Reviewerly/exHarmony huggingface dataset:
PASS: A Privacy-Aware Approach for Secure Smart Metering in Advanced Metering Infrastructure Networks

Huge amounts of sensitive data are generated and transmitted on a smart grid infrastructure. These data are used for acquisition of various parameters of interest from smart meters in the grid and control of the grid. However, wireless communication processes across the smart grid network are vulnerable to different attacks like any other wireless communication infrastructure, which has unavoidably given rise to concerns about how to efficiently secure services and preserve privacy in smart grid network especially in metering. In this article, an efficient approach and framework was introduced for secure metering and preservation of consumers' privacy in different neighborhood area networks of different service providers of the smart grid communication network. Our approach effectively introduced a communication framework for efficient and secure metering, privacy preservation, load balancing, and billing using perturbation and data aggregation techniques. Computational and security analysis were performed to ascertain its efficiency in terms of computational cost, energy consumption, and security. The results showed that the approach not only prevented gleaning of the metering but did so at a relatively low computational overhead compared with other approaches.
SIM: Open-World Multi-Task Stream Classifier with Integral Similarity Metrics

One of the key challenges of performing label predictions over a data stream is concerned with the emergence of instances belonging to unobserved (or novel) classes over time. Although existing studies have proposed various solutions to address this challenge, they mostly focus on streams with lowdimensional data and strongly rely on the intrinsic cohesion and separation data property, i.e., instances belonging to the same class are closer to each other (cohesion) than those belonging to different classes (separation) in the observed feature space, to detect instances from unknown classes. Unfortunately, such a property is typically not inherent in high-dimensional data such as images and texts. Thus, to perform classification and novel class detection on high-dimensional data streams, we need to address two main problems: 1) Finding a feature space that exhibit cohesion and separation properties, and 2) Training with limited amount of labeled data. In this paper, we propose a multi-task metric learning mechanism useful for identifying a latent space in which the cohesion and separation data property is valid and have designed a semi-supervised stream classifier called SIM based on this mechanism. We empirically measure the performance of SIM over multiple real-world image and text datasets, and demonstrate its superiority by comparing the performance with existing state-of-the-art frameworks.
A Shift-Dependent Measure of Extended Cumulative Entropy and Its Applications in Blind Image Quality Assessment

Recently, Tahmasebi and Eskandarzadeh introduced a new extended cumulative entropy (ECE). In this paper, we present results on shift-dependent measure of ECE and its dynamic past version. These results contain stochastic order, upper and lower bounds, the symmetry property and some relationships with other reliability functions. We also discuss some properties of conditional weighted ECE under some assumptions. Finally, we propose a nonparametric estimator of this new measure and study its practical results in blind image quality assessment.
Chemical permeation of similar disposable nitrile gloves exposed to volatile organic compounds with different polarities Part 2. Predictive polymer properties

A follow-up study evaluated the chemical and physical parameters of 10 disposable nitrile glove products in association with the observed variability in chemical permeation performance. The aim was to determine which polymer properties explained or were predictive of the observed wide variation in breakthrough time and steady state permeation rate. The physical and mechanical properties evaluated were thickness, area density, volume fraction and modulus 50–100%. The chemical composition properties evaluated were relative acrylonitrile content, relative carboxylation content, oily plasticizers, inorganic fillers and organic polymer content. A combination of correlation and multiple regression analyses were performed to evaluate the predictive nature of these parameters. For the regression analyses, stepwise, forward selection and backward elimination methods were used to determine an optimal regression fit. Both thickness and area density were strongly correlated with the breakthrough time. With the addition of volume fraction, these factors accounted for about 88–89% of the variation in breakthrough times. The correlation results for the steady-state permeation rate were largely inconclusive and only a moderate correlation with thickness was observed with one solvent. However, regression analyses revealed a moderate to strong association (R2 = 0.742; p < 0.001) between the permeation rate and thickness and volume fraction. With the inclusion of volume fraction in all regression models, the microstructure of the polymer played a critical role in chemical permeation, which requires further investigation. Based on these results, selection based on the availability of chemical permeation data for the product should always be preferred, especially when skin protection is critical. When chemical resistance ratings are based on general performance data, additional factors such as thickness and area density should be taken into consideration. In general, increases in thickness and area density are associated with increases in breakthrough time and decreases in the steady-state permeation rate. However, evidence in the literature and this study support the need for inclusion of additional factors associated with the microstructure of the polymer.
Deploying A Machine Learning Solution As A Surrogate

