Reviewerly/exHarmony huggingface dataset:
MemFRCN: Few Shot Object Detection with Memorable Faster-RCNN

At this stage, research in the field of Few-shot image classification (FSC) has made good progress, but there are still many difficulties in the field of Few-shot object detection (FSOD). Almost all of the current FSOD methods face catastrophic forgetting problems, which are manifested in that the accuracy of base class recognition will drop seriously when acquiring the ability to recognize Novel classes. And for many methods, the accuracy of the model will fall back as the class increases. To address this problem we propose a new memory-based method called Memorable Faster R-CNN (MemFRCN), which makes the model remember the categories it has already seen. Specifically, we propose a new tow-stage object detector consisting of a memory-based classifier (MemCla), a fully connected neural network classifier (FCC) and an adaptive fusion block (AdFus). The former stores the embedding vector of each category as memory, which enables the model to have memory capabilities to avoid catastrophic forgetting events. The final part fuses the outputs of FCC and MemCla, which can automatically adjust the fusion method of the model when the number of samples increases so that the model can achieve better performance under various conditions. Our method can perform well on unseen classes while maintaining the detection accuracy of seen classes. Experimental results demonstrate that our method outperforms other current methods on multiple benchmarks.
Motivational program-targeted management of education quality: from model to implementation

The article discusses the main aspects of the development of a methodology for the formation of an education quality management system: motivating monitoring of education management bodies, a model for analyzing data on the education management system, and a single federal personnel reserve of education system leaders. The developed methodology is based on a model of motivational program-targeted management of the quality of education. The system of motivational program-targeted management is the result of the development and integration of global areas of world management science - rationalistic and behavioral. The motivational component made it possible to enrich the traditional model of the management cycle with socio-psychological principles of motivational management and structure the management cycle based on methods and mechanisms of motivational management, which is reflected in the results of the model implementation.
The Problem of Convergence of Classifiers Construction Procedure in the Schemes of Logical and Algorithmic Classification Trees

The paper considers the problem of convergence in the procedure of classifier schemes synthesis by methods of logical and algorithmic classification trees.It suggests the upper evaluation of complexity for the scheme of algorithms tree in the problem of approximation of real data array by a set of generalized features with a fixed criterion of termination of the branching procedure at the stage of the classification tree construction.This approach provides the required accuracy of the model, evaluates its complexity, reduces the number of branches, and achieves the required performance features.The paper represents the evaluation of convergence for the procedure of recognition schemes construction is logical and algorithmic classification trees on conditions of weak and robust separation of primary initial sampling classes.
Accountability And Transparency in The Industrial Revolution 4.0 Era at Private Universities in Makassar

The Industrial Revolution 4.0 results from increasing demands for accountability and transparency for all organizations, especially private universities organizations.The research aims to change the organizational design paradigm in implementing accountability and openness at private universities in Makassar.In this 4.0 industrial revolution, adaptive power and responsiveness are the keys to achieving the vision and mission of private universities.It is a challenge for private universities to adapt to various problems in this era of disruption.Organizational transformation of private universities is one alternative to realize public services-oriented and prepare competent human resources in their fields.This research offers a solution for private universities in Makassar to determine how far universities are ready to face this era.This study uses a descriptive method with a qualitative approach.The result using purposive sampling and snowball data sources show that the transformation of higher education in Makassar must immediately adapt to the demands of the industrial revolution 4.0, recommended to improve the management of information technology services and the application of information security management to be more effective, increase the adaptive power of the industrial revolution 4.0.
Cross-Lingual Transfer Learning for End-to-End Speech Translation

End-to-end speech translation (ST) is the task of directly translating source language speech to target language text. It has the potential to generate better translation than those obtained by simply combining automatic speech recognition (ASR) with machine translation (MT). We propose cross-lingual transfer learning for end-to-end ST, where the model parameters are transferred from the ST pretraining stage for one language pair to the ST fine-tuning stage for another language pair. Experiments on the CoVoST 2 and multilingual TEDx datasets in many-to-one settings show that our model outperforms the model that uses English ASR pretraining by up to 2.3 BLEU points. Through an ablation study investigating which layer of the sequence-to-sequence architecture contains important information to transfer, it was demonstrated that the lower layers of the encoder contain language-independent information for cross-lingual transfer. Extensive studies were conducted on (1) ASR pretraining language, (2) ST pretraining language pair, (3) multilingual methods, and (4) model sizes. It was demonstrated that (1) Using the same language as the ASR pretraining language and the ST fine-tuning source language results in good performance. (2) A high-resource language pair is a good choice for the ST pretraining language pair. (3) The proposed method works well in conjunction with multilingual methods. (4) The proposed method can operate with different model sizes.
Pedestrian detection algorithm based on Faster RCNN

