Reviewerly/exHarmony huggingface dataset:
procd: A Privacy-preserving Robust Implementation to Discover Contacts in Social Networks

Current instant messengers store the users’ phone book contacts typically unencrypted or hashed on a central server. In case of a server’s corruption, all contacts are either directly available in plaintext or can be unmasked using a simple dictionary attack. To solve this problem, we present procd [pʁoːst], a python implementation for privacy preserving contact discovery. procd is a trustless solution that requires neither plaintext numbers nor hashes of single phone numbers to retrieve contacts. Instead, we transfer hashed combinations of multiple phone numbers, which increases the effort for dictionary attacks to an unfeasible level using today’s hardware.
APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time.&#x0D; Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox.&#x0D; Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures.&#x0D; Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc.&#x0D; Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.
A Solution to Text Hiding in Media with Hybrid Gravity Search Algorithm and Transposition Scheme

This paper suggests a new solution to conceal text messages in media by hybridizing a meta-heuristic algorithm of the Gravity search algorithm (GSA) and transposition method. Several stages are implemented, namely the concealment stage, extract stage, and evaluation metrics, whereas cover text and message text are split into blocks, and each block contains one letter, relying on the fitness value of related notes both of secret information and cover message. The best positions of the letters are taken optimization by the adjusting GSA that is used to hide the confidential message. The steganography process is then carried out through the transposition process between the secret and cover message. While in the extraction stage, the fitness value of steganography message letters is found by using GSA to determine the positions that represent the secret letters. Experimental results of the proposed scheme are compared with the other in the literature which shows that the proposed approach presents robust security, provides high capacity and resistance against several steganalysis attacks.
Elements of language creativity

Abstract This paper proposes a concept of creativity that stems from a semiotic and linguistic theoretical perspective, in which the formal frame of reference for variation and linguistic change considers and evaluates both the process of general interaction and the contact of languages as a global phenomenon. This method proposes an analysis of creativity that ranges from reflections of ancient philosophy to a contemporary linguistic perspective, incorporates international ideologies, and identifies, within the dimensions of use and social sharing, the principle capable of guiding potentially unlimited forms of linguistic creativity that are self-expressive and communicative, far beyond the grammatical patterns of regularity and norm. Interpreting the paradigm of creativity according to this model means placing the semiotic property in a position of prima inter pares , entrusted not only with the “role” of forming signs (words, sentences, texts), but the function of arbitraire , as a phenomenon of language creation. The following reading references the semiotic contribution of Tullio De Mauro, an Italian linguist who has contributed to the systematization of creativity, overcoming and synthesizing both Saussurian structuralism and Chomskyan generativism.
Circle Feature Extraction from Remote Sensing Satellite Images Based on Polar Coordinate Representation of Wavelets

Circle feature extraction method from remote sensing satellite images based on polar coordinate representation of Wavelets is proposed. The conventional two dimensional wavelet transformation is not effective to extract arc and circle features form the imagery data. In order to enhance the capability in an effective manner, a polar coordinates of representation of wavelet transformations is proposed. The proposed paper describes the proposed method of the aforementioned polar coordinates of representation of wavelet transformations followed by examples of the application of the method for circle feature extraction from Earth observation satellite data of Advanced Very High Resolution of Radiometer; AVHRR onboard National Oceanic and Atmospheric Administration: NOAA satellite. The results are compared to the conventional two dimensional wavelet transformations.
Cross Entropy Optimization of Action Modification Policies for Continuous-Valued MDPs

We propose an algorithm to search for parametrized policies in continuous state and action Markov Decision Processes (MDPs). The policies are represented via a number of basis functions, and the main novelty is that each basis function corresponds to a small, discrete modification of the continuous action. In each state, the policy chooses a discrete action modification associated with a basis function having the maximum value at the current state. Empirical returns from a representative set of initial states are estimated in simulations to evaluate the policies. Instead of using slow gradient-based algorithms, we apply cross entropy method for updating the parameters. The proposed algorithm is applied to a double integrator and an inverted pendulum problem, with encouraging results.
Value Analysis of BIM Cloud Platform in Project Information Integration and Collaborative Work

