Reviewerly/exHarmony huggingface dataset:
Analysis of Feature Enhancements in Visual Secret Sharing

Visual Secret Sharing (VSS) aims at sharing of secrecy among many shadows depending on the number of dealers. These encrypted shadows themselves cannot reveal secrecy. A group of a pre-qualified number of shadows is sufficient to obtain the secret image. The quality of this secret is usually approximate to the original confidential image. This technique has evolved, offering solutions to the demands of the matured Internet domain. This paper analyzes the feature advancements in the VSS field. The VSS approaches remain distinct from the conventional cryptography, thereby offering new security solutions in many applications. Security being the central and challenging demand in contemporary information systems, the analysis of the state-of-the-art VSS stands significant.
Recognizing Preferred Grammatical Gender in Russian Anonymous Online Confessions

We present annotation results for a dataset of public anonymous online confessions in Russian ("Overheard/Podslushano" group in VKontakte, posts tagged #family). Unlike many other cases with online social network data, intentionally anonymous posts do not contain any explicit metadata such as age or gender. We consider the problem of predicting the author's preferred grammatical gender for self-reference, a problem that proved to be surprisingly hard and not reducible to simple morphological analysis. We describe an expert labeling of a dataset for this problem, show the findings of predictive analysis, and introduce rule-based and machine learning approaches.
Improved Feed Forward Attention Mechanism in Bidirectional Recurrent Neural Networks for Robust Sequence Classification

Feed Forward Attention (FFA) in Recurrent Neural Networks (RNNs) is a popular attention mechanism to classify sequential data. In Bidirectional RNNs (BiRNNs), FFA concatenates hidden states from forward and backward layers to compute unscaled logits and normalized attention weights at each time step and softmax is applied to the weighted sum of logits to compute posterior probabilities. Such concatenation corresponds to the addition of individual unnormalized attention weights and unscaled logits from forward and backward layers. In this paper, we present a novel attention mechanism called the Improved Feed Forward Attention Mechanism (IFFA), that computes the probabilities and normalized attention weights separately for forward and backward layers without concatenating the hidden states. Finally, weighted probabilities are computed at each time step and averaged across time. Our experimental results show IFFA outperforming FFA in diverse classification tasks such as speech accent, emotion and whisper classification.
Background and Notation

In this chapter we establish the mathematical notation used throughout this book and introduce the basic foundation of machine learning that this text builds upon. Readers generally familiar with this field can cursorily read this chapter to become familiar with our notation. For a more thorough treatment of machine learning, the reader should refer to a text such as (Hastie, Tibshirani, & Friedman 2003) or (Vapnik 1995).
An Efficient Approach to Image Indexing and Retrieval Using Haar Cascade and Perceptual Similarity Index

Image retrieval is a critical task in many fields such as indexing and Content-Based Image Retrieval is a tried and tested method for image retrieval. With advances in graphics and visualization techniques, image retrieval should be easy, quick and flexible. The aim of this work is to develop an efficient image indexing and retrieval system to collate similar images or images based on conditioned queries in the form of an image itself. The indexing method has been performed using wavelet transform and related mechanisms. Its detailing can easily entail description from a broad range to a narrow range. The indexing algorithm applies a Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) for Image Quality Assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. Results show that the algorithm used is able to retrieve and display images with high retrieval accuracy and medium relevance.The proposed methodology has been tested against known image retrieval techniques and was found to be comparable and in some cases better than most prior known techniques. The testing was performed on images of varied types, from multiple datasets and sizes. The accuracy was found to be 99% in most cases. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.
A case study of search-based path planning for a semi-autonomous mobile robot using visibility algorithms

Machines' capability to reproduce human abilities is the new source of technological advances. One of the most discussed approaches is the autonomous vehicles, machines that can move from place to place without human intervention in a safer way, since they can predict accidents. This research's goal is to bring forward algorithms to a semiautonomous mobile robot that has the purpose of searching an object in a known environment. To this end, algorithms that fit the case were selected and implemented. Such elected algorithms proved being able to construct the robot's intelligence efficiently.
Individual Dynamic Graduation Requirements Achievements of Students

