Reviewerly/exHarmony huggingface dataset:
Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.
Blockchain Structure and Cryptocurrency Prices

I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinct features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility.
Various Data Deduplication Techniques of Primary Storage

In this emerging era of data-driven applications, volume of data has been significantly increased due to new types of data generation device as well as storage devices. So a large amount of repeated data will be generated and stored in cloud services. Repeated data will be drastically reducing the storage space of the cloud environment. So that we need to do the data deduplication that is data block or chunk exactly stored only once in the environment. Data deduplication can be applied in different storage types, archives or backup storage, primary storage, disk drives, and RAM. The contribution of this paper classifies the data deduplication techniques in the primary storage system and identifies the performance and challenges of each technique in primary storage. Finally, ongoing research issues and undefined design points of deduplication techniques in primary storage are recognized and discussed. Primary storage system contains mutable data that means data often changed or removed, but the backup storage system mainly contains immutable data that is which never changed frequently and also some data could not be deleted at any time. In this article, we are analyzed various deduplication techniques of primary storage.
3D Face Authentication Software Test Automation

The 3D face authentication has become a hot trend for researchers and developers in the recent years, due to its many advantages over the 2D face recognition feature. The 3D reconstruction of a human face using both near-infrared and depth sensors is a complex process on mobile phones. It commonly involves algorithms like face detection, face landmark detection, facial feature extraction, and depth information analysis. The 3D face authentication feature is critical for the user as per as security and also providing a convenient way to authenticate by the correct user. Therefore, the testing of 3D face authentication algorithms and applications in terms of functionality, performance, and stability is critical. However, the research on 3D face authentication application level validation and testing method is lacking in the field. Most testers are still validating the application manually. In this paper, we propose a robotic-arm-based test automation for testing the 3D face authentication feature on mobile phones. We programmed a 6 degree of freedom (6-DOF) robotic arm to perform 3D face authentication automated tests that were executed manually before. Our test automation also benchmarked the performance of an in-house developed 3D face authentication application and a 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> party application which yielded promising latency and accuracy comparison results under different performance-impacting test scenarios.
Data-driven learning and younger learners

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book provides a range of studies that seek to address how corpus-based data-driven learning with young learners is finally emerging, how it is being applied, and how it can be infused into classroom practice in innovative ways. It discusses the need for constructive alignment for data-driven learning (DDL) involving younger learners in the second languages (L2) classroom. The book focuses on the training of pre-service teachers in corpus use and DDL lesson planning in a secondary L2 German context in France, dealing specifically with the barriers to trainees’ appreciation and use of corpora for language learning. It outlines both technical and conceptual barriers to corpus innovations as evidenced in trainees’ attempts to create an L2 learning activity involving the use of corpora for DDL, with evidence from observations and interviews.
Analytical Study of Agile Methodology in Information Technology Sector

With increasing demand of agile methodology in software development, almost every software organization is trying to move far from previous methods to adopt agile methodology in software development. In place of being extrapolative, agile somewhat being adaptive and focuses on customer satisfaction. It supports self-organized groups that work for technical excellence to increase agility. But number of team members is an issue that is in turn controlled by team perspective. The technique used in this research focuses on three diverse sized agile groups making software's using the same technologies. Both objective and subjective actions are used and the outcomes are reinforced by a study. The result clearly demonstrates that for better results in agile software development, it is critical to select the right persons to be considered as a good team. This paper focuses on different programming advancement techniques, their benefits and demerits, and how to choose the appropriate strategies for a specific circumstance. In addition it examines agile methodologies with the help of a case study that focuses on the advantages of agile over the conventional methods used in the software industry. This paper introduces a concept namely “Two Way Assessment” an approach to boost the process to make it more effective by identifying and removing the defects.
A Novel Approach for Software Defect prediction Based on the Power Law Function

