Reviewerly/exHarmony huggingface dataset:
Technology, domestic and family violence: perpetration, experiences and responses

Digital media and devices are increasingly used by domestic and family violence perpetrators to coerce, control and entrap victim/survivors targets. Behaviours facilitated through technology are not divorced from but inextricably connected to other forms of harm. This brief provides definitions and frameworks to understand technology-facilitated domestic and family violence (TFDFV) and an overview of patterns and trends in perpetration and impacts on victim/survivors. In the interest of preventing violence and safeguarding and empowering those subjected to abuse, this is a crucial field of enquiry.
Malicious URL Detection Using Machine Learning

Throughout the years, internet usage has grown significantly.The internet continues to transform how we interact with people, organise the flow of goods, and communicate knowledge all across the world.The attackers have used this popularity to their advantage to participate in illegal activities that would lead to monetary advantage.There has been a rise in malicious websites that launch client-side attacks over time, which cannot be identified effectively by existing approaches such as blacklisting.As a result, an efficient solution to detect these malicious websites is required.In this study, we used the random forest method to develop a machine learning model while integrating lexical features, hostbased features, and content-based features.The model has an accuracy of 94.7%.
Predictive models of mineralogy from whole-rock assay data: case study from Productora Cu-Au-Mo deposit, Chile

Mineralogy is a fundamental characteristic of a given rock mass throughout the mining value chain.Understanding bulk mineralogy is critical when making predictions on processing performance. However, currentmethods for estimating complex bulk mineralogy are typically slow and expensive. Whole-rock geochemical datacan be utilized to estimate bulk mineralogy using a combination of ternary diagrams and bivariate plots to classifyalteration assemblages (alteration mapping), a qualitative approach, or through calculated mineralogy, apredictive quantitative approach. Both these techniques were tested using a data set of multielementgeochemistry and mineralogy measured by semiquantitative X-ray diffraction data from the Productora Cu-AuMo deposit, Chile. Using geochemistry, samples from Productora were classified into populations based on their dominant alterationassemblage, including quartz-rich, Fe-oxide, sodic, potassic, muscovite (sericite) and clay-alteration, and leastaltered. Samples were also classified by their dominant sulfide mineralogy. Results indicate that alterationmapping through a range of graphical plots provides a rapid and simple appraisal of dominant mineralassemblage in volcanic rocks, which closely matches the measured mineralogy. In this example, calculated mineralogy using linear programming generated robust quantitative estimates formajor mineral phases, including quartz and total feldspars, as well as pyrite, iron oxides, chalcopyrite, andmolybdenite, which matched the measured mineralogy data extremely well (R2 values greater than 0.78, lowmoderate root mean square error). The results demonstrate that calculated mineralogy can be applied in themining environment to significantly increase bulk mineralogy data and quantitatively map mineralogical variability.This was useful even though several minerals were challenging to model due to compositional similarities, andclays and carbonates could not be predicted accurately. Fig. 1. Comparison of mineralogy estimates by semiquantitative X-ray diffraction data corrected to assays byweighted least squares (WLSQ; measured mineralogy) against calculated mineralogy by linear programming.Regression line and R2 also shown. A) Quartz. B) K-feldspar. C) Plagioclase (including sodic and calcicplagioclase). D) Pyrite. E) Chalcopyrite. F) Molybdenite. G) Total chlorite and micas (including chlorite,muscovite, phengite and biotites). H) Total clay (including kaolinite and smectite-montmorillonite). I) Fe-oxides(including magnetite, hematite, goethite). Major minerals, including quartz, feldspars, pyrite and Fe-oxides showexcellent correlation between measured (WLSQ) and calculated (linear programming).
EVALUASI SISTEM PENGENDALIAN INTERNAL SISTEM PENJUALAN KREDIT PADA PT ARINGO COMPUTER

In order to achieve the company's purpose is to get maximum profit, the company needs a tool used in order to achieve the company. The tool s are accounting system  and reports of each section used to provide financial information for the company . The purpose is to know the appropriateness of applying credit sales system based on internal control system principle. In the data collection, the authors conducted several interviews, observation, interviews and study the source of the book (literature s ). The analytical tool used is the elements of internal control system of credit sales by Mulyadi and the main purpose of internal control system. The result of this research is there are some system applied by company which is not in accordance with Internal Control System so that can cause loss for company. Keywords: A ccounting S ystem, C redit S ales S ystem, I nternal C ontrol S ystem .
Image Haze Removal Using Dark Channel Prior based on Guided Image

