Reviewerly/exHarmony huggingface dataset:
A Machine learning Approach for Crime Rate Analytics: Survey

Criminal activities are present in every region of the world affecting the quality of life along with the social and economic development of the country. Thus, there is an urgent need for security representatives and agencies to fight and reduce crime. Many governments are trying to use advanced techniques to tackle such issues. Crime prediction practices historical data and after examining data, predicts the upcoming crime concerning location, time, day, season and year. The main aim of this paper is to describe a summary of the computational techniques that has been implemented toward crime data analysis and prediction. The survey of different techniques is performed besides with an abstract view of the proposed system that we are going to implement. The proposed system helps to predict and solve crimes at a much faster rate using machine learning techniques to reduce the crime rate. The system helps to prevent crimes or to improve the investigation efforts.
Study and Application of Seismic Risk and Exposure Model Based on AI Technologies

This paper investigated the numbers, taxonomy and vulnerability of the buildings in Southeastern Tibet and built pattern classes for difference types of buildings.Then we assessed the distribution of each type of buildings in every town based on 3D image pattern recognition, and made an event-based and a 50-year hazard-based seismic risk assessment for the towns in Southeastern Tibet.Our study indicated that areas with the highest seismic risk are urban areas of Lhasa and Nyingchi and Cuona Town, and urban Lhasa has the highest seismic risk of building structural economic losses.
Chinese Punctuation Prediction with Adaptive Attention and Dependency Tree

Punctuation prediction, as a key step of connecting speech recognition and natural language processing, has a profound impact on subsequent tasks. Although methods of bidirectional long short term memory and conditional random fields (BiLSTM+CRF) are proposed and remain advanced for a long time, it still suffers from the lack of ability of capturing long-distance interactions among words and extracting useful semantic information, especially in Chinese punctuation prediction. In this paper, considering the characteristic of Chinese punctuation symbols, we propose a novel method of punctuation standardization. In our BiLSTM+CRF based network, adaptive attention and dependency parsing tree are utilized to capture the long distance interactions and extract useful semantic information, and thus enhancing the word representation. As for the performance, the first proposal of Chinese punctuation prediction outperforms BiLSTM+CRF with a gain of 0.292% and 0.127% on accuracy in two datasets respectively. The second proposal outperforms existing methods with a gap of above 4.5% of accuracy and reaches state-of-the-art performance in two datasets.
Pemanfaatan Perpustakaan Keliling Di SD Negeri Sondakan Sebagai Penguatan Program Literasi Sekolah

The utilization of the mobile library as a reinforcement of literacy programs in schools need to be improved, because the seeing of reading comprehension in Indonesia is still relatively low. The purpose of this research is to describe the utilization of a mobile library in the ELEMENTARY school Sondakan as the strengthening of the school literacy program, describe the benefits obtained with the utilization of a mobile library in the ELEMENTARY school Sondakan as the strengthening of the school literacy program. This study uses a descriptive qualitative approach with the instrument to collect the data are observation, interview, and documentation. Qualitative data analysis techniques using the concept of Miles and Hubermen. The subjects of this study were students, Sondakan State Elementary School teachers,, and mobile library officers from the Archives Service and the Surakarta City Library. The results showed that: the Utilization of mobile library is to provide library materials, searching for information to help complete the task of teachers, provision of books in the form of fiction and non-fiction and as a means to read a book, write, discuss, and learn. The benefits obtained with the utilization of a mobile library in the ELEMENTARY school Sondakan as foster a passion for reading, make teachers happy and active to educate, foster innovation, digging potential, and getting used to reading. Based on these conclusions it is known that with adannya the bookmobile supports and strengthens the various literacy program that was designed by the school.
Efficient and Universally Composable Single Secret Leader Election from Pairings