A machine learning (ML) solution can be non-robust and when it is deployed, can make mistakes on the future unseen data. Consequently, deployment of a ML solution might demand continuous service from its ML developer. Using wafer image classification as an example, this paper presents the design of a ML solution where its deployment is facilitated by the continuous service from its ML expert.
Hybrid 2D and 3D Visual Analytics of Network Simulation Data

We present a visualization architecture to support 2D and 3D visual analytics applications. The architecture is designed to be data-flow-oriented and reconfigurable such that several diverse visualization components can operate as one integrated system. Our prototype application allows users to visually analyze the results of a complex 3D network simulation data both on large high-resolution display and HTC Vive Head Mounted Display. The network simulation outputs variables describing various characteristics of network connectivity between the moving nodes on the ground and in the air interacting in a dynamically changing 3D environment. Our system uses 2D charting tools to visualize the statistical relationships between simulation variables. We developed a Unity application to animate the network simulation in a virtual environment showing the timevarying results in a 3D environment. The Unity application runs on a complete-immersive Head Mounted Display device. The 2D visualization framework running on our Large High-Resolution Display system supports multiple coordinated views across all the different 2D visualization components including a 2D map. Preliminary results show our data-centric design provides a usercentric visualization tool that can greatly enhance the analytical process and speed up the derivation of insights from data.
Hybrid Key Generation for RSA and ECC

The Rivest, Shamir, and Adleman (RSA) is one of the most important types of the Asymmetric cryptography. In RSA the key size is bigger when compared to the algorithms in cryptography. The keys are generated with any two prime numbers. The unauthorized person can easily get the keys with a Greatest Common Divisor (GCD) Attack. This is one of the main drawbacks of RSA cryptography. The Elliptic Curve Cryptography (ECC) is also Asymmetric cryptography with a small key size. The proposed work is to overcome the drawback of the GCD Attack in RSA. The RSA private and public key can be combined with an ECC private and public key by using an Exclusive OR (XOR) and then the new hybrid private and public keys are generated. The RSA encryption can be done with a new hybrid public key. The RSA decryption can be done with a new hybrid private key. The advantages of the proposed work is the creation of three levels of key for cryptography, which can avoid the GCD attack in RSA. The key strength is higher when compared to the simple key generation of RSA algorithm.
A Multi-Scaled Receptive Field Learning Approach for Medical Image Segmentation

Biomedical image segmentation has been widely studied, and lots of methods have been proposed. Among these methods, attention U-Net has achieved a promising performance. However, it has drawbacks of extracting the multi-scaled receptive field features at the high-level feature maps, resulting in the degeneration when dealing with the lesions with apparent scale variations. To solve this problem, this paper integrates an atrous spatial pyramid pooling (ASPP) module in the contracting path of attention U-Net. This module employs multiple dilation rates for the purpose of obtaining several multi-scale receptive fields, which significantly improves the networks' ability to handle both large and small lesions. Evaluation experimental result shows that our approach significantly improves the performance of medical image segmentation and substantially outperforms the representative deep learning models on public datasets.
Forward and backward input variable selection for polynomial echo state networks

As extension of traditional echo state networks (ESNs), the polynomial echo state networks (PESNs) have been proposed in our previous work (Yang et al., 208) by employing the polynomial function of complete input variable as output weight matrix. In practice, the generalization performance and computational burden of PESNs are perturbed by redundant or irrelevant inputs. To construct output weights with a suitable subset of input variables, the forward selection based PESN (FS-PESN) and backward selection based PESN (BS-PESN) are proposed. Firstly, the forward selection method is used in FS-PESN to choose the input variable which incurs the maximum reduction on objective function, and the backward selection shame is introduced in BS-PESN to remove the input variable which leads to the smallest increment on objective function. Then, the iterative updating strategies are designed to avoid repetitive computations in FS-PESN and BS-PESN. Specially, an accelerating scheme is introduced into BS-PESN to simplify training process. Finally, numerical simulations are carried out to illustrate effectiveness of the proposed techniques in terms of generalization ability and testing time.
An empirical comparison of interaction styles for map interfaces in immersive virtual environments

