Reviewerly/exHarmony huggingface dataset:
Spectroradiometry as a tool for monitoring soil contamination by heavy metals in a floodplain site

Soil contamination by heavy metals is common in floodplains throughout the world. Apart from other assessment techniques available, hyperspectral remote sensing is widely used as it offers a lucrative and fast assessment. The current work explores the possibility of on-field and laboratory spectroradiometry investigations together with geochemical data of lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd) in quantifying and modeling heavy metal soil contamination (HMSC) for a floodplain situated in Wales, United Kingdom. The goal of the study was to (1) gather on-field- as well as lab-based spectra from contaminated soils using analytical spectral devices FieldSpec3, in the spectrum range of 350–2500 nm; (2) construct spectral libraries of on-field- as well as lab-based readings; (3) carry out geochemical analyses of Pb, Zn, Cu, and Cd with the help of an atomic absorption spectrometer; (4) recognize the explicit spectral areas accompanying the modeling of HMSC; and (5) develop and validate heavy metal prediction models (HMPMs) through the spectral features of the contaminants and their concentrations in the soil. Two spectral libraries were developed from the on-field- and lab-based spectral features, which were derived from 85 soil samples. These spectral libraries along with the concentrations of Pb, Zn, Cu, and Cd were joint to construct eight HMPMs by stepwise multiple linear regression. The output provided, for the first time, the viability to predict HMSC in a highly contaminated floodplain site through the combination of geochemical analyses and field spectroradiometry. The resultant model offered support for mapping heavy metal concentrations over a huge area using space-borne hyperspectral sensors.
Cross-modal retrieval via label category supervised matrix factorization hashing

Due to the emergence and development of big data, cross-modal hash retrieval has become progressively more important in large-scale multi-modal retrieval tasks depending on its accuracy and efficiency. It completes the retrieval task in a common low-dimensional space by finding a common semantic space for heterogeneous data of different modalities. Recently, many works have concentrated on supervised cross-modal hashing and achieved higher retrieval accuracy. However, there are still many challenges in how to maintain the local geometric structure of the original space in the public space and how to use the supervision information efficiently. To deal with such issues, this paper proposes a hash retrieval method that incorporates supervised information based on matrix factorization (LCSMFH) by maintain the inter-modal and the intra-modal similarity in the original space and make the most of the label information to improve the retrieval task effect. Through experiments on two benchmark data sets, our method is more effective and outperforms state-of-the-art methods.
Deep Learning-based Data Augmentation for Hydraulic Condition Monitoring System

It is very important to classify the anomaly data because most of data collected from the manufacturing plant are time-series data and can be analyzed for fault detection. Many researches have been conducted on deep learning over the last decades and it have shown good performance in solving many demanding classification problems. However most deep learning models require a lot of data for training because of the large number of parameters. When the amount of data is small, data augmentation can be a good solution. But data augmentation for hydraulic system has not been deeply studied yet. In this paper, a novel data augmentation is proposed to increase the amount of data for monitoring the condition of hydraulic system. Therefore, deep learning model based on our proposed method is applied to the classification of hydraulic system data and shows good performance in terms of accuracy and loss.
Double Blind <i>T</i>-Private Information Retrieval

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($\theta_1, \theta_2$, resp.), to efficiently retrieve a message $W(\theta_1,\theta_2)$ labeled by the two indices, from a set of $N$ servers that store all messages $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$, such that the two users' indices are kept private from any set of up to $T_1,T_2$ colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to $M$-way blind $X$-secure $T$-private information retrieval (MB-XS-TPIR) with multiple ($M$) indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots, T_M$), and arbitrary level of security ($X$) of data storage, so that the message $W(\theta_1,\theta_2,\cdots, \theta_M)$ can be efficiently retrieved while the stored data is held secure against collusion among up to $X$ colluding servers, the $m^{th}$ user's index is private against collusion among up to $T_m$ servers, and each user's index $\theta_m$ is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves $1-(X+T_1+\cdots+T_M)/N$ bits of desired message per bit of download.
A Reinforcement Learning Based System for Minimizing Cloud Storage Service Cost

