Reviewerly/exHarmony huggingface dataset:
Exploring Strategic Communication Challenges in the Era of the Voice Assistant

Using machine learning and artificial intelligence technologies, voice assistant (VA) has become a new social phenomenon and a market game changer in multiple industries. This thesis focuses on the strategic communication challenges brought about by the VA. The researcher applies the theoretical framework of the communication value circle (CVC) and the gatekeeping theory (GK), adopting the applied thematic analysis (ATA) as the overarching method. After selecting and examining 18 industry reports and transcripts of 19 interviewees who are experienced with VA, challenges are identified. The results point out five key challenges and reveal VA’s game changer position in the field of strategic communication, both theoretically and empirically. The challenges are: ‘VA gatekeepers in the consumer journey’, ‘the management of VA-merged communication’, ‘new stakeholder relationships and negative associations’, ‘VA-triggered change and crisis’ and ‘new roles and skills of strategic communicators’. This study also places technology-based communicators under the spotlight of organizational communication theory. It enriches the CVC framework by offering new application means and contributes to GK theory with a case study of algorithm-based gatekeepers. In addition, this study also proves the effectiveness of the ATA approach in abstracting a holistic picture of complex, technology-driven strategic communication issues. As well as three future research suggestions, the author also presents an expanded model for easy application by communication managers. (Less)
Interpretable Artificial Intelligence through the Lens of Feature Interaction

Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature representations. Despite this, many view interpretability as a key solution to trustworthiness, fairness, and safety, especially as deep learning is applied to more critical decision tasks like credit approval, job screening, and recidivism prediction. There is an abundance of good research providing interpretability to deep learning models; however, many of the commonly used methods do not consider a phenomenon called "feature interaction." This work first explains the historical and modern importance of feature interactions and then surveys the modern interpretability methods which do explicitly consider feature interactions. This survey aims to bring to light the importance of feature interactions in the larger context of machine learning interpretability, especially in a modern context where deep learning models heavily rely on feature interactions.
Research Productivity of Library Philosophy and Practice during the Period of COVID 19

The present research purpose is to investigate the publishing trends of LPP during the COVID-19 from 2020 to 2021 The quantitative research design and survey method have been used to retrieve data from the website of LPP with the full-text article The collected data has been analyzed using quantification tools The parameters were set for this study are the frequency of publication growth, author’s collaborations, degree of author collaboration, papers length, and most productive regions The data revealed 860 research papers were collected during the COVID-19 era Where 844 papers were published in 2020 and 16 were published in 2021 as per retrieved data from the LPP database The length of papers has confirmed with 290(33 72%) with a range of 16-20 pages The degree of collaboration was analyzed that is 0 76 The present study shows the author collaboration where two authors was in majority that is 380(44 18%) The data shows the most productive region was India with 389 ranked 1st and Nigeria ranked 2nd with 212 research papers during the COVID-19 © 2021
How to integrate with real cars -- minimizing lead time at Volkswagen

The most successful tech companies of the world release new software versions to production multiple times a day. Thereby, they are able to quickly fix emerging bugs and rapidly deliver new features to their customers. This leads to short development cycles, minimal lead times and a high customer-centricity. Short development cycles are easy to achieve if you start a software project on a green field. Nevertheless, this does not apply to brown field environments which are usually found in big corporates such as traditional car manufacturers. For instance, if you want to integrate with real cars you have to interface legacy systems with development cycles of up to several months. We present a solution, which worked for one of the world's largest car manufacturer, leveraging in-house core development teams, dynamic stages and feature-toggles to overcome a brown field environment, allow for short development cycles and minimize the lead time.
Co-simulation Technology Analysis of Efficiency Design of Agricultural Machinery Products Under Artificial Intelligence

Artificial intelligence is an important branch of computer science and has always been at the forefront of computer technology. It is known as one of the world’s three cutting-edge technologies. The theories and findings of artificial intelligence research can be integrated into all aspects of human life and work, improve people’s quality of life, reduce labor intensity, and reduce the risk of injury. This article aims to study the analysis of the joint simulation technology of the efficiency design of agricultural machinery products under artificial intelligence. This paper proposes co-simulation technology to design and analyze the efficacy of agricultural machinery products, compare the traditional agricultural machinery product design with the agricultural machinery product design under artificial intelligence, and draw the practicality of the design methods and techniques under artificial intelligence and effectiveness. In addition, this article analyzes the development direction of agricultural machinery intelligence based on the application of artificial intelligence in the field of agricultural machinery. The experimental results of this paper show that the efficiency design of agricultural machinery products under artificial intelligence has been greatly improved compared with the traditional design, and its design efficiency has increased by 25%.
POSSE (Predict, Organize, Search, Summarize, and Evaluate) Strategy Towards Reading Comprehension Achievement

