Reviewerly/exHarmony huggingface dataset:
A Survey of Few-Shot Learning and Its Application in Industrial Object Detection Tasks

Few-shot learning aims to obtain a better deep learning model through training with a smaller amount of data. In recent years, with the development and application of deep learning, deep learning has faced the problem of few-shot in many scenarios. Therefore, the study of few-shot learning will promote the application and deployment of deep learning in the industrial field. First of all, this survey specifically introduces two few-shot learning implementation methods: based on data augmentation and based on transfer learning. Then, this survey lists actual cases to illustrate the application of few-shot learning in industry. One case is about Few-shot parts surface defect detection based on metric learning. Another case is about poultry egg detection in few-shot based on meta learning. Finally, this survey summarizes the research achievements and research value of few-shot learning. Survey summarizes the problems and challenges faced by few-shot learning and the future development direction of few-shot learning.
Building a Blockchain-Based Social Network Identification System

With the rise of social network service (SNS) in recent years, the security of SNS users’ private information has been a concern for the public. However, due to the anonymity of SNS, identity impersonation is hard to be detected and prevented since users are free to create an account with any name they want. Until now, there are few studies about this problem, and none of them can perfectly handle this problem. In this paper, based on an idea from previous work, we combine blockchain technology and security protocol to prevent impersonation in SNS. In our scheme, the defects of complex and duplicated operations in the previous work are improved. And the authentication work of SNS server is also adjusted in order to resist single-point attack. Moreover, the smart contract is introduced to help the whole system runs automatically. Afterward, our proposed scheme is implemented and tested on an Ethereum test network and the result suggests that it is acceptable and suitable for nowadays SNS network.
Phishing Detection using Machine Learning

Abstract:- Email is a formal way of communication which is widely used. Mostly emails are used in offices for transferring sensitive data like credentials, work related documents etc. A hacker can perform a phishing attack by sending a spam email to retrieve the confidential information. Cyber criminals can use this data in a way to degrade the company by selling our data. This creates a huge loss. To prevent this type of attacks we are using machine learning. Naïve Bayes algorithm can be used to detect whether an email is spam or ham. We have also used several algorithms to test the accuracies. Several stages like dataset collection. data preprocessing and training algorithm are being performed, The accuracy is 98.33% when naïve bayes is used. Key words:- Naïve Bayes, Training, feature extraction, ham ,spam, phishing.
Symbolic Control System for a Mobile Robotic Platform Based on SOAR Cognitive Architecture

Problem statement: It is important to be able to share various types of information between robot modules, including high-level data received from the user and low-level signals from the sensors. This paper describes an implementation of such a system that uses semantic networks as a generic representation providing interpretability of instructions and flexibility in parameterization. Purpose of research: development of a robotic control system based on standard libraries for logical processing that can execute complex commands including high level instructions derived from human speech that involve objects, spatial relations and clarifying information. Results: The proposed semiotic control system consists of a database that stores knowledge about the robot's environment and its behavior using the RDF data model, inference tools, a pipeline of filter modules and the corresponding interfaces. SOAR provides a standard representation of facts and rules. The external interface of the control system based on the semiotic model transforms natural language commands into semantic networks. It can be easily expanded to support other input signals as eyetracking or encephalograph due to the ability to include clarifying information as extra nodes of the intermediate semantic network. The developed system can execute both direct control commands such as movement in a specified direction, and more complex procedures like moving to the named object with a heuristic choice between alternatives and parameterization of the trajectory. Practical significance: Using natural language voice commands, intonation, and looking at the target is essential for effective operator interfaces with mobile platforms and has applications in many areas including assistive and service robots. Using standard implementations and frameworks for logical processing makes the system more reliable, efficient and easier to understand.
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR

This paper proposes InterAug: a novel training method for CTC-based ASR using augmented intermediate representations for conditioning. The proposed method exploits the conditioning framework of self-conditioned CTC to train robust models by conditioning with "noisy" intermediate predictions. During the training, intermediate predictions are changed to incorrect intermediate predictions, and fed into the next layer for conditioning. The subsequent layers are trained to correct the incorrect intermediate predictions with the intermediate losses. By repeating the augmentation and the correction, iterative refinements, which generally require a special decoder, can be realized only with the audio encoder. To produce noisy intermediate predictions, we also introduce new augmentation: intermediate feature space augmentation and intermediate token space augmentation that are designed to simulate typical errors. The combination of the proposed InterAug framework with new augmentation allows explicit training of the robust audio encoders. In experiments using augmentations simulating deletion, insertion, and substitution error, we confirmed that the trained model acquires robustness to each error, boosting the speech recognition performance of the strong self-conditioned CTC baseline.
Feature-Aided SMC-PHD Filter for Nonlinear Multi-target Tracking in Cluttered Environments

The Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter is a permissive multi-target tracker, performing state estimation through particle filtering with implicit data association. This filter is thus effective even in presence of clutter and nonlinear dynamics, while remaining tractable for real-time applications due to its computationally efficient data association process. Sensors are sometimes capable of sensing target features, which add up to kinematic measurements, e.g. range and bearing. In this paper, the adaptive Feature-Aided-SMC-PHD filter is designed, making use of feature information to increase the SMC-PHD’s estimation performance with respect to clutter, detection probability and location precision. As suspected, further differentiating targets from clutter led to greater sample degeneracy, especially as the detection probability drops. An adaptive sampling scheme was hence developed in order to relax this phenomenon. A radar application is considered in this study to validate this paper’s approach using Monte Carlo simulations.
Blockchain-based Distributed Power Market Trading Mechanism

Distributed power market trading has the characteristics of large number of participants, scattered locations, small single trading scale, and point-to-point trading.The traditional centralized power trading model has the problems of large load, low efficiency, high cost, reliance on third parties and unreliable data.With the characteristics of decentralization and nontampering, blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems.Therefore, this paper proposed a distributed power market trading framework based on blockchain.In this framework, the distributed power supply characteristics and trading needs of each participant are analyzed, a complete distributed trading process based on blockchain is designed.In addition, we have studied the key technologies of distributed power market trading.With the goal of power service reputation and maximum revenue of distributed power providers, we have established a matching degree model, a distributed power market trading optimization model, and designed a smart contract-based power market trading optimization strategy and power trading settlement strategy.Finally, we designed experiments to verify the performance of the proposed framework.
Modern possibilities for science publication analysis and search

The advantages of AI-based processing of science publications in information retrieval systems are revealed. Science publications are seen as the Big Data component. The examples of AI-based e-catalogs are given. The possibilities of AI-based system of the Federal Institute of Industrial Property are discussed. Augmented reality can be used for library items visualization.
Resource Allocation Techniques in Cloud Computing

With the advent of the Internet, Cloud Computing has become a ubiquitous technology in the present scenario. It is a pay-per-use model for providing various resources such as networks, computing servers, storage, etc. Resource Allocation (RA) is crucial for the performance of the cloud, as it involves providing the resources necessary for processing requests from the client. As the popularity and usage of cloud are increasing over years, there is an increase in the usage of cloud resources via public, private, or hybrid community clouds. To leverage the large-scale of requests, there is a need for effective RA techniques. This chapter mainly focuses on the various types of RA strategies and also provides an insight to choose the appropriate technique.
Applications of digital speckle pattern shearing interferometry in characterization of fluids

Digital speckle pattern shearing interferometry is a robust optical technique mostly used for measuring small deformations in solid objects. In this paper we focus on applications of this technique in characterization of liquid and gaseous samples. We demonstrate this by a few examples: measurement of the density (temperature) profiles inside a candle flame and around a hot wire in air. Also, we study the diffusion process in a binary mixture and measure the diffusion coefficient. The results of these studies confirm that this highly sensitive, nondestructive, and real-time technique is ideal for investigation of fluid specimens.
An Efficient Technique for Detection and Intensification of Blood Vessels from Breast MRI

Breast diseases, in which lumps are present in breast tissue followed by swelling up the areas latterly leading to the symptom of cancer. Detecting blood vessels can make easy identification of breast diseases. Early detection can play a remarkable role in the treatment of cancer. Identification of vessel structure of breast becomes difficult task due to presence of noise and fatty tissues. Congenital and radiation free breast MRI is presently utilized to assess the blood flow through vascular structure. Our paper presents an algorithmic program for detection and intensification of blood vessels accurately, highlighting upon fatty vessel tissues in which breast MRI is utilized. Morphological operators and gradient characteristics of image are being used to extract the features correctly. This methodology has more efficacies in case of noise and precision.
Sonar Sensing-Based Human Leg Localization Using Gaussian Process Regression

Human detection and following problems are considered as one of the crucial problems in a human-robot coexisting environment. The present work focuses on a significant subproblem within this genre, where the measurements obtained with the onboard range sensing system of a mobile robot are used for localizing the positions of the leg pair of a walking person at a time instance, during human tracking. This paper demonstrates how such a human leg localization problem can be formulated as a nonlinear regression problem where two distinct prediction models are learned by using two different training datasets, and how the learned models can be used to localize the positions of the human legs. Although sonar sensors produce readings fraught with uncertainty and imprecision, they are still considered as an attractive range sensing solution for robotic applications, especially in developing countries, where cost, size, and power consumption associated with the sensors are the major constraints. Keeping the sonar behavior in mind, in this paper, we propose to employ Gaussian Process Regression (GPR), a popular Bayesian nonparametric method, to solve our formulated nonlinear regression problem. Here, we have aimed to suitably adapt the basic GPR framework as per our formulated problem to perform a statistical model calibration and then localize the human leg positions based on the calibrated model. Performance evaluations carried out for localizing the positions of human legs based on real-life data show the superiority of the proposed GPR-based approach over the conventional position estimation method.
Reading and Writing in a Diglossic Context: A Multifaceted Perspective

