Reviewerly/exHarmony huggingface dataset:
On-the-fly Learning of New Objects and Instances

This paper focus on efficient learning of new objects by robots, at both instance level and class level. Despite the great achievements in deep learning, new objects are not well handled due to the big data dependency and the closed set assumption, which are not satisfied in many applications. For example, in service robots, the desired objects vary among customers and environments, and it is hard to collect big data for all objects. In addition, new objects occur from time to time that are not contained in the training data. This paper aims to learn new objects on-the-fly after deployment of the robot, without the dependency on pre-defined big data. A practical system is proposed to learn a new instance in 1.5 minutes and a new class in 15 to 25 minutes, by integrating state-of-the-art works including online learning, incremental learning, salient detection, object detection, tracking, re-identification, etc. A novel incremental object detection framework is further proposed with better performance than the state-of-the-arts. Extensive evaluations are conducted to examine the contribution of each module. The proposed system is also deployed on a real robot for end to end performance test.
Keyphrase Extraction Using PageRank and Word Features

Keyphrase extraction is a fundamental task in natural language processing. Its purpose is to generate a set of keyphrases representing the main idea of the input document. Keyphrase extraction can be used in several applications such as recommendation systems, plagiarism checking, text summarization, and text retrieval. In this paper, we propose an approach using PageRank and word features to compute keyphrases’ scores. Experimental results on SemEval 2010 dataset show that our method provides promising results compared to existing works in this field.
Basics and Basic Concepts: How to Work with These Materials?

The reporting on the complex of issues surrounding escape, migration and integration has come under criticism. This has been revealed by the content analyses of the media on the one hand, and the surveys of children and young people on the other, from which the present materials were developed.
Invalid Scenarios of External Cluster Validity Indices: An Analysis Using Bell Polynomial

External cluster validity indices (CVIs) are used to evaluate various clustering algorithms by comparing the obtained clustering result with the gold standard. These indices provide some values for the obtained clustering result, indicating how good or bad the obtained clustering result is compared to the gold standard. For an external CVI, it is desirable to always provide the values within its range; otherwise, the clustering result cannot be correctly interpreted. Thus, in this work, our objective is to theoretically obtain the scenarios where these indices are unable to provide valid values. 26 commonly used indices identified in the existing literature are considered for our analysis purposes. For all these CVIs, we are able to determine the scenarios where these indices provide undefined values. Using the Bell number and Bell polynomial notion, we have attempted to represent the number of such scenarios.
Research on Knowledge Graph Based on Artistes’ Works

In order to fill the gaps in the field of artistes’ information display and increase the diversity of the artistes’ network space, this paper proposes a knowledge graph model based on the artistes’ works. The paper uses data mining and analysis techniques to obtain artiste’s information, evaluates the artistes in multiple dimensions, and extracts the collaboration between them. Finally, the paper uses interactive visualization methods to display the artistes’ collaboration network, and realizes the construction and realization of the knowledge graph based on the works.
Lowe's Progress1

This chapter is a largely personal reflection on Jonathan Lowe’s Four-Category Ontology. It begins with an attempt to explicate the nature of universals, powers, and natural necessity, and the relations (or ‘relationships’) between entities in each of the four categories. After exploring a number of apparent difficulties with the ontology – notable especially in light of his more recent work on relations and essences – the chapter advances the hypothesis that Lowe’s ideas were evolving in surprising ways subsequent to the publication of The Four-Category Ontology in 2006.
Function Point-Story Point an Approach for Estimating the Cost of Digital Game Applications