Currently, many web applications are deployed on cloud storage service provided by cloud service providers (CSPs). A CSP offers different types of storage including hot, cold and archive storage and sets unit prices for these different types, which vary substantially. By properly assigning the data files of a web application to different types of storage based on their usage profiles and the CSP's pricing policy, a cloud customer potentially can achieve substantial cost savings and minimize the payment to the CSP. However, no previous research handles this problem. Towards this goal, we present a Markov Decision Process formulation for the cost minimization problem, and then develop a reinforcement learning based approach to effectively solve the problem, which changes the type of storage of each data file periodically to minimize money cost in long term. We then propose a method to aggregate concurrently requested data files to further reduce the cloud storage service payment for a web application. Our experiments with Wikipedia traces show the effectiveness of the proposed methods for minimizing cloud customer cost in comparison with other methods.
A Quantitative Study of Faculty Views on the Goals of an Undergraduate CS Program and Preparing Students for Industry

Although elements of the academia-industry gap have been studied extensively, these studies have mostly ignored the primary stakeholder for changing academia: faculty. Building on a recent qualitative study that revealed a wide range of faculty views on the gap, this study quantitatively examines faculty views through a survey on the goals of CS education, how CS programs should address the academia-industry gap, and which barriers prevent adoption of remedies. Analysis of the 249 responses reveals that a majority of faculty share common goals in supporting student preparation for a career in industry. Moreover, faculty strongly view their own institutions as the prime party responsible for student preparation for careers in both academia and industry. We also find that whereas faculty are generally in agreement on what could be improved to provide students with better industry preparation, some reported far greater barriers to implementing those improvements than others.
Photonic Reservoir Computing in Optical Transmission Systems

Hardware-based reservoir computing offers an alternative approach to conventional digital signal processing. Here two different types of photonic reservoir computers are evaluated with respect to their equalization performance in optical transmission systems. Feasibility and problems are also analyzed and possible solutions are discussed.
Survey Paper: Sentiment Analysis for Major Government Decisions

On internet, there are plenty of social networking sites on which people express their perception regarding a topic every day which can be helpful for sentiment analysis. In this paper, we have discussed the general procedures in sentiment analysis and how we are going to use python and its libraries for performing sentiment analysis on data retrieved from twitter’s website so we can get the view of what public thinks of last ten government decisions like Demonetization, Goods and Service Tax (GST), Bharat Interface for Money (BHIM), Citizen Amendment Act (CAA).
Three-Dimensional Working Pose Estimation in Industrial Scenarios With Monocular Camera

Three-dimensional (3-D) pose data has drawn great attention owing to its wide range of applications. Internet of Things (IoT)-based techniques have been introduced to collect 3-D pose data. Though previous studies have yielded significant results, researchers have yet to use 3-D pose estimation in real-life applications. Since wearable sensors might be intrusive and infrared depth cameras are sensitive to sunlight, monocular-camera-based computer vision algorithms provide a possible solution. Previous algorithms are trained and tested with simple daily postures. There are industrial scenarios where the poses are more complex and irregular. An example is the poses of workers on construction sites, such as lifting, climbing, and rebar tying. These postures differ drastically from daily postures and vary from person to person. For instance, some workers prefer bending rebar tying, while others prefer squatting rebar tying. As a result, the previous monocular-camera-based-3-D poses estimation methods have proved to be inapplicable to industrial scenarios. Thus, this article developed a monocular-camera-based 3-D estimation method which is suitable for industry working poses. A residual artificial neural network (RANN) with flexible complexity and weighted training loss was designed. A 3-D pose data set, which consists of diversified working poses in worksites, was built to test the performance of the network in complex scenarios. Compared with previous 3-D pose capture methods, the mean per joint position error was reduced by 31.42%. The latency was 0.24 s. Thus, we conclude that the proposed monocular-camera-based method has great potential in industrial application scenarios.
Recommender system using Long-term Cognitive Networks

In this paper, we build a recommender system based on Long-term Cognitive Networks (LTCNs), which are a type of recurrent neural network that allows reasoning with prior knowledge structures. Given that our approach is context-free and that we did not involve human experts in our study, the prior knowledge is replaced with Pearson’s correlation coefficients. The proposed architecture expands the LTCN model by adding Gaussian kernel neurons that compute estimates for the missing ratings. These neurons feed the recurrent structure that corrects the estimates and makes the predictions. Moreover, we present an extension of the non-synaptic backpropagation algorithm to compute the proper non-linearity of each neuron together with its activation boundaries. Numerical results using several case studies have shown that our proposal outperforms most state-of-the-art methods. Towards the end, we explain how can we inject expert knowledge to the proposed neural system.
Big Data in Climate Change Research: opportunities and Challenges

