Reviewerly/exHarmony huggingface dataset:
Scale-aware CNN for crowd density estimation and crowd behavior analysis

Visual surveillance is a highly demanded system in various real-time applications for civil and military sectors. For huge gatherings such as rallies and stadiums, crowd monitoring becomes a tedious task. Moreover, crowd density estimation also plays an important role to improve the performance of crowd monitoring system. Several techniques have been presented for crowd counting or density estimation and crowd behavior analysis. The crowd scenes contain noise, occlusion, cluttered environment which creates complexity to analyze the crowd behavior. To overcome the challenges of crowd monitoring, we present a combined solution to perform the crowd density estimation and crowd behavior analysis. The crowd density estimation is carried out by developing a new CNN based architecture which considers scale information and overcome the issues of scale variations. In order to deal with this issue of scale variation, we introduce a scale-aware attention module where multiple branches of self-attention module are concatenated to improve the scale variation realization. Further, we present the crowd behavior analysis model which uses motion map and energy level distribution based features to detect the abnormal crowd behavior. The Proposed approach achieves average accuracy of abnormal event detection as 98.95, and crowd counting accuracy as 97.60. Furthermore, the average MAE and MSE is achieved as 58.60, 98.55 for ShanghaiTech Part A dataset and 7.55, 8.50 for Part B dataset, respectively. Similarly, the MAE and MSE for UCF 50 dataset is obtained as 210.20, and 260.80, respectively which shows a significant improvement over other techniques discussed in the experimental study.
Automatic classification with an autoencoder of seismic signals on a distributed acoustic sensing cable

This study probes the association between fluid injection in enhanced geothermal systems and certain kinds of seismicity that may result from hydraulic fracturing occurring at depth using unsupervised machine learning. In April and May 2019, a distributed acoustic sensing borehole array at the Frontier Observatory for Research in Geothermal Energy site near Milford, Utah recorded seismic data during hydraulic injection stimulation of a nearby well. Using an autoencoder, a type of deep neural network, we reduce the dimensionality of spectrograms of the detected signals to a lower-dimensional latent feature space with just nine dimensions. Next, Gaussian mixture model clustering is performed on this latent feature space, assigning each detected signal to one of 7 classes. For each signal class, we examine spatiotemporal distributions of the clustered results and find that total detections exhibit a bimodal distribution with respect to channel depth. The shallow mode occurs between 250 and 500 m, and the deep mode is centered around 750 m. In the temporal distribution, clustering results show the two best-clustered signal classes exhibit weak or no correlation with injection-related activities. More generally, we demonstrate the ability to discern not just when and where signals are detected, but also what kind, thus enabling rapid and targeted data exploration and providing constraints on source mechanisms.
Entropic herding

Herding is a deterministic algorithm used to generate data points that can be regarded as random samples satisfying input moment conditions. The algorithm is based on the complex behavior of a high-dimensional dynamical system and is inspired by the maximum entropy principle of statistical inference. In this paper, we propose an extension of the herding algorithm, called entropic herding, which generates a sequence of distributions instead of points. Entropic herding is derived as the optimization of the target function obtained from the maximum entropy principle. Using the proposed entropic herding algorithm as a framework, we discuss a closer connection between herding and the maximum entropy principle. Specifically, we interpret the original herding algorithm as a tractable version of entropic herding, the ideal output distribution of which is mathematically represented. We further discuss how the complex behavior of the herding algorithm contributes to optimization. We argue that the proposed entropic herding algorithm extends the application of herding to probabilistic modeling. In contrast to original herding, entropic herding can generate a smooth distribution such that both efficient probability density calculation and sample generation become possible. To demonstrate the viability of these arguments in this study, numerical experiments were conducted, including a comparison with other conventional methods, on both synthetic and real data.
PRo-Pat: Probabilistic Root–Pattern Bi-gram data language model for Arabic based morphological analysis and distribution

