Reviewerly/exHarmony huggingface dataset:
Enriching Secure Secret Data Deletion through Public Variability

As the volume of the data increases at the user's end, they often outsource the task of data storage to the third parties. Generally, these third parties may be a Cloud Service Provider or Data Repositories or may be small data storage vendors. Each and every data storage service provider offers better options for the end user and lure them to increase their business. This also includes data access, data sharing and much more and Most of the times due to these schemes, encryption keys of the users often compromised to lead to data theft or hacking. So a Data eraser, or key eraser systems are brought into light to solve this, Where the key that is used to decrypt the data is deleted secretly to maintain the integrity of the user data. Some methodologies do exist to achieve this by using the help of the some hardware modules. So it is always a challenging task to achieve the same by providing a software solution. As a tiny step towards this proposed model uses the MAC address to encapsulate the private key and then delete it at the Trusted Platform module end. The proposed model uses the RSA Asymmetric key scheme to generate the private and public keys.
Exploration and Construction of Effective Teaching Environment

In view of the shortcomings of traditional classroom teaching, an effective teaching environment with four dimensions which includes individualization, knowledge, skills and meta-learning, is constructed to cultivate students to possess wide and deep knowledge. The proposed teaching environment is applied to the Object-Oriented Programming Course. Continuous exploration and practice results show that the model has a good reference guide for training high-quality innovative talents.
Review on Block Based Approach for Unconstrained Face Identification

Many approaches to unconstrained face identification exploit small patches which are unaffected by distortions outside their locality. A larger area usually contains more discriminative information, but may be unidentifiable due to local appearance changes across its area, given limited training data. We propose a novel block-based approach, as a complement to existing patch-based approaches, to exploit the greater discriminative information in larger areas, while maintaining robustness to limited training data. A testing block contains several neighboring patches, each of a small size. We identify the matching training block by jointly estimating all of the matching patches, as a means of reducing the uncertainty of each small matching patch with the addition of the neighboring patch information, without assuming additional training data.We further propose a multi-scale extension in which we carry out block-based matching at several block sizes, to combine complementary information across scales for further robustness. We have conducted face identification experiments using three datasets, the constrained Georgia Tech dataset to validate the new approach, and two unconstrained datasets, LFW and UFI, to evaluate its potential for improving robustness. The results show that the new approach is able to significantly improve over existing patch-based face identification approaches, in the presence of uncontrolled pose, expression, and lighting variations, using small training datasets. It is also shown that the new block-based scheme can be combined with existing approaches to further improve performance.
Measuring Inter-VM Performance Interference in IaaS Cloud

Virtualization has enabled cloud computing to deliver computing capabilities using limited computer hardware. Server virtualization provides capabilities to run multiple virtual machines (VMs) independently in a shared host leading to efficient utilization of server resources. Unfortunately, VMs experience interference from each other as a result of sharing common hardware. The performance interference arises from VMs having to compete for the hypervisor capacity and as a result of resource contention, which happens when resource demands exceed the allocated resources. From this viewpoint, any VM allocation policy needs to take into account VM performance interference before VM placement. Therefore, understanding how to measure performance interference is crucial. In this paper, we propose a simple experimental approach that can be used to measure performance interference in Infrastructure as a Service (IaaS) cloud during VM consolidation.
Automatic Pen writer for 2D Drafting

A new technology of pen is introduced by us in this paper an automatic pen which has an alleged psychic ability allowing a person to produce written words without actual writing. The automatic pen setup uses a CNC software to produce words. Automatic writing machine is a robotic hand which is used to write the characters, words from document. Pen is used to write the document stored in hard-disk. This automatic pen writer concept overcomes all the drawbacks faced due to all the traditional elements like pen, hard-disk, battery etc. all in an innovative manner. Automatic pen writer is designed for two main purpose, first is search the document in the hard-disk and start writing that document
Pre-Service Teachers’ Reactions to Education Teacher Performance Assessment