Single Secret Leader Election (SSLE) protocols allow a set of users to elect a leader among them so that the identity of the winner remains secret until she decides to reveal herself. This notion was formalized and implemented in a recent result by Boneh, et al. (ACM Advances on Financial Technology 2020) and finds important applications in the area of Proof of Stake blockchains. In this paper we put forward new SSLE solutions that advance the state of the art both from a theoretical and a practical front. On the theoretical side we propose a new definition of SSLE in the universal composability framework. We believe this to be the right way to model security in highly concurrent contexts such as those of many blockchain related applications. Next, we propose a UC-realization of SSLE from public key encryption with keyword search (PEKS) and based on the ability of distributing the PEKS key generation and encryption algorithms. Finally, we give a concrete PEKS scheme with efficient distributed algorithms for key generation and encryption and that allows us to efficiently instantiate our abstract SSLE construction. Our resulting SSLE protocol is very efficient, does not require participants to store any state information besides their secret keys and guarantees so called on-chain efficiency: the information to verify an election in the new block should be of size at most logarithmic in the number of participants. To the best of our knowledge, this is the first efficient SSLE scheme achieving this property.
Efficient synergetic filtering in big data set using neural network technique

Presently, great accomplishment on speech-recognition, computer-vision and natural-language processing has been achieved by deep-neural networks. To tackle the major trouble in synergetic or collaborative-filtering we concentrated intensively on the techniques based on neural networks. Although a few recent researches have employed deep learning, they mostly used it to carve auxiliary facts, along with textual metaphors of objects and acoustic capabilities of music. We present a popular framework named Artificial Neural Synergetic Filtering (ANSF) to substitute the core makeup with a neural design which could be very efficient to analyse data with a random feature. ANSF is a prevalent matrix-factorisation framework. To improvise it with non-linearity we propose to leverage a multilayer perceptron to investigate customer-object communication function. In-depth experiments on actual global databases display big improvement over the latest techniques. Investigational results manifest that the application of core layers of artificial neural networks gives improved overall performance.
Research on High School Students’ English Learning Engagement Based on Blended Learning Mode

Under the traditional teaching mode, the phenomenon of “mute English” is common in high school students' English teaching, which leads to the low engagement of English learning. The development of “Internet + education” provides a new idea of blended learning mode. Teachers can explore how to improve high school students' engagement in English learning under the blended learning mode from four aspects: the integration of teaching forms, the change of learning process, the improvement of evaluation system and the creation of English learning environment.
Health and Sustainability of National Higher Education Evaluation Model Based on BP Neural Network and Grey Model

The higher education system is the driving force of social development, and the level of higher education is an important factor to judge whether a country is strong or not.In the process of promoting the popularization of higher education, the directions and policies of different countries are different.In order to evaluate the health and sustainability of each country's higher education system, this research has established an evaluation model based on BP neural network and gray model.First, we collected data on 10 indicators on higher education in six countries over the past decades, divided into two categories: national higher education investment and national higher education achievement.Then, we use the main component analysis method to reduce the dimensional processing of the data to solve the problem that too much data causes the model to converge too slowly.Second, we have established a national higher education health evaluation model based on BP neural network.Then we optimize BP neural network by PSO, which improves the speed and stability of the evaluation model.We get the six countries' higher education health ratings which are United States V, Germany III, Japan IV, Australia II, South Africa I and India I. Third, we have established a national sustainability evaluation model for higher education based on the grayscale model.We translate sustainability into a national higher education rating that predicts the future, and a high rating in the future means a strong sustainability.The final six countries to obtain the higher education health ratings are United States V, Germany III, Japan IV, Australia II, South Africa I and India II.By evaluating the health and sustainability of a country's higher education, we can give corresponding suggestions and policy support for the country to improve the level of higher education.
PEMBUATAN DAN SOSIALISASI WEBSITE SEKOLAH PADA SMP INDRIASANA

Indriasana Palembang Junior High School is a private school in the center of Palembang city. The school complained about the lack of information and promotion media to students and student guardians. During this time guardians of students need to come to school to get information about student exams, school report, admission of new students and extracurricular activities. Based on the background of the problem, we created a school website at Indriasana Middle School. Our method is conducting interviews with school principals, student representatives and teachers about the obstacles that have been happening all this time. After the website is created, we make a socialization website introduction to the teacher. Based on the results of the school website loading and socialization evaluation, all teachers (n = 14) said they were satisfied with the website. 86% of teachers think that school websites can be used as a means of school promotion, 14% think they can be used as information media.
Analysis of Students’ Problems in Determining Adjective Clause of Report Text at Grade XI of SMAN 2 Kerinci Academic Years 2015/2016