Based on 29,192,662 html files obtained from the ClueWeb a bi-gram data language model for Arabic is constructed. The created dataset is considering standard types of bi-gram analysis, however with focus on the root-pattern paradigm in Arabic. Root-Pattern distributions in form of P(root|pattern), P(pattern|root) and P(pattern|pattern) are additionally estimated. The aspect of considering the Maximum Likelihood Estimation (MLE) on the root-pattern level as a higher-level of abstraction, has been widely neglected in Arabic research community despite its advantage in reducing ambiguities within Arabic morphological analysis and its impact on cognitive aspect on Arabic word perception [1]. In the preprocessing phase, the html files were converted to 974 unfiltered raw text files with the size of about 180 GB. These files were morphologically analyzed towards extracting and counting frequencies of patterns, roots, particle, and stems and particularly root-pattern occurrences. Based on a resulting corpus containing around 18,482,719 raw words, a language data model is constructed containing 9,311,246 bi-grams of morphologically analyzed wordform, including around 3.49 million bi-directional P(root|pattern) and around 1.153 million P(patttern|pattern) bi-grams in form of conditional probabilities covering a subset of around 8086 roots with 20413 possible pattern-forms. As this data model is considering the root-pattern phenomenon in Arabic, the created data are useful for researchers working on cognitive aspect of Arabic such as visual word cognition, morpho-phonetic perception, morphological analysis, spell-checking, and resolving ambiguities in morphological parsing.
Enhancing organizational communication via intelligent voice assistant for knowledge workers: The role of perceived supervisor support, psychological capital, and employee wellbeing

In the world of globalization, knowledge workers have grown in importance to organizational development. Currently, the intelligence voice assistant (IVA) has a high degree of participation in people's lives, displaying a trend of becoming an emotional partner for humans. Therefore, whether the application of IVA can help enhance organizational communication for knowledge workers is worthy to be explored. This study adopted an interactive situation-simulation survey experiment to explore the impact of the application of IVA on knowledge workers' perceived supervisor support, psychological capital, and employee wellbeing, using a 2 (voice gender: male/female) by 2 (voice characteristics: machine-like/anthropomorphic) between-subject experimental design with two additional control groups (text-only/no-interaction). Besides, voice interviews were designed to understand what knowledge workers need from the IVA. Results show that: (1) There were pair-wise correlations among perceived supervisor support, psychological capital, and employee wellbeing of knowledge workers, and psychological capital played a complete mediating role between perceived supervisor support and employee wellbeing; (2) There were significant differences among the scores of different groups (the experimental groups, the text-only control group and the no-interaction control group) on perceived supervisory support and employee wellbeing, with the experimental groups overall better than the control groups. (3) Knowledge workers hope that the services provided by the IVA mainly include three categories: work support, emotional support and life support. In conclusion, this study shows that the use of IVA can significantly improve perceived supervisor support, psychological capital, and employee wellbeing of knowledge workers, and provide some useful directions for the product design.
Intelligent health management based on analysis of big data collected by wearable smart watch

Some problems still exist in health management and application such as insufficient data, limited technology, and lack of professional evaluation methods by physicians with medical theory. In this study, an intelligent method is based on an analysis of physiological big data collected by wearable smartwatches. Firstly, physiological data such as pulse, heart rate, and blood oxygen were collected continuously from individuals by wearing smartwatches, and the data was digitally transmitted. Secondly, the transmitted data was sent to a health management platform by Narrow Band Internet of Things. Analyzing the data, physicians evaluated individual situations via an intelligent math model. Finally, the results were fed back to individuals through a smartphone APP to finish a medical diagnosis, disease prediction, or warning. The intelligent health management method and technology created via years of studies have been verified and will provide a new and effective strategy for health management.
Zero-bit fragile watermarking for medical image tamper detection and recovery using RS code and lifting wavelet transform

Medical images are habitually composed of Regions of Interest (ROI) and Region of Non-interest (RONI). ROI means the area containing the crucial information for diagnosis. To achieve the fidelity and integrity of the ROI, we propose a novel watermarking approach based on two great concepts zero-watermarking and Error Correcting Code (ECC). Tamper detection and recovery information are stored in RONI which accepts some visual quality degradation. Experimental results prove that our proposed approach produces a good performance in terms of imperceptibility and the ability to resist several hard attacks. The proposed scheme can recover 100% of the ROI until 15% of distortion is caused by salt and pepper noise. For multiplicative and Gaussian noise, the scheme has also a good capability of recovery (between 70% and 100%). With close to 20% of distortion, the system can recover 50% of the ROI. The proposed method has also a good capability of recovery against cropping and image processing operations.
A comprehensive interpretation for medical VQA: Datasets, techniques, and challenges

