Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI DAN PENGUJIAN SISTEM PENDAFTARAN SISWA PADA SMK KESEHATAN MAHARISHI DENGAN FRAMEWORK LARAVEL

Maharishi Health Vocational School is one of the Educational Institutions in Bali, precisely in the City of Gianyar. In improving the quality of service to the general public, especially to prospective new students, a computerized system is needed, one of which is by creating a PSB or Registration process for new students that is not limited by place, time and energy. Until now Maharishi Health Vocational School still uses a manual system to register prospective students, so prospective students must, queue in registering at school. To increase the registration of prospective new students so that it runs smoothly and prospective new students do not need to queue in registering and make it easy to get information from schools, the means that can be used to overcome these problems is the use of an online registration system. This system will help prospective students, especially those who have great distances to go to school, can register online. So that makes the basis for researchers to create web-based new student registration applications with the concept of MVC (Model View Control) using the laravel framework. The method used in the development of this system is Waterfall and its design uses UML (Unified Modeling Language). The test results use the Black Box Testing method to ensure that the prospective student registration process is in accordance with the desired scenario, and student data and test scores can be stored in a database. Meanwhile information on prospective students who have passed or failed can be sent to all prospective students through the announcement menu. The process of registering prospective students and new students is expected to be done in a short time and can help the process of enrolling new students at the Maharishi Vocational Health School.
The evaluation of the performance of the poor students in colleges based on the fuzzy comprehensive evaluation method

In recent years, China’s colleges have made gratifying achievements in the funding work of poor students, but there are still some problems. In order to improve the accuracy of the funding work, the performance of the poor students in colleges should be evaluated effectively. This paper uses the design idea based on the whole process, and the fuzzy comprehensive evaluation method and the hierarchical analysis method, and constructs the performance evaluation index system of the poor students in colleges. Then, taking the performance evaluation of poor students’ support in Jiangxi University of Technology as an example, according to China’s national conditions, the empirical analysis shows that the poverty students’ support work in Jiangxi University of Technology is at the general level, and can be improved from four aspects: perfecting the mechanism of identifying poor students, broadening the funding channels, perfecting the supervision mechanism of financial aid for poor students, and combining financial aid with mental support. The research of this paper is of great significance to improve the management level of the funding of poor students in colleges and universities.
Introduction to the Digital Services Act, Content Moderation and Consumer Protection

In December 2020, the European Commission presented the Digital Services Act with the stated aim of ensuring a safe and accountable online environment. It mainly consists of a Proposal for a Regulation of the European Parliament and of the Council on a Single Market for Digital Services. This article provides an analysis of the historical and systematic context of this proposal, including a guided tour of its content and an overview of the relationship with other European legislative instruments. The issue of content moderation in digital services is also further addressed, referring to the historical context of the legal regime now proposed. The different EU and US perspectives are outlined. The topic of consumer protection is also dealt with in the text, with emphasis on the most relevant provisions in this field and the problems that may arise therein.
LibGuides: Faculty Support Services: Subject Guides

This portal provides information about library resources and services that can support faculty teaching and research
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards

Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent's exploration. However, these approaches typically operate on structured representations of the environment, and/or assume some structure in the natural language commands. In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy learning. Our experiments on the Meta-World robot manipulation domain show that language-based rewards significantly improves the sample efficiency of policy learning, both in sparse and dense reward settings.
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (\textsc{GeNME}). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate \textsc{GeNME} with the well known family domain consisting of kinship relations, the visual relational Winston arches domain and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.
Towards the score of communication

The exchange of verbal and non-verbal com- munication signals in face-to-face dialogue is complexly organised in several ways: each contribution is produced and processed incre- mentally, contributions may be consecutive (e.g. question-answer pairs) or overlapping (e.g. backchannelling), and the contributions themselves may be multimodal. Contributions nonetheless exhibit pairwise utterance coher- ence, and in two respects: across tiers and across discourse co-texts. For these reasons, we propose to distribute dialogue agents across different tiers and to \textquoteleftincrementalize\textquoteright the se- quential notion of turns according to the model of music-inspired communication scores
Improvement of Automatic Annotation for Semantic Retrieval of Images using Artificial Neural Networks

