Reviewerly/exHarmony huggingface dataset:
«BIG DATA» IN EDUCATION: INTERNATIONAL AND RUSSIAN EXPERIENCE

Big data analysis and their free circulation can serve as a basis for qualitative changes, the formation of a new modern and dynamically developing education system. The article analyzes data on the educational process, which will make it possible to better assess teachers and, if necessary, make changes to the content of their training. As data for the analysis, information about the traits of the student's character, his success in learning, and previous work experience are used. One of the significant points in terms of collecting and structuring big data in Russian education is the creation and implementation of the Moscow Electronic School project. Big data collection and analysis technologies open up great prospects for creating new positive learning experiences and highly effective expansion of lifelong learning competencies.
Teaching Recurrent Neural Networks to Infer Global Temporal Structure from Local Examples

The ability to store and manipulate information is a hallmark of computational systems. Whereas computers are carefully engineered to represent and perform mathematical operations on structured data, neurobiological systems adapt to perform analogous functions without needing to be explicitly engineered. Recent efforts have made progress in modelling the representation and recall of information in neural systems. However, precisely how neural systems learn to modify these representations remains far from understood. Here, we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory. Specifically, we drive an RNN with examples of translated, linearly transformed or pre-bifurcated time series from a chaotic Lorenz system, alongside an additional control signal that changes value for each example. By training the network to replicate the Lorenz inputs, it learns to autonomously evolve about a Lorenz-shaped manifold. Additionally, it learns to continuously interpolate and extrapolate the translation, transformation and bifurcation of this representation far beyond the training data by changing the control signal. Furthermore, we demonstrate that RNNs can infer the bifurcation structure of normal forms and period doubling routes to chaos, and extrapolate non-dynamical, kinematic trajectories. Finally, we provide a mechanism for how these computations are learned, and replicate our main results using a Wilson–Cowan reservoir. Together, our results provide a simple but powerful mechanism by which an RNN can learn to manipulate internal representations of complex information, enabling the principled study and precise design of RNNs. Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.
Optimized recurrent neural network based emotion recognition using speech for Assamese language

This paper deals with the design and development of a dataset for emotion recognition in Assamese language and its analysis using Machine Learning. This dataset comprises of spoken sentences in Assamese language which relates 7 different emotions of the speakers. It also focusses on the analysis of the dataset with Deep Learning architectures and classification algorithm like Recurrent Neural Network (RNN). In total these 7 emotions included calm/neutral, anger, sad, fear, disgust, happy and surprise. The performance of RNN is improved using Representation Learning where training of the model is done using the glottal flow signal to investigate the effect of speaker and phonetic invariant features on classification performance. The speech samples were experimented with different combinations of features and a variety of results were obtained for each of them. In this paper 2 experiments are performed to improve the performance of RNN training. The first one is the representation learning where the training is modelled on the glottal flow signal which is used to investigate the effect of the speaker and phonetic distinct features on the performance of the classification. The second is the transfer learning based RNN training which is done on the valence and activation that is adapted to a 7-category emotion classification. On the Assamese dataset the experimented approach results in a performance which is comparable and similar to the existing state of art systems for emotion recognition using speech.
Could you be a school governor?

Abstract Getting more IT professionals involved with schools will play a vital role in encouraging the next generation of computing practitioners, writes Victoria Temple, Press and Community Engagement Officer at BCS.
SISTEM INFORMASI BENGKEL KHAYANGAN MOBIL BEBASIS DESKTOP DENGAN JAVA DAN MySQL

Car repair shop is a business entity engaged in the maintenance and repair of motorized vehicles (automotive), namely cars. Repairs carried out by car repair shops include repairs to body, engine, electrical systems, car air conditioning, and painting. can assist users in using this application. The application information system starts with analysis, program design, program implementation and testing. For analysis, there are several stages including problem analysis, system architecture, system data flow, requirements. The design also has several stages including sequence diagram design, activity diagram design, database table design, relationships between database tables, and interface design. Then, implementation and testing also has stages including algorithm preparation, displaying source code, program screenshots, black box testing and white box testing.
LightWeight Deep Convolutional Neural Networks for Vehicle Re-Identification using Diffusion-based Image Masking

