Reviewerly/exHarmony huggingface dataset:
Peak Signal to Noise Ratio analysis in single image restoration technique

Images have a wide significance these days for detailed observation in different fields like remote sensing, and navigation. When images got captured by the image acquisition equipment, they got affected by environmental effects. These environmental effects are haze, smog, fog which cannot be ignored while acquisition. Such type of effects should be reduced so that images can be accurately observed. These effects can be considered as noise in our work. These effects are difficult to reduce efficiently while acquisition. Therefore, there arises a need for an algorithm to process the noisy image after capturing. In our work, Dark Channel Prior (DCP) technique is used for the de-noising purpose and our focus is on the refining of the estimated transmission map so that a noise-less image can be recovered. The efficient noise reduction is determined by the Peak Signal to Noise Ratio (PSNR) value. PSNR value is the evaluation parameter which is used to determine that the proposed work has improved the performance compared to recently introduced approaches. An analysis of our proposed method and other research work is shown.
Solving Nonlinear Equation Systems Using Multiobjective Differential Evolution

Nonlinear equation systems (NESs) usually have more than one optimal solution. However, locating all the optimal solutions in a single run, is one of the most challenging issues for evolutionary optimization. In this paper, we address this issue by transforming all the optimal solutions of an NES to the nondominated solutions of a constructed multiobjective optimization problem (MOP). In the general case, we prove that the proposed transformation fully matches the requirement of multiobjective optimization. That is, the multiple objectives always conflict with each other. In this way, multiobjective optimization techniques can be used to locate these multiple optimal solutions simultaneously as they locate the nondominated solutions of the MOPs. Our proposed approach is evaluated on 22 NESs with different features, such as linear and nonlinear equations, different numbers of optimal solutions, and infinite optimal solutions. Experimental results reveal that the proposed approach is highly competitive with some other state-of-the-art algorithms for NES.
Pfeiffer Library: Open Resources: Welcome

This guide walks users through the definition of open source and explains the various options for classroom use.
Gradient-Based Expanding Spherical Appearance Models for Femoral Model Initialization in MRI

While deep learning strategies for semantic segmentation increasingly take center stage, traditional approaches seem to take a backseat. However, in the domain of medical image processing, labeled training data is rare and expensive to acquire. Thus, traditional methods may still be preferable to deep learning approaches. Many of these conventional approaches often require initial localization of the structure of interest (SOI) to provide satisfactory results. In this work we present a fully automatic model initialization approach in MRI, that is applicable for anatomical structures that contain a near-spherical component. We propose a model, that encapsulates the difference between intensity distribution within the SOI’s spherical component and its proximity. We present our approach on the example of femoral model initialization and compare our initialization results to a diffeomorphic demons registration approach.
Academic Collaboration Recommendation Based on Sparse Distributed Representation

The researchers with similar research contents are helpful for understanding the relevant field and the promotion of further exchanges and collaborations. Find proper researchers is a complex task, we proposed an innovative approach of collaboration recommendation based on sparse distributed representation (SDR). According to the sparse distributed representation theory, the text contents of authors' thesis are characterized to vectors, then recommend collaborations based on the similarity of vectors. We also validate our approach using the data set of NIPS, the experimental results show that the method we proposed is effectively used for collaborative recommendation.
Humanistic Psychology Study of Abraham Maslow on the Main Character in Tiba Sebelum Berangkat Novel by Faisal Oddang

This article aims to analyze the main character in the novel Tiba Sebelum Berangkat by Faisal Oddang using the humanistic psychology approach of Abraham Maslow. The method used in this research was descriptive qualitative research method. The data source used was Tiba Sebelum Berangkat novel by Faisal Oddang. The data analysis technique in this study used the content analysis. The results of this study indicated that the main character in the novel Tiba Sebelum Berangkat by Faisal Oddang had and performed actions that can be analyzed by using Abraham Maslow's hierarchy of needs. This was shown by the content of the novel which presented about conflicts around human life; beliefs, religions, gender and love. Tiba Sebelum Berangkat novel tried to reflect on what might actually happen in people's real life in South Sulawesi. Based on the novel content, the main character built in the novel performed certain actions in obtaining multilevel needs that must be fulfilled as human beings. Tiba Sebelum Berangkat novel showed how humans obtained multilevel needs that must be fulfilled until human self-actualization was achieved.
Vocational Education Institution Leaders’ Virtues and Educational Leadership Profile in Latvia: a Personality Perspective of Education Quality

Considering the topicality of vocational education and training (VET) in Latvia, and the relevance of school leaders' personality and leadership abilities for education quality, the purpose of this research paper was to explore the associations between VET institution leaders' civic, intellectual, moral and performance virtues, and the different dimensions of their leadership (charismatic, cultural, emotional, formative, participatory, professional, administrative and anticipatory), using mixed methods research for exploring staff's and students' views about it.Two groups of research questions guided the study: one about the pedagogical leadership dimensions: What are the most salient pedagogical leadership dimensions of three high-level Latvian VET institutions' leaders; what are the differences between students' and staff's opinions about how their institution leaders embody those dimensions?; and the other group of questions about the associations between leaders' virtues and leadership dimensions: Which leadership dimensions and virtues are more often associated in respondents' discourse about their institution leaders; and what are the differences between respondents' groups regarding these associations?Data collected in 3 high-quality VET institutions from different fields (tourism, sports and maritime education) were used for secondary analysis.The quantitative part of the study was based on 83 answers to the standardized questionnaire "Pedagogical Leadership and Quality of Education".The qualitative part of the study was based on seventeen semistructured interviews: 6 with leading staff, 5 with teachers and 6 with students.Latent content regarding leaders' virtues was explored using qualitative data analysis software AQUAD 7.
Does Blockchain “Decentralize” Everything?: An Insight from Organizational Economics

There have been increasing expectations that blockchain technology would decentralize organizations in a wider range of services such as sharing economy, public ledgers, and electricity. On the other hand, decentralization is facing difficult challenges such as seen in the split of Bitcoin and the growing expectation on permissioned ledgers. This chapter aims to shed light on the economic mechanisms behind blockchain-enabled decentralization from the viewpoint of organizational economics. An analysis is provided with an in-depth case study of the Bitcoin ecosystem. Results reveal that blockchain technology decentralizes organizations by reducing uncertainty through codifying tasks and also by reducing the risk of opportunism through the governance by distributed participants, while those decentralization applies only to a fraction of the whole ecosystem. Additionally, decentralization sacrifices workers’ incentives such as income risk aversion and efficient decision-making. Therefore, the extent of blockchain-enabled decentralization is determined by the trade-offs between efficiency through tasks marketization and workers’ incentives.
Discipline Convergence in Networked Systems (Dagstuhl Seminar 18261)

This report documents the program and the outcomes of Dagstuhl Seminar 18261 Discipline Convergence in Networked Systems. This seminar explored emerging networked system design approaches, seeking to increase performance, efficiency and security through the convergence of disciplines: compute, storage and networking. With technologies such as network function virtualization (NFV) having started the convergence of computing technologies and networking technologies, this seminar discussed new research directions to embrace the convergence of disciplines that used to be mainly isolated in the past.
KALEMBO ADE AS AN INSTRUMENT OF RESOLVING CONFLICT AMONG MBOJO COMMUNITY: A STUDY ON SPEECH ACT

This study aims at describing variation of meaning carried in kalembo ade as an instrument of resolving conflict among individuals and groups in Mbojo community by applying speech act analysis. Data were collected from speech events in Mbojo community and further analyzed descriptively. One type of speech acts was taken by considering its relevancy towards to focus of this study, i.e. expressive. Results showed that kalembo ade dealt with one of types of speech acts, i.e. Expressive type. The expression was found to reflect some actions, including condolences, apologies, greetings, thanksgivings, refusal, and congratulations. Hence, by viewing the context of usage, members of Mbojo community used this expression as an instrument in communication to resolve conflict, in which the phrase was used as tool to control verbal interaction during a speech event.
A fast-blind IP watermark detection scheme based on position fuzzification

The rapid advancement in internet of things (IoTs) has created new demands for intellectual property (IP) protection. Existing zero-knowledge-based IP watermark detection schemes have low performance in real-time detection. To address this issue, we propose a position fuzzification-based IP watermark detection scheme mainly aiming to reduce the rounds of inquiry. Firstly, a random sequence is produced by chaos system to scramble all the resource positions of an IP design. Then watermark positions are fuzzified to make the scramble algorithm irreversible. A verifier can achieve zero-knowledge proof of IP ownership by one round of inquiry. Experiments show that the proposed scheme can greatly reduce computational complexity during blind IP watermark detection and enhance performance in real-time watermark detection.
Simplified experimental scheme of quantum algorithm for solving linear equations with single photons

Linear equations play an important role in nearly all fields of research in science and engineering. Recent studies have shown that quantum algorithms for solving linear systems of algebraic equations provide exponential speedups over their classical counterparts. Here, we present a simplified experimental scheme for implementing the quantum algorithm for solving linear equations with multiple degrees of freedom (DoF) of single photon. Compared with the previous studies, our scheme not only presents excellent efficiency, it requires fewer photons to solve the same problem. Our work highlights the potential applications of multiple DoFs of single photon in quantum information processes.
Do check your spam folders if using ...