One of them carries out important rule that is using adjective clauses especially who, which, when, and where. Based on the finding above, it can be seen that students who has understood what adjevtive clause is and how to find out in the report text. This research was aimed to find out students’ problems in determining adjective clause of report text at grade XI of SMAN 2 Kerinci Academic Years 2015/2016. And to find out the causes of students’ problems in determining adjective clause of report text at grade XI of SMAN 2 Kerinci Academic Years 2015/2016. The method of this research was descriptive reserach. The sample of this research was class XI IA1 of SMAN 2 Kerinci. There were 28 students. The data were collected by using grammar test for quantitative data and using interview for qualitative data. The result of this research showed that the students at XI IA1 of SMAN 2 Kerinci were very good in determining adjective clause of report text. It indicated the mean of students’ score. It was 71.5 The researcher found that most of the students were able to determine adjective clauses of report text and few of them didn’t understand how to use them. It can be concluded that the students problems in determining adjective clauses of report text were no gotten by all students. It was concluded that the students’ problems in determining adjective clauses of report text was good. It is caused the students did not understand what their teacher had taught about tenses especially adjective clauses, they felt bored to study grammar.
3D Perception for Autonomous Mobile Robots Navigation Using Deep Learning for Safe Zones Detection: A Comparative Study

Computer vision plays an important role in intelligent systems, particularly for autonomous mobile robots and intelligent vehicles. It is essential to the correct operation of such systems, increasing safety for users/passengers and also for other people in the environment. One of its many levels of analysis is semantic segmentation, which provides powerful insights in scene understanding, a task of utmost importance in autonomous navigation. Recent developments have shown the power of deep learning models applied to semantic segmentation. Besides, 3D data shows up as a richer representation of the world. Although there are many studies comparing the performances of several semantic segmentation models, they mostly consider the task over 2D images and none of them include the recent GAN models in the analysis. In this paper, we carry out the study, implementation and comparison of recent deep learning models for 3D semantic image segmentation. We consider the FCN, SegNet and Pix2Pix models. The 3D images are captured indoors and gathered in a dataset created for the scope of this project. Our main objective is to evaluate and compare the models’ performances and efficiency in detecting obstacles, safe and unsafe zones for autonomous mobile robots navigation. Considering as metrics the mean IoU values, number of parameters and inference time, our experiments show that Pix2Pix, a recent Conditional Generative Adversarial Network, outperforms the FCN and SegNet models in the
Editorial for the Launching of Dynamics

Nowadays, the subject of studying system dynamics behavior has become very important in many branches of technology [...]
LANN: an Integrated Online Annotation Tool for Information Extraction

The creation of high-quality annotated corpora with strong fidelity to the underlying concepts in a domain is a critical step in the development of machine- and deep-learning models for Information Extraction (IE) and Natural Language Processing (NLP). Often, these annotations are done by subject matter experts or highly trained annotators. To achieve high quality suitable for use as a gold-standard corpus, it is necessary that these annotators use common definitions for named entities and relationships, and that they apply them in a uniform manner. In this paper, we present our work on LANN, a text annotation tool that provides support for team-based annotation, quality controls and machine learning assistance across the workflow of an annotation project.
The Impact of a Canadian Financial Cybercrime Prevention Campaign on Clients’ Sense of Security

The purpose of this study was to evaluate the impact of a cybercrime prevention campaign that was run by a Canadian financial institution. More specifically, we examined how participants/clients perceived the financial institution’s initiative to inform them about cybercrimes. The study also explored whether or not the campaign had the desired effect, which was to reinforce the clients’ sense of security. This campaign took place on October 2018 and 1452 adults (831 males and 621 females) participated in the online web survey. The results indicated that the prevention campaign had been positively perceived by most of the respondents (93.2%). However, only a low percentage of individuals (18%) had seen the poster/campaign prior to the completion of the survey while the majority (82%) accessed the prevention campaign’s components during the survey. Further analysis has shown no gender differences in participants’ responses. In general, participants felt that the campaign has increased their sense of security, especially among older individuals (55 years old and over). Most participants have expressed an interest in receiving more information on cybercrime and how to take actions on protecting one’s self. Results suggest that it would be advisable to conduct targeted prevention campaigns in order to reach out to as many people as possible. Discussion also includes practical recommendations based on the results and the review of the literature.
Project: Reading on Different Media

Project: Reading on Different Media We are investigating how different media affect reading performance and experience. Status: ongoing Runtime: 2020 - Participants: Raphael Wimmer, Florian Bockes, Marie Sautmann, Michael Hebeisen Keywords: reading, media, eye-tracking, e-paper Background
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets.

