Reviewerly/exHarmony huggingface dataset:
Optimization of the lecture training strategy for students

Changing the needs of students in the consumption of information requires teachers to constantly search for new teaching methods, which, in turn, indicates the construction of their own learning strategy and its optimization. In this article, the authors present their own experience in developing a lecture learning strategy built on their own experience. Personal experience of teaching led to the emergence of the hypothesis that a change in the type of information provided (for example, from presentation to video, then to discussion, etc.) and the frequency of repetitions of important aspects of the topic can improve the learning. To test this hypothesis, the article attempted to use econometric modeling methods that made it possible to optimize the learning strategy taking into account the indicated factors.
Image registration with deep probabilistic classifiers: application in radiation therapy

We present the application of deep multi-class classifiers for registration of the pre-radiation image (CBCT) to the treatment planning image (planCT) in Radiation Therapy (RT). We train a multi-class classifier on different classes of displacement between 3D patches of images and use it for registration. As the initial displacement between images might be large, we train multiple classifiers for different resolutions of the data to capture larger displacements in coarser resolutions. We show that having only a few patients, the deep multi-class classifiers enable an accurate and fast rigid registration for CBCT to planCT even with significantly different fields of view. Our work lays the foundation for deformable image registration and prediction of registration uncertainty which can be utilized for adaptive RT.
Data Integrity Checking with File Sharing and Providing Privacy from Auditors

The advent of the cloud computing makes storage outsourcing becomes a rising trend that promotes the secure remote data auditing a hot topic that appeared within the analysis literature. Recently some analysis contemplates the problem of secure and economical public information integrity auditing for shared dynamic information. However, these schemes area unit still not secure against the collusion of cloud storage server and revoked cluster users throughout user revocation in practical cloud storage system. During this paper, we have a tendency to discover the collusion attack within the exiting theme and supply associate economical public integrity auditing theme with secure cluster user revocation primarily based on vector commitment and verifier-local revocation cluster signature. We style a concrete theme supported our theme definition. Our theme supports the general public checking and economical user revocation and additionally some nice properties, like with confidence, efficiency, count ability and traceability of secure cluster user revocation. Finally, the security and experimental analysis show that compared with its relevant themes our scheme is additionally secure and economical.
Survey on Reversible Data Hiding in Encrypted Images Using POB Histogram Method

This paper describes a survey on reversible data hiding in encrypted images. Data hiding is a process to embed useful data into cover media. Data invisibility is its major requirement. Data hiding can be done in audio, video, image, text, and picture. Here use an image for data hiding especially digital images and existing method (Histogram Block Shift Base Method) HBSBM or POB. Now a day's reversible data hiding in encrypted images is in use due to its excellent property which is original cover image can be recovered with no loss after extraction of the embedded data. Also, it protects the original data. According to the level and kind of application one or more data hiding methods is used. Data hiding can be done in audio, video, text, and image and other forms of information. Some data hiding techniques emphasize on digital image security, some on the robustness of digital image hiding process while other's main focus is on imperceptibility of a digital image. The capacity of digital information which has to hide is also the main concern in some of the applications. The objective of some of the papers mentioned below is to achieve two or more than two parameters i.e. Security, robustness, imperceptibility and capacity but some of the parameters are trade-off which means only one can be achieved on the cost of other. So the data hiding techniques aiming to achieve maximum requirements i.e. security, robustness, capacity, imperceptibility etc. and which can be utilized in the larger domain of applications is desired. Related work for techniques used for data hiding in a digital image is described in this paper.
CONVERGENT ANALYTICAL TOOLS FOR BIG DATA APPLICATIONS IN HADOOP ENVIRONMENT

