information, the CDEC constructs positive samples and negative samples with the data reconstructed from the data itself and other data, respectively. By maximizing the distance between positive and negative ones, the CDEC can not only obtain the most representative features but also explore the discriminative features. Extensive experiments on several public datasets demonstrate that our method achieves the state-of-the-art clustering effectiveness. Our codes are available at: https://github.com/guoshuaiS/contrastive-deep.
AdaInject: Injection-Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks

The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD)-based optimization techniques. The existing SGD optimizers generally suffer with the overshooting of the minimum and oscillation near minimum. In this article, we propose a new approach, hereafter referred as AdaInject, for the gradient descent optimizers by injecting the second-order moment into the first-order moment. Specifically, the short-term change in parameter is used as a weight to inject the second-order moment in the update rule. The AdaInject optimizer controls the parameter update, avoids the overshooting of the minimum, and reduces the oscillation near minimum. The proposed approach is generic in nature and can be integrated with any existing SGD optimizer. The effectiveness of the AdaInject optimizer is explained intuitively as well as through some toy examples. We also show the convergence property of the proposed injection-based optimizer. Furthermore, we depict the efficacy of the AdaInject approach through extensive experiments in conjunction with the state-of-the-art optimizers, namely AdamInject, diffGradInject, RadamInject, and AdaBeliefInject, on four benchmark datasets. Different CNN models are used in the experiments. A highest improvement in the top-1 classification error rate of 16.54% is observed using diffGradInject optimizer with ResNeXt29 model over the CIFAR10 dataset. Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark

Abstract Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (Begin), comprising 12k dialogue turns generated by neural dialogue systems trained on three knowledge-grounded dialogue corpora. We collect human annotations assessing the extent to which the models’ responses can be attributed to the given background information. We then use Begin to analyze eight evaluation metrics. We find that these metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. Our findings underscore the need for more sophisticated and robust evaluation metrics for knowledge-grounded dialogue. We make Begin publicly available at https://github.com/google/BEGIN-dataset.
Vehicle detection method with low-carbon technology in haze weather based on deep neural network

Abstract Vehicle detection based on deep learning achieves excellent results in normal environments, but it is still challenging to detect objects in low-quality picture obtained in hazy weather. Existing methods tend to ignore favorable latent information and it is difficult to balance speed and accuracy, etc. Therefore, the existing deep neural network is studied, and the YOLOv3 algorithm is improved based on ResNet. Aiming at the problem of low utilization of shallow features, DensNet is added in the feature extraction stage to reduce feature loss and increase utilization. An attention module is added in the feature extraction and fusion stage to better focus on potential information and improve the detection accuracy in haze weather. In view of the difficulty of vehicle detection in haze weather, focal loss is introduced to give more weights to difficult samples, balance the number of difficult and easy samples and improve detection accuracy. The experimental results show that the recognition accuracy of the improved network for vehicles reaches 75%, which proves the effectiveness of the method.
An Investigation of Shoulder Surfing Attacks on Touch-Based Unlock Events

This paper contributes to our understanding of user-centered attacks on smartphones. In particular, we investigate the likelihood of so-called shoulder surfing attacks during touch-based unlock events and provide insights into users' views and perceptions. To do so, we ran a two-week in-the-wild study (N=12) in which we recorded images with a 180-degree field of view lens that was mounted on the smartphone's front-facing camera. In addition, we collected contextual information and allowed participants to assess the situation. We found that only a small fraction of shoulder surfing incidents that occur during authentication are actually perceived as threatening. Furthermore, our findings suggest that our notions of (un)safe places need to be rethought. Our work is complemented by a discussion of implications for future user-centered attack-aware systems. This work can serve as a basis for usable security researchers to better design systems against user-centered attacks.
How Does Sleep Tracking Influence Your Life?