With the increasing growth of the Internet and the digital imaging industry, the need to organize and separate images is strongly felt. As a result, very large image databases were created. In such a situation, there is a great need for efficient tools and methods to search and retrieve the image. In this proposed method, an automatic image annotation method using artificial neural network is presented. In order to evaluate this proposed method, invocation criteria, accuracy and F criterion have been used. We have also used the standard database of images Msrc, Vistex, Corel as a benchmark database to evaluate the proposed method. The proposed method has the feature of simpler color extraction, has less computational complexity, is more accurate and has a higher speed. Image clustering with the help of SOM artificial neural networks, this clustering method has been seen in less AIA system and has been used in this method with higher quality. The results show that the proposed method of AIA has improved the performance in comparison with the conventional methods of automatic image annotation.
Study Guides: Information Technology: Finding Reliable Information on the Internet

This guide contains resources and guidance to help students studying Computer Science and Information Systems.
Auto Vulnerability Assessment and Penetration Testing Tools

The complexness of the system is increasing day by day. This results in a lot of vulnerability for Systems. The attackers use these being in danger of exploiting the victim's system. it's best to sight this danger earlier than time for the assailant. Attack risk assessment is usually under estimated. While Risk Assessment and Entry check will be used as cyber-defense technology to supply effective cyber protection. during this paper we've got shown that Vulnerability Assessment and Penetration Testing (VAPT) as a cybersecurity technology, on however we are able to give effective cyber protection penetration Vulnerability Assessment and Login check. we've got outlined the entire life cycle of Vulnerability Assessment and Penetration Testing in systems or networks and actions taken to resolve that risk and to prevent potential attacks. during this paper we've got explained customary risk assessment methods and alternative in style VAPT tools.
Observing seismic signatures of slow slip events with unsupervised learning

&amp;lt;p&amp;gt;Slow slip events are observed in geodetic data, and are occasionally associated with seismic signatures such as slow earthquakes (low-frequency earthquakes, tectonic tremors). In particular, it was shown that swarms of slow earthquake can correlate with slow slip events occurrence, and allowed to reveal the intermittent behavior of several slow slip events. This observation was possible thanks to detailed analysis of slow earthquakes catalogs and continuous geodetic data, but in every case, was limited to particular classes of seismic signatures. In the present study, we propose to infer the classes of seismic signals that best correlate with the observed geodetic data, including the slow slip event. We use a scattering network (a neural network with wavelet filters) in order to find meaningful signal features, and apply a hierarchical clustering algorithm in order to infer classes of seismic signal. We then apply a regression algorithm in order to predict the geodetic data, including slow slip events, from the occurrence of inferred seismic classes. This allow to (1) identify seismic signatures associated with the slow slip events as well as (2) infer the the contribution of each classes to the overall displacement observed in the geodetic data. We illustrate our strategy by revisiting the slow-slip event of 2006 that occurred beneath Guerrero, Mexico.&amp;lt;/p&amp;gt;
Pixel Level Instance Segmentation using Single Shot Detectors and Semantic Segmentation Networks

Object detection algorithms are vital in computer vision applications especially the one regarding segmentation of objects in a scene. Instance segmentation using mask RCNN is the state of the art computer vision application using deep neural networks. However it has a complex pipeline for training and real-time testing. Single shot detectors based on CNN such as YOLO are robust in real time object detection compared to Faster RCNN. Combining convolutional networks for semantic segmentation along with single shot object detectors can give superior instance segmentation in real time at less computational complexity. Independent training of instance detection and segmentation networks makes it possible to create application specific pipelines based on context. Lighter architecture using single shot detectors and segmentation networks reduces latency on common processors thereby achieving higher frame rate. Class specific training of detectors improves the average precision of detection. The entire architecture is developed in python over Keras deeplearning framework.OpenCV library is used to render and process the input and output images.
CHICA: A Chinese Calligraphy Mobile Learning Application for Beginners

CHICA is a Chinese mobile learning application for beginners. Most of us know that Chinese language is hard to learn especially the writing. Most beginners cannot differentiate the tones of Chinese characters. There are some Chinese characters that sound the same however it has different tones andmeanings. The writing of Chinese characters is difficult for beginners too since they need to memorize characters and the stroke orders. Particle of Chinese language is different with Bahasa Malaysia. Chinese language often uses 'ma' (chinese character) at the end of sentences while Bahasa Malaysia has many particles. This application was developed to assist beginners in learning basic Chinese with the main focus of Chinese handwritten lettering or calligraphy.  CHICA consists of three categories: hanyun pinyin (vocal, consonant and tone), learning basic strokes and practice writing. This application was developed using Adobe Animate with Actionscript 3.0. The evaluation is carried out among UniKL MIIT students in Mandarin 1 class. Results show that the application helps students in understanding the basic Chinese and help them in writing practice.
국제 제품 전략과 수출 성과 : 혁신 역량의 조절 효과를 중심으로

This study employs the resource-based view and the organizational learning perspective to understand how cost leadership and differentiation strategies influence on export performance and the distinctive roles of innovation capabilities on these links between international product strategies and export performance relatively. Using survey data from 216 Korean exporting firms, the findings indicate that cost leadership and differentiation strategies enhance export performance. Moreover, while exploitative innovation capability strengthens the relationship between cost leadership strategy and export performance, exploratory innovation capability enhances the link between differentiation strategy and export performance. This study suggests theoretical background for strategic fits between international product strategy and innovation capability to enhance export performance. Furthermore, the findings underscore needs for managers to invest in a certain type of international product strategy to ensure the development of innovation capability differently.
TOCO: A Systolic Network for Efficient Transposed Convolutions with Output-Reuse Paths

Transposed convolution is a learnable up-sampling operator widely-used in deep neural networks. It up-samples the input activations to generate useful information in applications like style transfer and super resolution. There exists a rising demand for accelerating transposed convolution layers since they occupy a large portion of computation in GAN-like networks.
Food Kiosk: Smart Food Ordering System for Restaurants.

The project is Food Kiosk: Smart Food Ordering System for Restaurants. In this project menu is going tobe available at customer’s seating area on Kiosk. Menu will be available in display of the screen/Tablet.Customer does not have to wait for the waiter for order. It is very easy and handy method for customer.Placed order will be sent to the chef’s display using Wireless Connectivity. This integration solution canadd or expand hotel software system in any size of hotel chains environment. This system increasesquality and speed of service. This system also increases attraction of place for large range of customers.Implementing this system gives a cost-efficient opportunity to give your customers a personalized serviceexperience where they are in control choosing what they want, when they want it from dining to orderingto payment and feedback with the help of trending technologies like internet of things, Cloud computingetc.
Metodología para la extracción distribuida de imágenes forenses incrementales de dispositivos móviles con sistema operativo Android a través de redes WIFI

Mobile devices, such as smartphones and tablets, have for some years become instruments not only for domestic or personal use, but as a work tool. At the same time, they also began to be susceptible to computer crimes, either as a facilitator or as a bridge to reach corporate information. The architectures of the different versions of Android have been analyzed to know their differences and to be able to make an exact copy of the content of the mobile device. An appropriate encryption method for the extracted image was also analyzed. Generally, the exact copies of the devices are made completely every time, but in this work the exact copy of the new or modified information on the mobile device is tested, so that in each extraction the files are of small size and facilitate the transport via WiFi. And, finally, the validation of the proposed methodology in a controlled environment that confronts it with the traditional wired methodology. The results are shown for each of the components analyzed, where it can be concluded that it is very possible that this methodology is adopted.
Application of Computer Information Management Technology in Network Security

The coming of the information age has brought great opportunity to widespread application of computer information technology.But we should pay attention to the management of information because it related to the users' benefit.So information management is very urgent and arduous in the field of information network.
Implementation of the Open Source Virtualization Technologies in Cloud Computing