Abstract In foggy weather, the interference of fog has a serious impact on the accurate monitoring of vehicle targets. To address this problem, this paper proposed a method which is image haze removal based on the guided image. In the process of this method, the clear images are as the guide images, which is designed to effectively redress the bright region which is not meet the dark color prior by the prior information of the guide image, to avoided the error estimates of this kind of area of transmissivity. Experimental results demonstrate that our method achieves significant improvement in both image quality and image detail when compared with classical image haze removal method.
Sequence to Sequence LSTM Modeli ile Telegram Bot Uygulaması

Chatbot is a software that chat with the user by audio or textual methods. Advanced chat bots are able to provide appropriate answers related issues. Using artificial intelligence methods in chat bots increases efficiency. In this context, telegram bot application was developed with LSTM (Long Short Term Memory) and seq2seq model. The use of LSTM in the study enabled the return to speech history to predict the next speech action. Time saving was achieved by using chatterbot dataset from kaggle.com in the study. Telegram integration is provided via pythonanywhere for user interaction. The loss rate and other performance parameters during the training of the study were visualized with TensorBoard. In the study, the 50-step training was completed in 13 seconds. The loss rate in the study decreased at each step and decreased to 0.2772 at the end of 50 steps and 79 percent accuracy rate was obtained. The study is designed modularly and open to development. By continuing the education process of the modular study, it will be possible to teach different linguistic expressions. Open source and free software were used in the study. The presented study has brought together the features of the outstanding studies in the literature using state of art technologies.
An Overview of Search and Match Algorithms Complexity and Performance

DNA data provide us a considerable amount of information regarding our biological data, necessary to study ourselves and learn about variant characteristics. Even being able to extract the DNA from cells and sequence it, there is a long way to process it in one step. Over past years, biologists evolved attempting to “decipher” the DNA code. Keyword search and string matching algorithms play a vital role in computational biology. Relationships between sequences define the biological functional and structural of the biological sequences. Finding such similarities is a challenging research area, comprehending BigData, that can bring a better understanding of the evolutionary and genetic relationships among the genes. This paper studied and analyzed different kinds of string matching algorithms used for biological sequencing, and their complexity and performance are assessed.
LibGuides: General Information Management: Dissertations

This guide is for the NCU specialization General Information Management. It contains resources available in the NCU Library.
Modified LCM’S Approximation Algorithm for Solving Transportation Problems

Now a day's text documents are advancing over internet, e-mails and web pages.As the use of internet is exponentially growing, the need of massive data storage is increasing from time to time.Normally many of the documents contain morphological variables, so stemming which is a preprocessing technique gives a mapping of different morphological variants of words into their base word called the stem.Stemming process is used in information retrieval applications accordingly as a way to improve retrieval performance based on the assumption that terms with the same stem usually have similar meaning.To do stemming operation on bulky documents, we require normally more computation time and power, to cope up with the need to search for a particular word in the data.In this paper, various stemming algorithms are analyzed with the benefits and limitation of the recent stemming methods or approaches.
Seismic Soundoff

The following is an excerpt from SEG's podcast, Seismic Soundoff. In episode 75, host Andrew Geary highlights David Lumley's President's Page column from the March 2020 issue of The Leading Edge titled “Geophysics and sustainability.” In this timely conversation, they discuss the future of geophysics education, areas SEG could develop to continue to support the science, how climate change could impact the industry, and the proposal for a new name for SEG. Listen to the full episode at https://seg.org/podcast/post/8963 .
Aplikasi Danger Message Daerah Rawan Kecelakaan Dengan Android GIS

There are several areas in Probolinggo District where accidents often occur because the location of the existing road is indeed very dangerous, because of sharp turns or because of the heavy traffic on the road, this problem can be reduced by giving messages through the Android mobile application. The number of accidents that occur in various places with the time of the incident in the district of Probolinggo can be obtained at the Office of Transportation. The purpose of this research is to create an GIS android application that can provide information about maps of accident-prone areas in Probolinggo Regency. The method used is using a spiral. With stages, Planning, Risk Analysis, Product Engineering, Evaluation by users. Information about traffic accident-prone locations is very much needed by the community, transportation department and law enforcement in this case the police. Android-based Geographic Information System as an appropriate tool to be applied in this case because it uses the Mapping location of accident-prone areas and will, sending messages (danger message), in the form of user distance from accident-prone locations.
Deep heterogeneous ensemble.