Geographical Information Systems (GIS) can be visualized using immersive technologies like Virtual Reality (VR). Before using this kind of technologies it is required to explore which interactions are affordable, efficient and satisfactory from the users' point of view. The purpose of this work is to provide insight on how to design efficient and natural interaction on GIS VR interfaces. This study presents a within-subjects comparative study that assesses the usability and performance of two popular interaction strategies: body-based interaction and device based interaction. In body-based interaction, participants use their hands and head orientation to control the VR map. In the second case, users interact with the Oculus Touch controller. Thirty two users participated in an experiment whose results suggest that interacting with the controller improves performance of the selection task, in terms of time spent and error rate. Also, the results show a preference of users for the controller in terms of perceived usability.
Extracting Feature Engineering Knowledge from Data Science Notebooks

Designing good features for machine learning models, which is called feature-engineering, is one of the most important tasks in data analysis. Well-designed features, which capture the characteristics of data, improve the predictive performance and explainability of the model. Since good features generally reflect the deep knowledge on business domains of the data and the analysis task, feature engineering is considered as one of the most difficult phases in data analysis. Nowadays, AutoML is trying to automate the data science process by producing good features by autonomous algorithms such as feature-synthesis and feature-selection. While AutoML is making success in some extent, it cannot reproduce all the features crafted by expert data scientists in reality because of its huge search space. In this paper, we take different approach for assisting feature engineering process: transfer expert data scientists knowledge as much as possible. Proposed approach extracts frequently used feature engineering operations from source codes or notebooks by pattern discovery. Since naive textual pattern discovery performs poor for the source code, our approach converts source codes into abstract syntax trees and discovers important feature engineering operations as subgraphs by performing frequent subgraph mining.
A general approach for designing the MWGS-based information-form Kalman filtering methods

The paper addresses a general approach to MWGS (Modified Weighted Gram-Schmidt) orthogonalization based Kalman filtering (KF) implementation methods. We propose two new numerically favored and convenient array information formulations of the MWGS-based KF that are the MWGS-based array Information Filter (algorithm MWGS-aIF) and the extended MWGS-based array Information Filter (algorithm eMWGS-aIF). To confirm the correctness of our results, we have proved that the newly constructed MWGS-based array computational schemes are algebraically equivalent to the “straight” (conventional) information filter. Although all these information-type algorithms are theoretically equivalent, their computational properties are different. The newly proposed algorithms are numerically robust to machine roundoff errors due to the numerically stable orthogonal transformations applied on each iteration. The obtained numerical results confirm this statement. Additionally, algorithm eMWGS-aIF has the extended array form, i. e., it allows for updating all required filter quantities with the use of the numerically stable MWGS orthogonalization procedure, only. Thus, our results extend the existing class of numerically efficient KF implementation methods and can be used in practical applications.
Ethical Considerations and Issues of Blockchain Technology-Based Systems in War Zones: A Case Study Approach

In the postwar era, the Syrian Civil War has become the bloodiest, prolonged, and protracted conflict in the 21st century. It inherently includes various dimensions, increasing complexity in the pattern, both in the defense sector and in the ensuing displacement-cum-migration crises, on a scale that the world has hitherto witnessed. This has pretty much caused a huge (direct and/or indirect) paradigm shift in the socioeconomic and political patterns and processes around the World. Although the political crisis has induced large-scale migration, including mass exodus, which many have described as the “The Great Humanitarian Crisis,” the defense sector is slowly imbibing various technologies including artificial intelligence and digitalization into it. The Syrian Civil War has resulted in the loss of nearly half a million Syrian lives, injuring over 1 million injured and many millions who are either internally displaced persons (IDPs) or have become refugees, besides degrading societal conditions and instability. An estimated 5.6 million Syrians have been forced to become refugees, around 6.2 million accounts for IDPs. Globally, almost one-sixth of the world population are without provable identity, while 20 trillion USD of unowned capital lay around. A prominent issue in resolving The Great Humanitarian Crisis is to legitimately identify the refugees and IDPs. But a large number of the refugees and IDPs lack evidence or verification documents like identity cards, property deeds, passports; most of which would have been lost while fleeing for their lives, amidst conflicts or bombardments. This creates a number of hindrances for their return, reentry, identity verification, validation, certification and rehabilitation, repatriation, and resettlement as well. This also leads to issues of increasing illegal entry points, lack of property restitution and/or compensation, along with the lack of livelihood opportunities or overdependence on aid. Moreover, issues like trust deficit, leakages in distribution, and accessibility to goods and services exacerbate the situation. A number of actors have taken initiatives to record, authenticate, and verify identities but the documentation processes and mechanisms have often been found practically lacking. This issue could be dealt with by providing verifiable universal identity (a global passport) to the population, particularly the refugees and IDPs. Blockchain Technology-Based System (BTBS) has been increasingly used by various actors to identify, provide support, funding, aid, and food to refugees and IDPs. At the same time, the disruptive nature of BTBS as a foundational technology into the defense sector has also been dealt with. Interestingly, the pros and cons of the system have been one that is debatable. In this context, the chapter seeks to discuss the features, deployment, technical innovations, ethical considerations, and issues related to the global use and implementation of BTBS.
Uncertainty-Aware Opinion Inference Under Adversarial Attacks

