Reviewerly/exHarmony huggingface dataset:
International Economic Forum as a Platform for Gaining Experience and Reinforcing Communication Skills

The paper discusses current trends in the Russian labor market. The question of the importance of communication and self-presentation skills for a modern young engineer is highlighted. The paper provides a definition of the engineering elite as universal specialists capable of solving diverse problems. An important component of the education of the elite is the stressful situation simulation for the development of skills for solving extraordinary tasks and teamwork. A visit to the forum is an important component of the training, in which students learn to apply the learned skills in practice to consolidate them and broaden outlook. The necessity of attending high-level public events for students striving for self-realization not only as specialists, but also as an educated person, is shown.
Exploring Optimal Model for Machine Learning by Differential Evolution

In the big data era, the data scientist or business analyst own business data can apply machine learning algorithm to train inference models easily since the application of big data attracts more and more attention and the technique of training models can be obtained easier and cheaper than ever. The focus of big data applications has gradually shifted from model training to the prediction and inference. For enterprise application scenarios, selecting the most precise model among many trained models has become a significant topic of research. Though ensemble methods have been proposed to discover best model by multiple training phase, studies of exploring best combination within multiple modes are still few. Finding the appropriate parameters to configure different machine learning models is an NP-hard problem that needs metaheuristic algorithm to solve. This study proposes a differential evolution algorithm to integrate multiple trained machine learning models into a hybrid model. For experiment, the regression model is taken as an example and the differential evolution algorithm is compared with the ant colony optimization algorithm in this paper. Three benchmark datasets are employed to examine, and the results discovered that the differential evolution algorithm outperforms ant colony optimization.
Hierarchical Clustering Based Medical Video Watermarking Using DWT and SVD

A video watermarking technique is proposed in for securing medical videos by adapting new clustering algorithm. As per law the information need to keep secure in order to protect the privacy of the patient. Cybercriminals may sell the medical video with the patient's fake identities which leads to data insecurity and various criminal activities. And so the technique is mainly for maintenance and confidentiality purpose of medical video. This is designed in a way to cluster the medical video frames which incorporates a Euclidean distance of frames. The Hierarchical representation is constructed for every cluster to choose key frame with the entropy and Probability Density Function (PDF) value of the frames. Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) improve the performance of the watermark embedding process, where the watermark image is chosen to embed into the selected key frames for every cluster. The experimental results show that the proposed scheme has higher robustness and imperceptibility against various image and video processing attacks.
A Study on Safety Awareness by Risk Avoidance in Building Facilities & Practicability of Risk Prediction Sheets under ISO45001

Abstract The study discussed in this paper aims to prevent hazards and risks which may occur during the installation of building facilities, by making the most of risk prediction training sheets (hereafter,risk prediction sheets) and analyzing the degree of safety awareness among trainees. This survey has been carried out for nearly five years, in which supervisors and workers who are engaged in equipment installation were tested with illustrated risk prediction sheets depicting typical equipment installation activities. And we suggested the practicability of risk prediction sheets under ISO45001.
The application of a decision support system in predicting flood hazard using the analytical hierarchy process method

Flood disasters are natural disasters that often occur in Indonesia because ofin the tropics. There are many flood-prone areas that have high population density, due to lack of understanding of the community regarding flood-prone areas and many people choose settlements in a flood area without prior knowledge of the territory These are flood-prone areas. For that purpose a built Prediction Decision Support System using Flood Vulnerability the Analytical Hierarchy Process method where on this system has five criteria, namely land slope, landform, bulk rain, land use, and soil texture. The results of this study are: the slope of land is flat, which is the criteria for the slope of the land is flat. with the results of 11.33.
Performance comparison of CATA’s information retrieval system application vs WTS

Abstract Searching for data or documents that are always a necessity in the world of education, work, research an others requires convenience so that time efficiency occurs. This is supported by the emergence of various Information Retrieval system applications, one of which is cata, which is built on the Java programming language and applies the Generalized Vector Space Model method which can search based on similar keywords. Other facilities need to be developed, one of which is by applying the stemmer porter algorithm applied to the WTS (Web Tokenization Stemming) web which is built with the laravel framework that can search data or documents simply by typing the basic words so that searches are easier and faster.
Flat Electroencephalography Image: Image Size Dependent Normalization versus Fuzzy Technique

