Reviewerly/exHarmony huggingface dataset:
Information keys of the education system

The progress of a technical civilization as a product of the development of social mentality is based on the synthesis of three components: a pair of respondents connected by means of communication - which in general is a single historically formed aggregate of cultural progress. The mechanism of action of this aggregate: the first respondent, who has accumulated the potential for useful achievements, transmits information through direct and feedback channels to the second respondent, who implements the received information about achievements, sending signals to the first to request updates and improvement of activity. The phenomenon of the evolutionary ladder is formed, the steps of which are the progress of civilization, imitated in a playful form by the educational system that supplies the society with trained specialists. Ideally, a professional school of the middle and higher levels is focused on the production of personnel with developed creative inclinations. However, in practice, special educational institutions produce specialists who are mainly ready for reproductive work. And only a small proportion of graduates, immediately after graduation or after working in the field of routine production, reveal their creative and organizational potential. The introduction of electronic information technology into education, furthermore stimulated by the requirements of self-isolation due to the pandemic, brings the problems of distance learning to the surface of professional education.
Retrieving Entities from Knowledge Graphs without Knowing Much: On Learning Generalizable Patterns and Indexing

Querying and extracting potentially a large number of entities that are the user's intention is a challenging problem for knowledge graphs. The conventional query mechanism of subgraph pattern matching would not work well as the user in general does not know the graph pattern to search for. Moreover, there may not be a single subgraph pattern that fits all the intended entities. Using keywords also may not be a viable approach, as it is very difficult to come up with the right set of keywords, and the results are often very diverse and overwhelming. We propose a novel approach that does not require users to know much about the knowledge graph but only simple keywords about the desired entities. We retrieve a sample of matches and learn the entity context patterns as what we call the subgraph sketch signatures. We provide clustered patterns for the user to prune. Moreover, we devise a novel index to finally perform efficient entity retrieval over the whole knowledge graph.
Online evaluation method based on open and short answers

The purpose of this article is to present a method for assessing a short and open Arabic question during a learning activity. The proposed method is based on a combination of data presentation and a conceptual map of ontologies. The proposed method is based on the following steps: Process the text of the student's response by removing the stop words and sound from the text. Extract information about the student's response and plan in the form of a conceptual table; comparison between the reference and the student's response and assignment of points by semantic proportions; the score is calculated on the basis of the positional similarity of the measurement results.
On the Use of Fuzzy Logic in Criminal Justice

The article attempted to raise questions about the possibility of using certain methods of fuzzy logic in the field of criminal justice, in the formation of modern criminal policy, as well as the criminal legal reaction to the results of the use of artificial intelligence in the disclosure and investigation of criminal activities in modern society.
Collection of Meta Information with User-Generated Question Answer Pairs and its Reflection for Improving Expressibility in Response Generation

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question-answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating responses reflecting the meta information, especially emotion.
Utilizing CNNs for Object Detection with LiDAR Data for Autonomous Driving

This project evaluates the feasibility of utilizing popular Convolutional Neural Networks (CNNs) to detect objects present in LiDAR (Light Detection And Ranging) data, and the resulting neural network's performance. This work aims to further existing experimentation using raw LiDAR data that is analyzed and represented in a two-dimensional frame. Using this method, hundreds of frames were generated to create a dataset that was used for neural network training and validation on an existing CNN architecture. The LiDAR dataset was used to train YOLOv3, a popular CNN model, to detect vehicles. This research aims to test a smaller version of the network, YOLOv3-tiny, to measure the change in accuracy between using YOLOv3 and YOLOv3-tiny on the LiDAR dataset. The results are then compared to the loss typically found when going from YOLOv3 to YOLOV3-tiny on camera-based images. In prior experimentation, a preprocessing method was also introduced to attempt to isolate target objects in the frame. The method will be evaluated in this paper to measure its effect on the final accuracy metric of the network. Lastly, the runtime performance of these networks will be evaluated on two embedded platforms to understand if the frame rate that the networks perform on is usable for real-world applications, based on the frame rate the sensor is capable of outputting and the inference speed of the network on the embedded platforms.
Text Recognition Using K-means Clustering and Support Vector Machine