Construction project management is a complex and tedious task that runs through the entire construction project production activities and involves a large number of information resources, which are also the basis of construction project management. Based on this, in order to ensure the effectiveness of construction project management, project management concepts are gradually applied to it, to improve the effectiveness and standardization of construction project management, and to ensure that the project construction is in the best state. However, in the process of integrated application of project management concepts in construction engineering management, through the use of BIM cloud platform in project information integration and collaborative utilization of modern management concepts, management methods, management methods, etc., the level of construction engineering management has been improved and the construction has been realized.
Quality Analysis the E-PASTI System to Determine the Condition of the System and Information Quality

Abstract The purpose of this study is to evaluate the quality of secretarial, financial and staffing information systems at the Directorate General of AHU, known as the E-PASTI system. There are two variables analysed, namely system quality and information quality. Data collection using questionnaires that distributed to 123 employees who used the E-PASTI system but returned 93 questionnaires. Data quality analyst uses descriptive quantitative method. The results of the study are several conditions related to the system and information quality of E-PASTI. The quality can be a reference for the AHU Directorate General to improve the quality of E-PASTI that suits the needs of employees.
HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data

Recent work based on Deep Learning presents state-of-the-art (SOTA) performance in the named entity recognition (NER) task. However, such models still have the performance drastically reduced in noisy data (e.g., social media, search engines), when compared to the formal domain (e.g., newswire). Thus, designing and exploring new methods and architectures is highly necessary to overcome current challenges. In this paper, we shift the focus of existing solutions to an entirely different perspective. We investigate the potential of embedding word-level features extracted from images and news. We performed a very comprehensive study in order to validate the hypothesis that images and news (obtained from an external source) may boost the task on noisy data, revealing very interesting findings. When our proposed architecture is used: (1) We beat SOTA in precision with simple CRFs models (2) The overall performance of decision trees-based models can be drastically improved. (3) Our approach overcomes off-the-shelf models for this task. (4) Images and text consistently increased recall over different datasets for SOTA, but at cost of precision. All experiment configurations, data and models are publicly available to the research community at horus-ner.org
Negotiation Based Resource Allocation in Cloud Computing Scenarios

Cloud computing has received a lot of consideration due to on-demand provisioning of resource-intensive applications. However, it is logical to assume that a single Cloud Service Provider (CSP) may not be able to satisfy the request of multiple Cloud Service User (CSU) as it may not have infinite resource in its Data Center (DC). Hence, resource bargaining or negotiation among CSUs and CSPs shall ensure optimal management of resources considering CSUs needs. In this paper, we propose negotiation based resource allocation to analyze percentage of successful job executions and resource utilization. Further, the proposed work paves the way for enhancements in performance of Cloud applications.
Teoreticheskiye osnovy formirovaniya kompetentsii sotsial'no-professional'nogo vzaimodeystviya u budushchikh bakalavrov sotsial'noy raboty

The purpose of the work is to theoretically substantiate the system of professional training of future bachelors of social work and analyze the main trends in modern education in Russia. On the basis of theoretical and empirical research methods, the author presents a comparative characteristic of qualification requirements in federal state educational standards, identifies trends that provide modern professional training for bachelors of social work. The results obtained create the basis for scientific and methodological support of the educational process aimed at optimizing the system of professional training of future bachelors of social work, and can be included in the educational practice of Russian universities that provide professional training for bachelors of social work. The article is addressed to teachers leading the training of students studying in the areas of a social profile, as well as specialists working in institutions of social protection and social services for the
RECONSTRUCTION OF HIGH-RESOLUTION HEPATIC TUMOR CT IMAGES USING AN AUGMENTATION-BASED SUPER-RESOLUTION TECHNIQUE

Improving the resolution of medical images is crucial in diagnosis, feature extraction, and data retrieval. A significant group of super-resolution algorithms is multi-frame techniques. However, they are not appropriate to medical data since they need several frames of the same scene, which bring a high risk of radiation or require a considerable acquisition time. We propose a new data augmentation technique and employ it in a multi-frame image reconstruction algorithm to improve the resolution of pathologic liver CT images. The input to our algorithm is a 3D CT-scan of the abdominal region. Neighboring slices are considered to increase the resolution of a single slice. Augmented slices are prepared using the nearby slices and the interpolation approach. The new data is aligned to the original slice, and it is used as an augmented version of the data. Then, a multi-frame scheme is utilized to reconstruct the high-resolution image. Our method’s novelty is to remove the need for multiple scans of a patent to employ multi-resolution techniques in medical applications. The results reveal that the proposed method is superior to conventional interpolation methods and available augmentation techniques. Compared to the tricubic interpolation, the proposed method improved the PSNR by 3.1. Concerning conventional augmentation techniques, it enhanced the SSIM measure by 0.06. The proposed algorithm improved the SSIM by 0.11 compared to traditional interpolation techniques and 0.1 for recent researches. Therefore, a multi-frame super-resolution technique has the potential to reconstruct medical data better.
Small Scale Face Detection Method in Dense Scene

To address the problem of low detection accuracy that still exists for small-scale face detection in dense scenes, this paper presents a face detection algorithm based on neighborhood system theory, which uses existing image processing techniques and logical relationships between neighborhoods. The face neighborhood and the head neighborhood of the human body are used as a neighborhood chain. And based on the correlation between them, a simple and easy-to-implement detection technique is used to extract the features in the different neighborhoods, respectively. The idea of the algorithm is similar to the correlation in big data thinking. The detection method is based on manual experience, easy to use with strong logical rules, avoids the learning process of large samples, and has low performance requirements on experimental equipment. The MATLAB experimental results show that the Precision is 88.8% and the Recall is 90.0%. It proves that the method can have a good accuracy even without relying on machine learning and only relying on simple manual experience.
An Improved Embedding Model for Zero-Shot Classification based on Attention Mechanism

It is well-known that the auxiliary information plays a key role in zero-shot classification. However, most of the existing popular methods do not make effective use of auxiliary information. To address this issue, we propose an improved embedding model for zero-shot classification based on attention mechanism, called EMAM. In the proposed EMAM, we first add an attention mechanism to effectively extract the key information of auxiliary information in zero-shot classification. Then optimizes the objective function to improve the recognition rate of this model. Finally the experimental comparison is implemented on the standard zero-shot learning datasets. The experimental results demonstrate that our proposed EMAM not only verifies its validity, but also achieves good results.
Sparse Gaussian Mixture Model Clustering via Simultaneous Perturbation Stochastic Approximation

In this paper the problem of a multidimensional optimization in unsupervised learning and clustering is studied under significant uncertainties in the data model and measurements of penalty functions. We propose a modified version of SPSA-based algorithm which maintains stability under conditions such as a sparse Gaussian mixture model. This data model is important because it can be effectively used to evaluate the noise model in many practical systems. The proposed algorithm is robust to external disturbances and is able to process data sequentially, “on the fly”. In this paper provides a study of this algorithm and its mathematical justification. The behavior of the algorithm is illustrated by examples of its use for clustering in various difficult conditions.
Modelado e implementación de un sistema distribuido para la simulación de reacciones químicas

This article describes the process of analysis, design and implementation of a distributed system consisting of a suite of applications, in which there is a web, a desktop and a mobile and two servers that perform the processing for the optical recognition of patterns, 2D and 3D simulation, session management and also provide the information of the periodic table. This distributed scheme establishes the frame of reference for the modeling, representation and simulation of chemical reactions through compounds and elements of the periodic table, for the support of classroom activities in the chemistry laboratories of the Educational Institutions that offer these subjects . The development of the software required the implementation of the agile SCRUM methodology for the production of the suite, which will help users through simulations to understand and understand the interaction of the elements with each other, providing visual support fundamental for the understanding of topics such as valence electrons and chemical bonds, which allows the development of the skills described in the graduation competencies of these Institutions.
Identifying negative language transfer in learner errors using POS information.

A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.
Educational STEM-project «Mini meteostation»

The reasons for the need for training highly qualified personnel in the world are identified. The role of science and pedagogy in the preparation of highly qualified specialists implementing technological processes. The main issues and suggestions for their solution are proposed in the article. Skilled professionals with skills of the 21st century and their training in the modern conditions of existence of some countries. Skills of the XXI century: digital, communicative competence, creativity and high productivity of activity and their formation in the modern educational process. STEMeducation is one of the approaches that allows you to prepare children for the actively progressing development of technology and not only. The introduction of the main directions of STEM-education in Malaysia, Indonesia, the United States of America, and Ukraine is highlighted. Countries have different levels of economic development and different potentials for the development of education. But each of the certain states embodies approaches consistent with the principle of STEM-education. Examples of the implementation of STEM-education in the form of the implementation of STEM-projects in the above states are given. In Malaysia, the stage of introducing STEM-education through the implementation of integrated learning in educational institutions of the country. Indonesia introduces STEMeducation through the implementation of STEM-projects in which the integration of individual branches of science and technology is implemented. The United States is introducing targeted STEM-projects that attract professionals, teachers, and children. Ukraine introduces STEM-education through the implementation of STEM training projects or research projects that integrate several academic disciplines. The article presents, as an example, an educational STEM-project for sixth graders “Mini Weather Station”. The purpose of which is to impoverish knowledge from several academic disciplines than to show the integration of knowledge as a whole. This STEM-project allows us to identify in children a tendency to design, invent, protect nature, organizational abilities and others.
Bag of Science: A Query Structuring and Processing Model for Recommendation Systems

Technological advancements and the changing needs drive the process workflow, meeting the need-of-the-hour requirements, and calibrating system components. While the perception evolves, the fundamental principles stay put and wrap around generational disparities. In a changing scenario of the physical market to an e-commerce site, the recommendation systems have had substantial roles. The present systems customarily use the item or user profiles for recommendations. The existing recommendation systems rely heavily on data and learning algorithms. An improved recommendation system given by considering the query’s semantics rather than using only historical data of numerous worldwide queries can create a paradigm shift in the technologies involved in computer recommendations. Bag of science attempts to take on this challenge. The model dwells on inferring a query’s meaning in all contexts to create an order in which the words relate. By constructing a word definition graph, the methodology explores the possibility of enhancing the recommendation systems to improve the e-commerce platforms’ business. The paper presents the model’s architecture with its chief components, including a parser and scraper, graph generator, graph traversal, and results. The model presents the traversal results and analysis of the constructed e-commerce graphs using hops as the threshold metric. The paper also presents the model’s abstract data type to make it applicable and extend to other domain contexts that involve query engines and need recommendations.
Research on Image Detection Algorithm Based on Improved Retinanet

In many applications, existing image detection algorithms detect small targets in complex scenes with low recognition rate and biased classification. To address this issue, we develop a multi-target detection algorithm based on improved Retinanet. It consists of the following four parts: 1) extract rich texture features using the information interaction module; and 2) extract high-level abstract features through the improved FPN+ module; and 3) make full use of contextual information for detection leveraging SSH detection head; and 4) adopt the weighted loss for regression. For the accuracy of large and small categories and avoid bias, we employ the ensemble of loss function to optimize parameters. The experimental results confirm the feasibility of our detection algorithm, with the improved performance on four metrics. The mAP50 value and mmAP value increase to 96.75% and 75.08% respectively, and the ACD value decreases by 0.907.