The purpose of study was to explore the experiences of teacher candidates about being assessed by the Education Teacher Preparation Assessment (edTPA) requirements during their student teaching practicum. Fifty-six elementary and adolescent majors working for a Master of Science Degree in Education participated in the study by responding to open-ended survey questions. The study aimed at answering two research questions: (1) What are the challenges/concerns that the student teachers report about their experiences of edTPA during their student teaching placements? (2) Do teacher candidates suggest edTPA remains as part of the teacher education program requirement? The findings of the study indicate that the teacher candidates are adamant about their unfavorable experiences of edTPA implementation. They expressed that they found edTPA requirements to be an additional burden, not beneficial, a distraction, and they suggest that edTPA should be discarded from current teacher education programing. While such findings call for considerations to revisit aspects of edTPA for improvement, further studies are suggested to add insight into the nature of edTPA implementation.
Multivariate Data Retrieval Modified by Random Noise using Lattice Autoassociative Memories with Eroded or Dilated Input Residuals

Lattice associative memories were proposed as an alternative approach to work with a set of associated vector pairs for which the storage and retrieval stages are based in the theory of algebraic lattices. Several techniques have been established to deal with the problem of binary or real valued vector recall from corrupted inputs. This paper presents a thresholding technique coupled with statistical correlation pattern index search to enhance the recall performance of lattice auto-associative memories for multivariate data inputs degraded by random noise. By thresholding a given noisy input, a lower bound is generated to produce an eroded noisy version used to boost the min-lattice auto-associative memory inherent retrieval capability. Similarly, an upper bound is generated to obtain a dilated noisy version used to enhance the max-lattice auto-associave memory response. A self contained theoretical foundation is provided including a visual example of a multivariate data set composed of grayscale images that show the increased retrieval capability of this type of associative memories.
Video Encryption and Decryption using Modified AES Algorithm

Video encryption is one of the most important technique nowadays for secured transmission. The proposed system uses modified AES algorithm as well as scrambling process in order to provide secured transmission of video. The AES algorithm is used because it is most secured standard for encryption and decryption. Hence the proposed system has been developed using modified AES algorithm and scrambling process, SPYDER (Python 2.7), ANACONDA2.
Penerapan Teknik Bagging Berbasis Naïve Bayes untuk Seleksi Penerimaan Mahasiswa

Students who graduate not on time create an imbalanced ratio between lecturers and students. The current selection system is ineffective because it has not been able to detect prospective students who have the possibility of not being able to complete their education on time so that many students who are accepted do not graduate on time and leave without completing their education. This condition causes a decrease in performance of study programs and institutions. The classification algorithm can use for classifying new students as graduate timely or not. Naïve Bayes classification algorithm can use to classify data in certain classes, using the history of alumni of informatics engineering at Pamulang university as training data and prospective student data as test data. Some attributes used to determine which label class to graduate on time and not on time are gender, school majors, year difference, math grades, English, Indonesian. To improve the results of the classification of Naïve Bayes, Bagging (Bootstrap Aggregating) technique is used. From the test results of the alumni dataset, the informatics study program using bagging techniques as an optimization of the Naïve Bayes classification algorithm has a lower failure rate than without using bagging techniques. The results of the calculation of performance data using bagging techniques can increase accuracy by 2.381% and AUC by 1.470% on the student graduation prediction model for new student selection using the Naïve Bayes classification.
Network Traffic Big Data Prediction Model Based on Combinatorial Learning

Network traffic is an unstable time series. Under uncertain conditions and the lack of sufficient data, traffic prediction is a complex problem. The grey markov model is a prediction model combining the classical grey theory and the state escape behavior of markov chains. In this paper, water conservancy information network data set provided by a company is used as experimental data for modeling. Considering the fast computing power of the statistical model and the non-stationary characteristics of the network flow time series, this paper first constructs the gray markov Verhulst model. By taking the difference between the predicted results and the real data as a new data flow, GRU neural network algorithm is introduced to construct the composite model. In this paper, three model algorithms proposed based on actual network traffic data, LSTM and GRU are respectively used for simulation experiments, and the prediction effects of the three models are analyzed and evaluated. Experimental results show that the three short-term network traffic prediction models proposed in this paper can achieve good prediction results, among which the grey verhulst-markov GRU neural network model has the best prediction effect.
APLIKASI METODE ROTATED GUMBEL COPULA UNTUK MENGESTIMASI VALUE AT RISK PADA INDEKS SAHAM PASAR ASIA