Pedestrians are the most frequent targets in video surveillance and vehicle camera shooting, and the safety of pedestrians is the most concerned issue of social and public safety.In order to avoid accidents and traffic accidents caused by dense personnel, real-time detection of pedestrians on the street is particularly important.For pedestrian detection in various actual scenes, accuracy and real-time have always been the key indicators.Aiming at the difficulty of target detection in video, this paper adopts fast r-cnn algorithm for pedestrian detection, solves the problem of real-time pedestrian detection in video, and improves the accuracy of detection.
Conversation in WhatsApp Media-Assisted Distance Learning: A Study of Directive Speech

Conversations between teachers and students in learning via whatsapp often occur lately because learning has turned to being brave.Speeches from teachers and students that often occur when communicating via whatsapp are directive speech acts.Directive speech acts are speech acts that have the intent of the narrative conveyed to the speech partner to do something such as ordering, advising, pleading, and so on.The purpose of this study is to describe the forms of directive speech acts.These speech forms are analyzed with the aim that teachers and students can create good communication so that the teaching and learning atmosphere becomes more active.This study uses a descriptive method with a qualitative approach.The data obtained are described in the form of words.The data in this study are directive utterances in the conversations of teachers and students in the whatsapp group.Objective data objectively with the technique of listening, recording, recording.Then the data were analyzed using the agih method because it is a determining tool of the language being studied.This study uses the agih method to find the form of directive speech acts that occur in teacher and student conversations in whatsapp groups.The agih method used in this study was applied with the basic BUL technique and the advanced lesap technique.The results of this study found that directive speech is described in several forms of directive speech acts.Based on the research that has been done, it can be found that as many as 17 directive speech act data were found in teacher and student conversations in media assisted whatsapp during September and October 2021.The forms of directive speech acts are divided into eight parts, namely the use of commanding, asking, inviting, instruct, inquire, advise, suggest, remind.
Mitigating to user cold-start issue in Recommendations System

This research paper uses a brand new popular model to resolve users' side issues within the recommendation process.the primary cold problem occurs when the target user doesn't have a rating history within the system.Today, the recommendation system has been utilized in various fields.However, they still suffer from various ailments, including cold sores and sparsity problems.The aim of the model is to recommend the highest five items to the user and therefore the performance of the model is evaluated.
Design principles of an open-source language modeling microservice package for AAC text-entry applications

We present MozoLM, an open-source language model microservice package intended for use in AAC text-entry applications, with a particular focus on the design principles of the library.The intent of the library is to allow the ensembling of multiple diverse language models without requiring the clients (user interface designers, system users or speech-language pathologists) to attend to the formats of the models.Issues around privacy, security, dynamic versus static models, and methods of model combination are explored and specific design choices motivated.Some simulation experiments demonstrating the benefits of personalized language model ensembling via the library are presented.
An Improved DBN Method for Text Classification

Traditional Deep Belief Networks (DBN) is prone to the problems of too long training time and falling into local extreme values when dealing with text classification. Therefore, the DBN is improved by introducing adaptive learning rate and additional momentum items, and is invested in text classification tasks: Propose a Deep Belief Network (LMDBN) based on an adaptive learning rate-additional momentum term. The algorithm can make the convergence of the network tend to the global minimum, and ensure that the convergence process is gentler and more stable, and at the same time accelerate the convergence process of the network. Experimental results show that the LMDBN network is better than the DBN in terms of classification accuracy and convergence time. Compared with several other traditional classification models, the LMDBN network also shows good classification performance.
Automation of the conversion of natural language to formalized node of knowledge record