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations such as needing a large number of labeled data for training and having a large number of parameters. Ensemble learning, meanwhile, provides a learning model by combining many different classifiers such that an ensemble of classifiers is better than using single classifier. In this study, we propose a deep ensemble framework called Deep Heterogeneous Ensemble (DHE) for supervised learning tasks. In each layer of our algorithm, the input data is passed through a feature selection method to remove irrelevant features and prevent overfitting. The cross-validation with K learning algorithms is applied to the selected data, in order to obtain the meta-data and the K base classifiers for the next layer. In this way, one layer will output the meta-data as the input data for the next layer, the base classifiers, and the indices of the selected meta-data. A combining algorithm is then applied on the meta-data of the last layer to obtain the final class prediction. Experiments on 30 datasets confirm that the proposed DHE is better than a number of well-known benchmark algorithms.
La crédibilité des politiques monétaires affecte-t-elle la croissance économique en Afrique subsaharienne?

This study proposes to highlight the effect of greater Monetary Policy Credibility (CPM) on economic growth in Sub-Saharan Africa (SSA) during the period 1980-2017. The econometric approach based on dynamic panel data is mainly used for our estimates. The main results show a limited and insignificant effect of the credibility indicator on economic activity, mainly due to the ineffectiveness of the monetary policy transmission channels. This contradiction has led us to recommend for our part the promotion of a monetary policy oriented towards improving economic growth in Sub-Saharan Africa, but without however prejudicing the constraint of price stability.
Cybersecurity impact of the growth of data in sports

As the sporting industry continues to increase dependence on new and often massive data sets, they also risk exposing themselves, as well as the leagues, teams, venues, players, and fans, to risks posed by not protecting the confidentiality and integrity of this data. The industry, especially true for professional sports, demands an ever increasing supply of new forms of data to achieve their goals of enticing new fans; enhancing experience; expanding the fan experience from venues and television to the internet and mobile devices; measuring and evaluating the performance and safety of players; and increasing revenue. New methods have emerged to collect data about a variety of aspects of sports ranging from players to fans to teams to venues. Data collected during sporting events is combined with historical data to also help teams analyze and predict performance. To synthesize these predictive analytical and historical memories, sports teams and organizations mix a variety of computer technologies. System integration tends to focus on ensuring the availability of the data and reliability of the technologies but might introduce risks to the confidentiality (privacy) and integrity of the data. Recognizing the potential risks requires review and evaluation. Much of the data is collected for publicly providing information to the fans, so threats to confidentiality pose fewer risks. The greatest risk is violating the privacy of players, such a private performance test, or using technology to gain a competitive advantage.
ПСИХОЛОГІЧНИЙ СУПРОВІД ФОРМУВАННЯ ПРОФЕСІЙНОЇ САМОСВІДОМОСТІ МАЙБУТНІХ ПСИХОЛОГІВ

Forming highly-developed professional self-consciousness is a task of contemporary significance in the process of developing the psychologists’ professionalism. Significant changes in the structure of consciousness occur during the study at a higher education institution: realization of life and professional goals, planning professional development, self-realization and self-improvement, perception of professional activity as a value in the hierarchy of personal motives.We have developed a technology of forming professional self-consciousness in students: the Program of psychological oversight of the formation of professional self-consciousness in future psychologists, aimed to raise professional self-evaluation, the level of aspirations, realization of one’s own “self”, and understanding of professional reality.The main principles of building psychological oversight include: the principle of simultaneousness of all mental processes driving the activity of consciousness; the principle of incomplete informational definitiveness of the formational impact; the principle of individual uniqueness of a situation; the principle of creativity.The following tasks were performed for the purpose of the Program’s methodological substantiation: to describe the principles, technologies and methods of forming professional self-consciousness in future psychologists, and to prove the Program’s effectiveness. The Program’s structure and the topic and forms of work were described.A formational experiment was held to verify the effectiveness of the proposed Program. 27 students of the third to fifth year of study were included to the experimental group in which the Program was going to be implemented. A reference group also consisting of 27 participants was established, in which no special developmental work was done. Students’ t-criterion for dependent samples was used for statistical verification of the significance of differences in the experimental/reference groups before and after the end of the formational experiment. A retest using the Self-Esteem Survey Test methodology by V. Stolin and S. Panteleev showed that since the start of the formational experiment, all test indicators in the experimental group reached statistically significant difference. The following scales showed especially positive dynamics: “self-confidence”, “self-acceptance”, “expected attitude of others”, “self-management” and “self-interest”.Given that the Program of psychological oversight of the formation of professional self-consciousness in future psychologists was not implemented in the reference group, no statistically significant difference was achieved in the reference group in the course of the aforementioned test. Therefore, information obtained on the basis of a post-experimental measurement of the Program’s effectiveness revealed significant differences between effective indicators of the experimental and reference samples.
New Fast and Efficient Techniques for Image Denoising Using VQ, PCA, SVD, Soft Thresholding and Wiener Filter