Value at Risk is a tool used to calculate the value of risk in investing. The purpose of this study was to estimate VaR in the portfolio using the rotated Copula Gumbel approach, which originated from the Archimedean copula family. Copula can provide an overview of the relationship between random VaRiables on a quantil scale which is very useful in explaining the interrelationships in extreme events. This VaR calculation is used in portfolios from the Indonesian stock index (JKSE), Malaysia (KLSE), Singapore (STI), and South Korea (KOSPI), in the period of June 1, 2016 to June 1, 2018 (519 data). VaR is calculated using a daily period with a confidence level of 99%. So that the VaR of each portfolio is obtained, JKSE-KLSE is 1.41%, JKSE-STI is 1.38%, JKSE-KOSPI is 1.39%, KLSE-STI is 1.44%, KLSE-KOSPI is 1.42%, KOSPI-STI is 1.48%. The highest risk level that can be derived from the portfolio contains a combination of the Singapore stock index (STI) and the South Korean stock index (KOSPI).
Algorithm Mismatch in Spatial Steganalysis

The number and availability of stegonographic embedding algorithms continues to grow. Many traditional blind steganalysis frameworks require training examples from every embedding algorithm, but collecting, storing and processing representative examples of each algorithm can quickly become untenable. Our motivation for this paper is to create a straight-forward, nondata-intensive framework for blind steganalysis that only requires examples of cover images and a single embedding algorithm for training. Our blind steganalysis framework addresses the case of algorithm mismatch, where a classifier is trained on one algorithm and tested on another, with four spatial embedding algorithms: LSB matching, MiPOD, S-UNIWARD and WOW. We use RAW image data from the BOSSbase database and and data collected from six iPhone devices. Ensemble Classifiers with Spatial Rich Model features are trained on a single embedding algorithm and tested on each of the four algorithms. Classifiers trained on MiPOD, S-UNIWARD and WOW data achieve decent error rates when testing on all four algorithms. Most notably, an Ensemble Classifier with an adjusted decision threshold trained on LSB matching data achieves decent detection results on MiPOD, S-UNIWARD and WOW data.
Features, Opportunities and Application of Control Cards Accumulated Sum. Part 1. Graphic Evaluation Method Dilution Technological Process

The article considers the possibility of maintaining in statistical controllable state the technological pro­cess with the use of control charts of accumulated sums. The principles of construction of these charts, features of the process disorder detection are shown. The feature of the control charts of accumulated sums is that, the decision regarding the statistical controllability of the pro­cess is made taking into account the preliminary information. The dots mapped do not correspond to indivi­dual measurement results or statistics, they display information about all the results obtained, from first to last, inclusive. This feature allows you to respond more quickly to signs of process disorder than when working with Shewhart control charts, where sample statistics are used for analysis, which are independent control results.&#x0D; The characteristics relation of the charts with the establishment of errors of the 1st and 2nd kind when interpreting them using the hypothesis testing apparatus is considered. The article presents the theoretical substantiation and approaches to the application of sequential LQ (likelyhood quotient) criterion in process monitoring, describes the main parameters that influence decision making. A graphical interpretation of the application of the LQ criterion is given to improve the perception of information.&#x0D; The method of graphical estimation of the process disorder with the use of V-mask, which is associated with the reversed LQ criterion, is also considered, if a disorder of the technological process is detected and it is necessary to set the time when it happened. At the same time, the right approach to constructing a V-mask template is very important, since in addition to the «opening» angle of the mask, the scaling factor must be taken into account. This coefficient is the ratio of the scale of the ordinate to the abscissa axis, and its failure to apply will lead to incorrect interpretation of the control results. A specific numerical example with an appropriate graphical representation of the results is considered.
A Corpus-Based Study of Hypotactic and Paratactic Thematic Relations in English and Urdu Clause Complexes