Abstract Flat Electroencephalography (fEEG) is a method for mapping high dimensional signal, namely Electroencephalography (EEG) into a low dimensional space. The image of fEEG which is in grayscale form is obtained from digital fEEG by using fuzzy approach. The main aim of this paper is to reduce the spread of the vague boundary and improve the visibility of the clusters of epileptic foci in terms of contrast enhancement via Image Size Dependent Normalization (ISDN) and fuzzy technique. Contrast performance comparison between both methods are carried out for an epileptic patient at varied time, t . It shows that fuzzy method gives better contrast compared to ISDN.
Recursive LCMVEs with Non-Stationary Constraints and Partially Coherent Signal Sources

In estimating an unknown parameter vector in a linear regression model, it is common to use linearly constrained minimum variance estimators (LCMVEs). For a long time, LCMVEs were studied in the context of stationary constraints under both stationary or non-stationary environments. Recently, a new family of non-stationary constraints leading to Kalman-like recursive LCMVEs has been introduced for fully coherent signal (FCS) sources. A noteworthy feature of this family is to allow the possibility of, at each new observation, incorporating new constraints. This article extends these results to the case of partially coherent signal (PCS) sources. Indeed, without ad hoc modifications of the Kalman-like recursion, estimation of the amplitudes of PCS sources exhibit a performance breakdown even for a slight loss of coherence. Last but not least, it is shown that PCS sources introduce a lower limit in the achievable performance in the large sample regime.
이슬람국가(IS)의 이중적 이념구축: 동원과 타자화의 양적 분석을 중심으로

On April 21, 2019, a series of attacks occurred at churches and hotels in the Sri Lankan capital of Colombo, killing about 250 people. The large-scale terrorist attacks show that the activities of the Islamic State(IS)’s remnants and supporting groups may continue for a considerable time in the future, despite the West''s announcement of the disappearance of ISIS in March 2019.Thus, it is urgent to clearly understand the nature and function of IS'' ideological Daeshism. The IS organization is a terrorist group and its ideology is radical Islamism. However, unlike past Islamist organizations, the IS has declared itself a state and ruled for a certain period of time. It has also developed and transformed its ideology through the phases of its establishment, expansion, occupation, state declaration, and governance.Therefore, this study looks at IS''s ideology from a functionalist perspective attempts. It attempts to demonstrate the hypothesis that IS has been seeking to maintain and expand its organization by using dual ideology of mobilization and otherization. To this end, this paper seeks to empirically understand the manufacturing of the organization''s dual ideology through quantitative analysis of the Internet magazine Daviq, the IS'' most representative public relations.The frequency of nouns, verbs and adjectives in the magazine will be measured using the KH Coder program. Furthermore, the co-occurrence of words tool in the KH code will be used to understand the duplex ideological manufacturing process of the nouns, verbs, and adjectives.
Research on Apple Yield Prediction Model

This paper has used a fuzzy comprehensive prediction method and taken tree vigor data from 29 orchards as samples. By using 23 of the total samples, the apple yield prediction model base on tree vigor is established. The test precision of the model is 96.36%. With other six orchards to be predicted, this paper calculated fuzzy division and eigenvalues of grade variables and obtained apple yield prediction accuracy which is 91.08%. This model has practical applied value in apple yield prediction.
Optimal geometries for multistatic target localization with one transmitter and multiple receivers

This chapter analyzes the optimal geometries for the problem of target localization using time-of-arrival (TOA) measurements collected from a multistatic radar system with one transmitter and multiple receivers. The optimal geometry analysis is conducted based on maximizing the determinant of the Fisher information matrix (FIM), which effectively minimizes the area of the estimation confidence region. Rotating the coordinate system so that the bearing angle of the transmitter relative to the target is zero, an optimal geometry is formed by placing some receivers collinearly with the target at the bearing angle of 60 degrees and the remaining receivers at −60 degrees. The accuracy of the analytical findings is confirmed via simulation studies including numerical solutions and sensor trajectory optimization.
Connected Vehicles in the Shark Tank: Rethinking the Challenge for Cybersecurity to Keep Pace with Digital Innovation