Big Data Analytics offers an about interminable wellspring of business and instructive understanding, that can prompt operational improvement and new open doors for organizations to give undiscovered income crosswise over pretty much every industry.From use cases like client personalization, to hazard relief, to misrepresentation discovery, to inward tasks investigation, and the various new use cases emerging close every day, the value covered up in organization information has organizations hoping to make a front-line examination activity.Finding an incentive inside crude information presents numerous difficulties for IT groups.Each organization has various needs and various information resources.Business activities change rapidly in a regularly quickening commercial center, and staying aware of new orders can require readiness and versatility.In addition, a fruitful Big Data Analytics activity requires tremendous figuring assets, innovative framework, and exceptionally talented faculty.These troubles can make various exercises flop before they pass on regard.Beforehand, a nonappearance of enlisting power and access to computerization made a certified age scale examination movement past the compass of most associations: Big Data was too much expensive, with a ton of issue, and no sensible ROI.With the climb of conveyed registering and new headways in figure resource the load up, Big Data gadgets are more accessible than some other time in ongoing memory.
HDCNN-CRF for Biomedical Text Named Entity Recognition

Biomedical named entity recognition (BNER) is one of the most basic and important tasks of biomedical text mining. LSTM does not take full advantage of parallelism, making recognition slower. This paper focuses on improving the model structure and proposes a HDCNN-CRF method which combines hybrid dilated convolutional neural network (HDCNN) and conditional random field (CRF). It can not only avoid the expensive cost of human participation in feature construction, but also greatly improve the speed compared with LSTM method in named entity recognition (NER). We use Adam for optimization during model training and the IOBES tagging method for labeling the sequence. The HDCNN-CRF model that does not rely on any costly feature engineering has shown good performances on the NCBI-disease corpus. Due to its high degree of parallelism, the model speed is four times higher than BLSTM.
ANALYSIS OF STUDENTS PERFORMANCE METHODS USING MODIFIED K-MEANS MODEL

:- Machine Learning is a field of artificial intelligence that can use past information for the future purpose. Machine learning is similar to data mining in the way that both are looking for the pattern. Machine learning can detect a pattern in data and adjust the action. Machine learning is a field that is used in every system. Machine learning is used in the educational system, pattern recognition, Games, Industries, Social media services, online customer support, Product Recommendation Etc. In the education system, its importance becomes more because of the future of the students. There is a huge amount of data in higher education because nowadays every student is looking for higher education, so there is more need for machine learning methods in the education system. Many methods are there for the analysis of the students ’ performance. Hidden information is carried out with the use of data mining, which will help in the analysis of students ’ information. There is a huge amount of data in education and all the data are useful for students as well as for teachers. With the growth of institute, it becomes more importance of machine learning technology in the educational field. Clustering is one of the basic techniques often used in analyzing data sets. Many clustering techniques are there but modified K-means is one of most efficient and used method. Classification techniques are also there and the most popular is the decision tree. A decision tree is also a method used for analysis of the students ’ performance but compared to modified K-means, it is less stable. The unsupervised algorithm is discussed. These make use of cluster analysis to segment students into groups according to their characteristics. Elbow method is there to determine the cluster size; it will help in the optimal solution. Elbow method looks over the arm and elbow point is there. With the help of machine learning concept, it is easy to improve the result and future of students. It is not only useful for students but also for teacher and institute to improve their result. Keywords: - Data mining, EDM, K-means, Decision Tree, Students data.
Learn to Imitate Using Sequence to Sequence Network with Attention

Imitation is an important beginning step for intelligent beings to learn more complicated concepts, especially from each other. Nevertheless, imitation by it self is arguably an ability that need to be learned. In this work, we demonstrate how simple imitation behavior can be learned by using sequence to sequence network with attention. As a proof of concept, an agent is developed that is capable of taking in an English word, letter by letter, as input and generating an output sequence of letters that make up to the same word as the input. By doing so, the agent is proved to possess the ability to imitate. The input English word is first transformed through an encoder network into a set of context vectors, which may either contain semantic information of the word or some other abstract concept meaningful only to the agent. Next, the context vectors are fed to a decoder network to generates an output sequence. It is observed that an attention mechanism in the decoder network can bring improved performance and much faster convergence during training. Imitation behavior was successfully created which in turn proved the relevance of the context vectors. Perhaps more importantly, the context vectors generated can be further processed to help with more advanced tasks such as complex concept learning and reasoning. The ideas presented in this work can be used to generate other more realistic robotic imitation behaviors as well. Tasks such as vocally repeating a spoken word/sentence heard by the agent, duplicating a sequence of moves performed by another robot, etc., can be broken down to the task of duplicating a sequence of sounds or moves codified by a sequence of letters, words, or numbers.
Преподавание математических дисциплин в условиях цифровизации