The digital game industry has been at the peak of its popularity in the past few years. Revenue of 91 billion in 2016 exceeded the combination of the film and music industry in the same year. Within only two years, the value had risen to 119.6 billion. This lucrative revenue had increased the number of parties and tightened the competitiveness of the digital game industry. The competition had resulted in an increased need to estimate costs and Efforts on digital game applications. The Function Point Analysis (FPA) method, which was an industry-standard method, had been deemed to fail because it ignored non-functional factors and did not fit the characteristics of digital games, some of which were related to non-functional elements. This research proposed to formulate a method that combines the FPA method with Story Point (SP) called Function Point-Story Point, which utilized the value of the team's ability to calculate non-functional components of digital games. Based on the results of this research, the FPS method had a better cost difference of 29.57% compared to the FPA original, which had a value of 53.51% and emphasized the importance of non-functional factors in digital games applications.
Evolutionary Dendritic Neuron Model Learned by A State-of-the-art Evolutionary Learning Algorithm

Recently, the role of dendrite structures in nerve calculation has attracted wide attention. As a single neuron model, Dendritic Neuron Model (DNM) is usually built to transmit information through imitating the mechanism and process network of biological nerves. The branches of a dendrite that distribute corresponding to the three coordinates are used to classify the training data based on demands. Contrarily, traditional artificial neural networks which use a couple of McCulloch and Pitts’ neurons are still difficult to be understood and trained. Commonly, evolutionary computing is adopted to solve nonlinear problems. In this paper, a recently proposed spherical search algorithm (SASS) is for the first time introduced as the training algorithm for DNM. It substitutes the traditional error back propagation (BP) learning method to alleviate the local minima trapping problem. Six benchmark classification datasets are tested to verify the accuracy of the well trained neural network. Experimental results suggest that SASS performs better as a learning algorithm for DNM in terms of solution accuracy.
Data Classification Using Combination of Five Machine Learning Techniques

Data clustering plays a vital role in object identification.In real life we mainly use the concept in biometric identification and object detection.In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica.Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW).The combination of five machine learning methods provides above 98% accuracy of class identification.
Beyond Accuracy: A Feature Crossing Method for Chinese Thesis Reviewer Recommendation

Peer review refers to the process of a group of reviewers jointly assessing a thesis or a journal article. Reviewer assignment constitutes the major challenge in this process. However, many studies only focus on the predictive accuracy of the matching degree between reviewers and theses. Recent works show that the unitary accuracy metric can not satisfy the requirement of maximizing the comprehensive performance of the reviewer recommendation system. In this paper, we introduce a Chinese thesis reviewer personalized recommendation model(TRPRM) for the reviewer assignment problem. The TRPRM focuses on an extra crucial metric in recommendation system evaluation: novelty. The model uses a feature crossing method to mine the interactions among different research interests of author. Moreover, we analyze the role of accuracy and novelty as indicators of recommendation quality and present novel ways of how to measure them. Lastly, we apply our model to the reviewer assignment problem using a real dataset from a university in China to verify the comprehensive performance of our proposed approach.
Credit Cards Theft Using Social Engineering over WhatsApp: A Survey Study

Social engineering is one of the most critical threats to security and people's privacy nowadays. Over the past years, attackers have been relying on this method to persuade people to provide and expose personal information willingly without them realizing that this could threaten them when this info is harmfully used. This study focuses on the impact social engineering can make on changing peoples' ideas and customs, especially in Jordan society. In addition to exploring the importance of social engineering and how criminals use it to influence events and pass its plans and goals. That is through studying social and understanding and explaining how it conveys hidden messages to influence society and its outlook. Finally, based on questionaries' results analysis, this study provides several concluding observations and recommendations that help deal with the harmful impact of social engineering in Jordan society, especially social attacks accomplished over WhatsApp for money fraud.
Research on UAV conflict resolution based on dynamic conflict detection model

UAV has been widely used in various industries, but at present, it mainly operates in isolated airspace, which limits the further development of UAV. This paper presents an improved UAV conflict detection model is proposed to establish a dynamic conflict detection model based on the operation status of UAV and intruders, and the conflict deconfliction of UAV is realized based on the detection results combined with ORCA(Optimal Reciprocal Collision Avoidance) algorithm, which further improves the success rate of conflict deconfliction and ensures the safe and efficient operation of UAV. Finally, Monte Carlo simulation is used to simulate the algorithm and verify the effectiveness of the algorithm.
Knowledge and Its Dimensions