The present research inquires the paratactic and hypotactic thematic relations in terms of their grammatical realization, functional significance (Halliday, 1994) and thematic progression (McCabe, 1999). In the paratactic clause complexes, two or more independent clauses are joined by the coordinating conjunctions while in the hypotactic clause complexes, two or more independent and dependent clauses are joined by the subordinating conjunctions. The specific objectives of this research are: (1) to define the grammatical realization of paratactic and hypotactic thematic structures in the English and the Urdu texts, (2) to describe the functional significance of paratactic and hypotactic thematic structures particular to information flow and thematic progression in the English and the Urdu texts, and (3) to discuss how effectively the paratactic and hypotactic thematic structures in the English text have been translated into the Urdu text. The English text, Things Fall Apart by Chinua Achebe and its translated Urdu text, Bikharti Duniya by Ikram Ullah have been selected for this study. These texts have been annotated through the annotation scheme of UAM Corpus Tool (O&amp;rsquo;Donnell, 2008). The results reveal that the Urdu text uses multiple equivalents of conjunction either paratactic or hypotactic in the English text. Thematic progression patterns in both texts are mostly constant, linear and peripheral. The unmotivated displacement of paratactic and hypotactic themes causes ambiguity and change the information flow in the Urdu text. The present research is significant to support the systemic functional grammar of Urdu taking into account of English.
A Match Method of Encircled Marker-Points on External Store Model

In the high-speed and complex flow circumstance, the recognition, location and matching of the encircled markerpoints on the aircraft external store model are the important basic data for determining the separation characteristics of the model and the flight trajectory. In order to solve the matching problem of the marker-points on the small-sized external store, a algorithm of encoding and decoding with encircling markerpoints is proposed. The decoding algorithm calculates the distance of marker-points between head and tail and the direction vector to match the homonymous points. Such algorithm has the advantages of keeping the aerodynamic shape, easy to implement, and low cost. The experiments show that the decoding accuracy is higher than 94% in circumstance of long distance and large field of view, complex background and high ambiguity. In addition, the efficiency of decoding is also improved. By applying the decoding with encircling marker-points in the monocular vision measurement system, the experiments show that the proposed method can achieve fast recognition and high-precision matching. It has engineering practical value.
Cost Estimation Method of Coastal Navigation Sign Measurement and Control Project Based on Big Data's Analysis

Zheng, S.; Wang, L.; Chen, R.; Xing, F., and Chen, J., 2019. Cost estimation method of coastal navigation sign measurement and control project based on Big Data's analysis. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 830–835. Coconut Creek (Florida), ISSN 0749-0208.The method for estimating and controlling the coastal navigation mark measurement and control cost of the coastal navigation mark measurement and control project is to maintain the safety of the coastal navigation mark measurement and control project, to reduce the cost of the project and to guarantee the service level. The traditional cost estimation method of the coastal navigation mark measurement and control project is based on the experience-based evaluation of the cost prediction model of the coastal navigation mark measurement and control engineering, which cannot effectively meet the principle of minimizing the present value of the measurement and control cost of the coastal navigation mark and the maximization of the state index during the service life period. In this paper, a method for estimating the cost of a coastal navigation mark measurement and control project based on the characteristics of large data correlation is proposed, and the measurement and control model of the conventional coastal navigation mark is designed. Cost estimation model of coastal navigation mark measurement and control project based on performance and energy supply survival period assessment model based on comprehensive coastal navigation mark measurement and control strategy. The cost estimation model of the coastal navigation mark measurement and control engineering is designed by using the first-order Bessel function, a reliable index evaluation equation of the cost all-sample regression analysis is obtained, the measurement and control cost of the coastal navigation mark, the user cost and the social cost are calculated. The cost present value of the measurement and control project of the coastal navigation mark is minimized as the objective function, and the financial constraint and the performance constraint are obtained as the boundary conditions. The plan optimization of the cost of the measurement and control engineering of the coastal navigation mark is realized, and the measurement and control scheme of the coastal navigation mark with the minimum cost present value is obtained under the limited financial resources. The numerical simulation results show that the model can improve the reliability and reliability of the measurement and control engineering structure of the coastal navigation mark, optimize the measurement and control cost of the coastal navigation mark, and obtain the minimum measurement and control scheme of the coastal navigation mark with the minimum cost present value under the limited financial resources, thus saving the measurement and control cost of the coastal navigation mark by 15%.
Explanation Chains Model Based on the Fine-Grained Data

With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems.