Reviewerly/exHarmony huggingface dataset:
A new methodology for Standard Evapotranspiration classification over mesoscale regions: Application and evaluation of SOM over remotely sensed data

&amp;lt;p&amp;gt;Standard Evapotranspiration (ETo) is an indicator of water losses given by evaporation and plant transpiration. Its quantification is particularly important for irrigation purposes, however in-situ data is not always accessible. This research aims to develop a methodology for Eto &amp;amp;#8216;weather&amp;amp;#8217; classification through clustering Eto zones over Ecuador using remotely sensed data and an unsupervised learning algorithm. Thus, we obtained climatological variables from the Weather Research and Forecasting model&amp;amp;#160; corresponding to years 2017, 2018, 2019, 2020, 2021. Following, we pre-processed the raw variables into eight parameters for Eto estimation, as in the Penman-Monteith equation, providing the model input variables for each year of study. Hence, we implemented a Self-Organizing Map (SOM) Artificial Neural Network over each dataset to obtain maps representing Eto clustered classes. Moreover, we tested the methodology's repeatability by applying SOM ten different times over each dataset and by applying the modified Cramers&amp;amp;#8217; V-index to quantify the differences between map comparisons. Accordingly, we selected the SOM parameters that produced a Cramer&amp;amp;#8217;s V-index &amp;gt; 0.9&amp;amp;#160; and differences between clustered maps &amp;lt; 0.0001. The outcomes of this research contribute to the classification of Eto &amp;amp;#8216;weather&amp;amp;#8217; in mesoscale regions with future prospects to Eto &amp;amp;#8216;climate&amp;amp;#8217; classification over larger temporal and spatial resolutions.&amp;lt;/p&amp;gt;
Look Ma, no constants

With the advent of JIT compilers, code-injection attacks have seen a revival in the form of JIT spraying. JIT spraying enables an attacker to inject gadgets into executable memory, effectively sidestepping W⊕X and ASLR.
PHUSICOS-VR: Using virtual reality to explore mitigation measures for geohazards

&amp;lt;p&amp;gt;PHUSICOS-VR is a virtual reality experience to learn how nature-based solutions (NBS) can be used to mitigate natural hazards, including rock falls, floods, avalanches and agricultural run-off. &amp;amp;#160;The game is set in real locations in Norway, Italy, France and Spain, and is a digital representation of actual demonstration sites in the EU project PHUSICOS (EU contract nr). &amp;amp;#160;The underlying development strategy for PHUSICOS-VR was to create an interesting and fun pedagogical tool to learn about natural hazards, and the effect that NBS can have on both mitigating the hazard but also providing co-benefits such as improving biodiversity and creating public use spaces.&amp;amp;#160; The primary audiences for the VR experience are the general public, politicians, planning authorities and other decision-makers.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Game development is more than programming, and during the fast-track production of PHUSICOS-VR the development team followed a SCRUM project philosophy and transitioned through creative phases and processes including audience/personas definitions, scientific content, pedagogical considerations, visual/audio expression, and of course the technical implementation in the UNITY engine to realize the game for the Oculus VR platform.&amp;amp;#160; Covid19 has been an added complication, resulting in the entire development process running virtually &amp;amp;#8211; the core team never met as a complete group, and all development works were coordinated through collaborative platforms such as TEAMS and Miro Board.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This article will present the development phases in detail, describing the challenges encountered and solutions implemented to realize the PHUSICOS-VR game.&amp;amp;#160; PHUSICOS-VR is currently under review for public release and will soon be available on the Oculus app store.&amp;amp;#160; A browser version is also available.&amp;lt;/p&amp;gt;
DSS: Dynamic Sample Selection by Utilizing Object Shape and Scale

