Reviewerly/exHarmony huggingface dataset:
THE GEODIVERSITY INDEX MAP OF PUERTO RICO – A PRACTICAL REFERENCE IN LAND DEVELOPMENT AND ENVIRONMENTAL CONSERVATION

Geodiversity represents the abiotic counterpart of biodiversity; it encompasses the geological, geomorphological, pedological, paleontological, hydrological and mineralogical features, assemblages, properties and systems. The quantification and mapping of geodiversity have a wide application in various fields of sustainable development and nature conservation, and most often take the form of a Geodiversity Index as a compilation of partial indices – one for every segment of abiotic nature. The Index for Puerto Rico was assessed and mapped using ArcGIS and QGIS software, and a SRTM Digital Elevation Model. The methodology is straightforward and involves a simple algorithm, but relies and depends on pre-existing thematic maps. These are overlapped with a grid and the number of units in each grid cell is extracted following a semi-automatic process. The values of the partial indices are summed and accommodated into a 5-grade (low-high) scale. The resulting raster model is accessible to and can be interpreted by a wide range of people with little or no background in geosciences. The Geological Index was obtained from geologic provinces, lithostratigraphic formations and lithodemic units, and major depositional and structural contacts. The Geomorphological Index was based on physiographic units and geoclimatic zones. The Pedological Index was obtained from soil orders. The Hydrological Index is represented by hydrology and hydrogeology – surface water bodies, rivers and watersheds, ground water provinces and aquifers. The Paleontological Index was given by fossiliferous stratigraphic units. The Mineralogical Index was given by the spatial distribution of metallic and non-metallic mineral resources. Puerto Rico has well-defined areas of high and very high geodiversity that are vulnerable to the rapid expansion of built-up land, and contrast with the almost exclusive focus on biodiversity of the existing protected areas. To better illustrate this, the Geodiversity Index map is displayed against a map of urban and rural land use and the map of Natural Protected Areas. This study reinforces the need for more efficient land-use planning across the main island of Puerto Rico and for an integrated approach of biodiversity and geodiversity for conservation purposes.
Deep Motion Blind Video Stabilization

Despite the advances in the field of generative models in computer vision, video stabilization still lacks a pure regressive deep-learning-based formulation. Deep video stabilization is generally formulated with the help of explicit motion estimation modules due to the lack of a dataset containing pairs of videos with similar perspective but different motion. Therefore, the deep learning approaches for this task have difficulties in the pixel-level synthesis of latent stabilized frames, and resort to motion estimation modules for indirect transformations of the unstable frames to stabilized frames, leading to the loss of visual content near the frame boundaries. In this work, we aim to declutter this over-complicated formulation of video stabilization with the help of a novel dataset that contains pairs of training videos with similar perspective but different motion, and verify its effectiveness by successfully learning motion blind full-frame video stabilization through employing strictly conventional generative techniques and further improve the stability through a curriculum-learning inspired adversarial training strategy. Through extensive experimentation, we show the quantitative and qualitative advantages of the proposed approach to the state-of-the-art video stabilization approaches. Moreover, our method achieves $\sim3\times$ speed-up over the currently available fastest video stabilization methods.
A Chaotic Confusion-Diffusion Image Encryption Based on Henon Map

This paper suggests chaotic confusion-diffusion image encryption based on the Henon map. The proposed chaotic confusion-diffusion image encryption utilizes image confusion and pixel diffusion in two levels. In the first level, the plain image is scrambled by a modified Henon map for n rounds. In the second level, the scrambled image is diffused using Henon chaotic map. Comparison between the logistic map and modified Henon map is established to investigate the effectiveness of the suggested chaotic confusion-diffusion image encryption scheme. Experimental results showed that the suggested chaotic confusion-diffusion image encryption scheme can successfully encrypt/decrypt images using the same secret keys. Simulation results confirmed that the ciphered images have good entropy information and low correlation between coefficients. Besides the distribution of the gray values in the ciphered image has random-like behavior.
Komparativni osvrt na visoko obrazovanje tokom pandemije kovid-19

The 2020 crisis caused by the global COVID-19 pandemic brought the current and emerging weaknesses of the education system in both developed countries and countries in transition to the surface. This paper focuses on a comparative overview of the sector of higher education, especially in terms of the possibilities for achieving equality of educational opportunity, mobility, quality of scientific research, and education quality in general. We conducted an analysis of available theoretical papers, empirical studies, and reports that focus on the impact of the pandemic on institutions of higher education, the process itself, and the results of online teaching/learning, research and internationalization of education. The results of the analysis indicate differentiated responses of institutions of higher education to the new situation and the changed social context with regard to all observed aspects. Previous studies and analyses should indicate directions for post-pandemic reactions of institutions of higher education, so they could adequately and flexibly respond to global crises and changes in the future.
Third Time's Not a Charm: Exploiting SNMPv3 for Router Fingerprinting