Power law describes a common behavior in which a few factors play decisive roles in one thing. Most software defects occur in very few instances. In this study, we proposed a novel approach that adopts power law function characteristics for software defect prediction. The first step in this approach is to establish the power law function of the majority of metrics in a software system. Following this, the power law function’s maximal curvature value is applied as the threshold value for determining higher metric values. Furthermore, the total number of higher metric values is counted in each instance. Finally, the statistical data are clustered into different categories as defect-free and defect-prone instances. Case studies and a comparison were conducted based on twelve public datasets of Promise, SoftLab, and ReLink by using five different algorithms. The results indicate that the precision, recall, and F-measure values obtained by the proposed approach are the most optimal among the tested five algorithms, the average values of recall and F-measure were improved by 14.3% and 6.0%, respectively. Furthermore, the complexity of the proposed approach based on the power law function is O ( 2 n ) , which is the lowest among the tested five algorithms. The proposed approach is thus demonstrated to be feasible and highly efficient at software defect prediction with unlabeled datasets.
PERCEPTUAL METHOD FOR MRI MEDICAL IMAGES IMPROVEMENT IN PRESENCE OF IMPULSE NOISE

Medical imaging technology gives a good solution in the diagnosis and treatment of patients suffering from a serious illness. Noise removal from medical images is an important task in medical examination to increase the accuracy in disease detection. Diagnostic imaging is a common term for a wide field of medical examinations such as X-ray, ultrasound images, computed tomography and magnetic resonance imaging. Creating clinical human body images by magnetic resonance imaging (MRI) is an important technique in modern medical science. Unfortunately, medical images suffering from various types of noise. This paper focuses on impulse noise detection and removal system in medical scanned images. The designed system presents a new approach to impulse noise detection in medical images based on visual perceptibility. The detected noise is removed by applying a nonlinear filter to the corrupted pixel only. The proposed method is examined compared with classic filtering procedure and it acts effectively in preserving image details while removing the noise.
Identification of Static and Dynamic Signs of the Mexican Sign Language Alphabet for Smartphones using Deep Learning and Image Processing

The Mexican Sign Language (MSL) is a language with its own syntax and lexicon.It is used by the deaf people, who use it to express thoughts, ideas and emotions.However, most of hearing people are unable to understand this language.The alphabet of any Sign Language (SL) is composed of signs where each sign corresponds to a letter of the alphabet of the dominant language in the region, for example, Spanish or English.Most signs of a signed alphabet are static, that means, they are only composed by the configuration of the hands.However, there are letters that are represented by signs that include movement.The present work proposes a system that, using artificial vision techniques and image processing, identify the 27 letters -including dynamic and static signs-of the Spanish alphabet in a mobile application.To solve the problem of sign identification it was used a combination of image processing techniques and deep learning.Canny and Camshift algortihms was implemented for the recognition of edges and trajectories in signs with movement.Once the characteristics were identified, the K-means and Tensorflow algorithms were used to classify the signs.The system achieves a 92% accuracy in the alphabet sign detection.
Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

Abstract Many have argued that datasets resulting from scientific research should be part of the scholarly record as first class research products. Data sharing mandates from funding agencies and scientific journal publishers along with calls from the scientific community to better support transparency and reproducibility of scientific research have increased demand for tools and support for publishing datasets. Hydrology domain‐specific data publication services have been developed alongside more general purpose and even commercial data repositories. Prominent among these are the Hydrologic Information System (HIS) and HydroShare repositories developed by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). More broadly, however, multiple organizations have been involved in the practice of data publication in the hydrology domain, each having different roles that have shaped data publication and reuse. Bibliographic and archival approaches to data publication have been advanced, but both have limitations with respect to hydrologic data. Specific recommendations for improving data publication infrastructure, support, and practices to move beyond existing limitations and enable more effective data publication in support of scientific research in the hydrology domain include: improving support for journal article‐based data access and data citation, considering the workflow for data publication, enhancing support for reproducible science, encouraging publication of curated reference data collections, advancing interoperability standards for sharing data and metadata among repositories, developing partnerships with university libraries offering data services, and developing more specific data management plans. While presented in the context of CUAHSI's data repositories and experience, these recommendations are broadly applicable to other domains. This article is categorized under: Science of Water &gt; Methods
Selecting Optimal Submatrix for Syndrome-Trellis Codes (STCs)-Based Steganography With Segmentation

