Reviewerly/exHarmony huggingface dataset:
Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model.

Recent years have witnessed a rapid increase in Question Answering (QA) research and products in both academic and industry. However, geographic question answering remained nearly untouched although geographic questions account for a substantial part of daily communication. Compared to general QA systems, geographic QA has its own uniqueness, one of which can be seen during the process of handling unanswerable questions. Since users typically focus on the geographic constraints when they ask questions, if the question is unanswerable based on the knowledge base used by a QA system, users should be provided with a relaxed query which takes distance decay into account during the query relaxation and rewriting process. In this work, we present a spatially explicit translational knowledge graph embedding model called TransGeo which utilizes an edge-weighted PageRank and sampling strategy to encode the distance decay into the embedding model training process. This embedding model is further applied to relax and rewrite unanswerable geographic questions. We carry out two evaluation tasks: link prediction as well as query relaxation/rewriting for an approximate answer prediction task. A geographic knowledge graph training/testing dataset, DB18, as well as an unanswerable geographic query dataset, GeoUQ, are constructed. Compared to four other baseline models, our TransGeo model shows substantial advantages in both tasks.
TWEEZER – Tweets Analysis

Twitter is one in all the foremost used applications by the people to precise their opinion and show their sentiments towards different occasions. Sentiment analysis is an approach to retrieve the sentiment through the tweets of the general public. Twitter sentiment analysis is application for sentiment analysis of information which are extracted from the twitter(tweets). With the assistance of twitter people get opinion about several things round the nation .Twitter is one such online social networking website where people post their views regarding to trending topics .It s huge platform having over 317 million users registered from everywhere the globe. a decent sentimental analysis of information of this huge platform can result in achieve many new applications like – Movie reviews, Product reviews, Spam detection, Knowing consumer needs, etc. during this paper, we used two specific algorithm –Naive Bayes Classifier Algorithm for polarity Classification & Hashtag classification for top modeling. this system individually has some limitations for Sentiment analysis. The goal of this report is to relinquish an introduction to the present fascinating problem and to present a framework which is able to perform sentiment analysis on online mobile reviews by associating modified naive bayes means algorithm with Naive bayes classification.
A Hybrid Evolutionary Algorithm for Offline UAV Path Planning.

This paper investigates the offline path planning problem of unmanned aerial vehicles (UAVs) for surveillance mission in complex urban environments. A new idea by coupling the differential evolution (DE) with A* algorithm is suggested to address the problem in large urban areas with narrow street and infrastructure of built environment. The proposed method consists of two phase: the first phase adopts DE to divide the straight line between source and destination into several smaller regions, while the second one utilizes A* for each region to find a collision-free and shortest path in parallel. In order to assess the efficiency of the suggested algorithm, a real-world scenario is examined. Evaluations exhibited promising results with proper accuracy and minimum computational time.
Comparative Study on the Use of the Educational Resources of PeruEduca by Teachers from Arequipa and Moquegua

PeruEduca is an educational platform implemented by the Ministry of Education of PerÃº for providing free resources for teaching at different educational levels. This research presents the analysis of the use of PeruEduca in the teaching-learning process. This study began with a survey carried out with two groups of teachers belonging to the regions of Moquegua and Arequipa, using the criteria of mandatory Educational Quality Examination of the Ministry of Education, which is applied annually. The teachers are graduates of the masterâ€™s degree course in the management of virtual learning environments offered by the Catholic University of Santa MarÃ­a. The analysis was carried out considering two aspects: digital competencies and the teaching-learning process. Â The results of this survey indicate that students from the Moquegua region are having better performances in their learning than students of the Arequipa region. This difference could be attributed to the extensive use of digital resources from the PeruEduca platform in the teaching-learning process. Thus, teachers must be sensitized at the local level to reflect on their pedagogical practices and incorporate methodological strategies in which they can use virtual environments in the development of their learning sessions. https://doi.org/10.26803/ijlter.19.4.7
LibGuides: Effective Meetings: Increase Productivity, Collaboration, and Innovation: Home

If you want to improve your skills to lead effective meetings and be a valuable contributor to meetings, then join us for this workshop.
Determination of the stability of osmium(IV) hexachloride ICP-OES standards in HCl acid matrix