The paper describes the improvement of the system for automatic question answering based on the knowledge expressed in natural language sentences. The system is implemented using a relational database. This system will be the basis for the development of a web application for obtaining answers to the posed questions. In order for natural language sentences to be entered into a relational database, they must be prepared and formally recorded. The development of a question-answering system is based on the application of the conceptual framework Node of Knowledge (NOK), whose formalized record is suitable for input to a relational database from which answers to questions can be obtained. This paper presents an application for automated conversion of English sentences into a formalized record. The application was tested on one hundred simple English sentences, and the results of the automated conversion were compared with those obtained by processing the same sentences manually. Ključne riječi Node of Knowledge (NOK), Formalized Node of Knowledge (FNOK), Knowledge Representation, Question-Answering (QA) system
Density Based Traffic Control System with Convolutional Neural Network

We present a real-time traffic flow controller structure that can keep traffic under control using image processing techniques. In this way, a camera is used in every section of the robot to take pictures of the traffic where traffic jams will appear. The number of vehicles in these images is designed using image processing tools. In the proposed image, green and red signals are represented using LEDs and the diminished green signal supervisor is signified by a specific presentation.
An improved approach to resolve a combinatorial optimization problem based CoronaVirus Optimization Algorithm

Combinatorial optimization problems refer to intractable problems that can’t be performed using exact methods. The resolution of combinatorial problems geared towards the application of heuristics, metaheuristics also matheuristics, in order to provide good enough approximations. As exact methods provide resolution corresponding to small problem scale, the approximation methods target large scale of complex problems. Metaheuristics are used to deploy intelligent methods to solve complex problems in a reasonable amount of time. The performance of a metaheuristic is improved by means of parameters adjustment as well as, hybridization within heuristics, iterative improvement methods or various metaheuristics. The cooperation of several optimization algorithms leads to improve resolution, also to overcome the limitations reported in resolving NP-hard problems. The resolution of complex problems, is thus constrained by stagnation on local optimums, as the optimization process is possibly stagnant on a specific search space region. In fact, traveling salesman problem is a combinatorial problem, that arises problematics related to the efficiency of its resolution methods. The aim of this work is to investigate on the improvement of a new bio-inspired method so-called coronavirus optimization algorithm in order to provide improved resolutions to traveling salesman problem. Various intelligent approaches are investigated and hybridized within coronavirus optimization algorithm, namely random replicate operator, elitist selective replicate operator, iterated local search, stochastic hill-climbing also improved self-organizing map. The numerical results are obtained using symmetric TSPLIB benchmarks.
Petrochemical features of acid volcanism of the Sablegorsk Complex (Subpolar Urals)

The study of the composition of the rock using petrochemical, geochemical and other analytical studies makes it possible to obtain quantitative information about the content of chemical elements, more accurately diagnose the object under study and create an idea of its genesis. Acidic volcanics of the sablegorsk subvolcanic complex (Subpolar Urals) are represented by rhyolites, trachyrhyolites, rhyodacites, trachydacites and dacites with high silicon contents, up to ultra-acidic varieties, from low-alkaline to moderately alkaline calc-alkaline series of rocks. By the nature of their concentration, trace elements have a very similar distribution with inner continent.
Practice-oriented tasks as a means of forming schoolchildren’s economic literacy: problems and opportunities

The study is devoted to the problem of forming economic literacy of schoolchildren. In the text-books specializing on preparing for the State Examination there are many practice-oriented tasks that contain the information contradicting the economical reality. Here we innumerate several contradictions and propose methods of improving such tasks in order to form the correct economic literacy. In our opinion, mathematical problems presented in this article can help to improve students’ mathematical knowledge, make them confident in making decisions in the financial sphere, and even attract their attention to ecological problems.
DESIGN OF THE FUNCTIONAL ENTITIES OF THE BloHeS SYSTEM

The increased number of Internet services and the exposure of the personal information to the public network, create a problem with the privacy of the personal data. This problem is extremely impactful in the healthcare systems where the sensitive medical data circulate in the network. The emerging Blockchain technologies offer properties and tools for better personal data access management. The integration of the Blockchain technologies into the healthcare systems can provide better data privacy. However, beside the attractive properties, the current Blockchain technologies cannot achieve sufficient scalability to serve a global public healthcare system and cannot provide compatibility with the GDPR and HIPAA regulations. To overcome these limitations, this paper introduces new deducted Blockchain-based public healthcare system, the BloHeS, with main focus on several functional entities, such as optimal consensus mechanism, better storage organization, improved network organization and compatibility with GDPR and HIPAA regulations. It also defines a procedure for patient-centric data access management, protocols for Island management and new block structure.
Unsupervised MRI Images Denoising via Decoupled Expression