Syndrome-Trellis Codes (STC), as the de facto state-of-the-art steganography framework, has drawn huge attention recently.However, the original STC merely developed with the general form of cover and message, and almost all the subsequent improvements for STC focused on the cover selection and design of the distortion cost function.How to select the optimal submatrix by the features for the given cover and message is an important issue and requires to be solved urgently.In this work, we propose to divide both the cover and message into segments and embed each message segment into its optimally matched cover segment.We name the proposed method as Segment-STC steganography.Specifically, by investigating the features of the submatrix used in STC, we first select the optimal submatrix, which could effectively reduce the number of distorted elements during the embedding.Then, the given cover and message will be split into a series of segments.Each message segment is adaptively matched with an optimal submatrix.We conduct the experiments on both the benchmark BOSS dataset and our collected on-line songs dataset.The experimental results show that, compared with the original STC, our method could effectively reduce the distortion and improve the undetectability of the stego.
Elite Representative Based Individual Adaptive Regeneration Framework for Differential Evolution

Differential evolution (DE) is a popular optimization algorithm which attracts numerous research studies and is applied widely. To address the problem of population stagnation in DE, this paper proposes an elite representative based individual adaptive regeneration framework (EIR) that can be incorporated into any DE variant easily. EIR is able to renovate stagnated individuals to regions centering around the weighted value of the individuals themselves and the elite representatives based on the Gaussian distribution with distinct crossover rates. Elite representatives are sampled from dynamically varied elite swarms for different individuals. A less greedy substitution mechanism considering the proximity of fitness values is introduced to avoid exclusion of some valuable information from inferior solutions. The whole regeneration and substitution mechanism is dynamically adapted according to the evolutionary process and the fitness ranking status of current individuals to balance between exploitation and exploration capability. Experimental results on CEC 2014 benchmark functions of $30D$ , $50D$ , $100D$ and two real world optimization problems show that the proposed EIR framework can significantly improve the performance of two standard DEs and six state-of-the-art DE algorithms.
A SERENDIPITOUS RESEARCH PAPER RECOMMENDER SYSTEM

In recent times, the rate at which research papers are being processed and shared all over the internet has tremendously increased leading to information overload. Tools such as academic search engines and recommender systems have lately been adopted to help the overwhelmed researchers make right decisions regarding using, downloading and managing these millions of available research paper articles. The aim of this research is to model a spontaneous research paper recommender system that recommends serendipitous research papers from two large and normally mismatched information spaces using Bisociative Information Networks (BisoNets). Set and graph theory methods were employed to model the problem, whereas text mining methodologies were used to process textual data which was used in developing nodes and links of the BisoNets graph. Nodes were constructed from weighty keywords while links between these nodes were established through weightings determined from the co-occurrence of corresponding keywords originating from both domains. Final results from our experiments ascertain the presence of latent relationships between the two habitually incompatible domains of magnesium and migraine. Word clouds indicated that there was no obvious relationship between the two domains, but statistical significance investigations on the terms indicated the presence of very strong associations that formed information networks. The strongest links in the established information networks were further exploited to show bisociations between the two habitually incompatible matrices. BisoNets were consequently constructed, exposing terms and concepts from two discordant domains that were bisociated. These terms and concepts were utilised in querying the one domain for recommendations in another domain. Hence, serendipitous recommendations were made since our bisociative knowledge discovery methodologies revealed hidden relationships between research papers from diverse domains. Finally, it was postulated that latent relationships exist between two incompatible domains, and when well exploited, it leads to the discovery of new information and knowledge that is useful to researchers in various fields, especially those engaged in multi-disciplinary research. Further research is being conducted to identify outlier linkers and connectors between domains of diverse subjects.
Convolutional Neural Networks and Impact of Filter Sizes on Image Classification