This research aimed at finding out whether or not Predict, Organize, Search, Summarize, and Evaluate (POSSE) Strategy influence the students’ reading comprehension achievement of the eighth-grade students of SMP Muhammadiyah 2 Karang Tengah. The population consists of 99 students. The sample was 69 students taken through purposive sampling. In this research, the researcher used multiple-choice as an instrument for collecting the data. The data obtained from Independent t-test analysis, between the result of the posttest in the experimental group and the control group. Based on the calculation by using an Independent t-test, the researcher found that t-Obt. was higher than the t-table (4,115 &gt; 2,000) and less than at the significant level α = 0.05(0.000 &lt; 0.05) in a two-tailed test. So, the null hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. It can be concluded that the POSSE Strategy influence to teach reading comprehension achievement.
THE EFFECT OF USING PICTURES MEDIA IN STUDENTS’S WRITING ABILITY

Learning English as a second language can be useful for school, for work, or personal interest in an exciting. There are four basic skills in mastering the language namely listening, speaking, reading, and writing. The researcher is very sure that in making this research by implementing pictures media in learning writing could become fun and interesting. Pictures media is one of techniques to get more motivated and can carry out more their own ideas . Thus, the writer intends to make a research about the effect of using pictures media in student’s writing ability at VI grade in SD Negeri Kampung Baru Kutacane. This study will be conduct by using the experimental design. In this study, there are two variables : Picture as independent variable and writing ability as dependent variable. In conducting the experimental research, the sample be divided in two groups : the experimental group taught by using pictures and control group taught without using the pictures. Both of the groups will get pre-test and post- test with the same items. Based on the data analysis, the two score of both experimental and control groups were different. The mean score of the post-test of experimental group was higher than control group (68,7 > 54,3). However, the differences were tested by using t-test. The result of the t-test calculation showed that t-observed value (9,7) was higher than t-table value (1,980) at p = 0,05 , df = 78. The differences indicate that Measure Picture Media significantly affects on students writing ability.
La matemática en tiempos de la covid-19: retos e implicaciones para su enseñanza-aprendizaje/ Mathematics in times of covid-19: challenges and implications to the teaching and learning process

Objective: This article aims at promoting critical thinking on mathematics relevance in times of COVID-19, its challenges and implications to make teaching and learning closing to actual events.Methods: The authoress rely mainly on documentary analysis assisted by analytic and synthetic procedures. Results: Arguments to prove the relevance of Mathematics for society are given; its role to understand and transform the surrounding reality, specifically, those related to COVID-19. Likewise, the authoress devises the essential didactic guidelines for teaching Math in present-day context.Conclusions: The instrumental and educative character of mathematic concepts for modern society poses a challenge for teaching and learning to improve the quality of life. This challenge demands teacher’s commitment and awareness of their decisive role in achieving such goals.
Finitely-Valued Propositional Dynamic Logic.

We study a many-valued generalization of Propositional Dynamic Logic where formulas in states and accessibility relations between states of a Kripke model are evaluated in a finite FL-algebra. One natural interpretation of this framework is related to reasoning about costs of performing structured actions. We prove that PDL over any finite FL-algebra is decidable. We also establish a general completeness result for a class of PDLs based on commutative integral FL-algebras with canonical constants.
Multi-Image Crowd Density Estimation using Multi Column Deep Neural Network

Crowd density estimation is a challenging problem for security applications which is a regression task consisting of feature extraction and regression steps. In this paper, multicolumn deep neural networks (MDNN) is proposed for crowd density estimation. Regression task is performed using the feature vector obtained from MDNN. 2000 images selected from the UCSD pedestrian dataset are used in the experiments. In the images, the region of interest (ROI) is filtered and the remaining parts are removed. In order to avoid distortions due to camera position, perspective normalization is applied as a pre-processing step which yields considerable performance improvement.
The Use of Ebru Drawing in the Psychological Correction of Anxiety in Adolescents with Traumatic Experience