Do check your spam folders if using askimet and you can always go directly to the site endpoin to check by sending a manual webmention:
Design of a Classifier System using Machine Learning Techniques for Diffuse Goiter Diagnosis

A Diffuse goiter is usually an auto immune disease where the thyroid gland situated in the neck becomes enlarged. It is one of the most common thyroid disorders. In this paper, a diagnostic classifier system is designed to diagnose diffuse goiter using Thyroid Ultrasound (TUS) images.  Normal and goiter TUS images are pre-processed using Matlab software to extract Region of Interest (ROI). Relevant features were extracted from ROI. These features are validated by classifier system using different machine learning techniques- Naive Bayes, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Instance Based K-nearest neighbor (IBK) and J-48. The performance evaluation of the developed system is estimated using classification accuracy, time taken to build the model, Mean Absolute Error (MAE), Kappa Statistic and ROC Area. The obtained classification accuracies of different classifiers are in the range 88%-93%, 85%-91% and 83%-90% using 3-fold, 10-fold and hold-out (60%-40%) methods respectively. The results are very promising, indicating selected features were robust for diagnosis of diffuse goiter and also demonstrating the utility of machine learning techniques in medical domain.
Passing The Buck, the Bane of Corporate Social Responsibility in Nigeria

Corporate Social Responsibility in Nigeria has gained recognition as obtained in the developed economies of the world as higher proportion of organizations, both local and multinational operating therein have grafted it in their business philosophies.However, the weak legal system and lack of advocacy by the concerned stakeholders have been relegating the actual performance to the background.The host communities of these organizations (safe the explorative organizations) have been in a state of uttermost negligence while the little real CSR initiatives have been concentrated in already developed areas of big cities and health initiatives outside the reach of the uneducated community members of these organizations who have been avoiding their responsibilities to local communities by passing same to the local, state and federal governments who are the original owners of most resources and infrastructures in such communities.
An Enhanced Region-Based Model for Segmentation Images with Intensity Inhomogeneity

Segmentation of images with intensity inhomogeneity is always challenging due to low resolution, blurred boundaries and poor illumination. Although existing image segmentation methods were widely used, there exists some shortcomings in segmenting intensity inhomogeneous images, such as not considering the spatial interrelation within the central pixel and its neighborhood. Therefore, this paper proposes an enhanced region-based active contour model for segmenting images with intensity inhomogeneity. In this model, a range-based adaptive bilateral filter is utilized to preserve edge structures and resist the noise of the image. Then an effective energy functional is constructed into the level set framework. With the permission of keeping the original shape of the image, a regularization term is utilized to refrain from the process of re-initialization and speed up the curve evolution. In the end, some experiments on synthetic and real images and contrast with the classic segmentation models are executed. The proposed model is more accuracy than other classic models.
Automating Data Entry Forms for Banks Using OCR and CNN: Survey

Digitalization of money transfer is a must in the present situation of banking operations. Clients have a variety of ways to carry out transactions, such as credit, wiring money, and so forth. However, depositing cash requires the physical presence of the depositor at the bank, and cashier needs to enroll the transaction into the system, which slows down the rate of money deposit and teller's activity. To accelerate the process, banks around the world have to adapt and construct guidelines for a digital deposit. To accurately digitize and transmit deposit slip information from smartphones to the bank, a scheme called "Automating Data Entry Forms for Banks Using OCR and CNN". The deposit slip scanner algorithm is based on input from the Smartphone camera.
LibGuides: Grade 4: Exploring Connections

The Draft K-4 Curriculum (2018) is built around essential understandings that are consistent across the grades. The Guiding Questions that lead to outcomes are unique for each grade. Use New.LearnAlberta.ca for complete outcomes.
A Study on Efficient Training Method for Named Entity Recognition Model with Word Embedding Applied to PCA

The Bidirectional LSTM CRF model used for Named Entity Recognition takes much time to train Named Entity. The hyper-parameters of Word Embedding used as input data in this model affect performance and training time. However, there is very little research on the number of dimensions, which is one of the parameters of Word Embedding. In this paper, we obtain proper number of 4-Word Embeddings (fastText, GloVe, skipgram, CBOW) considering performance and training time in Bidirectional LSTM CRF which can input large amount of data. Next, apply the PCA to the word vector in Word Embedding to reduce the dimension to small dimensional (10 dimensions) intervals. Therefore, applying PCA to conventional Word Embedding and training Word Embedding with small dimensional intervals shows that the model can be trained by maintaining or improving performance based on stable training time in fewer dimensions than conventional Word Embedding.
Unified framework for data management in multi-cloud environment