The article focuses on the issues of modernizing the teaching of mathematical disciplines in the context of the digitalization of pedagogical objects: the educational process, methodological systems for teaching mathematical disciplines, educational paths for the development of key and subject competencies in the field of mathematics, mathematical and simulation modeling. The demand for digital technologies and digital products for the practice of mathematical preparation of the future bachelor is noted. From a methodological and research point of view, digital technologies are described that are essential for improving the quality of mathematical training.
Research Guides: Library Workshop Descriptions: Wordpress

Information, materials, and schedules for all currently offered library workshops Workshops offered by UO Libraries on using Wordpress
ATR and Lethal Autonomous Robots

The term autonomous stems from the Greek autos (self) and nomos (law). Autonomous implies reduced human control as compared to automatic (or automated) control. An automated car will be less intelligent and independent than an autonomous car. It will be less able to learn, understand, and adapt to new situations. Driverless and autonomous are nearer to synonyms, as are self-driving and automated. When developed, a truly driverless car will not require a human to take control in difficult situations. It will be able to deliver packages without any humans onboard. Virtually all current automatic (a.k.a. aided) target recognizers (ATRs) leave decisions about weapons delivery to humans. A future autonomous platform could have an embedded ATR, but no humans in direct control. That is the premise of this chapter.
Identifying the Forged Regions on the Images Using Shared Memory Model

In this paper, the representation of some of the regions on the image which were using without the information of the actual image users was given.The images which were using on now days are with good quality and the forging of these images is a little bit difficult rather than the old images.Most of the existing systems are struggling to identify the same for the images.Hence in the current paper, a model known as copy move forge detection using the shared memory model is given thought and implemented.The primary job of us is to identify the number of region son the images were being copied and to inform the developers about the number of regions on the images forge and using for other purposes.Several image formats like the JPEG, PNG are given thought for checking the current model.As per result, it is observed that the current model is working excellent and the results shows that the model is well suited for identifying the various numbers of forged regions on the images.
Geometric Discriminant Analysis for I-vector Based Speaker Verification

Many i-vector based speaker verification use linear discriminant analysis (LDA) as a post-processing stage. LDA maximizes the arithmetic mean of the Kullback-Leibler (KL) divergences between different pairs of speakers. However, for speaker verification, speakers with small divergence are easily misjudged. LDA is not optimal because it does not emphasize on enlarging small divergences. In addition, LDA makes an assumption that the i-vectors of different speakers are well modeled by Gaussian distributions with identical class covariance. Actually, the distributions of different speakers can have different covariances. Motivated by these observations, we explore speaker verification with geometric discriminant analysis (GDA), which uses geometric mean instead of arithmetic mean when maximizing the KL divergences. It puts more emphasis on enlarging small divergences. Furthermore, we study the heteroscedastic extension of GDA (HGDA), taking different covariances into consideration. Experiments on i-vector machine learning challenge indicate that, when the number of training speakers becomes smaller, the relative performance improvement of GDA and HGDA compared with LDA becomes larger. GDA and HGDA are better choices especially when training data is limited.
Research Guides: Tips and Tricks for Teaching Remotely: Home

This guide is intended to introduce you to material and resources that may help you when teaching a class remotely.
Linear Support Vector Regression in Cloud Computing on Data Encrypted using Paillier Cryptosystem

The use of linear support vector regression on private data in cloud computing must consider data privacy. Homomorphic encryption is an approach to address the problem. However, most of the existing approaches still use inefficient fully homomorphic encryption, in which both the training data and the testing data must be encrypted using the same public key. This leads to the repetition of the training process. The problem is addressed in this paper by applying partially homomorphic encryption using Paillier cryptosystem. Operations in linear support vector regression are modified so that they can be applied to process encrypted data. The model is used to predict the motor and total UPDRS (Unified Parkinson's Disease Rating Scale) scores. To assess the performance of the model, the MRSE (Mean Root Square Error) of the prediction on encrypted data is then compared with the MRSE of the prediction on unencrypted data. The evaluation shows that the MRSE of the prediction on encrypted data is exactly the same as that on unencrypted data, which proves that the modification on the operations in linear support vector regression has been done correctly.
DIFFERENT TECHNIQUES FOR HEALTHCARE USING IoT SURVEY