Arabic is considered a classical case of diglossia because conventionally, one form of Arabic is spoken (SA) and another is used in the domain of written language (MSA). In the recent past, globalization of English-based technology, together with the absence of Arabic supporting keyboards, resulted in the reliance on Latin script as the main writing system when communicating through computers (CMC). This writing is known as ‘Arabizi’, which represents Spoken Arabic (SA). The advent of Arabic supporting software has allowed the writing of SA in Arabic letters, but has not completely eradicated Arabizi. Although the use of Arabizi today is less ubiquitous than it was half a decade ago, its effects on the cognitive processes involved in literacy are scientifically interesting. The present chapter explores the way that Arabizi affected reading, writing, and personal and social dynamics in a sample of Arabic-speaking adolescents collected in 2014. We focused on three areas of inquiry: The first aimed to provide a description of writing practices, perceptions, and attitudes for the two writing systems, Arabizi and MSA. The second examined literacy skills and abilities in MSA and Arabizi. The third tried to determine the stability of the Arabizi orthography in order to evaluate to which extent it is standardized.
A modified type-2 neuro-fuzzy SVM-based inverter fed IM drive

In indirect vector control, the conventional speed and current controllers operate satisfactorily when the operating point is constant. But the operating point is always dynamic; the reference voltages obtained in a closed-loop system feeding to the inverter contains more harmonics. Due to this, the pulses which are going to produce are uneven and in turn, understudy creates the inverter output voltages produced with more harmonics. In order to produce a better output voltage from the inverter, this paper presents the neuro-fuzzy type-2 (NFT2) space vector modulation technique. Performance comparison of the inverter is done with conventional space vector modulation (SVM) and neuro-fuzzy type-1 (NFT1) system using MATLAB simulation and experimental validation. The performance parameters of the induction motor based on current, torque, and speed with neuro-fuzzy type-2 space vector modulation is compared with conventional and type-1 neuro-fuzzy SVM. The % THD of inverter output voltages are also compared. The experimental validation of a DSpace-1104 is used to analyse the performance of the induction motor which is obtained by the simulation. The experimental validations are carried out considering 2HP induction motor in the lab.
Toward Pervasive Computer Vision for Intelligent Transport System

The Smart Passenger Center (SPaCe) is a fully integrated platform that aims to overcome the complexity of centralized management of public transport infrastructure and vehicles. The SPaCe artificial intelligence engine predicts threats and critical events and proposes countermeasures by examining the daily flows of people and correlating different data and events, thanks to machine learning and big data analytics. All this massive data comes from a pervasive smart camera network that constantly monitors activities in stations, trains, buses and other places of interest. In this work, we present the idea of this computer vision distributed sub-system, the state of the art of the techniques involved and the advanced functionalities that this intelligent surveillance system offers to the upper layers. Everything is developed following the privacy-by-design paradigm; namely, no real image is recorded or transmitted, but all the elaborations take place on the edge nodes of the system.
Towards effective preservation of robust safety properties

Secure compilation investigates when compilation chains preserve security properties. Over the years, different formal criteria and proof techniques have been put forward for proving a compiler secure. However, these proposals require a lot of manual effort by compiler designers. This paper introduces a formal approach to mechanically support these efforts. We focus on the specific class of robust safety properties and we propose a translation validation approach that leverages program analysis techniques to check that a compilation run preserves security.
On language-agnostic abstract-syntax trees

As modern software systems have grown in complexity and heterogeneity, static code analysis of these systems has become more difficult. Due to increasing heterogeneity of software systems and static analysis tools being language specific, multiple of these tools are typically required to fully analyze these systems. Due to this, current tools for static code analysis are no longer well-suited to analyzing these modern systems. This paper presents an approach to solving the problem of needing multiple language-specific static analysis tools to fully perform static code analysis of such systems. This can be done by converting the software system's source code into an intermediate representation or abstraction known as a Langage-Agnostic Abstract-Syntax Tree. This abstraction provides a common interface for static analysis tools to operate with, no longer requiring the usage of multiple language-specific static analysis tools for a given heterogeneous system. The methodology for creating such an abstraction is presented here, along with an evaluation of two microservice system testbeds, DeathStarBench and TrainTicket, written in C++ and Java, respectively. By utilizing a higher abstraction for source code representations, we are much better prepared to perform static code analysis of modern software systems.
On the impact of data sampling on hyper-parameter optimisation of recommendation algorithms