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker. In practice, a \emph{single} pair of networks is typically trained in order to find an approximate equilibrium of a highly nonconcave-nonconvex adversarial problem. However, while a classic result in game theory states such an equilibrium exists in concave-convex games, there is no analogous guarantee if the payoff is nonconcave-nonconvex. Our main contribution is to provide an approximate minimax theorem for a large class of games where the players pick neural networks including WGAN, StarCraft II, and Blotto Game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, these games are concave-convex with respect to the actual models (e.g., functions or distributions) represented by these neural networks.
Undergraduates’ Reading Habit: A Study Based on South Eastern University of Sri Lanka

Reading makes a man perfect and it is very essential component to University students. Hence, students face various obstacles in developing reading habits. Therefore, with the aim of assessing students’ reading habits and identifying the changes and the factors infl uenced when they pursue education through online learning tools due to corona fears, the study was conducted with second year students of Department of Islamic Studies of South Eastern University of Sri Lanka. For this study, which was carried out using quantitative research methods, a fi lled questionnaire was obtained as primary data from 121 of the 174 students studying in the year through Google Forms, analyzed through MS Excel and the results were described. Secondary data such as research articles, journals, and web articles related to the topic were also used. At the end of the study, it was found that students’ reading habit was healthy, the amount of time they spent on daily reading varied, their reading habits have declined due to continuing education through online from home and factors such as lack of library, diffi culty in reading on electronic devices, home works, social media, laziness, workload, sleepiness and increase in data cost have more infl uence on their reading decline, they have the habit of reading from both hard and soft sources, most of them use their smartphones to read the soft copy, while reading through electronic devices they face various problems such as not being able to read for a long time, headache, numbness like reading a book in hand, boredom, inability to read small letters, wasting more time and forgetting easily,. The conclusion of this study was that the continuation of educational activities from home had a negative impact on the reading habits of the students for various reasons. However, the positive change in their reading habits after entering university has been a motivating force for them to continue reading under any circumstances. At the same time, where guidance is provided to overcome challenges, they face in developing reading habits, further positive improvements can be expected in their reading habits.
Subject Guides: LabArchives: Managing Data

LabArchives is an innovative cloud-based ELN (electronic lab notebook) to enable researchers to store, organize, and publish their research data.
Estratégias de ensino para ações de formação da competência em informação em bibliotecas escolares

It aims to present teaching strategies that can contribute to the development of information literacy in the context of the school library. Firstly, it contextualizes information literacy regarding the relation between school library and school culture, and demonstrates how important it is for the librarian to own teaching techniques that may subsidize the development for information literacy. It contemplates the school librarian’s operation, pointing out that he lacks a better suiting formation for the pedagogical practice, but on the other hand, it evinces the educational functions practiced by the librarian, the educative aspect of his actions and how the pedagogical forms categorized as elementary and complex are applied in the library. Sequentially it highlights the relevance of collaborative work between librarians and teachers, which directly influences the formation of abilities concerning the use of information. The methodology employed is exploratory and descriptive research with qualitative approach and bibliographic outline. It concludes that taking into account the students’ goals and cognitive development is required before choosing which strategies will be applied, favoring methodologies that promote active student participation in the learning process. It highlights the relevance of integrating information literacy with the school curriculum, not ignoring the contribution of extracurricular actions and non-formal education to the development of all dimensions of information literacy.
XML External Entity Attacks and Mitigation in XML Parsers

The increasing dependency of almost every organization in web applications has introduced a whole new set of vulnerabilities and attack vectors. Due to the ever-evolving nature of the information security space, new vulnerabilities, attack methods, and mitigation strategies are being introduced each day. XML External Entity Attack is such an attack. Being recently featured in the updated Open Web-Application Security Project (OWASP) Top 10 2017 list, this attack is prevalent in modern-day web applications and XML parsers. Recent statistics have shown a steep increase in XXE injections. But in spite of the fact, limited literature is available regarding this vulnerability. As XXE usually has a high-security impact on web applications, it is important to implement protective measures against XXE attacks. In this paper, we focus on XXE attack mitigation in various XML parsers. Firstly, we give a brief introduction to various XXE injection attacks and XML parsers. Then we test popular XML parsers for XXE injections. In addition to this, we suggest preventive measures for XML parsers in popular programming languages.