In the engineering education accreditation system, it is clearly required that the major applying for certification should prove the achievement of graduation requirements with appraisal. The common appraisal methods of graduation requirements achievements (GRAs) focus on the whole situation of graduates, which lacks personalized guidance for students at school. Based on the analytical method of exam scores in the common methods, the paper presents the achievement of individual graduation requirements and designs algorithms to dynamically appraise every student with real examples, which may provide detailed data and immediate guidance for the major and its own students. The dynamic appraisal method can improve the GRAs of the major, and also can strengthen the core concepts of engineering education accreditation.
Approaches for Video Dehazing

This is a basic audit of dehazing impacts of pictures and videos, which is influenced by terrible climatic conditions. A video caught outside is influenced by the nearness of fog-like mist, fog and residual particles in the air. A few dehazing strategies are used to expel this undesirable dimness from recordings and constant video. From these perceptions, it is inferred that cloudy picture and video has low differentiation, re-establishment of the murky picture and video by upgrading its complexity will solve this issue. This strategy registers the airlight on an information murky picture and video then evaluates the transmission guide to amplify the differentiation of yield video and picture. latest research has demonstrated the capability of utilizing convolutional neural systems (CNNs) to achieve single picture dehazing. Here the system that made one stride further to investigate the probability of abusing a system to perform cloudiness expulsion for recordings is designed. Not like single picture dehazing, video-based methodologies can exploit the abundant data that exists crosswise over neighboring casings. The evaluated transmission map is therefore used to Get better a murkiness free casing through a climatic dispersing model. Broad tests exhibit that the proposed calculation performs well against the cutting edge techniques on both manufactured and certifiable recordings.
Practical Align Overview of the Main Frameworks Used by the Companies to Prevent Cyber Incidents

Among the biggest cybercrime or information security challenges, the information security professionals must be up to date with the new risks, cases, and different ways of attacks. Being up to date in this complex and aggressive scenario is a huge challenge and is a necessity to the security professional to fight against the cybercriminals. Additionally, based on this standard of requisites to start an information security program, an immature professional may be confused on the different frameworks used by the industries, mainly ISO/IEC 27000 family, NIST 800-53, NIST Cybersecurity Framework, COBIT, etc. This chapter will help the information security professional to decide where is important to focus efforts, to decide what is feasible and which control does not demand any additional investment. Additionally, this grade helps the InfoSec professionals to compare the information security maturity level within the companies and between the companies, comparing with benchmarks.
Shape Description of FDG uptakes in Pre and Postoperative fused PET/CT Images

Automatic object recognition with shape descriptors help to interact with the real-time environment. This paper describes the shape analysis of radioactivity Fluoro Deoxy Glucose (FDG) uptakes present in pre and postoperative stages of Fused PET/CT images using shape feature extraction approach. Shape features are invariant to various affine transformations such as translation, rotational, flipped, scale, etc., are more robust for shape analysis. Geometrical properties of the FDG uptakes are extracted at various levels of spatial resolution by hierarchical K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> abstraction from the segmented PET/CT images. Rotational invariants considered are the Zernike moments magnitudes and the shape signatures of Fourier descriptors are investigated. The discrimination power of features between pre and post-operative images is examined and evaluated.
Identifying Recent Telemedicine Research Trends Using a Natural Language Processing Approach

Conventional literature review processes undertaken by human experts require considerable effort. Automating them is elusive due to subtlety of concepts and complexity of interrelationships implicit in text semantics. However, when assessing topics relating to current trends, these factors are less important as there is inherent breadth and diversity in the text which countermands the need for expertise. This paper presents an approach for trend topic analysis using simple bibliometrics of Term Frequency and Keyword Selection, extracted with natural language processing tools NLTK and AntConc. This approach is applied to a case study of identifying trends in Telemedicine research in South Africa based on 2019 publications included in PubMed. Lists of topics generated by the analysis methods show consistency in identified trends and suggest their suitability to categorise small focussed corpora.
Comparison between Knowledge Network System and Collaborative Filtering in Recommender System