Recent technological developments and improvement in the medical domain demands advancement, to address the issue of early disease detection. Also, the current pandemic has resulted in considerable progress of improvement in the medical domain, through online consultation by physicians for different diseases using clinical reports and medical images. A similar process is adopted in developing a Visual Question Answering (VQA) system in the medical field. In this paper, existing medical VQA datasets, appropriate techniques, suitable quantitative metrics, real time challenges and, the implementation of one VQA approach with algorithms and performance evaluation are discussed. The medical VQA datasets collected from multiple sources are represented in different perspectives (organwise, planewise, modality-type and abnormality-type) for a better understanding and visualization. Then the techniques used in VQA are subsequently grouped and explained, based on evolution, complexity in the dataset and the need for semantics in understanding the questions. In addition, the implementation of a VQA approach using VGGNet and LSTM is carried out for existing and improved datasets, and analyzed with accuracy and BLEU score metrics. The improved datasets, created through dataset reduction and augmentation approaches, resulted in better performance than the existing datasets. Finally, the challenges of the medical VQA domain are examined in terms of datasets, combining techniques, and modifying the parameters of existing performance metrics for future research.
Deep Metric Learning with Adaptively Composite Dynamic Constraints

In this paper, we propose a deep metric learning with adaptively composite dynamic constraints (DML-DC) method for image retrieval and clustering. Most existing deep metric learning methods impose pre-defined constraints on the training samples, which might not be optimal at all stages of training. To address this, we propose a learnable constraint generator to adaptively produce dynamic constraints to train the metric towards good generalization. We formulate the objective of deep metric learning under a proxy Collection, pair Sampling, tuple Construction, and tuple Weighting (CSCW) paradigm. For proxy collection, we progressively update a set of proxies using a cross-attention mechanism to integrate information from the current batch of samples. For pair sampling, we employ a graph neural network to model the structural relations between sample-proxy pairs to produce the preservation probabilities for each pair. Having constructed a set of tuples based on the sampled pairs, we further re-weight each training tuple to adaptively adjust its effect on the metric. We formulate the learning of the constraint generator as a meta-learning problem, where we employ an episode-based training scheme and update the generator at each iteration to adapt to the current model status. We construct each episode by sampling two subsets of disjoint labels to simulate the procedure of training and testing and use the performance of the one-gradient-updated metric on the validation subset as the meta-objective of the assessor. We conducted extensive experiments on five widely used benchmarks under two evaluation protocols to demonstrate the effectiveness of the proposed framework.
How to Manage Cyber Risks: Lessons Learned From Medical Science

In this article, we propose an approach for managing cyber risk that borrows from how medical professionals perform risk management.
A Predictive Cyber Threat Model for Mobile Money Services

Mobile Money Services (MMS), enabled by the wide adoption of mobile phones, offered an opportunity for financial inclusion for the unbanked in developing nations. Meanwhile, the risks of cybercrime are increasing, becoming more widespread, and worsening. This is being aggravated by the inadequate security practises of both service providers and the potential customers' underlying criminal intent to undermine the system for financial gain. Predicting potential mobile money cyber threats will afford the opportunity to implement countermeasures before cybercriminals explore this opportunity to impact mobile money assets or perpetrate financial cybercrime. However, traditional security techniques are too broad to address these emerging threats to Mobile Financial Services (MFS). Furthermore, the existing body of knowledge is not adequate for predicting threats associated with the mobile money ecosystem. Thus, there is a need for an effective analytical model based on intelligent software defence mechanisms to detect and prevent these cyber threats. In this study, a dataset was collected via interview with the mobile money practitioners, and a Synthetic Minority Oversampling Technique (SMOTE) was applied to handle the class imbalance problem. A predictive model to detect and prevent suspicious customers with cyber threat potential during the onboarding process for MMS in developing nations using a Machine Learning (ML) technique was developed and evaluated. To test the proposed model's effectiveness in detecting and classifying fraudulent MMS applicant intent, it was trained with various configurations, such as binary or multiclass, with or without the inclusion of SMOTE. Python programming language was employed for the simulation and evaluation of the proposed model. The results showed that ML algorithms are effective for modelling and automating the prediction of cyber threats on MMS. In addition, it proved that the logistic regression classifier with the SMOTE application provided the best classification performance among the various configurations of logistic regression experiments performed. This classification model will be suitable for secure MMS, which serves as a key deciding factor in the adoption and acceptance of mobile money as a cash substitute, especially among the unbanked population.
The Theory of Probabilistic Hierarchical Learning for Classification

Providing the ability of classification to computers has remained at the core of the faculty of artificial intelligence. Its application has now made inroads towards nearly every walk of life, spreading over healthcare, education, defence, economics, linguistics, sociology, literature, transportation, agriculture, and industry etc. To our understanding most of the problems faced by us can be formulated as classification problems. Therefore, any novel contribution in this area has a great potential of applications in the real world. This paper proposes a novel way of learning from classification datasets i.e., hierarchical learning through set partitioning. The theory of probabilistic hierarchical learning for classification has been evolved through several works while widening its scope with each instance. The theory demonstrates that the classification of any dataset can be learnt by generating a hierarchy of learnt models each capable of classifying a disjoint subset of the training set. The basic assertion behind the theory is that an accurate classification of complex datasets can be achieved through hierarchical application of low complexity models. In this paper, the theory is redefined and revised based on four mathematical principles namely, principle of successive bifurcation, principle of two-tier discrimination, principle of class membership and the principle of selective data normalization. The algorithmic implementation of each principle is also discussed. The scope of the approach is now further widened to include ten popular real-world datasets in its test base. This approach does not only produce their accurate models but also produced above 95% accuracy on average with regard to the generalising ability, which is competitive with the contemporary literature.
Encrypting for Time- and/or Location-Based Decryption

Encrypting and decrypting documents is routinely performed by thousands of computers practically every moment. We describe a time- and location-based decryption algorithm using elliptic curve public-key cryptography that caters for a flexible and accurate range of decryption times and/or locations.
Robust Multiple-Path Orienteering Problem: Securing Against Adversarial Attacks

The multiple-path orienteering problem asks for paths for a team of robots that maximize the total reward collected while satisfying budget constraints on the path length. This problem models many multirobot routing tasks, such as exploring unknown environments and information gathering for environmental monitoring. In this article, we focus on how to make the robot team robust to failures when operating in adversarial environments. We introduce the robust multiple-path orienteering problem (RMOP), where we seek worst case guarantees against an adversary that is capable of attacking at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> robots. We consider two versions of this problem: RMOP offline and RMOP online. In the offline version, there is no communication or replanning when robots execute their plans, and our main contribution is a general approximation scheme with a bounded approximation guarantee that depends on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> and the approximation factor for single-robot orienteering. In particular, we show that the algorithm yields a: 1) constant-factor approximation when the cost function is modular; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> factor approximation when the cost function is submodular; and 3) constant-factor approximation when the cost function is submodular, but the robots are allowed to exceed their path budgets by a bounded amount. In the online version, the RMOP is modeled as a two-player sequential game and solved adaptively in a receding horizon fashion based on Monte Carlo tree search. In addition to theoretical analysis, we perform simulation studies for ocean monitoring and tunnel information-gathering applications to demonstrate the efficacy of our approach.
Quantum Computing: Progress and Innovation

The field of quantum computers has reached the point of having real systems available for experimentation to a broad community. In this article, we review recent developments in hardware technology, outline challenges and innovations in quantum systems, and discuss efforts to cultivate and educate a quantum-ready community.
An Efficient Distributed Machine Learning Framework in Wireless D2D Networks: Convergence Analysis and System Implementation

Facing the heavy traffic burden and data privacy, distributed machine learning (DML) has been envisioned as a promising computing paradigm to enable edge intelligence by extracting and establishing models collaboratively in wireless networks. Unlike centralized methods, DML involves frequent local model exchanging among distributed devices, which confronts low efficiency referring to the convergence rate and delay. To enable edge intelligence, how to train DML efficiently has recently attracted extensive interest in wireless networks. Rather than over a fixed or centralized topology, we study the convergence and system implementation of DML over a wireless device-to-device (D2D) network in this paper. First, we introduce the DML training process and system model in this network, where the total delay in reaching convergence is minimized. Second, we analyze the convergence rate to figure out the special effects of synchronization frequency and network topology. To improve the efficiency of DML training, we propose a system implementation approach to reduce the convergence rate and delay of one iteration, where the network topology and synchronization frequency are set, followed by an optimal resource allocation policy. At last, we perform experiments with an image classification task. Simulation results indicate that our proposed D2D framework can effectively reduce training delay and promote computation efficiency by 39 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in a heterogeneous environment, which is concordant with the theoretical analysis.