The one-stage detector ATSS improves positives matching quality and small-sized object accuracy by setting dynamic IoU threshold. ATSS samples candidate positives used for calculating dynamic IoU threshold with center distance metric. However, designing metric for sampling candidate positives and determining the number of candidates for ground-truth remain unexplored. ATSS only considers the relative position between objects and anchors in terms of center distance, and hence the anchors of large shape difference but similar position may be easily sampled. Also, the same number of candidate detection boxes may not work well for the objects of different sizes. For small-sized objects, a large number of candidates may incorrectly include some that have low IoU with the ground-truth boxes. To address the above problems, we propose Dynamic Sample Selection (DSS) consisting of two components: Candidate Positive Sampling Module (CPSM) which is a metric considering the relative position and shape variation together; and Dynamic Selection Module (DSM) which is a module adjusting the the number of candidate positives for an object (topK) used for calculating dynamic IoU threshold. DSS brings ATSS about 0.7% mAP improvement on MS COCO dataset.
Application and Mitigation of the Evasion Attack against a Deep Learning Based IDS for IoT

An Internet of Things (IoT) network is characterized by ad-hoc connectivity and varying traffic patterns where the routing topology evolves over time to account for mobility. In an IoT network, there can be an overwhelming number of massively connected devices, all of which must be able to communicate to each other with low latency to provide a positive user experience. Various protocols exist to allow for this connectivity, and are vulnerable to attack due to their simple nature. These attacks seek to disrupt or deny communications in the network by taking advantage of these vulnerabilities. These attacks include Blackhole, Grayhole, Flooding and Scheduling attacks. Intrusion Detection Systems (IDS) to prevent these routing attacks exist, and have begun to incorporate Deep Learning (DL) to bring near perfect accuracy of detection of attackers. The DL approach opens up the IDS to the possibility of being the victim of an Adversarial Machine Learning attack. We explore the case of a novel evasion attack applied to a Wireless Sensor Network (WSN) dataset for subversion of the IDS. Additionally, we explore possible mitigations for the proposed evasion attack, through adversarial example training, outlier detection, and a combination of the two. By using the combination, we are able to reduce the possible attack space by nearly two orders of magnitude.
Research on Fusion Method of Nonlinear System

In order to improve the estimation accuracy of general nonlinear systems, the fusion method of nonlinear systems has been studied. Fusion methods include centralized fusion and covariance cross fusion methods. Combined with the unscented Kalman filter (UKF) algorithm, the centralized fusion UKF (CF-UKF)algorithm and the covariance fusion UKF(CI-UKF) algorithm are further proposed. And in terms of accuracy and calculation amount, the advantages and disadvantages of two fusion methods are compared. The examples are given to show the effectiveness of the proposed algorithm.
Towards the Development and Deployment of Community LTE Networks in Rural Areas

The evolution of community cellular network (CCN) initiatives over the years has created a distinct paradigm for developing, operating, and sharing network infrastructure and for promoting the fundamental human right to internet access. In this paper, we focus exclusively on the status of the local development and deployment of community LTE Networks in the Philippines, dubbed as LokaLTE, as a component of a larger research project, Resilient Education Information Infrastructure for the New Normal (REIINN). Here, we will present a review of the past and present CCN initiatives in the Philippines and other countries. The sub-components of LokaLTE will be briefly discussed to signify their synergy with the project in general. LokaLTE operates at LTE Band 71 (617 &#x2013; 698 MHz), which is supported by the 3rd Generation Partnership Project (3GPP) for 4G mobile broadband but has yet to be adopted by the Philippines for community LTE networks. Hence, a proposed open-source solution for LTE-based CCNs will be introduced to facilitate the deployment and operation of small-scale, community-owned and operated LTE networks in rural areas. The preliminary results of the measurements and characterization will be described. Accordingly, the plan towards an agile yet regulatory-compliant deployment will be elaborated. CNNs involve a large number of promising research opportunities in the country and, therefore, this paper aims to provide an overview of other possible directions towards future research.
Book Retrieval Navigation System Based on Internet of Things Technology

This paper mainly studies in the following aspects: first, the Internet of things Hadoop big data technology and C # language data analysis technology are applied to the reader interest analysis and guidance analysis. At the same time, in order to solve the high cost problem of big data storage and operation, this paper selects Microsoft azurez as the server group to build Hadoop data platform.
Fusion of Bag of Visual Words with Neural Network for Human Action Recognition