This chapter studies the properties and dimensions of knowledge. We treat knowledge in the context of epistemic structures and from a semiotic basis. Symbols and signs and their interpretations are necessary to acquire meaning. Knowledge representation and acquisition are key issues. To understand and process knowledge, it is important to know that there are various types, sorts, dimensions, and kinds of knowledge. This chapter emphasizes the multidimensionality of knowledge and discusses dimensions such as ontological, semantic, temporal, and reference. Formalization and quantification of knowledge are discussed in the last two sections.
Time-Graded Avoidance Map for Constrained Aerial Traffic

Integration of Unmanned Aerial Vehicles (UAV) to airspace is an ongoing work and has many safety-critical issues associated with it. Mid-air collision avoidance is one among them. Urban environment presents many obstacles and no-fly zones which make the movement of the aerial vehicles constrained. Geo-fencing in such cluttered environment defines the safe boundary within which the UAVs could fly. Avoidance map is a tool for conflict resolution among UAVs. We propose a new version of avoidance map which accounts for inter-UAV collision avoidance and boundary keeping. Considering a structured UAV traffic, a corridor like environment is envisaged which consists of intersections and lanes. Conflict resolution at intersections and during lane changing using avoidance map is also presented. Further, we present time graded version of avoidance map which predicts the time to collision for a pair of control inputs. The time grading provides more feasible solutions for the UAVs to avoid conflict. The proposed solution for conflict resolution in a constrained environment is a reliable method to ensure safety and bounded flight.
Research on remote sensing information recommendation technology based on collaborative filtering

With the rapid development of remote sensing platform technology, the amount of remote sensing data information has increased explosively. Solving the data recommendation for user specific behavior and application is the key to the application efficiency of remote sensing products. Aiming at the user's basic portrait model, this paper proposes a remote sensing information recommendation technology based on collaborative filtering, which uses the adjusted cosine after noise elimination to calculate the similarity between a product and another product. The noise elimination process added to this similarity calculation method is to subtract from the user's scoring mean, so that all scoring curves tend to be stable and ensure the accuracy and stability of the recommendation results. The application analysis shows that this method has certain feasibility and popularization, and is helpful to improve the overall efficiency of remote sensing application.
Improving Primary Pre-teachers Instructional Design Ability Focused on Cultural Intelligence

본 연구는 다문화 사회를 살아가는 다양한 화자들과 효과적으로 소통하고 협력할 수 있는 역량을 함양하고 내실있게 문화지능을 함양하는 방안을 제안하는데에 있다. 이를 위해, 문화지능(cultural intelligence: CQ)의 개념과 구성요소인 상위 인지적, 인지적, 동기적, 행동적 CQ의 관점에서 예비교사들의 수업지도안을 분석하였다. 무작위로 추출된 수업지도안은 예비교사들이 초등영어지도법 강좌에서 5학년 또는 6학년 학습자를 대상으로 마이크로티칭을 위해 제출한 것이다. 분석 결과, 첫째, 인식단계에 해당하는 초등영어학습자를 위해 상위인지 CQ와관련된 내용이 거의 담겨져 있지 않았다. 초등영어학습자들이 문화적 배경이 다른사람들과 적절하게 의사소통할 수 있도록 하기 위해서는 문화적 지식과 관련된내용이 교육과정의 소재에 설정되어야 한다. 둘째, 영어학습자들이 인지와 행동적CQ를 부분적으로 학습하고 있기 때문에, 문화적 배경이 다른 사람들의 가치, 규준, 신념과 밀접한 관련이 있는 교실활동들을 통해 의사소통을 지도할 필요가있다. 셋째, 학습자들이 상위인지와 인지 CQ와 밀접한 관련이 있는 비판적 사고능력를 함양하기 위해서는 CQ기반 교실활동을 통해 비판적 사고능력을 제대로측정할 필요가 있다. 이러한 결과들을 바탕으로, 예비교사들이 초등영어학습자들의 CQ를 제대로 함양할 수 있도록 하기 위해 학습자들의 요구와 수준을 점검하고, 그에 터하여 수업을 설계하고 구현하는 방안을 제안하였다.The purpose of this study is to suggest the direction of improving pre-teachers instructional design ability focused on cultural intelligence (CQ). To achieve this purpose, pre-teachers four lesson plans presented for microteaching in the lecture of Methodology of Primary English Language Teaching were analysed in terms of CQ composed of meta-cognitive, cognitive, motivational, and behavioral CQ. The findings are as follows: (1) Contents related to meta-cognitive CQ aren t designed in the awareness stage corresponded to primary classes, because English language learners (ELLs) don t have experiences of being conscious of and checking their cultural knowledge when they interact with people from diverse cultures. (2) As ELLs partially foster their cognitive and behavioral CQs, they cannot communicate with classmates appropriately through classroom activities closely associated with values, beliefs, norms, and social customs of speakers from different cultural backgrounds. (3) Critical thinking ability needs to be assessed through classroom activities based on CQ, because ELLs should improve their critical thinking ability closely related to meta-cognitive and cognitive CQs. Thus, in this paper, it was suggested that pre-teachers need to design contents and classroom activities for improving primary ELLs’ CQ considering cognitive and affective levels of learners corresponded to the awareness stage.
Smart Secure Healthcare Cloud Data Model By Fog