Although vehicle connectivity reveals significant potential to enrich mobility stakeholders, besides enhancing road safety, reducing congestion, and improving accessibility of personal mobility, it bears certain risks. On the one hand, societies, industries, firms, and economies can benefit from the globally growing market for connected vehicles enabled by digitization. On the other hand, cybersecurity threats emerge due to the increasing complexity of products with a rising number of electronic control units and lines of codes. Furthermore, the fragmented supply chain results in more complex policing and security. The rise of connected vehicles implies that stakeholders potentially have to reposition themselves within the mobility ecosystem and establish their roles along the value chain. Adapting across functions and reflecting upon new partnerships will be pivotal for companies to sustainably succeed on the market. Consumer, technology, and regulatory conditions will likely determine the full deployment of connected vehicles. This paper first distinguishes the relevant terms, explores the market environment, and studies the shift in the mobility ecosystem and value chain. Fundamentals of cybersecurity threats and connectivity features are investigated. Hacking the mind of a hacker, the discussion of potential cybersecurity solutions follows.
Interactive Building Metadata Normalization

Having standardized metadata is the first step toward deploying smart building applications over heterogeneous buildings. Such a conversion process is highly manual because of different conventions in existing building metadata and diverse building configurations. Many machine learning methods have been attempted to ease the process by reducing the amount of experts' training examples and reusing the knowledge in different data sets. However, many of the end-users, such as building managers and commissioning practitioners, are unfamiliar with machine learning and programming interfaces. We implement and demonstrate a web-based graphical user interface whose workflow is designed based on a common programming interface, Plaster, for building metadata normalization. We implement three algorithms, Zodiac, BuildingAdapter, and Scrabble, though any new algorithms can be added. Users are instructed for proper actions with information visualization at each step to easily complete the procedure. The service is freely available at https://plaster.ucsd.edu.
수업설계안에 나타난 예비 중등수학교사의 통계적 소양 수준 분석연구

This study aims to investigate the statistical literacy of pre-service mathematics teachers and to discuss the impact of statistical literacy in lesson plan. For the purpose of this study, we analyzed 13 pre-service mathematics teachers' statistical literacy tests and lesson plans. The results of th...
Quantum Entanglement Criterion for Rank of Block Matrix Vector Group Based on Density Matrix

Abstract The determination of quantum state separability is the basic problem of quantum entanglement theory. By judging the rank of the matrix vector group formed by the density matrix block, the necessary and sufficient conditions for the separation of quantum pure States are given, and the necessary conditions for the separation of quantum mixed States are given, too. In the composite system 2 × 2, the rank of the vector group corresponding to the divisible pure state is 1, and the rank corresponding to the entangled state is 4.
Applying Surveys and Interviews in Software Test Tool Evaluation

Despite the multitude of available software testing tools, literature lists lack of right tools and costs as problems for adopting a tool. We conducted a case study to analyze how a group of practitioners, familiar with Robot Framework (an open source, generic test automation framework), evaluate the tool. We based the case and the unit of analysis on our academia-industry relations, i.e., availability. We used a survey (n = 68) and interviews (n = 6) with convenience sampling to develop a comprehensive view of the phenomena. The study reveals the importance of understanding the interconnection of different criteria and the potency of the context on those. Our results show that unconfirmed or unfocused opinions about criteria, e.g., about Costs or Programming Skills, can lead to misinterpretations or hamper strategic decisions if overlooking required technical competence. We conclude surveys can serve as a useful instrument for collecting empirical knowledge about tool evaluation, but experiential reasoning collected with a complementary method is required to develop into comprehensive understanding about it.
Path Planning for Unmanned Vehicles Based on Value Function Approximation Algorithm

This paper deals with the path planning problem for unmanned vehicles based on reinforcement learning. Considering the unmanned vehicles' dynamic model, the neural network is used to approximate the value function. Besides, in order to make it more suitable for practical applications and speed up the learning process, the recursive least squares algorithm is used to eliminate the inverse operation. Then some experiments are implemented to verify the effectiveness of the proposed improved value function approximation algorithm. It is proved to have improved the generalization performance of reinforcement learning in continuous space.
Analysis of skill fashion designer

