Reviewerly/exHarmony huggingface dataset:
Comparison of Matrix Factorization and Graph-Based Models for Summary Extraction

With increasing number of users contributing in digital text generation, it becomes necessary to have some tools to extract relevant information from these text documents. Manually performing this task is a time consuming and tidious process. Both Matrix Factorization Based and Graph-Based Models are unsupervised models for extracting a summary from text documents. As the training phase is not required for both these methods, they are extremely fast. Both these methods construct an intermediate representation of a text document and use it to assign a score to each sentence present in the text document. In this paper, we discussed the underlying concept behind both the methods and compare them on the basis of quality of summary extracted.
Human-Robot Interaction Based on Battle Management Language for Multi-robot System

Commanding and controlling a multirobot system is a challenging task. Static control commands are difficult to fully meet the requirements of controlling different robots. As the number of robots increases, it is difficult for the robot’s motion-level commands to simultaneously satisfy the demands of commanding multi-robot system. In this paper, we propose to use a limited natural language to control multi-robot systems, and propose a framework based on Battle Management Language (BML) to command multi-robot systems. Based on the framework, the capabilities and names of the robot can be dynamically added to the dictionary, and the limited natural language can be converted into a standard BML command according to the dictionary to control the multi-robot system. In this way, the robot can execute motion-level commands, such as movement, steering, etc., and can also perform task-level commands such as enclosing, defense, etc. The experimental results show that the system composed of different types of robots can be commanded using the interactive framework proposed in this paper.
Crowd Density Estimation Using Taylor Expansion and Local Texture Feature

Crowd density estimation from images or videos is an important subject for crowd monitoring and safety control. In this paper, we propose a crowd density estimation method based on the Taylor expansion and the local binary count operator. Crowd density classification is performed using a support vector machine. Experiments on the PETS 2009 dataset are provided to demonstrate the feasibility of the proposed approach.
Comparison of digital image denoising method using various Transforms on Satellite Imagery

Satellite imagery normally affected by Gaussian, speckle, Poisson and salt & pepper noise that occurred in images of different background and qualities with random variation over the pixels of the images. Removal of these noises from a digital image is an important task for scientist and engineers. Image de-noising is a basic problem in digital image processing particularly satellite images. There are several image de-noising methods are proposed. The aim of this paper is to study various de-noising methods using wavelet transform. Further, a relative examination has been made between Daubechies, Haar, Symlet and coiflet wavelets to get most reasonable outcomes utilizing wavelets image de-noising techniques with reference to satellite pictures. Execution of image de-noising technique is estimated utilizing quantitative parameters, for example peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity index (SSIM).
Method for Author Attribution Using Word Embeddings

In this paper we look at a methodology of revealing an unknown document’s author through the use of extracting the author's characteristics from their writing style The method used explores identifying sources of unknown documents, using a model of distributive semantics to form a set of queries to a search engine. The dataset used is the PAN @ CLEF 2019 shared task on Cross-domain Authorship Attribution are in the following languages: English, French, Italian, and Spanish, each of which contains 5 problematic questions, which gives a total of 20 problematic questions. The problem relates to Natural Language Programming where the process is done through the attribution of the user that can be used to identify an author’s work. The method explores identifying sources of unknown document, using a model of distributive semantics to form a set of queries to a search engine. The method used to reveal the unknown authors is done through distributional semantics; this is based on the following hypothesis: the linguistic units that are observed in close contexts have similar semantic meaning, in this area when looking at linguistics this is calculated based on the proximity of linguistic elements in terms of semantic load based on their distribution in large textual boxes.
Hiding secured text data in audio signals using 3-LSB technique

Steganography is that the limit and examination of encompassing lined messages during a perspective that no one, nearby the sender and foreseen beneficiary, interfaces the part with the message, a kind of security through nonattendance of clearness. A lot of work has been developed for audio steganography. But there are various problems in the existing techniques. Existing frameworks utilize just word reference-based pressure method which can be supplanted by the mixture pressure systems to accomplish progressively precise outcomes. Existing frameworks have low SNR values which must be improved to utilize the framework in real circumstances. In the proposed work we have used the I-LSB Technique and Adaptive Huffman Compression Technique on audio signals to obtain secure stego-signal. I-LSB uses three bytes of the audio signal to hide the text data. The results are improved as compared to the existing approach which reflects that the new approach is better in terms of security and speed in data transmission.
Characteristics of Flood Water Level Based on Hydrologic Soil Group Analysis in Temef Watershed