Deep Neural Networks (DNN) in the past few years have revolutionized the computer vision by providing the best results on a large number of problems such as image classification, pattern recognition, and speech recognition. One of the essential models in deep learning used for image classification is convolutional neural networks. These networks can integrate a different number of features or so-called filters in a multi-layer fashion called convolutional layers. These models use convolutional, and pooling layers for feature abstraction and have neurons arranged in three dimensions: Height, Width, and Depth. Filters of 3 different sizes were used like 3×3, 5×5 and 7×7. It has been seen that the accuracy on the training data has been decreased from 100% to 97.8% as we increase the filter size and also the accuracy on the test data set decreases for 3×3 it is 98.7%, for 5×5 it is 98.5%, and for 7×7 it is 97.8%. The loss on the training data and test data per 10 epochs could be seen drastically increasing from 3.4% to 27.6% and 12.5% to 23.02%, respectively. Thus it is clear that using the filters having lesser dimensions is giving less loss than those having more dimensions. However, using the smaller filter size comes with the cost of computational complexity, which is very crucial in the case of larger data sets.
Predicting and reining in application-level slowdown on spatial multitasking GPUs

Predicting performance degradation of a GPU application at co-location on a spatial multitasking GPU without prior application knowledge is essential in public Clouds. Prior work mainly targets CPU co-location, and is inaccurate and/or inefficient for predicting performance of applications at co-location on spatial multitasking GPUs. Our investigation shows that hardware event statistics caused by co-located applications strongly correlate with their slowdowns. Based on this observation, we present Themis with a kernel slowdown model (Themis-KSM), which performs precise and efficient online application slowdown prediction without prior application knowledge. The kernel slowdown model is trained offline. When new applications co-run, Themis-KSM collects event statistics and predicts their slowdowns simultaneously. In addition, we also propose a two-stage slowdown prediction mechanism (Themis-TSP) for real-system GPUs without any hardware modification. Our evaluation shows that Themis has negligible runtime overhead, and both Themis-KSM and Themis-TSP can precisely predict application-level slowdown with prediction error smaller than 9.5% and 12.8%, respectively. Based on Themis, we also implement an SM allocation engine to rein in application slowdown at co-location. Case studies show that the engine successfully enforces fair sharing and QoS.
Practical Anonymous Attestation-based Pseudonym Schemes for Vehicular Networks

Vehicular communication systems increase traffic efficiency and safety by allowing vehicles to share safety-related information and location-based services. Pseudonym schemes are the standard solutions providing driver/vehicle anonymity, whilst enforcing vehicle accountability in case of liability issues. State-of-the-art PKI-based pseudonym schemes present scalability issues, notably due to the centralized architecture of certificate-based solutions. The first Direct Anonymous Attestation (DAA)-based pseudonym scheme was introduced at VNC 2017, providing a decentralized approach to the pseudonym generation and update phases. The DAA-based construction leverages the properties of trusted computing, allowing vehicles to autonomously generate their own pseudonyms by using a (resource constrained) Trusted Hardware Module or Component (TC). This proposition however requires the TC to delegate part of the (heavy) pseudonym generation computations to the (more powerful) vehicle's On-Board Unit (OBU), introducing security and privacy issues in case the OBU becomes compromised. In this paper, we introduce a novel pseudonym scheme based on a variant of DAA, namely a pre-DAA-based pseudonym scheme. All secure computations in the pre-DAA pseudonym lifecycle are executed by the secure element, thus creating a secure enclave for pseudonym generation, update, and revocation. We instantiate vehicle-to-everything (V2X) with our pre-DAA solution, thus ensuring user anonymity and user-controlled traceability within the vehicular network. In addition, the pre-DAA-based construction transfers accountability from the vehicle to the user, thus complying with the many-to-many driver/vehicle relation. We demonstrate the efficiency of our solution with a prototype implementation on a standard Javacard (acting as a TC), showing that messages can be anonymously signed and verified in less than 50 ms.