In this paper, we show that adoption of the SNMPv3 network management protocol standard offers a unique -- but likely unintended -- opportunity for remotely fingerprinting network infrastructure in the wild. Specifically, by sending unsolicited and unauthenticated SNMPv3 requests, we obtain detailed information about the configuration and status of network devices including vendor, uptime, and the number of restarts. More importantly, the reply contains a persistent and strong identifier that allows for lightweight Internet-scale alias resolution and dual-stack association. By launching active Internet-wide SNMPv3 scan campaigns, we show that our technique can fingerprint more than 4.6 million devices of which around 350k are network routers. Not only is our technique lightweight and accurate, it is complementary to existing alias resolution, dual-stack inference, and device fingerprinting approaches. Our analysis not only provides fresh insights into the router deployment strategies of network operators worldwide, but also highlights potential vulnerabilities of SNMPv3 as currently deployed.
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

We introduce regularized Frank-Wolfe, a general and effective algorithm for inference and learning of dense conditional random fields (CRFs). The algorithm optimizes a nonconvex continuous relaxation of the CRF inference problem using vanilla Frank-Wolfe with approximate updates, which are equivalent to minimizing a regularized energy function. Our proposed method is a generalization of existing algorithms such as mean field or concave-convex procedure. This perspective not only offers a unified analysis of these algorithms, but also allows an easy way of exploring different variants that potentially yield better performance. We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone component and as an end-to-end trainable layer in a neural network. We also show that dense CRFs, coupled with our new algorithms, produce significant improvements over strong CNN baselines.
THE USE OF DIRECT METHOD TO IMPROVE THE EIGHTH GRADE STUDENTS’ READING COMPREHENSION OF SMP N 1 SATU ATAP TAMPAHAN IN ACADEMC YEAR 2021/2022

This study analyzed the use of the Direct Method to improve the eighth grade students’ reading comprehension and perspective of SMP N 1 Satu Atap Tampahan. The objective of this study was to find out whether the Direct Method improves the students’ reading comprehension and the eighth grade students’ perspective of being taught by Direct Method. The subject of this study were 30 students. The methodology of this study was Classroom Action Research. The qualitative and quantitative data were gathered by reading test and diary notes, observation and interviews. The result of the quantitative data obtained that the score of the students improved from cycle I to cycle III which the mean of pre-test and post-test I were (71,75 to 75,23), the mean score of post-test in cycle II were (78,1) and the mean score of the post-test cycle III were (81,26).The qualitative data drew that the use of Direct Method made sense to the students’ reading comprehension in English and to their perspective of being taught by Direct Method. It means that the use of the Direct Method improves the students’ reading comprehension and the students’ perspective of being taught by it.
Sentiment Analysis in Online Products Reviews Using Machine Learning

Online Shopping is a phenomenon that is growing rapidly. It refers to the act of buying and selling products or services over the internet. Since customers are shopping online, there are some problems with this process. Firstly, is that customers can fall into fraud and security concerns as there is an inability to inspect the goods that you are purchasing beforehand. There is also the other issue on the quality of the product, this is because when selling online, only simple pictures and or descriptions of the product are all a customer can rely on when purchasing. There is also another factor customer can look at before purchasing a product and those are reviews left by previous customers that have purchased the same product from the same seller. Reviews are left by a consumer that has experienced or purchased a product from the store. Thus, by reading the reviews of a product, the new customer can see whether people liked the product or not, or to see if the product that was delivered was the promised product by the store. With the help of Machine Learning Techniques, the researcher can then try to find the best technique that can be used for Sentiment Analysis on Online Product Reviews.
A Review Paper onWireless Image Transmission

The need for a mobile radio link is to build an innovative wireless communication system for picture and video signals other than voice signals. The transmission of the pictures through a wireless channel permits an image to be compliant with the characteristics of the channel, such as the bandwidth. A large space in the storage unit occupies the image and video signals and takes a long time to transmit over a wireless channel. In order to reduce the redundant data from an acquired image to make it compliant with channel bandwidth, compression techniques are used. Various compression methods and communication models are analyzed in this article. During image acquisition and in the channel, numerous noises are added. Various compression methods and communication models are analyzed in this article. During image acquisition and in the channel, numerous noises are added. These noises need to be minimized during the transmitter and receiver image formatting and de-formatting process, respectively.
A new algorithm for solving linear programming problems with bipolar fuzzy relation equation constraints