Extraction of text characters and strings from the nameplates, sign boards, printed names on bank cheques, printed postal envelope addresses and office boards can give useful information for numerous applications. The process to extract text characters from all the categories mentioned above is very complex task due to divergence in the text patterns in terms of text fonts used. Text recognition always starts with text detection. Therefore, text recognition is the process of text detection followed by text recognition. This can be done by combining image processing and machine learning together. This paper proposes a method for text detection and identification mainly on nameplates in the offices and home doors written in English language which uses Roman script. Initial step of text detection of Roman characters is carried out by using image processing and K-means clustering algorithm while later part of identification is carried out with the help of multiclass feature of support vector machine.
MatDC: A Multi-turn Multi-domain Annotated Task-oriented Dialogue Dataset in Chinese

The modern conversational agent requires high-quality datasets, which are often the bottlenecks when building models. This paper introduces MatDC, an entirely human-produced dialogue dataset with full semantic annotations in Chinese. The dataset features linguistic variations given users' intents and fully annotated semantic slots. MatDC dataset was completely human-edited, and the curation comprises two stages. At first, templates design stage, domain editors first construct schemas and compose ten dialogues between the agents and the users based on the back- end database. Secondly, in the dialogue rewrite stage, rewriters generate sentential variations for each template, under the constraints that the normalized slot values are kept unchanged. The underlying methodology of the MatDC is more open to extension and more adaptable to different domains. To demonstrate the applicability of the dataset, we build a dialogue agent with conventional pipeline architecture. We expect the MatDC dataset to provide additional training data and testing ground for dialogue agent studies.
Extraction of handwritten and printed text from a form

Abstract A lot of offline information or request forms available at certain government firms and similar companies use text boxes to fill the information. The data from these manually filled forms has to be manually filled in the machine database requiring a person to type the data. The proposed system uses a blend of machine learning technologies to extract the text data from such forms and automatically convert it to text that can be processed and understood by the computer.
Combining BERT with Contextual Linguistic Features for Identification of Propaganda Spans in News Articles

Recent endeavours at detection of propaganda in news articles treat this as a fine-grained problem of detecting it within fragments; and hence, transformer based embeddings perform decently in such detection. We build our propaganda detection framework on top of a transformer model simultaneously enriching it with contextual linguistic information of surrounding part-of-speech tags and LIWC categories the word itself belongs to. The evaluation outcomes being encouraging indicate a huge potential for this line of reasoning in natural language processing of news text.
The Transformation and Development of Vocational Education in the Age of Artificial Intelligence

Artificial intelligence, as a universal technology that supports the transformation of all industries, promotes changes in various fields such as politics, economy, and society. Naturally, it also puts forward the requirements of transformation and innovation for the development of vocational education. This article explores the transformation and development of vocational education in the era of artificial intelligence in order to promote the ecological development of vocational education in my country. This article understands the status quo and changes of the development of vocational education in this profession by issuing questionnaires to the teachers and students of the professional robotics profession in A vocational colleges. It is concluded that 86.42% of teachers and students believe that the major is now more focused on training comprehensive talents; 93.71% of teachers and students believe that this major is now more concerned about the development of future society, and put forward the development strategy of vocational education in the era of artificial intelligence. The research in this article will help vocational colleges optimize their talent training programs and keep up with the development of the times to achieve the sustainable development of colleges.
Advanced ICT for Access of Visual Impaired People to Computers, Knowledge, Education and Culture

The paper presents methods and tools for tactile access of people with visual impairments to a graphical computer interface, cultural and historical heritage and temperature in the home. Different size of graphical Braille screens on the base of linear electromagnetic micro drives, tactile graphical tiles and thermometer for visual impaired are described. The presented devices give access of visual impaired people to graphical computer interface (Windows), objects of cultural and historical heritage, as well as give possibilities for better education and perception of knowledge.
Static Taint Analysis for JavaScript Programs

Web applications have become an essential component of many different fields. As a client-side scripting language, JavaScript is ubiquitous across the web. Malicious JavaScript code can exploit a user’s browser, cookies, and security permissions. In this paper, we propose a static taint analysis approach for precise detection of taint-style vulnerabilities, such as DOM-based Cross-site Scripting (XSS), in JavaScript programs. The approach divides sinks into contexts to ensure that untrusted data passed to a certain context has been sufficiently sanitized. We reengineered TAJS resulting in a new analyzer, $${\text {TAJS}}_{\text {taint}}$$ , that adopts the new approach and uses finite state automata as its abstract string domain in order to track tainted flows more precisely. We run $${\text {TAJS}}_{\text {taint}}$$ on a set of real Web pages and show that $${\text {TAJS}}_{\text {taint}}$$ can precisely detect taint-style vulnerabilities, especially those that are caused by insufficient input sanitization.
Things to Consider to Enable Dynamic Graphs in Processing-in-Memory

With the ubiquity of graphs to represent large, sparse data sets, a recent focus has been placed on making graph processing more efficient. Processing-in-Memory (PIM) is an alternative solution to reduce the data movement between memory and processors, and it results in better performance and reduced energy consumption. However, prior PIM-based graph processing work operated only on static graphs. In fact, real-world graphs are constantly changing as their data is updated. In this paper, we will discuss design considerations for adapting dynamic graph processing to PIM.
Image Super Resolution in Real World Using Variational Auto Encoder

Most of the existing super-resolution methods trained only by simulated datasets are difficult to achieve good performance in real-world scenes. Besides, it is difficult to obtain well-aligned real-world image pairs between high-resolution and low-resolution spaces for training. To tackle this problem, we proposed a novel super-resolution framework based on variational auto encoder. In particular, we firstly utilized a variational auto encoder to map the degraded low-resolution images and the real-world low-resolution images to the same latent space. Meanwhile, the high-quality images were mapped to another latent space by another variational auto encoder. An additional convolutional neural network was used to learn the mapping between the two latent spaces. After that, the information in the mapped latent space was decoded and the high-resolution images were reconstructed by the decoder. We have compared the performance of our proposed method and those of state-of-the-art methods including SRGAN., ESRGAN., and CycleGAN algorithms. The experimental results demonstrate that the proposed method outperforms the above methods in the super-resolved task in real world.
Question Paper Generator and Result Analyzer

An examination may be a crucial activity for any educational establishments that is accustomed to notice the individual’s performance and their growth in progress. This online examination method is to be implemented so as to improve their knowledge in all the aspects thoroughly. Preparing the questions manually is extremely difficult, hard, and time taking process for the staff. Therfore, we introduced the process of conducting online mode of examination, so that the task for the staff to prepare the questions is reduced and machine takes all the responsibilities after loading the questions. This technique includes many modules like login, subject, questions, ranking, etc. Machine takes all the responsibilities after loading the questions. These modules assist the teacher in generating question papers and in anlyazing the results, the entire process is been implemented in a user friendly approach. The automated Question Paper Generation makes the work of teachers simple and also the automated result evaluation and analyser, saves a lot of their time which could be efficiently used in improving their skills.
A Safe Semi-supervised Ensemble Method Combined with Active Learning Strategy

In view of the limitations of traditional semi-supervised ensemble learning and the characteristics of active learning, this paper proposes a safe semi-supervised ensemble method combined with active learning strategy (S3E-AL). The algorithm is improved on the basis of the traditional Tritraining algorithm, where three different classification algorithms are used to enhance the difference between the learners, and the classifier is integrated through the weighted voting method based on the classification accuracy, which improves the prediction accuracy. In combination with the active learning strategy, the samples with high confidence were selected for marking, and the reliability of the new markers was verified to reduce the addition of noise samples. At the same time, the low confidence samples with inconsistent votes were manually marked to make up for the defect that traditional semi-supervised ensemble learning only considers the samples with high confidence while ignoring those with low confidence but rich information. Finally, we use the data set on UCI to verify the availability and reliability of the proposed method.
Auto regression Integrated moving average (ARIMA) to estimate temporal series of global solar radiation from measured data

The Measurements of the solar radiation is important in choosing sites to install projects with solar systems. Adrar region is classified from the high potential solar areas in the world. In this study, we using the measurements of global solar radiation on horizontal surface in Adrar region and Auto regression Integrated moving average (ARIMA) model to estimate temporal series of global solar radiation. The results of estimation were validated with observed data of Adrar station (latitude=27.88 North, longitude = -0.28 EST). The correlation between predicted and expected values of global solar radiation is generally significant. But the comparisons with other model demonstrate less efficiently of the model.
Integrating Block Chain with Social Network &amp;amp; Tracking False Information: Challenges and Open Issues