The article presents the analysis of psychological and pedagogical correctional work with children and adolescents in difficult life situations and having traumatic experience. The work is carried out by pedagogical psychologists of the pivotal schools of the Volgograd educational cluster using the ebru-drawing technology.
Geochemical and Mineralogical Characterization of Ore â€“ Mineral Assemblages from Black Sands in Akure South Area, South - Western Nigeria

The geochemical and mineralogical features of Black sand in Akure South was investigated. The head samples were sourced from four different locations and mixed properly for homogenization. 200 g crude sample was sampled out, finely ground using the Denver Ball Mill, Model: Denver D10 and prepared towards characterization. Chemical Characterization of the crude sample was carried out using Energy Dispersive X-ray Fluorescence Spectrometry (ED-XRF) - Minipal 5. Mineralogical Analysis was carried out using Scanning Electron Microscopy with Energy Dispersive X-Ray Spectrometer (SEM-EDS) and X-ray Diffraction Analysis (XRD). ED-XRF analysis revealed that the crude sample contains 17.55 % Fe2O3, 63.7 % SiO2, 15.5 % TiO2, and other compounds in trace form. Mineralogical analysis of the black sand via XRD revealed the present of Quartz (87.07 % SiO2), Magnetite (0.59 % Fe3O4), Ilmenite (9.89 % FeTiO3) with other associated minerals such as Rutile (0.53 % TiO2), Anatase (0.25 % TiO2), Kaolinite (0.09% Al2 (Si2O5(OH)4) and Albite (1.57% Na(AlSi3O8)). SEM images revealed interlocking of some minerals within the crystal aggregates of the crude black sand and the EDS of these microstructures revealed the presence of Al, Mn, Cu, O, Si, C, Fe and Ti; silicon, titanium and iron are the major elemental constituents of the matrix.
LibGuides: Creative Commons: A Summary : Home

This guide summarizes Creative Commons licenses for graduate students at Washington State University.
Inference for Generative Capsule Models.

Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge and reason about the relationship between an object and its parts. % In this paper we focus on a clean version of this problem, where data is generated from multiple geometric objects (e.g. triangles, squares) at arbitrary translations, rotations and scales, and the observed datapoints (parts) come from the corners of all objects, without any labelling of the objects. We specify a generative model for this data, and derive a variational algorithm for inferring the transformation of each object and the assignments of points to parts of the objects. Recent work by Kosiorek et al. [2019] has used amortized inference via stacked capsule autoencoders (SCA) to tackle this problem -- our results show that we significantly outperform them. We also investigate inference for this problem using a RANSAC-type algorithm.
Stochastic model for prediction of microgrid photovoltaic power generation

In this article, a stochastic model for prediction of microgrid photovoltaic power generation, using statistical and stochastic methods is presented. The study is performed in the following steps: Processing of a large database of historical data (Data Mining); Construction of a stochastic forecasting model; Reporting a symmetric mean absolute percentage error in forecasting.
STEM Education in the Age of “Fake News”: A John Stuart Mill Perspective

If a nation expects to be ignorant and free, in a state of civilization, it expects what never was and never will be."Thomas Jefferson, 1816 It is widely recognized that American society is deeply polarized along political and ideological lines, and the polarization has become increasingly adversarial, even hostile, in recent years.As my colleague and I have stated elsewhere, "The lack of engagement, understanding, and appreciation between people across these groups is stunning and potentially catastrophic." 1 Such lack of understanding and deliberation on both sides, it seems, originates not only from an unwillingness to engage and a tendency to demonize and dismiss the other's viewpoint, but also from a lack of the ability to discern true statements from false ones.The so-called "dumbing down" of America in the age of "fake news," which is playing an increasingly significant role in shaping the current political culture, is disheartening.Since American democracy relies on a public that is capable of actively expressing ideas and participating in discussions with people who have different perspectives, a citizenry unable to deliberate rationally and meaningfully poses a great threat to its success.Education in particular has been tasked with equipping youngsters to become contributing members of a democratic society.Specifically, math and science education has been the central place where students learn the skills to discern facts, process information, and draw conclusions and practice ways to search for true knowledge.Such an "art of thinking," apparently, is vital for democratic societies to thrive.With the current strong nation-wide push for
Simple online and real-time tracking with occlusion handling