Re-Identification of Vehicles (Vehicle Re-ID) in surveillance videos is an important and emerging topic in computer vision and intelligent systems. In Vehicle Re-ID, a specific vehicle is searched through non-overlapping camera viewpoints, which is a difficult task due to varying image conditions (e.g. scale, variations of appearance, illumination). For vehicle Re-ID, low level features such as shape and color can be utilized. Recently, deep learning-based vehicle Re-ID solutions became popular. Many of these deep learning solutions use pretrained large convolutional neural networks (CNNs) to perform vehicle Re-ID. Most of the pre-trained networks for object recognition and retrieval work with a large input image size (e.g. 224 x 224 x 3) and many hidden layers/parameters. In this work, we propose a lightweight deep CNN model together with diffusion-based image masking. The lightweight network trains input images of size 32 x 32 x 3, it also has a smaller number of parameters to train in comparison to large pre-trained networks. We use three lightweight CNN networks, the first network trains original colour vehicle images, the second network trains vehicle images after masking with fully diffused shape information image, and the third network trains vehicle images after masking with partly diffused shape information image. Feature vectors extracted from these three networks are used to generate three similarity scores. Then, these three similarity scores are fused to produce an overall similarity score for retrieval. We demonstrate that the proposed work performs better than many large pretrained networks on VeRi-776 dataset.
Emoji Your Story: The Advertising Effectiveness of Emoji-Based Narratives

Emojis are often used as single symbols to express emotions. Moreover, they serve as paralanguage in mass media and digital communication. Emojis are also used to tell narratives in advertising. Thus far, the latter usage of emojis has not been investigated. In two studies, we investigated the effectiveness of emoji-based narratives compared with textual narratives. Based on the data obtained from a thought-listing task, we found that consumers focus on solving the emoji puzzle when emojis are presented, whereas textual narratives are seldom replicated in such detail and induce additional thoughts about product features. We found the following five mediating effects: emoji-based narratives influence brand attitudes and the propensity to follow recommendations (provided in social-marketing campaigns) through 1. higher levels of narrative transportation, 2. higher perceptions of ad originality, 3. lower message comprehensibility, 4. stronger curiosity, and 5. lower perceptions of brand/organization trustworthiness. In total, emoji puzzles proved to be advantageous compared with textual narratives, with one exception: if the ad promoted advice that had no immediate and direct relevance for to the consumers’ lives (e.g., avoiding the use of animal-tested cosmetics and contributing to the preservation of the Amazon rainforest), the participants showed a low propensity to solve the emoji puzzle.
동아시아 국가들의 구매력평가 변수가 빅맥지수에 미치는 영향에 관한 연구

Our study is to identify the effectiveness of the Big Mac index of East Asian countries which is the representative index of Law of one price(Loop) by using variables related to the price index and exchange rates. Purchasing power parity(PPP) which is the theoretical background of Loop is that price indices of various countries determine the long-term exchange rate. It is classified into absolute purchasing power parity(APPP) and relative purchasing power parity(RPPP). APPP is described the exchange rate difference between countries’ price indices, but RPPP is described the change in the exchange rate with a change of the countries' prices indices. The theory used for empirically testing is RPPP. In our study, the Big Mac Index which is MacDonald's representative product sold for the same quality and specifications all over the world for more than 30 years is used to evaluate the effectiveness of PPP. Empirical tests by using yearly data from 2000 to 2017 to Korea, Japan, Hong Kong, Singapore, Thailand, Malaysia. Indonesia, and the Philippines were conducted through panel analysis. As a result of the empirical analysis, variables relating to the price index of did not show statistical significance for the Big Mac index, but statistical significance was shown with variables such as the rate of change in the nominal exchange rate and the exchange rate of purchasing power. In order to hold the robustness, OECD countries and the countries using the floating exchange rate system in samples are analyzed. Then, the results are also similar. Unlike previous empirical studies where the difference the change in the level of price indices between countries related long-term the effectiveness of purchasing power, our study used 17-year time series data to analyze the effect of the change in price index on the Big Mac index however long-term effectiveness has not been verified. The deviation of the Big Mac index and purchasing power is also explained by transaction costs, non-comparative barriers, and legal barriers. Our study implies that the Big Mac Index, which is representative of Loop, did not show statistical significance with the changes in the price level of East Asian countries since 2000, but it is related to the exchange rate change of the nominal exchange rate and the purchasing power. Moreover the latter has a positive relationship. Future research will be conducted by comparing PPP between OECD countries in Asia and Europe and the study of the effectiveness of PPP using the Latte Index by Starbucks and the iPhone Index by Apple.
Efficient and Secure Group Data Sharing Model Using Cloud Computing

Cloud computing is alleged to be the service homeward-bound computing technology, that ar reasonable and versatile over the web. In the past few years, the cloud has become more experienced and provided several services, one in all the first service is a knowledge sharing in the cluster, wherever the information is often simply shared from one member to a different. However, whereas sharing information security is one of the first concerns. In past, many methodologies have been planned. However, these strategies lacked feasibleness. Hence, during this paper, we’ve proposed a methodology that relies on the chosen theme. Here General cluster secret’s generated and what is more General Key agreement protocol is redistributed primarily based model wherever the information ar controlled by the owner inside the constant cluster. Moreover, the planned methodology is evaluated by analyzing the comparative analysis supported by the assorted range of parameters. Result Analysiscounsel that our methodology merely outperforms the present one.
Perception Beyond Sensors Under Uncertainty

My research aims to enable spatiotemporal inference in mobile robot perception systems. Specifically, the proposed thesis presents learning-based approaches to the tasks of behavior prediction and occlusion inference that explicitly account for the associated aleatoric and epistemic uncertainty.
Impact of Electronic Media on Reading Habits of Teenagers: A Pilot Study Done in Two Schools in Colombo District, Sri Lanka

The study aimed to discover the impact of e-media on reading habits of teenagers aged 13-19 in Colombo district, Sri Lanka. A questionnaire was distributed among two schools to find out the demographic information, the nature and extent of reading habits, the media other than books that they are interested in and the attitudes towards Reading and E-media. Main conclusions of the study are that both groups using e-media as well as books and having positive attitudes towards reading, books and e-media. Using E –media does not affect their studies or social life. Since the majority of teenagers are heavy media users, the researcher suggests using e-media to enhance reading habits among them and to improve the quality of reading.
Access and Use of Electronic Information Resources by Faculties of RGUKT, Andra Pradesh: A Study

The research paper intends to determine the usefulness of e-resources to the faculties of Rajiv Gandhi University of Knowledge Technologies (RGUKT), and their skills in using various search methods and techniques to access and utilize these resources. The study further aims to investigate the level of satisfaction with the information accessed by the faculties through the available e-resources and various challenges faced by them in their field. The survey was conducted with the help of a questionnaire and personal interview. Questionnaire was distributed to a random sample of 88faculties from different fields of Science and Technology available at the time of study and the response rate was 75%. The responses received from the available faculties are presented in tables and figures and data is analyzed by using simple calculation of percentage method. The findings reveal that access and use of e-information is an important component of research activities for faculties, also qualitative and quantitative developed e-collections overcome conventional resources with the characteristic of fast accessibility.
Sistem Pakar Diagnosa Hama dan Penyakit Pada Jamur Tiram dengan Metode Case Based Reasoning Berbasis Web

Mushrooms are useful plants for humans, mushrooms as one of the food needs that are often hunted by the community. Lately, many people want to cultivate oyster mushrooms, but to cultivate oyster mushrooms is not easy because oyster mushrooms are easily attacked by pests and diseases. Most farmers only mimic the concept of how to grow other oyster mushrooms without knowing how to properly grow oyster mushrooms. Most oyster mushrooms that are stricken with disease are only left to die, this method can potentially spread to other mushroom plants. Another factor that can cause oyster mushrooms to die is in terms of humidity, oyster mushrooms must be cultivated in a humid room because the habitat of oyster mushrooms grows in humid temperatures. With this the authors took the initiative to create a system that can help oyster mushroom farmers know pests and diseases by creating an expert system. Expert system is a system that functions to transfer knowledge from experts and then it is applied to computer programs. In the research conducted using the case-based reasoning method, there are weight values for each symptom, the weight values obtained from experts. After testing 100 data samples, the calculation results get an accuracy value of 93% and a precision value of 93% which shows the system is functioning properly according to expert identification.
Multi-Objective Cooperated Path Planning of Multiple UAVs Based on Revisit Time

This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the 3D mountainous environment. As a contribution to solving trade-offs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms including a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multiagent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.
Maker Space: A Beginner Way to Imparting Library for Adani Institute of Infrastructure Engineering

Adani Institute of Infrastructure Engineering (AIIE) is established to equip the students to become modern-day engineers and managers primarily for the infrastructure and allied sectors. The Institute is presently conducting three Bachelor Programmes in Engineering. 1. BE- Civil &amp; Infrastructure Engineering, 2.BE-Electrical Engineering, 3.BE -Information &amp; Communication Technology. Adani Institute of Infrastructure Library embarked on a change journey in 2008 for services in support of the education. The concept for creating a maker space for engineering students within the library to foster activity -based learning. Maker spaces are over just places to create things; a maker space may be a physical location wherever people gather to share resources and knowledge, work on projects, network, and build. The NMC Horizon Report: 2015 higher education the paper examines the awareness and importance of library maker spaces. A questionnaire was used for collection of data. Around 50 questionnaires were distributed among the students. A total of 30 stuffed in questionnaires were selected for analysis of the data. While there is a push to include maker spaces in universities, very little is understood regarding the expertise of establishing maker spaces on engineering campuses. Typically, these maker spaces contain 3D printers and laser cutters, and offer facilities to conduct work, personal and collaborative projects. The idea for creating a maker space for engineering library is promotional activity-based learning. Maker spaces are over just places to make things; a maker space may be a physical location wherever people gather to share resources and knowledge, work practical projects, network, and build. Libraries that have lengthy been substitutable with peace and tranquillity are currently thought of as a part in which there have to be a stability between quiet surroundings and social dynamic mood. The intention of this study is to observe the importance of maker space of engineering libraries that is tremendously needed in technological advancements and alteration of ancient systems with new ones. This analysis consists of reviewing the domestic samples, conforming examples to library maker space and inspecting numerous libraries over a duration of time. The outcomes of this find out about indicate that generally, modern libraries have fewer areas for paper books and publications and individual study, instead, bigger spaces are committed to locations for cluster studies and the use of computer and maker space etc. This trade has influenced the function of the library space and maker space.
A New Approach to Secure IoT Devices Using Blockchain and SmartContract

The conception of Block chain has penetrated a large vary of scientific areas, and its use is considered to rise exponentially within the close to future. death penalty short scripts of predefined code called sensible contracts on Blockchain will eliminate the requirement of intermediaries and might conjointly raise the multitude of execution of contracts. During this paper, we have a tendency to discuss the conception of Blockchain on with sensible contracts and discuss their pertinence within the web of Medical Things (IoMT) in the e-healthcare domain.In this paper we propose a Smart Solution for Securing IOT Devices using Block chain and Smart Contract. Existing System has many issues. To overcome these various Issues like DDOS attacks, Device Authenticity and Proper Governance and On Time Management, Block chain has potential to solve all the issues within its own Ecosystem and at much lower cost. Leveraging the exponential potential of Block chain we can overcome all the drawbacks faced by IoT Industry Currently.Our proposed framework relies on smart contracts and distributed trust to maintain security and privacy while making it more suitable for the specific requirement of IoT. Ethereum in our case as a Best Feasible Solution to overcome above mentioned issues by eliminating the concept of Centralization and the need for Intermediaries being the Priority.
Multi-facet information processing algorithms for news video based on event combing

As an important and effective media of communication, online news videos are being widely and incessantly consumed by innumerable audience through the Internet. However, identifying content of interest from massive online resources remains a quite challenging and time-consuming task due to the continuous explosive growth of news video data, increasingly diverse production modes and the shortage of powerful tools for efficient video navigation, which are seriously affecting user’s experience and information seeking efficiency. In this paper, a multi-facet information processing algorithm for news video based on event combing is reported. By establishing causal relationship and subordinate relationship between news events can help users comb news events and acquire the desired information from massive news video data both effectively and efficiently. The algorithms are mainly made up of three functional modules: the first one is the news timeline. The news videos is associated with their corresponding time nodes to reflect the times of occurrence of events. According to the sequence of news time nodes, users can better grasp the whole news events in the occurrence sequence of news events. The second is the news content keyword cloud module. It represents the content keywords of video within a specific time period, and helps users quickly appreciate the theme of video through the word cloud. The third is news topic clue clustering. According to the retrieval keywords entered by users, the system retrieves relevant videos from the database and carry out topic clustering operation to them. Every cluster is used to generate news clues in the form of card stack, reflecting the main content of the whole news event from different views. The advantages of the multi-facet information processing are demonstrated through benchmarked experimental results in comparison with peer methods.