Image denoising is process of removal or reduction of noise from degraded images. During formation of images itself, images get degraded, so it is unavoidable. In this paper two fast, easy and efficient techniques are developed for image denoising. These techniques use VQ, PCA/SVD, Soft thresholding, Wiener filtering for denoising on non-overlapping blocks. These techniques are compared with existing VQ techniques and results are found better in terms of PSNR, SSIM objective criteria. The proposed techniques improved PSNR by 12% and 9% for color and gray images respectively as compared to noisy images while existing VQ technique has improvement of 9% and 0.47% respectively. SSIM is improved by 67% and 45% respectively for color and gray images by proposed methods while existing VQ technique has improvement of 27% and 3% respectively. These techniques are also compared with state-of-art LPG-PCA technique, and it is found that the proposed techniques have given 5% better PSNR than LPG-PCA, but SSIM is not improved. The key advantage of these techniques is time required to execute is 97% less than LPG-PCA. So, these techniques can be considered as promising improvements in terms of computational complexity.
A Study of Recombination Operators for the Cyclic Bandwidth Problem.

This work is dedicated to a study of the NP-hard Cyclic Bandwidth Problem with the paradigm of memetic algorithms. To find out how to choose or design a suitable recombination operator for the problem, we study five classical permutation crossovers within a basic memetic algorithm integrating a simple descent local search procedure. We investigate the correlation between algorithmic performances and population diversity measured by the average population distance and entropy. This work invites more research to improve the two key components of the memetic algorithm: reinforcement of the local search and design of a meaningful recombination operator suitable for the problem.
On the Effect of the Proximity Parameter on Property Testers.

This work refers to the effect of the proximity parameter on the operation of (standard) property testers. Its bottom-line is that, except in pathological cases, the effect of the proximity parameter is restricted to determining the query complexity of the tester. The point is that, in non-pathological cases, the mapping of the proximity parameter to the query complexity can be reversed in an adequate sense.
Staying Safe and Cyber-Secure Through Telehealth

As social distancing has become the new norm due to COVID-19, the new normal for healthcare has also seen a rise of telehealth services. However, there are security risks involved for patients’ personal data and companies’ private information.
LibGuides: K-12 Learning at home during COVID-19: Talking with kids about COVID-19

This library guide outlines resources used to support BC K-12 curriculum-focused learning in home settings.
LibGuides: Digital Resources for Families: Tweens and Teens

Links to resources and activities for fun and education at home. The fundamental information for your guide
LibGuides: Passwords, Two-Factor Authentication, and Security Questions: Two-Factor Authentication

This guide goes into more detailed looks at passwords, password managers, and two-factor authentication. Using two factor authentication to increase online security.
Natural Language Interfaces for Procedural Content Generation in Games

Author(s): Mobramaein Kano, Afshin | Advisor(s): Whitehead, Jim | Abstract: Mixed-Initiative Procedural Content Generation (MI-PCG) focuses on developing systems that allow users with diverse technical backgrounds to co-create interesting and novel game content in collaboration with a computational agent. These systems provide a front-end for users to interact with a generator by means of placing different constraints, or modifying a variety of the generator's parameters. While these systems provide significantly enhanced design support over traditional design tools, there exists areas of opportunity to address shortcomings in these systems such as high user interface complexity (too many controls presented, little feedback provided) and the lack of a model of designer intent (the system can reason over constraints but does not understand the expressive intent of the user). We believe that natural language interfaces can provide a way of addressing these areas by utilizing the expressiveness of natural language as an input for mixed-initiative systems in a way that it can reduce interface complexity by converting natural language queries into design space movements or constraints for the generator to act upon. By reducing all input to a single query the natural language interface can make the appropriate selection of parameters and controls that can result in the desired result for the user compared to the traditional modification of one control at a time in a traditional graphical user interface. Furthermore, the issue of designer intent can be addressed by creating a mapping of natural language concepts into a series of parameter combinations that allows for multi-dimensional movements in the design space of the generator, rather than manipulating a series of controls sequentially to achieve the same effect. In this thesis we explore the design and implementations of natural languages in MI-PCG systems through the development of a design methodology for encoding natural language understanding into MI-PCG systems and the implementation of two proof of concept systems named CADI and WATER4-NL for different use case scenarios such as automated game design and shader manipulation respectively. Furthermore a user study based evaluation of WATER4-NL and its results are presented and discussed.