The Virtualization and Cloud Computing is a recent buzzword in the digital world. Cloud computing provide IT as a service to the users on demand basis. This service has greater flexibility, availability, reliability and scalability with utility computing model. This new concept of computing has an immense potential in it to be used in the field of e-governance and in the overall IT development perspective in developing countries like Bangladesh.
CURRENT PROGRESS AND TRENDS IN OPTO-ELECTRONIC SENSOR-BASED MONITORING IN GEO-ENGINEERING—A SUMMARY OF 6 TH OSMG-2017

This paer summarizes the 6th International Forum on Opto-Electronic Sensor-Based Monitoring in Geo-Engineering(6th OSMG- 2017)held in Nanjing University on 3-5 November 2017 under the themeFrontiers and Applications of Infrastructure Monitoring Technologies. Attended by over 350 delegates from nearly 20 countries/regions, the 3-day forum was organized around 35 invited presentations. The forum showed the recent breakthroughs achieved worldwide pertaining to geo-engineering monitoring:(1)opto-electronic demodulation technologies are undergoing numerous developments and the monitoring accuracy and reliability are significantly enhanced; (2)a host of new sensors have been recently developed for geo-engineering monitoring such as smart geotextiles with integrated plastic optical fibers; (3)opto-electronic sensing can provide huge data for health diagnosis of infrastructures including tunnels, dams, nuclear power plants, and bridges, hence insuring the safety operation of these infrastructures; and (4)opto-electronic sensing is playing an increasingly important role in monitoring and early warning of geohazards. Suggested future research includes the need for:(1)cost-effective demodulation techniques for distributed fiber optic sensing technology; (2)special opto-electronic sensors for geo-engineering monitoring and their field installation methods; and (3)real-time huge monitoring data processing and early warning system of natural and anthropogenic hazards.
Discussion on the Application of Modern Surveying and Mapping Technology in Engineering Survey

With the development of modern surveying and mapping technology,the methods and means for engineering survey will be progressively updated and upgraded.This paper outlines the work?procedures of the modern surveying and mapping technology,and connecting with the work practice,probes into the rational application of modern surveying and mapping technology in engineering survey.
MuseumQA: A Fine-Grained Question Answering Dataset for Museums and Artifacts

In this paper, we present a fine-grained museum artifact question-answering (QA) dataset, which serves as the cornerstone for developing museum question-answering systems. Creating these systems is essential for the advancement of museums and can enhance the visitor experience. Nevertheless, research reveals the current absence of domestically available datasets for museum artifacts in China. To ensure data authenticity and validity, we meticulously collected and screened 3,416 raw data entries from the official websites of provincial museums across China. Using these raw data, we annotated annotatable QA pair information to create the final QA dataset. Initially, a small batch of QA pairs was generated with the assistance of ChatGPT. Subsequently, the remaining QA pairs were annotated using an enhanced QA generation model, yielding 23,149 QA pairs. To mitigate overfitting due to dataset-model size disparities, a noise factor was incorporated into the enhanced generation model. Additionally, a Chinese grammar correction module was integrated to enhance the accuracy of the generated statements. Ultimately, the model achieved optimal performance, and the dataset demonstrated the highest semantic relevance.
Printed Multilingual Document Image Retrieval Based On Improved SURF

In the process of printed document image retrieval, the traditional algorithm, SURF algorithm combined with violent matching, has the problems of low retrieval accuracy and low retrieval efficiency. This paper proposes a FAST + PCA-SURF combined with KNN algorithm based on FLANN for multilingual document image retrieval. Based on FAST, the feature points are detected, and the feature descriptors after dimensionality reduction are extracted by PCA-SURF.Then, the KNN algorithm based on FLANN is used for feature matching, and finally the appropriate matching results are output. The experimental results show that the proposed algorithm is improved in terms of time complexity and retrieval accuracy compared with the traditional algorithm. The average time complexity and retrieval accuracy of the traditional algorithm are 0.1783 s and 71.8%, respectively, while the proposed algorithm is 0.0464 s and 77.8%, indicating that the proposed algorithm achieves better experimental results in multilingual document image retrieval.