Recommender system is an effective tool to avoid amount of useless information given large-scale websites, which has achieved commercial success. Existing recommendation methods can be roughly divided into the content based filtering and collaborative filtering, and the latter one actually takes advantage of collective intelligence and is particularly popular because of its flexibility and independence of the item content description. Despite its popularity, there are at least two disadvantages with the collaborative filtering, respectively about the diversity of interest points and cold start. In this paper, a knowledge network system is introduced into the recommender system, which can be formed by collective intelligence in an evolutional way. Also using the collective intelligence, the proposed method is able to tackle or relieve the above disadvantages of the collaborative filtering. Specifically, it is able to recommend to users the closely related items based on an inherent interest structure and push items with diverse interest points. And by including a multi-round voting mechanism in the evolutional collective intelligence algorithm, the accumulation requirement of new records is reduced. Two more benefits with the proposed method are respectively the higher recommendation accuracy also based on the above mentioned multi-round voting mechanism, and the generalizable knowledge structure automatically extracted in the evolutional process. Explanative comparison with the collaborative filtering is made, and finally experimental comparison witnesses the efficacy of the proposed method.
Fuzzy Set Similarity for Feature Selection in Classification

A problem for machine learning research occurs when many possible features exist but the training data examples are very few. For example, microarray data typically have a much larger number of features, the genes, as compared to the number of training data examples, the patients. One approach is to first determine the best features for prediction and then to group features based on a measure of their relatedness. The concordance correlation coefficient has been used to place somewhat correlated features into disjoint groups of similar features. Multiple base classifiers are created by randomly picking one feature from each of the feature groups and then the collection of base classifiers is used in an ensemble classifier. Each classifier in the ensemble provides a vote. The majority vote is used to produce the final class prediction. This paper investigates grouping features using fuzzy set similarity measures as well as the concordance correlation coefficient as a relatedness measure. The performance of these different measures is compared in terms of accuracy, sensitivity, specificity, and F-measure using the ensemble classifiers created with the different relatedness measures. Four microarray gene expression data sets are used in the experiments to determine the usefulness of fuzzy set similarity measures and how they compare with the concordance correlation coefficient. Using the concordance correlation coefficient to guide clustering is not superior to fuzzy set similarity measures. Depending on the particular data set and performance measure being used, different fuzzy set similarity measures perform better than or just as well as the concordance correlation coefficient.
Mapping Items between Information Classification Standards Based on Semantic and Structure Similarities: a Pilot Study

Automatic mapping items between different versions of classification standards is challenging and has great values in practice, as these information classification standards play key roles in data exchange between different application systems in e-government, e-commerce, and other domains. In this paper, we propose an automatic method for mapping items between standards based on semantic and structure similarities: starting with mapping items of the same name, and employing thoroughly the semantic constraints embedded in the hierarchical category structures. Experiments on the Chinese national standard of classification and codes for fixed assets demonstrate that the proposed method could help to finish more than 74% of the task for mapping items between two versions of that Chinese national standard (v1994 and v2010), and is expected to dramatically reduce the time and lower the cost of manual mapping by domain experts.
A study of existing use case extensions and experience: a systematic review

Use case has embedded potentials that enable the diversity in its adoption to addressing other issues in software engineering. This objective of this paper is to gathered and classified use case researches carried out over the years and defines the state-of-art. We identified and analysed 62 relevant articles with deep insights into six different initiatives. The publications included 29 journal articles, 18 conference articles, 3 reports, 5 book chapters, 5 workshops and 2 symposiums. This study deduced that as many as 15 different extensions are proposed from use case ranging from formal descriptions to subjective methodology. Use case has evolved from initial functional requirement elicitation to a more formal and extensive structure. There are fewer publications in cloud computing and use case-based systems. This study contributed to the use case body of knowledge by assessing the extended initiatives and giving an overview of the challenges that are not yet resolved.
Ameliorated Reversibility in RDH-DSC of AES Encrypted Images for providing Authentication