The advent of fourth industrial revolution has come at a cost; our planet earth is enduring concurrent changes in population, increase in urban infrastructure, depleting natural resources, depleting forest covers, increase in pollution etc., and its cumulative ill effects on global climate. These changes coupled with advancements in technology viz. Sensors and Data Mining techniques have led to a growth in study of Climate Data as a method to thwart Climate Change. Climate change study is a data intensive subject which has seen numerous innovations in the data collection methods, and study of that data using Big Data Analytics. There has been a considerable shift from traditional study methods like random sampling of climate data to data intensive methods where complete datasets are analyzed by means of Big Data Analytics. The study of climate change and its effects has been one of the top priorities in Big Climate Data Analytics implementations and detailed research has been done covering a plethora of topics. However, the study of climate data by recent methods has revealed several challenges previously unrecorded in data mining literature, unless these challenges are addressed, big data analytics will not have the same meaningful impact that it has on other fields. The purpose of this paper is to provide an overview of use of Big Data in Climate Change research and the opportunities and challenges pertaining to it, by studying and summarizing the current state of applications of Big Data in Climate Science.
A Survey of Deep Learning based Online Transactions Fraud Detection Systems

With the advancement of technology, today most of the modern commerce is relying upon the online banking and cashless payments. Due to adaption of online payment among businesses, the fraud cases are also increasing which cause financial losses to them. Fraudsters are inventing new techniques to perform fraudulent transaction which seem legitimate. Hence, there is an urgent need to develop fraud detection measures which can deal with these fraudsters on real time basis. Deep learning techniques have the capability to detect these fraudulent transactions efficiently and has a huge scope in fraud detection. However, there are many challenges faced by the researchers in online transactions fraud detection because the datasets are not publicly available due to privacy issue of the financial institutions as customers data is sensitive and it can be misused and the datasets which are available are imbalanced. This paper presents a review of deep learning techniques used for online transactions fraud detection. It also provides the information about datasets used by the researchers and the results achieved by them in their research work.
The Designing of Warehouse Management Information System

Abstract Warehouse Management Information System or WMIS primarily aims to control the movement and storage of materials in the warehouse and process related transactions, including sending, receiving, storing, and taking. This research has the objective to analyze the running system and design a warehouse management information system in Zaeni Convection. The development methodology used is Rapid Application Development with an object-oriented approach. While the tools used in design are UML (Unified Modeling Language). Problems in the Zaeni Convection are; there is no automatic calculation of raw materials, the process is not yet maximized to regulate the entry and exit of goods, there are no reports on inventory and expenditure of goods, and there is no computerized data storage. In designing a warehouse management information system, there are four actors involved namely Owner, Warehouse Section, Production Manager and Supplier. There are four function that are described, including: Ordering of Raw Materials, Purchase Orders, Production, and Management of Finished Goods. Based on this research, it can be concluded that the results of the warehouse management information system design can be a solution for the process of managing goods at Zaeni Convection
Improving BERT with Focal Loss for Paragraph Segmentation of Novels

In this study, we address the problem of paragraph segmentation from the perspective of understanding the content of a novel. Estimating the paragraph of a text can be considered a binary classification problem regarding whether two given sentences belong to the same paragraph. When the number of paragraphs is small relative to the number of sentences, it is necessary to consider the imbalance in the number of data. We applied the bidirectional encoder representations from transformer (BERT), which has shown high accuracy in various natural language processing tasks, to paragraph segmentation. We improved the performance of the model using the focal loss as the loss function of the classifier. As a result, the effectiveness of the proposed model was confirmed on multiple datasets with different ratios of data in each class.
eXogenous Kalman Filter for State Estimation in Autonomous Ball Balancing Robots