Abstract Fashion is always evolving. A fashion designer must have a corresponding hardskill and sofskill were with the character of each fashion. The purpose of this research was analyzed fashion designer. This type of research is qualitative, descriptive. Data retrieval method using interviews and documentation. The interview was conducted against 5 persons with educational background designer. The results showed that was fashion designer is as follows. the ability to communicate, creativity, reckless, capable of working together, confident, have a good time management, negotiation ability, being able to have a create a stress is able to analyze the market
Evasion Attack for Fingerprint Biometric System and Countermeasure

Currently, biometrics is being widely used for authentication and identification of an individual. The biometric systems itself needs to be more secured and reliable so it they can provide secure authentication in various applications. To optimize the security, it is vital that biometric authentication frameworks are intended to withstand various sources of attack. In security sensitive applications, there is a shrewd adversary component which intends to deceive the detection system. In a well-motivated attack scenario, in which there exists an attacker who may try to evade a well-established system at test time by cautiously altering attack samples, i.e., Evasion Attack. The aim of this work is to demonstrate that machine learning can be utilized to enhance system security, if one utilizes an adversary-aware approach that proactively intercept the attacker. Also, we present a basic but credible gradient based approach of evasion attack that can be exploited to methodically acquire the security of a Fingerprint Biometric Database.
Supervised Hashing for Retrieval of Multimodal Biometric Data

Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopted for most unimodal and multimodal biometric identification tasks.
On the Insecurity of Generalized (Rivest-Shamir-Adleman) - Advance and Adaptable Cryptosystem

Abstract This paper explores the security claims of the Generalized (Rivest-Shamir-Adleman) - Advance and Adaptable Cryptosystem, in short the GRSA-AA cryptosystem. In the GRSA-AA design proposal, the public key n is defined as the multiplication of two large prime numbers, while the values of encryption key E and decryption key D are relying on the result of multiplying 2 k large prime numbers called N where n divides N . The GRSA-AA claimed that the brute force is necessary to break the cryptosystem even if the integer n was factored. Nevertheless, this paper aims to show that this scheme is insecure once n is factored. The mathematical proof is presented to show that it is easy to generate an alternative value to the private key D without brute-forcing, yet successfully break the system.
Fast Tree-Based Classification via Homogeneous Clustering

Data reduction, achieved by collecting a small subset of representative prototypes from the original patterns, aims at alleviating the computational burden of training a classifier without sacrificing performance. We propose an extension of the Reduction by finding Homogeneous Clusters algorithm, which utilizes the k-means method to propose a set of homogeneous cluster centers as representative prototypes. We propose two new classifiers, which recursively produce homogeneous clusters and achieve higher performance than current homogeneous clustering methods with significant speed up. The key idea is the development of a tree data structure that holds the constructed clusters. Internal tree nodes consist of clustering models, while leaves correspond to homogeneous clusters where the corresponding class label is stored. Classification is performed by simply traversing the tree. The two algorithms differ on the clustering method used to build tree nodes: the first uses k-means while the second applies EM clustering. The proposed algorithms are evaluated on a variety datasets and compared with well-known methods. The results demonstrate very good classification performance combined with large computational savings.
Research on Ontology Perception Technology in Recommendation of Professional Books

Abstract A book recommendation method in the paper is proposed by using the professional book ontology perception method. By establishing a model framework based on ontology technology, the ontology triggering mechanism of professional books is combined to obtain relevant information parameters and improve the higher recommendation accuracy, so as to more accurately calculate the regional weights and preferences of professional books, and complete the professional books and the recommendation process. Through the evaluation and testing of the theoretical methods, the feasibility and accuracy of the research content in the paper is proved, which contributes to the improvement of book recommendation service.
Line and Ligature Segmentation for Nastaliq Script

The accuracy rate of recognizing ligatures in Urdu Character Recognition mostly relies on the accuracy with which the segmentation has been performed to convert Urdu text into lines and ligatures. Generally, it has been seen that the ligature based segmentation yields better results rather than character based segmentation. In this paper, we present a technique for segmenting Urdu text images into lines and then to ligatures. A hybrid approach has been used, which employs top-down technique in order to perform line segmentation and bottom-up technique in order to segment lines into ligatures. The various issues like broken lines, diacritic association, overlapping has also been discussed.