Fog computing provides a security model for privacy-preserving healthcare data in the cloud, such as video (surgery footage, endoscope, ultrasound) and images (X-ray, CT scan, MRI). Here, a decoy approach is used to prevent data tampering and information leakage. In the case of video, hiding AES encrypted video inside an image then the output becomes uploaded image and for images use decoy algorithm, blowfish, and Hmac. This paper provides integrity, confidentiality, and availability
SoK: Secure Memory Allocation

Heap-related memory corruption vulnerabilities are a severe threat that continues to wreak havoc in widespread software despite a few decades of research. Research in hardening memory allocation yielded several proposed designs and a large number of techniques designed to mitigate common heap-related vulnerabilities. However, rigid performance requirements imposed by the majority of vulnerable workloads are a severe hindrance to the practical use of secure memory allocation techniques and systems.
Decomposing Relief Maps to Detect Counterfeit Coins Using a Hybrid Deep Learning Method

In this research, we take advantage of deep learning approaches to improve the performance of counterfeit coin detection. As most of the pre-trained networks accept three channels for their input, we propose a new method to represent the relief map (height-map image) in three geometrical forms with Steep, Moderate, and Gentle slopes. This can also make our proposed method more understandable for both coin and AI experts by an eXplainable AI strategy. We compensate for the lack of fake coins by using a proposed Generative Adversarial Network. Then, we proposed a hybrid method using fine-tuning pre-trained deep neural networks to detect fake coins and provide a rejection option to increase the reliability of the system. While a few fake coins available in this research are used in the training process, the model is mostly trained by the images that are generated as fake coins from genuine ones. However, the system produces remarkable results to classify the coins.
Extending Two-Dimensional Markers with Functional Color Information

With the Revolution of the Internet of Things (IoT), it's possible to deploy IoT solutions on a large scale in most industries. In Fast-Moving-Consumer-Goods (FMCG), current IoT solutions still cannot meet the requirements of the industry. FMCG items use mainly paper-based labels with traditional barcodes. These labels are cheap and can be used in high-speed manufacturing. FMCG producers still do not see the shift towards advanced solutions, like RFID/NFC smart tags, worthy investment. This is mostly because of higher price of advanced solutions, or they require costly changes in production lines.Recent research has shown that functional inks can act as cheap IoT-sensor in FMCG's. But more research is needed so that functional inks can be taken into production. This paper presents one technical solution for a smart tag for FMCG's. The proposed smart tag is different from the existing ones. It is based on traditional QR-Code and Datamatrix markers, printed with standard inks. But it also has functional ink embedded inside the marker as a sensor. This research experiments how embedding impacts the overall performance of the smart tag decoding. And how CIEDE2000 colour difference algorithms can be used to calculate the state of the sensor. Three colour parameter options, CIEDE2000(1, 1, 1), CIEDE2000(2, 1, 1), CIEDE2000(2.76, 1.58, 1) and their accuracy are compared in this research. Experiments show that the proposed approach doesn't negatively affect the decoding process. And that a colour comparison can detect sensor states. In the comparison between CIEDE2000-algorithms CIEDE2000(2.76, 1.58, 1) performed best, especially in the low-intensity test. However, some future research needs to address absolute value detection of the sensor and the accuracy of the colour recognition. Especially when there is a small number of pixels in use, or the colour has low intensity.
Cognitive Radio for Aeronautical Mobile Telemetry: A Machine Learning-Based Approach