Recognition of human actions is one of the important tasks in various applications. It finds a number of applications in a range of areas. Some of the challenges confronted are noise that peeps in and shape of the action being performed by the subject. In this paper, a bag-of-visual-words is extracted with the help of Scale Invariant Feature Transform (SIFT) and optical flow. It represents spatial as well as time-dependent feature points. Sobel edge filter and median filtering is used to minimize shadow effect and suppress background noise respectively. The classifier used is multiclass Learning-vector Quantisation-based. The well-known KTH dataset is used as benchmark.
Solution of Schrodinger equation for screened modified Poschl-Teller potential in spherical quantum dots and its application in linear and nonlinear optical properties

The optical properties of the spherical quantum dots have been studied by applying screened modified Poschl-Teller potential to the Schrodinger equation. The equation in this work was solved using the supersymmetric quantum mechanics method to determine the energy spectra and wave functions. The results show that the energy level comparison for screened and non-screened modified Poschl-Teller potential has insignificant differences or in other words when the screened potential is applied to the Schrodinger equation, the result returns to its original form. The change in the orbital quantum number has a more significant effect on the energy spectra rather than the potential parameter. Furthermore, the density matrix formalism was applied to obtain the absorption coefficient and refractive index changes for the linear and third-order nonlinear case.
Centralized Machine Learning Versus Federated Averaging: A Comparison using MNIST Dataset

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era.Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily.Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more.However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training.This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training.Furthermore, the training of machine learning and deep learning models requires massive computing resources.Thus, many researchers have jumped to a new model known as "Federated Learning".Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner.Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data.Moreover, we compare the centralized machine learning model with federated averaging.The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center.The MNIST dataset was used in this experiment.
Commentary on “Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters”

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS Journal on Data ScienceVol. 1, No. 1 Commentary on “Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters”Edward McFowland III Edward McFowland III Published Online:9 Mar 2022https://doi.org/10.1287/ijds.2021.0010"Commentary on “Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters”." INFORMS Journal on Data Science, 1(1), pp. 21–22 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 1, Issue 1April-June 2022Pages 1-113, C2 Article Information Metrics Information Received:November 16, 2021Accepted:November 25, 2021Published Online:March 09, 2022 Copyright © 2022, INFORMSCite asEdward McFowland III (2022) Commentary on “Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters”. INFORMS Journal on Data Science 1(1):21-22. https://doi.org/10.1287/ijds.2021.0010 Keywordscasual inferencetreatment effect estimationtreatment assignment policyfairnesshuman-in-the-loopPDF download
Revisiting Neighborhood-based Link Prediction for Collaborative Filtering

Collaborative filtering (CF) is one of the most successful and fundamental techniques in recommendation systems. In recent years, Graph Neural Network (GNN)-based CF models, such as NGCF [31], LightGCN [10] and GTN [9] have achieved tremendous success and significantly advanced the state-of-the-art. While there is a rich literature of such works using advanced models for learning user and item representations separately, item recommendation is essentially a link prediction problem between users and items. Furthermore, while there have been early works employing link prediction for collaborative filtering [5, 6], this trend has largely given way to works focused on aggregating information from user and item nodes, rather than modeling links directly. In this paper, we propose a new linkage (connectivity) score for bipartite graphs, generalizing multiple standard link prediction methods. We combine this new score with an iterative degree update process in the user-item interaction bipartite graph to exploit local graph structures without any node modeling. The result is a simple, non-deep learning model with only six learnable parameters. Despite its simplicity, we demonstrate our approach significantly outperforms existing state-of-the-art GNN-based CF approaches on four widely used benchmarks. In particular, on Amazon-Book, we demonstrate an over 60% improvement for both Recall and NDCG. We hope our work would invite the community to revisit the link prediction aspect of collaborative filtering, where significant performance gains could be achieved through aligning link prediction with item recommendations.
Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones

The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddles. In this paper, we characterize a number of state-of-the-art FCN models on mobile embedded platforms for self-navigation at these sites containing extremely irregular obstacles. We evaluate the models in terms of accuracy, performance, and energy efficiency. We present a few optimizations for our designed vision system. Lastly, we discuss the trade-offs of these models for a couple of mobile platforms that can each perform self-navigation. To enable vehicles to safely navigate earthquake-struck zones, we compiled a new annotated image database of various earthquake impacted regions that is different than traditional road damage databases. We train our database with a number of state-of-the-art semantic segmentation models in order to identify obstacles unique to earthquake-struck zones. Based on the statistics and tradeoffs, an optimal CNN model is selected for the mobile vehicular platforms, which we apply to both low-power and extremely low-power configurations of our design. To our best knowledge, this is the first study that identifies unique challenges and discusses the accuracy, performance, and energy impact of edge-based self-navigation mobile vehicles for earthquake-struck zones. Our proposed database and trained models are publicly available.
Proposal for Generating Complex Microwave Graph States Using Superconducting Circuits

We propose to use transmon qubits to construct microwave 2D cluster states and repeater graph states. We design the generation circuits and compare the state fidelities of fixed- versus tunable-frequency transmon devices.
Digital Media

This chapter aims to show that digital media contributes to such communicative constitution of organizing. It briefly describes the different perspectives on studying digital media, it critiques those perspectives—in particular, the affordances and the entanglement perspectives that dominate its study—and then it brings into focus CCO’s materialization perspective as a framework for studying digital media as agents that make a difference. This perspective, and CCO’s key notion of “matters of concern”, is illustrated using the case of an open university’s online learning environment.
The transmission efficiency analysis for quantum secure direct communications over noisy channels

The concept of quantum secure direct communication (QSDC) is to distribute secure message directly between distant users securely, which is an important branch of quantum information science. The purpose of QSDC is to investigate the large capacity of quantum channels and fast speed of information transmission. However, the fidelity of qubits and the efficiency of QSDC will inevitably be affected by the channel noise and the decoherence of the quantum system. In this work, we focus on the performance of QSDC under different noisy channels, and investigate the evolution of quantum states. By numerically studying the performance for two QSDC protocols, we show the efficiency of information transmission over different noisy channel models.
A spatio-temporal emotional framework for knowledge extraction and mining in digital humanities

Purpose This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method. Design/methodology/approach The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities. Findings The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations. Originality/value The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.
Device Programming for IoT

The chapter focuses on Python as language of choice for beginners in the Internet of Things (IoT) in reception of the fourth industrial revolution, as more devices become interconnected. The problem focuses on the rising popularity of the IoT. More devices, such as smart fridges and televisions, are being granted access to the internet. There is an increased demand of computer power on smaller and smaller chips. Hence, the language to teach for the preferred embedded systems, discussed in this chapter, is Python. A desk review literature search methodology was employed to collect relevant information. Henceforth, the viability of Python as a language of choice for beginners was assessed. The results highlighted issues such as programming language popularity; competitive advantages of Python for IoT development; and limitation of C/C++, and Python for IoT development. The discourse concluded that Python is the preferred language for beginners. It is recommended that due to the rise and popularity of internet and the information communications technologies (ICT) demands of the fourth industrial revolution, more research on computer languages like C/C++ and Python needs to be conducted.
Implementation of a User-Friendly Drone Control Interface Using Hand Gestures and Vibrotactile Feedback

With the increasing use of drones, drone-related accidents have also increased. The main cause of these accidents is associated with the operational issue due to the difficulty of manipulation, especially for a novice. In this paper, a user-friendly drone control interface with hand gesture recognition and vibrotactile feedback is proposed to present additional information on obstacles distributed in the flight environment. Sensitivity analysis was performed to determine the key parameters of hand gestures and with these results, deep learning-based gesture recognition was developed. To effectively avoid obstacles around a drone, the vibration intensity was adjusted according to the distance between the drone and its obstacles. To demonstrate the effectiveness of the proposed system, drone flight simulation and a preliminary experiment were conducted. Results indicate that the proposed interface is useful for controlling drone more intuitively and safely.