With the recent emergence of multi-cloud environments, it has become crucial to manage data in them. This work proposes a data archival scheme which initially picks optimal clouds from a multi-cloud environment based on the data access patterns. The voluminous data obtained is then split into chunks and their access frequency is efficiently predicted. Depending on the prediction, suitable clouds are picked based on QoS attributes and weighted threshold coefficient. Further, the data is placed into the selected clouds by checking their resource availability. Data retention policies are proposed and applied to the data along with homomorphic encryption to provide security and to place the highest priority data into the appropriate cloud. The least frequently accessed data are also archived in suitable clouds. Results obtained in a heterogeneous cloud environment indicate the cost effectiveness of storing huge data in a multicloud environment to provide the best of services.
Acquiring Digital Evidence

The main task of a computer forensics investigator is to acquire and analyze computing devices’ memory images. In a nutshell, a memory image—widely known as a forensic image—is a static snapshot of all or part of the data on a computing devices’ secondary storage (e.g., HDD, SSD), attached storage device (e.g., USB thumb drive, external hard drive, magnetic tape), or RAM memory (when performing live acquisition on running systems). We can think of this image as a container of data, where you can store individual files or the whole drive/live memory files in one image file.
Initial Response and First Responder Tasks

When an incident that involves digital evidence is reported, the entity (public agency or private laboratory) responsible for conducting the investigation will send one or more individuals to investigate the case; this person is called the “first responder,” and he/she is responsible for conducting the initial investigation of the incident to determine its root cause.
Local Search-Based Recommender System for Computing the Similarity Matrix

Recommender systems reduce the users' effort in finding their favourite items among a great number of items. In collaborative-based RSs, there are different similarity measures like: genetic algorithms, Pearson and cosine-based similarity techniques. The number of items and personal attributes (e.g., environment, sex, job, religion, age, county, education, etc.) that are used by the similarity metric algorithms are increasing significantly which makes the recommendation task more difficult. In our project, we introduce a new RS that uses the local search algorithms to compute the similarity matrix. As far as we know, we have not found any work in the RS literature that uses local search algorithms techniques. We show that our new RS computes the similarity matrix and outperforms the other techniques like the Pearson correlation and cosine similarity and some of the recent genetic-based recommender systems.
Short Text Classification based on Feature Extension using Information in Images

With the quick development and extensive application of the Internet, there is a growing desire for people to share their life or opinions on social networks, which produces a mass of short texts.Short texts are characterized by short length, sparse features, and a lack of contextual information.Thus, it is difficult for conventional methods to achieve high quality classification performance.To achieve a higher classification accuracy, this paper proposes a novel short text classification method based on feature extension by incorporating the information of the images.Specifically, we first generate a sentence that descripts the images by image caption technology, and then we combine the generated sentence with the text as the input of the classifier.Meanwhile, we introduce a similarity module in terms of the correlation between the image and the short text so as to determine whether the two sentences are combined or not.Simulation results show that our proposed model significantly outperforms the state-of-the-art methods in terms of classification accuracy.
Russian Resources on the Internet

The explosive growth of foreign language resources on the Internet has been reflected in the Network Update column of th e [ALL Journnl over the last severa l years.However, as an instructor of Russian language, I was somewhat disappointed that the discussion lists dealing with Russian and other Slavic languages, as well as numerous Russian sites on the Inte rnet, have not even been mentioned in th e column.That prompted me to write this brief report.
HYPERGRAPH GRAMMAR BASED MULTI-THREAD MULTI-FRONTAL DIRECT SOLVER WITH GALOIS SCHEDULER

In this paper we analyze two dimensional grids with point and edge singularities in order to develop an efficient graph grammar based multi-frontal direct solver algorithm. We express these grids by hypergraph models. For these meshes we define a sequence of graph grammar productions expressing the construction of frontal matrices, elimination of fully assembled nodes, merging of resulting Schur complements, and repeating the process of elimination and merging until a single frontal matrix remains. The dependency relation between graph grammar productions is analyzed, and the dependency graph is plot, which is equivalent to the elimination tree of the multi-frontal solver algorithm. We utilize classical multi-frontal solver algorithm, and the graph grammar productions allows us to construct an efficient elimination tree, based on the graph representation of the computational mesh, and not the global matrix itself. The graph grammar productions are assigned to nodes of the dependency graph, and they are implemented as tasks in the GALOIS system and scheduled according to the developed dependency graph over the shared memory parallel machine. We show that our graph grammar based solver outperforms parallel MUMPS solver.