Abstract Magnetic Resonance Imaging (MRI) is widely adopted in medical diagnosis. Due to the spatial coding scheme, MRI image is degraded by various noise. Recently, massive methods have been applied to the MRI image denoising. However, they lack the consideration of artifacts in MRI images. In this paper, we propose an unsupervised MRI image denoising method called UEGAN based on decoupled expression. We decouple the content and noise in a noisy image using content encoders and noise encoders. We employ a noising branch to push the noise decoder only extract the noise. The cycle-consistency loss ensures that the content of the denoised results match the original images. To acquire visually realistic generations, we add an adversarial loss on denoised results. Image quality penalty helps to retain rich image details. We perform experiments on unpaired MRI images from Brainweb datesets, and achieve superior performances compared to several popular denoising approaches.
Development of recommendation systems using game theoretic techniques

In the present work, we inquire the use of game theoretic techniques for the development of recommender systems. Initially, the interaction of the two aspects of the systems, query reformulation and relevance estimation, is modelled as a cooperative game where the two players have a common utility, to supply optimal recommendations, which they try to maximize. Based on this modelling, three basic recommendation methods are developed, namely collaborative filtering, content based filtering and demographic filtering. The different methods are then combined to create hybrid systems. In the weighted combination, the use of game theoretic techniques is extended, as it is modelled as a cooperative game. Finally, the methods are combined with the use of a genetic algorithm where game theory is used for the parent selection process. Our work offers a baseline for the efficient combination of recommendation methods through game theory and in addition the novelty method, Choice by Game, for the parent selection process in genetic algorithms which offers consistent performance improvements.
Aircraft Trajectory Tracking Method Based on Terminal Angle Constraint and Inverse Proportional Guidance Obstacle Avoidance

Aiming at the issue of autonomous path tracking and obstacle avoidance of aircraft, in order to suppress the significant change of the speed direction during the path tracking process, the terminal angle constraint item is introduced into the guidance law to realize the smooth tracking of each path point. In addition, based on the relative positional relationship and movement direction between the aircraft and the obstacle, the obstacle avoidance guidance law is established in the target line of sight coordinate system in this paper. At the same time, the speed control item in the process of avoiding obstacles has been added. Through simulation, the aircraft which manipulated by the guidance law can achieve smooth tracking of the preset path and effectively avoid unexpected obstacles in the path as well.
A VIRTUAL MUSEUM AS AN EDUCATIONAL TECHNOLOGY

The research conducts a historical and cultural review of neo-Kantians’ division of science into humanities and natural sciences. However, that division is not relevant in the 21st century. The author considers the reasons for the demand for specialty “Engineering and Arts”. Using the example of modern music, a new type of organization of opera material, which has changed the listening experience and general communication between expert and nonexpert, can be traced. Such transformations took place in other spheres as well – education, science, and culture. According to the author, digital tools and media modified the methods of presentation, dissemination, and production of scholarly knowledge and culture. Cultural analytics as a new methodology for studying modern culture, which was proposed by media researcher Lev Manovich, is emphasized. Consequently, it is noted that the use of new technologies in art contributes to the emergence of innovative discourse, and one of the promising research programs in this area is Digital Humanities.
Computer Vision Based Roadside Traffic Convex Mirror Validation for Driver Assistance System

On the roadside, there is a plethora of information that can aid drivers in driving safely. One such thing is a traffic convex mirror which are usually found in T-junctions, turns and parking areas. The Advance Driver Assistance System (ADAS) will play a key role in detecting such mirrors and alerting the driver in advance. At present, there is no such function available in driver assistance systems which can analyze the information available in traffic convex mirrors for autonomous and semi-autonomous vehicles. Thus, it is currently not possible to extract information that is readily available to the driver. However, every traffic convex mirror on the road is not useful for the driver, so in this paper, a computer vision-based solution is provided to identify a valid convex mirror which extracts and provides only the required information to the driver. The ADAS camera can detect the presence of a convex mirror on the roadside. This convex mirror provides information of the incoming objects and obstacles in a blind spot that is not in the direct field of view. By analyzing the region which is present inside the mirror for oncoming road participants, it is possible to determine whether the mirror is useful for driver assistance or not.