The ideal HCl concentration used in stabilizing osmium standards was investigated in this study. Additionally, mathematical models were tested to predict the decomposition rate of osmium standards at room temperature. Osmium(IV) hexachloride stock solutions stabilized in 7 and 15% HCl were used to assess the decomposition rate using ICP-OES. Several sets of calibration standards were prepared from the original stock solutions. The first calibration set was prepared with no additional HCl added to the final solutions and other sets with a total HCl acid concentration of 0.2, 0.4, 0.6, 1.0 and 1.5% v/v. Emission intensities were measured at 0, 24, 48, 72 and 96 hours. Standards with no additional HCl acid added kept at room temperature showed increase in emission intensities after 24 hours. These changes in emission intensities indicated chemical instability of osmium standards at low acid concentration levels. Standards stabilized in 1.0–1.5% HCl and refrigerated below 10 ⁰C showed consistent emission intensities and had a shelf life of up to 48 and 72 hours, respectively. Four mathematical models were tested to determine the decomposition rate of the osmium standard. The first used rate of change concept and the second a stochastic approach. Both failed to capture osmium decay. The third was based on non-local operators with power law kernel and was able to capture the decay at earlier, but not later stages. The fourth, based on non-local operators with non-singular kernel was able to capture earlier and later decay due to its crossover properties in waiting time distribution.&#x0D; &#x0D; Bull. Chem. Soc. Ethiop. 2020, 34(1), 193-202.&#x0D; DOI: https://dx.doi.org/10.4314/bcse.v34i1.18
Cyber-Risk Management

Having legal remedies facilitates bringing in the state machinery for dealing with such crimes. The various acts do work to some extent in preventing the nefarious acts. The race to develop security measures and being ahead of the perpetrators is one kind of challenge the FIs have to meet. On the other hand, through periodic tests, being vigilant about employee discontent and learning from experience of others are the steps that FIs can take to build up their resilience.
Tyll on Unconditional Teaching

In this entry, I draw a parallel between the mindset of Unconditional Basic Income (UBI) and our concept of Unconditional Teaching.
The Contrivance of Prism Rule-Based Algorithm using ADLs Dataset in Context Database Design

The concept of context-awareness in the database is the contemporary issue which is under discussion and so much of work is in progress, but yet to achieve much progress in context reasoning and behavior adaptability.This research work mainly focuses on context data reasoning using Prism rule-based algorithm in which rules are been generated with the aid of ADLs dataset and context database design with the support of CEAR diagram, Context-aware use case diagram, context dimension tree.Queries are been executed in RDBMS environment by keeping context-awareness scenario in mind and tuple calculus queries are implemented.This research work briefs about query visualization where attribute value block has been introduced here.We have used ADLs dataset of Ordenza et.al [5] which was been captured by different sensors and devices within the living room with two users for 35 days.Our novel contribution is enactment of PRISM rule-based algorithm using ADLs data and instigated attribute value block in the query visualization part.This research work throughout perpetually portrays stratagem of prism rule-based algorithm of context database in ubiquitous computing environment designed for a specific application ADLs.
An Enhanced Privacy Preserving Techniques for Asynchronous Streaming Data Mining in Distributed Environment

This research deals with the study and analysis of privacy preserving techniques in collaborative data mining in order to improve the efficiency and effectiveness of asynchronous streaming data mining in distributed environment. Data mining is a process which uses different data analysis tools that discover patterns and relationships in data that can be used to make predictions. Security and Privacy protection has been a public policy firm for decades. However, rapid technological changes, the fast growing of the internet source and electronic digital source, and the development of more sophisticated methods of collecting, analyzing, and using personal data have made privacy a major public and government issues. The field of data mining is yield significance recognition to the availability of large amounts of data, easily collected and stored via computer systems.
Mechanisms for the generation of HREE mineralization in carbonatites: Evidence from Huanglongpu, China.

The attached document is the authors’ submitted version of these conference proceedings. You are advised to consult the publisher’s version if you wish to cite from it.
A taxonomy and empirical analysis of clustering algorithms for traffic classification

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are “similar” according to specific metrics. There is a vast body of knowledge in the area of clustering and there have been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that created a confusion amongst the practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this chapter introduces concepts and algorithms related to clustering, a concise survey existing (clustering) algorithms as well as providing a comparison both from a theoretical and empirical perspective. From a theoretical perspective, we come up with a categorizing framework based on the main properties pointed out in previous study. Empirically, extensive experiments are carried out where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. Additionally, we highlighted the set of clustering algorithms that are the best performing for big data.
Using Machine Learning with IoT and IIoT in Python

The purpose of this chapter is not to get you going on the hardware and the software but to show you how the interconnection between the hardware and software happens in the IoT. You’ll get an introduction here to this concept and then later in Chapter 5 you’ll learn how to do the hardware and software setup when I show you how to install Raspberry Pi, Arduino, and other devices from scratch.
Classification of Breast Lesions in Combination with Metamorphic Segmentation and Saliency Feature Block

Breast cancer is leading disease of females and every year death rate is increased gradually due to the breast cancer. Early diagnosis and treatment of breast cancer is an effective way to reduce the death rate of women. The development of the CAD systems improved the mortality rate by reducing false assumptions. This proposed work presents a computer-aided diagnosis (CAD) system for early detection of tumor in digitized mammograms. A novel classification method for breast lesions is proposed using transformative segmentation and saliency feature block. Experimental results show that proposed methods outperformed the existing method and provide timely diagnosis which greatly reduces the mortality rate in medical informatics.
Evolving a Weighted Combination of Text Similarities for Authorship Attribution.

Authorship Attribution (AA) also known as Authorship Identification is the problem of identifying the author of an anonymous text based on its characteristics or features. Among notable features extraction methods used to this end, one can cite, the bag of words methods (BOW) and the semantic and syntactic methods (SSM). BOW methods consider the text as a sequence of tokens and disregard the semantics of the language, whereas SSM rely on advanced natural language processing (NLP) techniques. The features extracted from an anonymous text are compared to features extracted from a corpus of texts written by known authors using several similarity measures. In this paper, we combine multiple results generated using conventional methods (chosen from the literature) and we use a genetic algorithm (GA) to find the optimal weighting distribution. The optimal combination obtained by the GA is then applied, and the author attributed to the anonymous text is selected among a set of known authors based on the highest similarity. The fitness of our GA is the resulting accuracy of the authorship attribution task. A numerical application on a corpus consisting of 3036 books written by 142 authors shows that the proposed method has higher accuracy than conventional methods and achieved satisfying performance.
Graduate Prediction in University using Fuzzy Logic

STIMIK Sepuluh Nopember Jayapura is an educational organization engaged in technology and information.Student graduation rate from 2011 -2018 seen from the number of incoming students has decreased.These conditions become reference material for the majors to conduct evaluations and strategic steps in an effort to increase the level of graduation of students so that quality objectives can be achieved.Evaluation of majors needs to be improved by analyzing the factors that cause late graduation for a student.3.For the purposes of training data Student data is used from 2011 -2015, while for testing data Student data is used for 2016.The method used to predict is the Adaptive Neuro Fuzzy Inference System method.Based on the test results, training is conducted on the amount of training data as much as 50%, 60% and 70% of the total data available.While testing is conducted on the remaining training data consisting of percentages of 50%, 40% and 30%.For each data combination ten times (Epoch value) and then the average RMSE value is taken from the test.The smallest RMSE value was found in a combination of 60% training data and 40% test data which was 0.006380.The more training data shows the higher the level of accuracy.
A Predictive Algorithm

A generalized algorithm that permit to predict the input of a system that acts as simple de-convolution algorithm.
Cyber Forensic Challenges: Escaping Holes for Cyber Attackers Series 01: Extracting Forensic Evidences from Non- Functional Computer Devices