This paper studies the linear optimization problem subject to a system of bipolar fuzzy relation equations with the max-product composition operator. Its feasible domain is briefly characterized by its lower and upper bound, and its consistency is considered. Also, some sufficient conditions are proposed to reduce the size of the search domain of the optimal solution to the problem. Under these conditions, some equations can be deleted to compute the minimum objective value. Some sufficient conditions are then proposed which under them, one of the optimal solutions of the problem is explicitly determined and the uniqueness conditions of the optimal solution are expressed. Moreover, a modified branch-and-bound method based on a value matrix is proposed to solve the reduced problem. A new algorithm is finally designed to solve the problem based on the conditions and modified branch-and-bound method. The algorithm is compared to the methods in other papers to show its efficiency.
Experiencias en el diseño didáctico en entornos virtuales de enseñanza-aprendizaje en la Universidad Tecnológica de La Habana

Currently, the use of virtual teaching-learning environments constitutes a requirement for professional training, since the ways of teaching and learning impose their use to enable students' autonomy and creativity. The article aims to assess the pedagogical experiences in didactic design in Moodle through the subject Pedagogy and Didactics of Higher Education and the optional-elective subject Virtual training environments in the Geophysical Engineering career of the Technological University of Havana Jose Antonio Echeverria (Cujae). The methods used were analytical-synthetic, modeling, observation and experiential. As the main result, the structural functional unit is recognized as a unit of analysis, the establishment of the didactic requirements and the essential methodological aspects for its implementation with students and teachers.
Applicability of systems and software quality engineering methods and models to information security in cloud computing services

Industry continues to aggressively adopt cloud computing services, motivated by the potential benefits of their native characteristics. Additionally, information technology (IT) outsourcing experts consider security to be the most important non-functional requirement of cloud computing services. Consequently, a growing number of systems and software engineers must confront the quality engineering of cloud-based information systems (IS) to mitigate or avoid potential user or stakeholder risks. It has been well-established that the early application of quality engineering to IS development during requirement activities is a major milestone and contributor to building high quality IS. Furthermore, the application of quality engineering requires the use of a quality model with the capacity to support both the definition of quality requirements and their subsequent evaluation. However, the extent to which ISO/IEC 25000 quality models are applicable to information security in cloud computing services has not been identified. Accordingly, this thesis proposes the use of the ISO/IEC 25030 quality requirements framework as a systematic approach to answer the research question “To what extent do ISO/IEC 25000 quality models support the definition of quality requirements related to information security in cloud computing services?” Here, we use IS research design science practices to evaluate and extend the applicability of the ISO/IEC 25030 quality requirements framework to information security in cloud computing services. We also use the extended framework to answer the research question while illustrating its applicability to the quality requirement definition of cloud-based IS. Results from this thesis show that on the one hand ISO/IEC 25000 quality models cannot adequately mitigate the top threats to cloud computing services. On the other hand, the application of the extended framework demonstrates that these quality models can be tailored to the quality requirements definition of cloud-based IS for the purposes of mitigating or avoiding potential user or stakeholder risks.
TINTIC DISTRICT: A MINERAL SYSTEMS APPROACH AS A GUIDE TO VECTORING INTO BLIND PORPHYRY DEPOSIT

The Southwest Tintic porphyry system is a blind porphyry Cu-(Mo-Ag) deposit, located in the Tintic mining district in the East Tintic mountains, west-central Utah. Reported combined production from various veins, breccias, and carbonate replacement deposit (CRD) ore bodies in the Tintic district is an estimated 25 million tons of ore containing 2.85M oz of Au, 291M oz of Ag, 278M lbs of Cu, 2.4G lbs of Pb, and 575M lbs of Zn. The undeveloped Southwest Tintic deposit has a reported historic non-compliant (to any regulatory institution) mineralized volume of >6G lbs of Cu, within an estimated 1.5G tons of mineralized material at 0.21% Cu, 0.01% Mo. Recent deep exploratory drilling intersected three distinct, spatially-related deposit types: 1) pyrite ± enargite, high-sulfidation epithermal veins; 2) CRD-style mineralization that contains chalcopyrite-pyrite-pyrrhotite-sphalerite mineralization; and 3) porphyry copper-style alteration and mineralization. The porphyry (Southwest Tintic) is hosted in the 34.9-32.8 Ma aged caldera complex principally consisting of three volcanic units: an upper air fall latite tuff, a middle andesite, and lower welded latite tuff. Porphyry copper-style mineralization and alteration are associated with various intrusive phases chiefly composed of monzonite (33.5 ± 0.3) and monzodiorite (34.4± 0.3Ma) porphyry dikes and plugs, and cut by sparsely mineralized, megacrystic monzonite porphyry dikes. Evidence from mineral textures, and cross cutting relations indicate that the distal epithermal and CRD mineralization are connected to the development of the blind porphyry system. This has led to the development of a refined geologic-paragenetic model for the district. Through the study of the Tintic district we have a better understanding for the systems that influence the distribution and timing of the mineralization events that drive the formation of diverse deposit types present throughout the East Tintic Mountains. Porphyry copper and related satellite deposits contain significant amounts of the metals which are necessary for the green economy. An increased understanding of how these mineral systems develop and relate to one another in time and space will aid us in providing those critical metals that will power the incoming global economic shift in energy production.
Research on Constructing the Cultivation Mode of College Students' Innovative and Entrepreneurial Psychological Quality in Mental Health Education