The block chain has become the next cyber security revolution. The integration of block chain with social network is an effective way to resolve many issues. The huge amount of userâ€™s data available on social network can be protected by integrating block chain with the information system governing the social network. As social network gains more publicity on both client-server and peer-to-peer network to provide information in a more dynamic and distributed manner, so it would be beneficial for us to take its security seriously. This paper provides an overview of integration of block chain into the social network by highlighting the integration benefits and implementation challenges. Discussion will also focus on how to track the culprit (origin) responsible for spreading the false information that is a bigger challenge we face today.
Combining Sparse Data with Reaction Kinetics Using Fuzzy Logic to Predict Reservoir Souring

Abstract Hydrogen Sulphide (H2S) is a colourless, flammable and highly toxic gas with a strong odour of rotten eggs that is found in many reservoir fluids and aquifers in the world. This gas is commonly a result of "reservoir souring" – a process which increases the H2S concentration. Increasing amounts of this gas pose serious health, safety and environmental concerns. This can result in significant costs associated with replacement of downhole and surface equipment and increased processing costs, but more lethally a potential loss of life. Many reservoirs particularly those undergoing waterflooding face increasing levels of hydrogen sulphide (H2S) production with time. H2S is a highly toxic gas that can be fatal even at low concentrations. Being able to predict the risk potential of a particular reservoir to increasing H2S production with time would be highly valuable. The objective is to determine apriori whether a reservoir would likely see dangerously high levels of H2S being produced during the lifetime of the reservoir, and if so, be a catalyst in supporting further investigation and mitigation of H2S early in the reservoir development. There is very little published field data with regards to reservoir souring, hence a purely data driven model would not be possible to create. However, we do have a good understanding of the reaction kinetics that goes into the biological process that generates H2S. To this end the best modelling paradigm that can assimilate sparse data with first principles dynamics is fuzzy logic. A fuzzy logic model has been built around the reaction kinetics and then conditioned to the published field data. The model created matches the published field data fairly well. It is now a ready tool that can be used by engineers to make a quick assessment of their reservoirs before going into full blown expensive sampling and laboratory analysis. The novel aspect of this paper is being able to use fuzzy logic to combine the first principles chemistry together with sparse data to produce a model that can be used practically. Fuzzy Logic has been out of the news of late as machine learning and neural networks are the current hot potatoes, however it is often overlooked that fuzzy logic can still be used in low dimensional cases where only sparse data is available.
Fundamentals of Lithostructural Mapping: Example from the SW Part of the Proterozoic Bhima Basin, Karnataka, India: A Note on Dharwarian Crustal Evolution

Lithostructural mapping in any type of geological terrains like the younger Himalayan mountain or the older Archaean Cratons has always been an immensely helpful tool. There are some challenges for geologists in mapping certain geological terrain. Although the use of remote sensing, GIS and other feasible technology is enhancing day by day, accurately produced field survey data cannot be challenged and considered as most reliable and fundamental. The fundamentals of projection techniques, toposheet preparation, components and working principles for brunton compass, magnetic declination effects, Global Positioning System (GPS), litho contact tracing, outcrop correlation by extrapolation and interpolation, etc. are of prime significance for field geologist. Though there are a number of publications available on these aspects, systematic examples with sketches, data tables and outcrop photos with specific case studies remained pending. In this chapter, comprehensive descriptions with a different approach on a few less commonly available but useful combination of techniques for lithostructural mapping are demonstrated with reference to a study area along with the SW basement of Proterozoic Bhima basin. Evolution of Archaean crust is also explained with a brief description.
Profiling Astroturfing Facebook Users During Three Contiguous Israeli Election Periods

Astroturfing is the practice of using a fake online social media (OSM) profile in order to influence public opinion, while giving the impression that the profile belongs to an authentic human user. The proliferation of such profiles in the political OSM scene is becoming more and more common, and as such, poses a threat to democratic processes, especially around election time. We present here a unique technique for exposing astroturfing profiles for what they are. This technique was developed and tested using a large dataset which we collected from Facebook, containing over four million comments, posted within a time span of 15 months, during which three rounds of elections took place in Israel. This political situation provided a rare opportunity to create and research what is-to the best of our knowledge-one of the largest datasets of authentic and fake profiles curated from Facebook to this day. These unique circumstances of three elections held contiguously within a short time span brought forth a set of previously unrecognized features, creating a temporal layer in the feature set, with regard to the date of elections, such as pre-election and post-election, which greatly improved classification results. By creating and selecting the right features, and using various machine learning algorithms, we are able to identify such profiles, with good precision and recall. This setup can serve as a platform for large scale astroturfing profiling in regular situations. Our unique dataset is planned to be made publicly available to the research community.