According to Forensic Terminology, Cyber Forensic Challenge is a state, a competitive situation, unavailability of reliable evidences and bottleneck constraint in Computer Forensic Investigation and its Procedures. A Forensic Challenge can either be Technical, Admistrative or Legislative/Legal and Anti-Forensic is an example of Technical Challenge, it adds complexity in acquiring and accessing an evidence or sets of evidences during Forensic Investigation. In real-world scenarios, it hurdles Forensic Examiners from acquiring an image of evidences to continue with Forensic Examinations and makes the Attackers or Adversaries "Scape Goats" during Criminal Prosecution or in a Corporate Forensic Investigation, as where it arises. In Public Computer Forensic domains, one of the Challenges is unavailability of Public Repositories of MACs, Bluetooth, IMEI and NFC Addresses -of Mobile Phones, WiFi Routers, Printers, Biometric Scanners and other IOT Devices(such as Amazon Alexa, Smart Watch and FitBit). Globally, not adequate, but there are reasonable numbers of such Repositories to overcome the challenges in the concerned fields and countable numbers of systems dedicated to such collections and innovative ones to name are CEIR(Central Equipment Identity Repository) for IMEI Databases(India), WIGLE for WiFi SSID(Global) but there is still shortfalls of ethical Repositories of Bluetooth Addresses (Private and Public) and Identifiers associated with NFC and RFID !
On Subgraph Signal Processing

Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph. However, in practice, only signals at a subset of vertices may be available. We propose a subgraph signal processing framework that relates a graph shift operator or filter on a subgraph with a filter on the ambient graph through an operator loss. It allows us to define shift operators for the subgraph signal, which has a meaningful interpretation and relation to mixtures of shift invariant filters restricted to different subgraphs of the ambient graph (which we call semi shift invariant). This leads to a notion of frequency domain for the subgraph signal consistent in some sense with that of the full graph signal. We apply the subgraph signal processing machinery to several applications and demonstrate the utility of this framework in cases where only partial graph signals are observed.
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.
Delegation-based conversion from CPA to CCA-secure predicate encryption

In 2011, Yamada et al. proposed CPA to CCA-secure conversions for attribute-based encryption (ABE) based on the properties, verifiability and delegation. Later, the verifiability-based conversion was generalised from ABE to predicate encryption (PE) by Yamada et al. (2012) and Nandi et al. (2017). We observe that for bilinear-pairing based PE schemes, the cost of CCA-decryption blows up to the double of the cost of CPA-decryption due to verifiability testing. Therefore, the conversion based on delegation is mostly acceptable whenever a delegation-based conversion is available for the primitive PE scheme. In this paper, we investigate a generic delegation-based conversion from CPA to CCA-secure predicate encryption schemes. Our conversion generalises the delegation-based conversion of Yamada et al. (2011) from ABE to PE. We show that our conversion captures many subclasses of PE, e.g., (hierarchical) inner-product encryption, (doubly-)spatial encryption and functional encryption for regular languages.
Feature Recalibration in Deep Learning via Depthwise Squeeze and Refinement Operations

Feature recalibration is a very effective strategy of further improving performance in deep networks. The commonly used global pooling operation will lose the information of distinguishing features, which requires additional fully connected layers to adjust the relationship between feature maps. In this paper, we propose a novel architectural unit that aims to recalibrate feature maps based on the discriminative feature information contained in each feature map. To achieve this, we use two-layer depthwise convolution instead of global pooling to extract the distinguishing feature information of each feature map. Since convolution can discriminately learn the global information of each feature map, we discard the fully connected layer to ensure independence when adjusting the feature map. After getting the global information of each feature map, the nonlinear function is used directly to implement the refinement of the feature map to enhance the distinguishing feature map and suppress useless ones. Based on the design characteristics of the new unit, we codename it as the “Depthwise Squeeze and Refinement” (DSR) block. And it can be easily embedded into existing state-of-the-art deep structures to significantly improve the performance at a slight computational cost.
ForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting Questions

Textual data are often accompanied by time information (e.g., dates in news articles), but the information is easily overlooked on existing question answering datasets. In this paper, we introduce ForecastQA, a new open-domain question answering dataset consisting of 10k questions which requires temporal reasoning. ForecastQA is collected via a crowdsourcing effort based on news articles, where workers were asked to come up with yes-no or multiple-choice questions. We also present baseline models for our dataset, which is based on a pre-trained language model. In our study, our baseline model achieves 61.6% accuracy on the ForecastQA dataset. We expect that our new data will support future research efforts. Our data and code are publicly available at this https URL.