A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one's sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.
Enhanced Moth-flame Optimizer with Quasi-Reflection and Refraction Learning with Application to Image Segmentation and Medical Diagnosis

Background: Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks. Objective: To overcome the above shortcomings, this paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields. Method: In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase; refraction learning improves the accuracy of the potential optimal solution. Results: Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper; first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms; afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case. Conclusion: Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy.
A new “Wall Street Darling?” effects of regulation sentiment in cryptocurrency markets

We analyze the prospect of cryptocurrency regulation affects cryptocurrency prices, volatility and trading using the Google Trend technique to construct the Crypto Regulation Sentiment Index (CRSX) reflecting investors' attitude towards Crypto regulation. Our analysis encompasses over 75% of the crypto market's daily activity. We find CRSX has no statistically significant long-term impact on cryptocurrency prices, consistent with investors having overly positive attitudes towards crypto assets, making crypto a "Wall Street Darling". Nonetheless, CRSX has a large impact on cryptocurrency price volatility and trading volume. The effects of CSRX on crypto markets largely depends on the coin's key blockchain characteristics.
Conv-transformer architecture for unconstrained off-line Urdu handwriting recognition

Unconstrained off-line handwriting text recognition in general and for Arabic-like scripts in particular is a challenging task and is still an active research area. Transformer-based models for English handwriting recognition have recently shown promising results. In this paper, we have explored the use of transformer architecture for Urdu handwriting recognition. The use of a convolution neural network before a Vanilla full transformer and using Urdu printed text-lines along with handwritten text lines during the training are the highlights of the proposed work. The convolution layers act to reduce the spatial resolutions and compensate for the $$n^{2}$$ complexity of transformer multi-head attention layers. Moreover, the printed text images in the training phase help the model in learning a greater number of ligatures (a prominent feature of Arabic-like scripts) and a better language model. Our model achieved state-of-the-art accuracy (CER of $$5.31\%$$ ) on publicly available NUST-UHWR dataset (Zia et al. in Neural Comput Appl 34:1–14, 2021).
Can Industrial Intrusion Detection Be SIMPLE?

Cyberattacks against industrial control systems pose a serious risk to the safety of humans and the environment. Industrial intrusion detection systems oppose this threat by continuously monitoring industrial processes and alerting any deviations from learned normal behavior. To this end, various streams of research rely on advanced and complex approaches, i.e., artificial neural networks, thus achieving allegedly high detection rates. However, as we show in an analysis of 70 approaches from related work, their inherent complexity comes with undesired properties. For example, they exhibit incomprehensible alarms and models only specialized personnel can understand, thus limiting their broad applicability in a heterogeneous industrial domain. Consequentially, we ask whether industrial intrusion detection indeed has to be complex or can be SIMPLE instead, i.e., Sufficient to detect most attacks, Independent of hyperparameters to dial-in, Meaningful in model and alerts, Portable to other industrial domains, Local to a part of the physical process, and computationally Efficient. To answer this question, we propose our design of four SIMPLE industrial intrusion detection systems, such as simple tests for the minima and maxima of process values or the rate at which process values change. Our evaluation of these SIMPLE approaches on four state-of-the-art industrial security datasets reveals that SIMPLE approaches can perform on par with existing complex approaches from related work while simultaneously being comprehensible and easily portable to other scenarios. Thus, it is indeed justified to raise the question of whether industrial intrusion detection needs to be inherently complex.
A Novel High-Performance Implementation of CRYSTALS-Kyber with AI Accelerator

Public-key cryptography, including conventional cryptosystems and post-quantum cryptography, involves computation-intensive workloads. With noticing the extraordinary computing power of AI accelerators, in this paper, we further explore the feasibility to introduce AI accelerators into high-performance cryptographic computing. Since AI accelerators are dedicated to machine learning or neural networks, the biggest challenge is how to transform cryptographic workloads into their operations, while ensuring the correctness of the results and bringing convincing performance gains. After investigating and analysing the workload of NVIDIA AI accelerator, Tensor Core, we choose to utilize it to accelerate the polynomial multiplication, usually the most time-consuming part in lattice-based cryptography. We take measures to accommodate the matrix-multiply-and-add mode of Tensor Core and make a trade-off between precision and performance, to leverage it as a high-performance NTT box performing NTT/INTT through CUDA C++ WMMA APIs. Meanwhile, we take CRYSTALS-Kyber, the candidate to be standardized by NIST, as a case study on RTX 3080 with the Ampere Tensor Core. The empirical results show that the customized NTT of polynomial vector ( $$n=256,k=4$$ ) with our NTT box obtains a speedup around 6.47x that of the state-of-the-art implementation on the same GPU platform. Compared with the AVX2 implementation submitted to NIST, our Kyber-1024 can achieve a speedup of 26x, 36x, and 35x for each phase.