The need for a cognitive radio in aeronautical mobile telemetry is motivated by the simultaneous use of three different versions of continuous phase modulation and bitrates that vary from test article to test article. The cognitive radio envisioned in this article comprises two parts: a machine learning algorithm that determines modulation type and bitrate, and a software-defined radio that performs demodulation and detection. The experiments presented in this article show that a quadratic discriminant classifier operating on power spectral density (PSD) estimates can successfully identify the modulation. Following the classifier, a Gaussian process regression model operating on the same PSD estimate can estimate the bitrate.
A Deep Learning Framework for Multi-Class Objectrecognition Using Feature Fusion and Neigborhoodcomponent Analysis

Object recognition is a computer vision technique for identifying objects inan image. Deep neural networks have demonstrated remarkable recognitionresults on the basis of the features extracted from a single image of the object. In this paper, we present a feature fusion-based deep learning method forclassifying and recognizing the multi-class objects. Specifically, we first adopttwo Convolutional Neural Network models: DenseNet 201 and ResNet 101, forfeature extraction. Then, to acquire a more compact presentation of featuresand reduce the dimensions, we have utilized Neighborhood Component Analysis(NCA). Furthermore, feature fusion is performed in a hierarchical manner byapplying the concatenation operation. Finally, we classify the multiple objectsin an image by using Support Vector Machine (SVM) classifier. We demonstrate the effectiveness of our methodology on two benchmark datasets; MSCOCO and wild animal camera trap dataset. The experimental results showthat the proposed framework achieved an accuracy of 98.1% and 97.5% on MSCOCO and wild animal camera trap datasets respectively. Results showed thatour proposed framework effectively improved the performance favorably in bothrobustness and accuracy. Furthermore, a fair comparison with the existing techniques reported in the literature is also provided
Захист інформації відомчої інформаційно-телекомунікаційної мережі за допомогою парольної системи

The approach to quantitative estimation of stability of password systems taking into account power of space of passwords and length of the password is theoretically substantiated. The formalized idea of information entropy as an approach to measuring the amount of information that is unknown through random variables is determined by the randomness of a variable based on the knowledge contained in another part of the message. It is established that the greater the entropy in a given distribution of passwords, the more difficult it is to guess the password that was chosen from this distribution; passwords with higher entropy values require more expected assumptions, which makes entropy useful as a measure of password strength. Proposals for password management of the departmental information and telecommunication network of the object of critical information infrastructure are given. Studies show that much of the entropy introduced by uppercase and lowercase characters is created by users who exceed the minimum requirements of the password strength policy. Secure password creation is complicated by the trade-off between developing passwords that are both difficult to crack and use. Accordingly, the access control policy is important. Studies show that much of the entropy introduced by large and non-alphanumeric characters is created by users who exceed the minimum requirements of the password strength policy: the use of more digits than necessary, different positions of special characters. It is concluded that text passwords remain the dominant method of authentication in computer systems, despite significant improvements, including smart cards, RFID cards, USB tokens and graphic passwords, which have their advantages and are suitable for use in a particular environment or for a specific program. It is noted that there are few published empirical studies that would examine the strategies used by users under different password policies. Further research is planned in this direction/