Under the background of entrepreneurship, college students have obvious entrepreneurial advantages and become the main force in the entrepreneurial army. Universities actively respond to the call of the state.Methods: In recent years, they have continuously expanded their enrollment, which has made many students exposed to higher education courses and increased the knowledge reserve of Chinese students. Entrepreneurial psychological quality refers to a kind of psychological quality level which is formed and continuously developed and matured under the influence of people's entrepreneurial environment and corresponding society. Results:It is a reflection of people's psychology and behavior. In the actual process of entrepreneurship, due to the lack of social experience and entrepreneurial psychological quality, it is often difficult for college students to achieve the desired goals, often ended in failure. It is important to cultivate the psychological quality of college students. Conclusion: Based on this, this paper constructs the cultivation path of College Students' innovation and entrepreneurship psychological quality by improving the innovation and entrepreneurship health education curriculum, improving the psychological consultation mechanism, and doing a good job of psychological health dynamic tracking.
Conditioning Sparse Variational Gaussian Processes for Online Decision-making

With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making. However, Gaussian processes typically require at least $\mathcal{O}(n^2)$ computations for $n$ training points, limiting their general applicability. Stochastic variational Gaussian processes (SVGPs) can provide scalable inference for a dataset of fixed size, but are difficult to efficiently condition on new data. We propose online variational conditioning (OVC), a procedure for efficiently conditioning SVGPs in an online setting that does not require re-training through the evidence lower bound with the addition of new data. OVC enables the pairing of SVGPs with advanced look-ahead acquisition functions for black-box optimization, even with non-Gaussian likelihoods. We show OVC provides compelling performance in a range of applications including active learning of malaria incidence, and reinforcement learning on MuJoCo simulated robotic control tasks.
Impact of Job Satisfaction on Turnover Intentions among University Teaching Staff

The present study measures the impact of job satisfaction on turnover intentions in the private universities teaching staff of Punjab. A sample of 300 university teaching faculty was studied. Proportionate stratified random sampling technique was used for the selection of respondents. After ensuring the normality of the data, factor analysis was conducted to reduce the data into factors. After that, multiple regressions were applied in order to find out the impact of job satisfaction on turnover intention. The results of the study highlighted that different factors of job satisfaction viz; time management and organizational support, leave policy and job security, organizational culture and job pressure were found to have a significant negative impact on urgency of quitting job and have a negative impact on the intention to leave among the teaching staff. Besides these, the paper also presents the managerial implications, limitations and future research of the study.How to cite this article: Sehrawat P, Saluja PD. Impact of Job Satisfaction on Turnover Intentions among University Teaching Staff. J Adv Res Humani Social Sci 2020; 7(4): 6-19.
How can classical multidimensional scaling go wrong

Given a matrix $D$ describing the pairwise dissimilarities of a data set, a common task is to embed the data points into Euclidean space. The classical multidimensional scaling (cMDS) algorithm is a widespread method to do this. However, theoretical analysis of the robustness of the algorithm and an in-depth analysis of its performance on non-Euclidean metrics is lacking. In this paper, we derive a formula, based on the eigenvalues of a matrix obtained from $D$, for the Frobenius norm of the difference between $D$ and the metric $D_{\text{cmds}}$ returned by cMDS. This error analysis leads us to the conclusion that when the derived matrix has a significant number of negative eigenvalues, then $\|D-D_{\text{cmds}}\|_F$, after initially decreasing, will eventually increase as we increase the dimension. Hence, counterintuitively, the quality of the embedding degrades as we increase the dimension. We empirically verify that the Frobenius norm increases as we increase the dimension for a variety of non-Euclidean metrics. We also show on several benchmark datasets that this degradation in the embedding results in the classification accuracy of both simple (e.g., 1-nearest neighbor) and complex (e.g., multi-layer neural nets) classifiers decreasing as we increase the embedding dimension. Finally, our analysis leads us to a new efficiently computable algorithm that returns a matrix $D_l$ that is at least as close to the original distances as $D_t$ (the Euclidean metric closest in $\ell_2$ distance). While $D_l$ is not metric, when given as input to cMDS instead of $D$, it empirically results in solutions whose distance to $D$ does not increase when we increase the dimension and the classification accuracy degrades less than the cMDS solution.