Abstract Temef Watershed is located in the Benanain River Basin with a catchment area of 551.50 km 2 . River characteristics in the Temef Watershed are influenced by three main river streams from upstream to downstream. Conventional collection of data on watershed characteristics requires a relatively long process and time. However, when based on Geographic Information Systems (GIS), the cost and time will be more efficient. The floodwater level is affected by the amount of rainwater runoff that occurs in the Temef Watershed. The amount of runoff depends on the level of soil permeability and land cover. To determine the level of soil permeability, hydrogeological maps are used. Based on this, a study of the characteristics of flood waters from GIS-based hydrogeology is needed. The analysis method used ArcGIS overlay on hydrogeological maps, while data on watershed characteristics used the Archydro and HEC-GeoHMS features. The results of the analysis showed a level 3 permeability type with limestone lithology, solid volcanic breccia, sandy marlene, conglomerate, and alluvium consisting of sand and gravel having moderate to high permeability. The metamorphic and basalt rocks have low to moderate permeability and the scaly clay has low to very low permeability. This condition causes runoff that turn into flood streams.
Hot Money Inflow Monetary System and Inclusive Growth in Nigeria

This study examines the interactive effect of hot money inflows and the monetary system on inclusive growth in Nigeria. The structural vector autoregressive (SVAR) technique is employed to examine this interactive effect. Findings from the study reveal that inclusive growth is positively and significantly impacted by hot money inflows passing through the monetary system. The study therefore, recommends that policies aimed at attracting more inflows of short term capital such as interest rate policies, exchange rate deregulation policies, lower inflation targeting policies, targeting higher GDP growth rates, and the development of stock market structure are to be uncompromisingly pursued.
Review on Face Recognition by Machine Learning and Deep Learning Approaches

In these days face recognition is a latest approach for the security and authentication of the users in different fields. The main use of the face recognition is in the field of commercial sector and law enforcement applications. The automatic recognition system achieves great success in real time applications. In recognition system, the main limitation is to acquire the outdoor images and their properties are different in each image due to illumination. The main aim of this work is to understand and discuss the recognition process of similar faces. The other aspects related to the recognition system also discussed in this work. The work is based on the recognition from images and videos.
Random Vector Functional Link with Naive Bayes for Classification Problems of Mixed Data

A common approach to handle categorical attributes in neural networks is to use the one-hot encoding. Depending of the number of categorical attributes, and the number of levels that each one of these can have, will increase significantly the number of input nodes of a neural network. In this paper we analyze a type of randomized neural network called random vector functional link which has a non-iterative learning approach, in particular, it can be trained in one step. In order to avoid any time consuming preprocessing for categorical attributes, or increase the dimensionality of the input layer of the neural network, we propose a two-stage learning approach. In the first stage, we use the naive Bayes, that is also trained in one step, to compute the posterior probabilities for each class, using all the attributes. Then, in the second stage, we train a random vector functional link using as inputs the continuous attributes and including as additional hidden units the posterior probabilities obtained in the previous stage. We compare using benchmark datasets, the classification performance of the neural network with only continuous attributes versus the combination with naive Bayes to include the information of the categorical data. The experimental results show the effectiveness of the proposed approach for mixed data handling in random vector functional link networks.
Problematic Aspects of Studying Russian Language Abroad

Russia has a rich experience of cooperation with diff erent countries in the fi eld of culture, science, and education. The strategic goal of foreign policy is to popularize the Russian language. The most urgent problems of studying the Russian language abroad (in China, Turkey, Cyprus, Mongolia, Tajikistan, Belgium, Holland) have been analyzed: the complexity of the educational process, the necessity of logical thinking in the learning process, the essential features of the phonetic system and grammar. The modern approaches of teaching Russian language have been analyzed, including the use of tongue twisters, radio broadcasts, and curatorship of foreign students by native Russian speakers. New educational technologies (including theory of multiple intelligence) have the potential to reduce the negative impact of the identifi ed problematic aspects.
A Fuzzy Approach to Spatio-temporal Analysis for Pedestrian Surveillance

This article proposes a type-2 fuzzy system that works on the different spatio-temporal features (obtained by analyzing stills captured from surveillance videos) and uses it to differentiate a pedestrian from a human, a human from non-human etc. Existing systems propose a solution based on factors that can be influenced by several other environmental factors, for example data captured at infrared spectrum are influenced by weather conditions. Moreover, existing systems often neglect the differences between a human and a pedestrian. In this proposed work we propose a method specifying differences between non-human, human, and pedestrian as a case of human showing locomotion features in temporal domain. To achieve this, we have considered a type-2 fuzzy system because rather than calculating the probability of a subject being a human(human being pedestrian) we assess the subset of features that determine the subject’s chances of being a human (human’s chances of being a pedestrian), thereby emphasizing the need for a type-2 fuzzy system. Here the crisp output set is assumed to be {Pedestrian, Human, and Non-Human}. The achieved results are motivating towards these precise classifications and the use of fuzzy systems for the same.
Service Composition and Customization of Its Features based on Combined Classification

By promoting service-oriented architecture in e-services of organizations and inter-organizational relationships, service quality is more focused. To provide high quality combined service, it is necessary to identify quality requirements of users and offer service in line with those. Service users tend to choose a combined service among the huge collection of available services based on the quality of service. In competition among rivals, service providers must customize features of service as one of the key strategies. Customization involves the combination of service features based on user requests, these strategies raise new problems on the expression and dissemination of quality information, service identification and setting qualitative offers to service users. In the previous methods, pre-processing step was not performed in the services set, and false service suggestions to the user were possible. In this study, nearest neighbor algorithm is offered for identifying consumers and customize their quality of service. In addition, Isodata has been used to cluster and filter the services. At the end, a case study is presented to illustrate the proposed method. The results of the evaluation have shown that the proposed method has tried to solve the existing shortcomings.
Brief Introduction of Error Systems

In this chapter, we put error events and issues in the context of a system and treat X $$=$$ X ($$\{W_i\}$$, $$T(t_1$$, $$t_2)$$, J, GY, MG, R) as an object system, where $$\{W_i\}$$ is the set composed of all the issues associated with research objects; T is the set of conditions with $$t_1$$ stands for constrained conditions and the $$t_2$$ represents conditions borne by the issue; J is the set for all conclusions of research objects; GY is the set of intrinsic features of the system formed by all research objects; MG is the set of objective features of research objects; R is the set containing all relationships connecting all research objects. In the object system, concept and classification of error, operation and transformations of error system are investigated accordingly. Within the context of object system, we examine the critical subsystems, major subsystems, important subsystems, critical structures, major structures, important structures, critical elements, major elements, important elements, and independence of subsystems. Next, we discuss the theory and method for conducting system optimization which should be carried on a non-erroneous system. For a system without errors (or a system tolerating errors), a programming model is built where the expected overall intrinsic features are the objectives; each intrinsic feature contained in objective features is defined as an independent variable; and the system’s conditions are constraints. By solving the programming model, the value or the range of values of each intrinsic feature is obtained. Feature-based system optimization is then conducted given the value or the range of values of each intrinsic feature.
An Approach for Thwarting Malicious Secret Channel: The Case of IP Record Route Option Header-Based Covert Channels

The Internet constitutes actually one of the main communication platforms for cybercriminals and terrorists to exchange secret messages and hidden information. The use of clear or non-encrypted network traffic to communicate over the Internet allows steganalysis process and surveillance agencies to easily identify the presence of secret messages and hidden information, and classify the involved entities as potential cyber criminals or terrorists. However, covert channels can be an efficient and remedial communication solution for cybercriminals and terrorists to exchanged secret messages and hidden information. In fact, most covert channels attempt to send clear and non- encrypted messages embedded in the fields of network packets in order to offer robust communication channels against steganalysis. Nevertheless, covert channels are an immense cause of security concern and are classified as a serious threat because they can be used to pass malicious messages. This explains why detection and elimination of covert channels are considered a big issue that faces security systems and needs to be addressed. In this paper, a novel approach for detecting a particular type of covert channels is discussed. The covert channel uses the IP Record route option header in network IP packets to send secret messages and hidden information. The paper demonstrates that this type of covert channels is not robust enough against steganalysis. The proposed detection approach is based on the IP Loose source route option header. Conducted experiments show that the proposed approach is simple and straightforward to implement and can contribute to identifying malicious online activities of cyber criminals and terrorists.
A Memory Model for Visual Image Classification Based on Residual Neural Network and Bayesian Decision

Though traditional classification methods show well performance in classification tasks, most of them mainly lay emphasis on ‘classification’ rather than ‘cognition’. When a new object that has never been seen is encountered, the traditional methods falsely default the image as a certain category that has been studied, however, humans can first identify the image as new. In this paper, we present a memory model for visual images classification based on residual neural network and Bayesian decision (VICRB). First, the feature vectors of the visual images for each category are extracted with residual neural network and each feature component may be correctly copied or randomly produced. Then the processes about how the visual images represent, store and retrieve are modeled. The feature vector of test images is matched with the feature vectors of learned images and the likelihood ratio is computed according to probabilistic inference theory. Finally, the odds value in favor of an old over a new image is computed by all likelihood values. According to the odd value, the Bayesian decision rule is applied to the image classification. Experimental results on two benchmark images datasets show that the presented memory model performs well in images classification tasks.
PhysTech Development Strategy — 2024

In 2017, the Government of the Russian Federation approved the National Program "Digital Economy of the Russian Federation". Five basic directions for the development of the digital economy in Russia for the period until 2024 were identified. To implement the decision of the Government of the Russian Federation, a competitive selection was held for the organization and state support of the centers of the National Technological Initiative on the basis of educational organizations of higher education and scientific organizations. As a result of the competitive selection, it was decided to create the Center for the National Technological Initiative in the field of "Artificial Intelligence" on the basis of the Moscow Institute of Physics and Technology (MIPT). National Research University MIPT is among the 50 best universities in the world in natural sciences (THE). The article sets out the main points of the development strategy of the Center for the National Technological Initiative "Artificial Intelligence" for the period up to 2024. To implement the Program in the field of artificial intelligence, a consortium was formed, which included MIPT partners: enterprises of the real sector of the Russian economy, leading research organizations and National Research Universities. The center is also interested in collaborating with foreign companies and universities and implementing joint research and educational projects.
Multi Independent Steering System-based Robot to Help Persons with Disability

Abstract - Open research in the field of robotics encourages individual creativity to create creative solutions to help people with disabilities. Robots are technologies that can relieve or replace human power. The need to provide food for persons with disabilities is very important because of its limitations. Nurses are the general solution to the limitations of people with disabilities, but what happens in America is a frequent weather problem and brings nurses to help people with disabilities. So you need a tool that can replace the work of a nurse. The solution to this problem is to make robots that can replace several servants' tasks, such as taking, moving and putting food and beverage boxes from one place to another. Robotic maneuverability is an important part of the job. A multi-independent steering system is used to reduce the robot's movement space when maneuvering. Besides being able to maneuver, robots must be able to pick up and transport food and beverage boxes using a gripping system. The success rate of robots completing their assignments reached 93.75% of 112 tests. Keyword : robot, prototype, multi-independent steering, person with dissability
JPEG DECOMPRESSION USING BAYESIAN MAP APPROACH WITH SECURITY

JPEG decompression can be considered as a image reconstruction problem. Standard decompression of JPEG images produces disturbing checkboard pattern and also artifacts along edges. Such problems can be reduced by formulating decompression as an image reconstruction problem using Bayesian maximum a posteriori probability by iterative optimization algorithms. In this type of problem, the prior knowledge about an image is usually given by L1 norm of learned frame prior. Solution of the problem can be achieved very efficiently using alternating direction method of multipliers as iterative optimization algorithm. The proposed robust method of using Bayesian approach on transform matrix compression algorithm restores images very efficiently even for small image size without disturbing JPEG artifacts. In addition to this, for security an algorithm is presented for lossless image encryption using a private key, with variable bit wise rotation image encryption. This is resistance to any statistical related attacks and also fast and efficient for large amount of data. The quality of reconstruction by this approach is shown both visually and interms of SNR.
NEEDS ANALYSIS OF INFOGRAPHIC AS A LEARNING MEDIA FOR PHYSICS TEACHERS IN INDONESIA

Teachers are required to be professional teachers who have the skills and have basic competencies. One of which is the teacher must be keen in choosing and using the media by utilizing the innovation of educational technology that is sophisticated today. This study aims to identify the needs of infographic media for Physics lessons in high school. Earlier it began with an analysis of the availability of Physics lesson media at a school located in Sambas district, followed by interviews with 25 Physics teachers on infographic media needs. This study is a simple descriptive survey. Data were collected using interview techniques and analyzed qualitatively. Membercheck is used to ensure the validity of the findings of this study. The results showed that based on experience, learning styles of students, and the principle of necessity states that Physics teachers in Sambas district need innovation, especially in selecting and using learning media. All teachers said they really need and are interested in using infographic. They hope to use it to facilitate students in understanding the Physics concept, both in and outside classroom.