This paper presents discrete-time eXogenous Kalman Filter (XKF) for state estimation in an autonomous Ball Balancing Robot (Ballbot). The Ballbot has four omni-wheels and four motors attached to a ball. The objective is to estimate the position and attitude using measurement from a low cost Inertial Measurement Unit (IMU). To this end, we model the dynamic of the Ballbot as a nonlinear uncertain system. We derive a sufficient condition for stability of the XKF in form of Linear Matrix Inequality (LMI). Experimental tests show the proposed XKF algorithm provide better results than the Extended Kalman Filter (EKF), which is the de facto standard for nonlinear state estimation. The algorithm developed in this paper is written for systems with Lipschitz nonlinearities, which represents a wide class of nonlinearity arising in robotics and autonomous systems.
The Battle of Frame Building: The Reciprocal Relationship between Journalists and Frame Sponsors

The idea that journalists make use of framing is widespread. However, systematic studies of the role of frame sponsors—that is, nonmedia actors who advocate a certain frame package—in influencing the patterns in frame package use by journalists are limited. Which characteristics make frame sponsors successful in frame building, and why? In this study, we propose a new way of understanding the relationship between journalists and frame sponsors, by studying to what extent high authority and having a strong stake in an issue are important predictors of frame coverage, and whether a bidirectional relationship between frame sponsors and journalists can be discerned in frame building. We examine the two court cases against Geert Wilders, leader of the Dutch Freedom Party (PVV), for alleged hate speech (2009–2020). Based on a content analysis of media input, such as statements, social media posts, and press releases ( N = 220) of relevant frame sponsors as well as news stories about the court cases ( N = 691), we demonstrate that there is a reciprocal relationship between frame sponsors and journalists in frame building. Frame sponsors influence journalists in the use of frame packages, but framing in news reports also stimulates frame sponsors to communicate similar frame packages in the future. Actors with high levels of authority and a strong stake in the issue are more successful in getting their preferred frame packages across. By acknowledging the bidirectional relationship and expanding knowledge on who benefits most from this relationship, this study advances literature on frame building.
Incorporating Psychology into Cyber Security Education: A Pedagogical Approach

The role of the human in cyber security is well acknowledged. Many cyber security incidents rely upon targets performing specific behavioural actions, such as opening a link within a phishing email. Cyber adversaries themselves are driven by psychological processes such as motivation, group dynamics and social identity. Furthermore, both intentional and unintentional insider threats are associated with a range of psychological factors, including cognitive load, mental wellbeing, trust and interpersonal relations. By incorporating psychology into cyber security education, practitioners will be better equipped with the skills they need to address cyber security issues. However, there are challenges in doing so. Psychology is a broad discipline, and many theories, approaches and methods may have little practical significance to cyber security. There is a need to sift through the literature to identify what can be applied to cyber security. There are also pedagogical differences in how psychology and cyber security are taught and also psychological differences in the types of student that may typically study psychology and cyber security. To engage with cyber security students, it is important that these differences are identified and positively addressed. Essential to this endeavor is the need to discuss and collaborate across the two disciplines. In this paper, we explore these issues and discuss our experiences as psychology and cyber security academics who work across disciplines to deliver psychology education to cyber security students, practitioners and commercial clients.
An Empirical Analysis of Blockchain Cybersecurity Incidents

The establishment of Bitcoin in 2008 has introduced Blockchain technology to become the predominant new ways in digital transactions in many fields, such as energy, healthcare, and financial services. Although blockchain technology promised many advantages, it has suffered from various attacks with significant financial loses. We analyse 65 cybersecurity incidents that have been happening on the blockchain networks between 2011 and the first half-year of 2019. We categorize those incidents against the key cybersecurity vulnerabilities in blockchain technology, which showed an upward trend of an increasing number of attacks which dues to lack of security controls in the digital currency exchange and vulnerability in smart contracts. This result of this research highlights the needs in a major overhaul of the blockchain technology.
Exploring Quantum Reversibility with Young Learners

Quantum computing is poised to revolutionize some critical intractable computing problems; but to fully take advantage of this computation, computer scientists will need to learn to program in a new way, with new constraints. The challenge in developing a quantum computing curriculum for younger learners is that two dominant approaches, teaching via the underlying quantum physical phenomenon or the mathematical operations that emerge from those phenomenon, require extensive technical knowledge. Our goal is to extract some of the essential insights in the principles of quantum computing and present them in contexts that a broad audience can understand.
Behavioral Model based Trust Management design for IoT at Scale

With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities' decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle's sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device's perceived trustworthiness without the need to rely on a majority consensus.