Multiple object tracking is a challenging problem in computer vision due to difficulty in dealing with motion prediction, occlusion handling, and object re-identification. Many recent algorithms use motion and appearance cues to overcome these challenges. But using appearance cues increases the computation cost notably and therefore the speed of the algorithm decreases significantly which makes them inappropriate for online applications. In contrast, there are algorithms that only use motion cues to increase speed, especially for online applications. But these algorithms cannot handle occlusions and re-identify lost objects. In this paper, a novel online multiple object tracking algorithm is presented that only uses geometric cues of objects to tackle the occlusion and reidentification challenges simultaneously. As a result, it decreases the identity switch and fragmentation metrics. Experimental results show that the proposed algorithm could decrease identity switch by 40% and fragmentation by 28% compared to the state of the art online tracking algorithms. The code is also publicly available.
Deep Graph Matching under Quadratic Constraint

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of graph structures, which may lead the model to be trapped into local minimum in training. In this paper, we propose to explicitly formulate pairwise graph structures as a \textbf{quadratic constraint} incorporated into the DGM framework. The quadratic constraint minimizes the pairwise structural discrepancy between graphs, which can reduce the ambiguities brought by only using the extracted CNN features. Moreover, we present a differentiable implementation to the quadratic constrained-optimization such that it is compatible with the unconstrained deep learning optimizer. To give more precise and proper supervision, a well-designed false matching loss against class imbalance is proposed, which can better penalize the false negatives and false positives with less overfitting. Exhaustive experiments demonstrate that our method competitive performance on real-world datasets.
POLYNOMIAL-TIME PLAINTEXT-RECOVERY ATTACK ON THE MATRIX-BASED KNAPSACK CIPHER

The aim of the present paper is to propose a polynomial-time plaintext-recovery attack on the matrix-based knapsack cipher. The aforesaid algorithm uses only public information and has time complexity O(t1.34), where t is the decryption time of the attacked cryptosystem. The matrix-based knapsack cipher is a novel additively homomorphic asymmetric encryption scheme, which is a representative of group-based knapsack ciphers. This cryptosystem is based on the isomorphic transformation’s properties of the inner direct product of diagonal subgroups of a general linear group over a Galois field. Unlike the classical knapsack cryptoschemes, the cryptographic strength of the aforesaid cipher depends on the computational complexity of the multidimensional discrete logarithm problem. Due to the attack proposed in the given paper, the matrix-based knapsack cipher can be considered broken and should not be used as a privacy tool. However, this cryptosystem is still suitable for educational purposes as an example of the application of linear and abstract algebras in asymmetric cryptography.
Closed Frequent Itemsets Mining Based on It-Tree

In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting &amp; intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.
Differential Tracking Across Topical Webpages of Indian News Media

Online user privacy and tracking have been extensively studied in recent years, especially due to privacy and personal data-related legislations in the EU and the USA, such as the General Data Protection Regulation, ePrivacy Regulation, and California Consumer Privacy Act. Research has revealed novel tracking and personal identifiable information leakage methods that first- and third-parties employ on websites around the world, as well as the intensity of tracking performed on such websites. However, for the sake of scaling to cover a large portion of the Web, most past studies focused on homepages of websites, and did not look deeper into the tracking practices on their topical subpages. The majority of studies focused on the Global North markets such as the EU and the USA. Large markets such as India, which covers 20% of the world population and has no explicit privacy laws, have not been studied in this regard. We aim to address these gaps and focus on the following research questions: Is tracking on topical subpages of Indian news websites different from their homepage? Do third-party trackers prefer to track specific topics? How does this preference compare to the similarity of content shown on these topical subpages? To answer these questions, we propose a novel method for automatic extraction and categorization of Indian news topical subpages based on the details in their URLs. We study the identified topical subpages and compare them with their homepages with respect to the intensity of cookie injection and third-party embeddedness and type. We find differential user tracking among subpages, and between subpages and homepages. We also find a preferential attachment of third-party trackers to specific topics. Also, embedded third-parties tend to track specific subpages simultaneously, revealing possible user profiling in action.