Hyper-parameter optimisation (HPO) is a fundamental task that must be performed in order to achieve the highest accuracy performance that a recommendation algorithm can provide. In the recent past, with the the growth of dataset sizes, the amount of resources and time needed to perform the optimisation dramatically increased. Sampling the data used during the HPO procedure allows reducing the required resources, but it impacts the accuracy metric score. In this paper, we study the effects of optimising the hyper-parameters through a random search, sampling the users in a dataset. The results of our experiments show that sampling reduces the amount of time needed to conduct HPO, but it also influences differently the accuracy of the best configuration found by HPO, depending on the algorithm optimised and the dataset selected.
A study on the reproduction of national R&amp;D projects and regional imbalance : Focusing on the policy linkage effect of the Korean Social Science Research Support Program (SSK)

연구개발 수준과 지역발전 간 상관성이 중요해졌다. 하지만 연구개발과 지역불균형의 관계에 관한 연구성과는 상대적으로 미미한 실정이다. 연구개발 분야에서 심화하는 지역차이 및 그것과 지역 불균형의 관계, 그리고 그 재생산 기제를 밝히는 작업은 지역사회학 연구의 주요 과제이다. 이 연구는 주요 국가연구개발사업의 하나인 한국사회과학연구지원사업의 정책적 활용을 중심으로 연구개발사업이 지역 불균형구조의 재생산에 미치는 영향을 고찰한다. 이를 위해 여기서는 국내 대학 연구비 수주, 한국연구재단의 연구비 지원, 한국사회과학연구지원사업, 정책연계, 정책활동 등의 지역별 현황을 비교 분석했다. 연구 결과, 서울을 중심으로 한 수도권은 다른 지역에 비해 압도적으로 많은 연구비를 수주하고 있었고 더불어 높은 수준의 연구성과를 산출하고 있었다. 특히 연구성과의 사회적·정책적 환류를 주요 목적으로 하는 한국사회과학연구지원사업에서는 더 심각한 수도권 편중 현상을 보였다. 한국사회과학연구지원사업 사례에서 보듯이 지역별 연구개발비 수주 격차가 지역 소재 대학의 연구역량 차이, 더 나아가 지역에서 생산·유통되는 정책지식의 양과 질의 차이를 초래한다. 지식의 가치와 중요성이 그 어느 때보다도 강조되는 지식기반사회에서는 지역별 연구개발비 격차가 지역 불균형을 재생산하고 심화하는 주요 기제 중 하나가 될 수 있다.It has become important in the correlation between R&D level and regional development. However, research results on the relationship between R&D and regional imbalance are insignificant. The relationship between regional imbalances and regional imbalances, which are deepening in the field of R&D, and their reproduction mechanisms, are major tasks of community studies. This study examines the effect of R&D projects on the reproduction of regional imbalance structures, focusing on the policy use of the Korea Social Science Research Support Program(hereinafter referred to as SSK), one of the major national R&D projects. To this end, this paper compared and analyzed the status of university orders for research funds, the status of support for research funds from the Korea Research Foundation, research funds for SSK, policy linkage, and policy activities by region. As a result of the study, the Seoul metropolitan area received an overwhelming amount of research funds compared to other regions and produced a high level of research results. In particular, the SSK project, which aims at social and policy reflux of research results, shows a more serious concentration phenomenon in the metropolitan area. As shown in the case of the SSK project, the gap in R&D costs by region leads to differences in research capabilities of universities located in the region, and furthermore, differences in the quantity and quality of policy knowledge produced and distributed in the region. In a knowledge-based society where the value and importance of knowledge are emphasized more than ever, regional R&D cost gaps can be one of the main mechanisms for reproducing and deepening regional imbalances.
Research on underwater target tracking method based on SIFT and KCF algorithm

In the process of water quality monitoring, qualitative evaluation can be carried out according to the changes in the number of underwater animals and the state of motion and other parameters, because of the underwater image resolution is low, mistracking and loss can occur when tracking underwater targets, this paper proposes a matching algorithm based on scale invariant feature transform (SIFT) and nuclear correlation filter (KCF) of underwater target tracking algorithm. SIFT algorithm was used for feature extraction to obtain the detection frame, and the improved HOG feature was used for KCF training and tracking. According to the experimental results, it can effectively detect and track underwater fish under complicated underwater conditions.