Inference of unknown opinions with uncertain, adversarial (e.g., incorrect or conflicting) evidence in large datasets is not a trivial task. Without proper handling, it can easily mislead decision making in data mining tasks. In this work, we propose a highly scalable opinion inference probabilistic model, namely Adversarial Collective Opinion Inference (Adv-COI), which provides a solution to infer unknown opinions with high scalability and robustness under the presence of uncertain, adversarial evidence by enhancing Collective Subjective Logic (CSL) which is developed by combining SL and Probabilistic Soft Logic (PSL). The key idea behind the Adv-COI is to learn a model of robust ways against uncertain, adversarial evidence which is formulated as a min-max problem. We validate the out-performance of the Adv-COI compared to baseline models and its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean absolute error in the expected truth probability while producing the lowest running time among all.
Blockchain Technology in Higher Education

Blockchain, which is also called a distributed ledger technology (DLT), is an emerging and ever advancing technology having flourishing potential for nourishing and revolutionizing higher education. It stems in decentralization and distributed learning with characteristics of permanence of records, pursuit and transfer of knowledge, authority of institutions, and reliability of teaching and learning. These characteristics of blockchain attract educational institutions particularly the higher education institutions to adopt it. However, in spite of all potential and benefits of blockchain technology, the higher education stakeholders currently seem to be less aware of the social benefits and educational/instructional potential of blockchain technology. It can be addressed through proper advocacy and campaign. The complete chapter will demonstrate possibilities of blockchain technologies in higher education along with its issues and challenges.
WaveNet Factorization with Singular Value Decomposition for Voice Conversion

WaveNet vocoder has seen its great advantage over traditional vocoders in voice quality. However, it usually requires a relatively large amount of speech data to train a speaker-dependent WaveNet vocoder. Therefore, it remains a challenge to build a high-quality WaveNet vocoder for low resource tasks, e.g. voice conversion, where speech samples are limited in real applications. We propose to use singular value decomposition (SVD) to reduce WaveNet parameters while maintaining its output voice quality. Specifically, we apply SVD on dilated convolution layers, and impose semi-orthogonal constraint to improve the performance. Experiments conducted on CMU-ARCTIC database show that as compared with the original WaveNet vocoder, the proposed method maintains similar performance, in terms of both quality and similarity, while using much less training data.
Load Balancing Performance in Distributed Storage with Regular Balanced Redundancy

Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems. Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balancing should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate load balancing performance of natural storage schemes in which each data object is stored at d different nodes and each node stores the same number of objects. We find that load balance in a system of n nodes improves multiplicatively with d as long as d = Θ(log(n)), and improves exponentially as soon as d = Θ(log(n)). We show that load balance improves the same way with d when the service choices are created with XORs of r objects rather than object replicas, which also reduces the storage overhead multiplicatively by r. However, unlike accessing an object replica, access through a recovery set composed by an XOR'ed copy requires downloading content from r nodes, which increases load imbalance additively by r.
Efficient Password Guessing Based on a Password Segmentation Approach

Most cracking tools against alphanumeric passwords conduct password guessing based on sophisticatedly constructed password dictionaries. The rule-based methods which continuously expand the size of dictionaries based on simple permutation and concatenation is the traditional way to construct password dictionaries. To increase the intelligence in dictionary generation, some password cracking tools extract password patterns from the training passwords based on machine learning, and thus construct dictionaries using the extracted patterns. However, these tools either have low guessing efficiency, or produce password generation models with low interpretability. Usually, a password could be split into several meaningful segments each of which represents particular personal information or a grammatically correct word, and the password patterns could be extracted from these segments. In this paper, we propose a novel password cracking tool, which breaks each training password to meaningful segments, learns the patterns from the password segments, and generates personalized high-efficiency password dictionaries based on the learned patterns. The experimental results show that the proposed tool is more efficient than the traditional rule-based tools as well as alphanumeric patterns-based tools. Furthermore, to evaluate the impact of personal information leakage on password security, we use personal information of the target users as the inputs for the proposed tool and analyze the password guessing efficiency.