The healthcare industry is in a state of great despair. Healthcare services are costlier than ever, global population is aging and the number of chronic diseases are on a rise.What we are approaching is a world where basic healthcare would become out of reach to most people, a large section of society would go unproductive owing to old age and people would be more prone to chronic disease.In Healthcare it has several applications.IoT explores new dimensions of patient care through real-time health monitoring and access to patients' health data. This data is a goldmine for healthcare stakeholders to improve patient's health and experiences while making revenue opportunities and improving healthcare operations.This paper will help to understand the flow of working of applications in healthcare domain also this paper enable the user to learn different IoT healthcare application and uses of the application. This paper also focuses on the use of IoT in remote primary healthcare services.
Noise reducing performance of anisotropic diffusion filter and circular median filter in digital images

The speckle noise is one of the prominent features found in ultrasound images and it tends to degrade the quality of the image. There is a historic success in the works towards this approach particularly in the medical field related to image de-speckling. In this paper, a scale-space nonlinear anisotropic diffusion process and circular median filter is being combined with un-sharp masking to filter out the speckle noise from the ultrasound images. However, the performance evaluation and the comparison of both the filters are being done at each level of the process by comparing the various parameters of image quality measures. The nonlinear anisotropic diffusion filter has found to be performing better by two decimal point values in comparison to the circular median filter.
User Modeling Systems Adapted to Virtual Environments

The user model represents a set of personal characteristics related to a user or types of users. One of the objectives when modeling a user within a virtual environment is to study their behavior during the development of a real-world activity or task. The challenge of modeling a user is complex because effort is required to represent their skills, behaviors, and knowledge among other user’s characteristics during the interaction with virtual environments. Adapting a virtual environment to achieve the maximum degree of efficiency in the development of tasks or processes requires structuring of the contents of the environment in such a way that it meets the usability, accessibility, and user adaptability criteria. The personalization of a virtual environment allows us to adapt the appearance and functionality of that environment according to the tasks, values, culture and needs of a user or a group of users.
초등학교 통합교과 실행 개선을 위한 교육과정 설계 방안 연구

The purpose of this study is to apply narrative and backward design 2.0 version as a way to improve the implementation of integrated subjects in elementary school. For this study, the researcher analyzed the integrated subjects textbook in accordance with themes to find the problems of textbook-based implementation of integrated subjects. The researcher developed the 'go to school' unit, which corresponds to the 'school' theme of the first grade of elementary school, using the narrative and backward design 2.0 version. As a result, the possibility of improvement of problems that can occur in existing integrated subjects implementation such as lack of understanding due to activity-based instruction and absence of awareness in the process of building knowledge due to fragmented knowledge acquisition has confirmed.
Analisis Sitiran Karya Ilmiah Dosen IAIN Purwokerto Tahun 2015-2017

This study aims to make a citation analysis of the scientific paper of a IAIN Purwokerto lecturer that has been published in an internal journal within three years (2015-2017). Using a quantitative approach is bibliometrics, a branch of library science that deals with statistics (mathematics). The population is all literature cited in scientific paper by the IAIN Purwokerto lecturers. Sampling of all lecturers' scientific papers that have been published in the journals Ibda, Almanahij, JPA, el-Jizya, Communica and Insania (6 journals) at 2015-2017. The data was obtained from the bibliography (citation) of the scientific paper of lecturers who had cited a number of 3,460 literature. The results of this study indicate that the form of literature that is widely cited is in the form of books (73.06%), the half-life of the literature is 13.5 years, and the availability of collections in the library is 54.42%. In addition, there are still alternative titles / library collection that make it possible to become an alternative reference material, namely a number of 156 library collection. It is recommended to lecturers in the IAIN Purwokerto environment to take part in the reference manager training, namely the application of writing citations (such as zotero or mendeley) so that writing of citation is more basic. This study recommends that an advanced bibliometric study be carried out, to dig deeper into the scientific paper of the IAIN Purwokerto academic community.
An Efficient And Expressive Keyword Search Over Encrypted Data In Cloud For Data Secuirity

Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient sincethey are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security, and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm [37], and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups.