Reviewerly/exHarmony huggingface dataset:
MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment

The organizational valuable data needs to be shared with multiple parties and stakeholders in a cloud environment for storage, analysis, and data utilization. However, to ensure the security, preserve privacy while sharing the data effectively among various parties have become formidable challenges. In this article, by utilizing encryption, machine learning, and probabilistic approaches, we propose a novel model that supports multiple participants to securely share their data for distinct purposes. The model defines the access policy and communication protocol among the involved multiple untrusted parties to process the owners' data. The proposed model minimizes the risk associated with the leakage by providing a robust mechanism for prevention coupled with detection. The experimental results demonstrate the efficiency of the proposed model for different classifiers over various datasets. The proposed model ensures high accuracy and precision up to 97% and 100% relatively and secures a significant improvement up to 0.01%, 103%, 151%, 87%, 96%, 43%, and 186% for average probability, average success rate, detection rate, accuracy, precision, recall, and specificity, respectively, compared to the prior works that prove its effectiveness.
Diagnosis of Lung Cancer Based on CT Scans Using CNN

Abstract Lung cancer is one of the most lethal cancer types; thousands of peoples are infected with this type of cancer, and if they do not discover it in the early stages of the disease, then the chance of surviving of the patient will be very poor. For the suggested reasons above and to help in overcoming this terrible, early diagnosis with the assistance of artificial intelligence procedures most needed. Through this research, a Computer-aided system introduced for detecting lung cancer in a dataset collected from the Iraqi hospitals by using a convolutional neural network technique with AlexNet architecture for helping with the diagnosis of the patient’s cases: normal, benign, or malignant. The proposed model gives high accuracy ups to 93.548%. The other performance metrics comes with high values such as 95.714% for sensitivity and 95% for Specificity.
Security Architecture and Protocols for Secure MQTT-SN

Message Queuing Telemetry Transport (MQTT) is a de facto standard for various Internet of Things (IoT) and industrial IoT applications. The data produced by a publisher are delivered to several subscribers via an MQTT broker. However, the MQTT standard lacks security-related functionalities, such as mutual authentication, access control, control message security, and end-to-end security. According to a recent report, many security threats and flaws associated with MQTT have been identified worldwide. This paper proposes security architecture and protocols to bootstrap MQTT security in the wireless sensor network. Security bootstrapping for MQTT includes security credential generation and distribution; registration protocol for joining MQTT entities such as publishers, subscribers, and brokers to the security controller; and rekeying protocol for group membership management. Special attention is given to the end-to-end security between the publishers and subscribers because the data from the publishers should not be corrupted by, and exposed to, the compromised broker. Both security analysis and performance evaluation show that our proposed security architecture and protocols for secure MQTT can be a viable solution to enhance MQTT security.
Violence detection and face recognition based on deep learning

With the emergence of the concept of “safe city”, security construction has gradually been valued by various cities, and video surveillance technology has also been continuously developed and applied. However, as the functional requirements of actual applications become more and more diverse, video surveillance systems also need to be more intelligent. The purpose of this article is to study methods of brute force detection and face recognition based on deep learning. Aiming at the problem of abnormal behavior detection, especially the low efficiency and low accuracy of brute force detection, a brute force detection method based on the combination of convolutional neural network and trajectory is proposed. This method uses artificial features and depth features to extract the spatiotemporal features of the video through a convolutional neural network and combines them with the trajectory features. In view of the problem that face images in surveillance video cannot be accurately recognized due to low resolution, two models are proposed: the multi-foot input CNN model and the SPP-based CNN model. By testing the performance of the brute force detection method proposed in this paper, the accuracy of the method on the Crow and Hockey datasets is as high as 92% and 97.6%, respectively. Experimental results show that the violence detection method proposed in this paper improves the accuracy of violence detection in video.
On oversampling imbalanced data with deep conditional generative models

Class imbalanced datasets are common in real-world applications ranging from credit card fraud detection to rare disease diagnosis. Recently, deep generative models have proved successful for an array of machine learning problems such as semi-supervised learning, transfer learning, and recommender systems. However their application to class imbalance situations is limited. In this paper, we consider class conditional variants of generative adversarial networks and variational autoencoders and apply them to the imbalance problem. The main question we seek to answer is whether or not deep conditional generative models can effectively learn the distributions of minority classes so as to produce synthetic observations that ultimately lead to improvements in the performance of a downstream classifier. The numerical results show that this is indeed true and that deep generative models outperform traditional oversampling methods in many circumstances, especially in cases of severe imbalance.
Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality

The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.
Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery

Solar energy captured by solar photovoltaic (PV) systems has great potential to meet the high demand for renewable energy sources in urban areas. A photovoltaic noise barrier (PVNB) system, which integrates a PV system with a noise barrier, is a promising source for harvesting solar energy to overcome the problem of having limited land available for solar panel installations. When estimating the solar PV potential at the city scale, it is difficult to identify sites for installing solar panels. A computational framework is proposed for estimating the solar PV potential of PVNB systems based on both existing and planned noise barrier sites. The proposed computational framework can identify suitable sites for installing photovoltaic panels. A deep learning-based method is used to detect existing noise barrier sites from massive street-view images. The planned noise barrier sites are identified with urban policies. Based on the existing and planned sites of noise barriers in Nanjing, the annual solar PV potentials in 2019 are 29,137 MW h and 113,052 MW h, respectively. The estimation results show that the potential PVNB systems based on the existing and planned noise barrier in 2019 have the potential installed capacity of 14.26 MW and 57.24 MW, with corresponding potential annual power generation of 4662 MW h and 18,088 MW h, respectively.
Embedding Perspective Analysis Into Multi-Column Convolutional Neural Network for Crowd Counting

The crowd counting is challenging for deep networks due to several factors. For instance, the networks can not efficiently analyze the perspective information of arbitrary scenes, and they are naturally inefficient to handle the scale variations. In this work, we deliver a simple yet efficient multi-column network, which integrates the perspective analysis method with the counting network. The proposed method explicitly excavates the perspective information and drives the counting network to analyze the scenes. More concretely, we explore the perspective information from the estimated density maps and quantify the perspective space into several separate scenes. We then embed the perspective analysis into the multi-column framework with a recurrent connection. Therefore, the proposed network matches various scales with the different receptive fields efficiently. Secondly, we share the parameters of the branches with various receptive fields. This strategy drives the convolutional kernels to be sensitive to the instances with various scales. Furthermore, to improve the evaluation accuracy of the column with a large receptive field, we propose a transform dilated convolution. The transform dilated convolution breaks the fixed sampling structure of the deep network. Moreover, it needs no extra parameters and training, and the offsets are constrained in a local region, which is designed for the congested scenes. The proposed method achieves state-of-the-art performance on five datasets (ShanghaiTech, UCF CC 50, WorldEXPO'10, UCSD, and TRANCOS).
Tools for quantum network design

Quantum networks will enable the implementation of communication tasks with qualitative advantages with respect to the communication networks we know today. While it is expected that the first demonstrations of small scale quantum networks will take place in the near term, many challenges remain to scale them. To compare different solutions, optimize over parameter space and inform experiments, it is necessary to evaluate the performance of concrete quantum network scenarios. Here, we review the state of the art of tools for evaluating the performance of quantum networks. We present them from three different angles: information-theoretic benchmarks, analytical tools, and simulation.
Representative Task Self-Selection for Flexible Clustered Lifelong Learning

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then: 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multitask data sets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multitask learning models.
Towards a Definitive Measure of Repetitiveness

Unlike in statistical compression, where Shannon’s entropy is a definitive lower bound, no such clear measure exists for the compressibility of repetitive sequences. Since statistical entropy does not capture repetitiveness, ad-hoc measures like the size z of the Lempel–Ziv parse are frequently used to estimate repetitiveness. Recently, a more principled measure, the size $$\gamma $$ of the smallest string attractor, was introduced. The measure $$\gamma $$ lower bounds all the previous relevant ones (including z), yet length-n strings can be represented and efficiently indexed within space $$O(\gamma \log \frac{n}{\gamma })$$ , which also upper bounds most measures (including z). While $$\gamma $$ is certainly a better measure of repetitiveness than z, it is NP-complete to compute, and no $$o(\gamma \log n)$$ -space representation of strings is known. In this paper, we study a smaller measure, $$\delta \le \gamma $$ , which can be computed in linear time. We show that $$\delta $$ better captures the compressibility of repetitive strings. For every length n and every value $$\delta \ge 2$$ , we construct a string such that $$\gamma = \varOmega (\delta \log \frac{n}{\delta })$$ . Still, we show a representation of any string S in $$O(\delta \log \frac{n}{\delta })$$ space that supports direct access to any character S[i] in time $$O(\log \frac{n}{\delta })$$ and finds the occ occurrences of any pattern $$P[1{.\,.}m]$$ in time $$O(m\log n + occ\log ^\varepsilon n)$$ for any constant $$\varepsilon >0$$ . Further, we prove that no $$o(\delta \log n)$$ -space representation exists: for every length n and every value $$2\le \delta \le n^{1-\varepsilon }$$ , we exhibit a string family whose elements can only be encoded in $$\varOmega (\delta \log \frac{n}{\delta })$$ space. We complete our characterization of $$\delta $$ by showing that, although $$\gamma $$ , z, and other repetitiveness measures are always $$O(\delta \log \frac{n}{\delta })$$ , for strings of any length n, the smallest context-free grammar can be of size $$\varOmega (\delta \log ^2 n/\log \log n)$$ . No such separation is known for $$\gamma $$ .
A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.
Augmented reality in education. A scientific mapping in Web of Science

Augmented reality is a technology that is bringing great benefits in the educational field. This study focuses on knowing the performance and scientific production of augmented reality in the field of education. This research has been approached from a bibliometric perspective. A novel documentary analysis technique based on scientific mapping and co-word analysis has been used. We have worked with an analysis unit of 777 reported publications of Web of Science. The results reveal the language, knowledge areas, type of document, institutions, authors, sources of origin, countries and most cited articles on augmented reality in the entire educational field. In addition, it is shown that research on augmented reality has focused on teaching people to use this technology effectively, in the learning environments it generates, in its educational application, and in attending to the diversity of students.
Real-Time Synchrophasor Data Anomaly Detection and Classification Using <i>Isolation Forest, KMeans, and LoOP</i>

Power grid operators assess situational awareness using time-tagged measurements from phasor measurement units (PMUs) placed at multiple locations in a network. However, synchrophasor measurements are prone to anomalies which may impact the performance of phasor based applications. Anomalies include any deviation from expected measurements resulting from power system events or bad data. Bad data include data errors or loss of information due to failures in supporting synchrophasor cyber infrastructure. It is necessary to flag bad data before utilizing for an application. This work proposes a tool for the detection and classification of anomalous data using an unsupervised stacked ensemble learning algorithm. The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP. The method classifies the data as anomalies or normal data with more than 99% recall. The method also provides a probability of the data to be an event or bad data with more than 99% recall. Results for the IEEE 14 and 68 bus systems with synchrophasor data obtained using Real-Time Digital Simulator and data of industrial PMUs highlight the superiority of the algorithm to detect and classify anomalies.
Temporal Cross-Effects in Knowledge Tracing

Knowledge tracing (KT) aims to model students' knowledge level based on their historical performance, which plays an important role in computer-assisted education and adaptive learning. Recent studies try to take temporal effects of past interactions into consideration, such as the forgetting behavior. However, existing work mainly relies on time-related features or a global decay function to model the time-sensitive effects. Fine-grained temporal dynamics of different cross-skill impacts have not been well studied (named as temporal cross-effects). For example, cross-effects on some difficult skills may drop quickly, and the effects caused by distinct previous interactions may also have different temporal evolutions, which cannot be captured in a global way. In this work, we investigate fine-grained temporal cross-effects between different skills in KT. We first validate the existence of temporal cross-effects in real-world datasets through empirical studies. Then, a novel model, HawkesKT, is proposed to explicitly model the temporal cross-effects inspired by the point process, where each previous interaction will have different time-sensitive impacts on the mastery of the target skill. HawkesKT adopts two components to model temporal cross-effects: 1) mutual excitation represents the degree of cross-effects and 2) kernel function controls the adaptive temporal evolution. To the best of our knowledge, we are the first to introduce Hawkes process to model temporal cross-effects in KT. Extensive experiments on three benchmark datasets show that HawkesKT is superior to state-of-the-art KT methods. Remarkably, our method also exhibits excellent interpretability and shows significant advantages in training efficiency, which makes it more applicable in real-world large-scale educational settings.
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining

There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant responses for any given context. However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives). To allow for better training and robust evaluation of model-based metrics, we introduce the DailyDialog++ dataset, consisting of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context. Using this dataset, we first show that even in the presence of multiple correct references, n-gram based metrics and embedding based metrics do not perform well at separating relevant responses from even random negatives. While model-based metrics perform better than n-gram and embedding based metrics on random negatives, their performance drops substantially when evaluated on adversarial examples. To check if large scale pretraining could help, we propose a new BERT-based evaluation metric called DEB, which is pretrained on 727M Reddit conversations and then finetuned on our dataset. DEB significantly outperforms existing models, showing better correlation with human judgments and better performance on random negatives (88.27% accuracy). However, its performance again drops substantially when evaluated on adversarial responses, thereby highlighting that even large-scale pretrained evaluation models are not robust to the adversarial examples in our dataset. The dataset 1 and code 2 are publicly available.
What Drives Herding Behavior in the Cryptocurrency Market?

This paper uses the cross-sectional absolute deviation (CSAD) in static and time-varying versions to examine herding in the cryptocurrency market from April 2013 to November 2019. Results from the static model confirm the evidence of an anti-herding behavior over the considered period. However, the time-varying analysis suggests the presence of herding behavior around the end of 2013 and persists until the end of the sample period. Furthermore, by examining the factors relating to market microstructure and general economic conditions that can drive herding, we find that the level of herding in the cryptocurrency market rises as volatility, the S&P500, and the dollar index increase. However, the rise in the trading volume, gold price, and the economic policy uncertainty index (EPU) reduce the herding in the cryptocurrency market.