The proposed paper discusses a technique to retrieve the encrypted images to obtain a better efficiency and embedding rate than other methods. This method infuses the Distributed source coding technique to improvise the hiding technique thereby ameliorates the reversibility rate. The flow of the technique goes as follows: Original image which is encrypted with the Advanced Encryption Standard (AES) and is hidden using Distributed source coding (DSC) technique. The former process is done by the possessor of the image and the latter is done by the data hider. The receiver can felicitate the image using the embedding key. If the receiver is provided with the encryption and embedding key, he can recover the original image with high security and lossless original data. Thus, this method is an exception compared to the existing technologies.
The Measurement System of DC Voltage and Ripple Based on C8051F350

For voltage instability problem of the industrial field, a measurement system for DC voltage and ripple was designed and a method realized by software was reported. The system was composed by voltage range-select circuit, DC voltage and ripple separate circuit, A/D and microcontroller. The method of voltage-separated made an adaptive measurement range and an accuracy of 0.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> / <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">00</sub> . It plays an important monitoring role in some devices which require accuracy DC voltage, and can provide timely equipment information to the users.
A Singular Value Decomposition based Maximal Poisson-disk Sampling for adaptive Digital Elevation Model simplification

The proposed method is to do simplification for Digital Elevation Model (DEM), which uses a few of original nodes representing the terrain surface while maintaining the accuracy. The original DEM nodes are sampled using the Maximal Poisson-disk Sampling (MPS), in which, the disk's size of each sample is computed on basis of the Singular Value Decomposition (SVD). MPS can generate the hyper-uniformly distributed samples and was taken to do DEM adaptive sampling by being combined with the geodesic metric. However, the geodesic distance computation is complex and the requirement for memory is high. As such, this paper proposes an extension of the classic MPS based method for selecting quasi-randomly distributed points from DEM nodes based on the distribution of eigenvalues, accounting for surface heterogeneity. To achieve this objective, uniform MPS is conducted to sample the DEM nodes by setting the related disk radius to be inversely proportional to the local terrain complexity, which is defined as an index expressing the local terrain variation. Then, the geodesic metric related parameters are implicitly contained in the defined index. As a result, more samples are concentrated in the rugged regions, and vice versa. The proposed method shows better perfermance, at least the results are comparable with the geodesic distance based Poisson disk sampling method. Meanwhile, it greatly accelerates the sampling process and reduces the memory cost.
Forensic Analysis of Bangla Handwritten Letters

Handwriting says a lot about a person. Sometimes the information hidden there becomes the most important clues for an investigation. Traditionally detectives take help from the Graphologists. However, given that handwriting analysis is mostly based on visual features, machine learning algorithms should be able to find out important features. In this research, we used a Bangla handwriting dataset to identify the age, gender, and location (district) of the writer using deep learning algorithms. To the best of our knowledge, we are the first to address these three features from Bangla handwriting. We found that age could be identified with around 87.2% accuracy, location with 65.2% accuracy, and gender with 55.8% accuracy. The main causes of the low accuracy are the complex geometric shapes of Bangla letters. Mastery of those shapes is clearly reflected by the age of the writers.
Transitional and Parallel Approach of PSO and SGO for Solving Optimization Problems

Optimization is finding minimization or maximization of a decision variable for a given problem. In most of the engineering problems, there is a requirement of optimizing some variables or other to obtain a desired objective. Several classical techniques are existing in optimization literature. However, when the optimization problem is complex, discrete, or not derivable there is a need to look beyond classical techniques. Swarm intelligence techniques are overwhelmingly popular in current days for targeting such kind of optimization problems. Particle Swarm Optimization (PSO) and Social Group Optimization (SGO) are belonging to this category. PSO being a popular and comparatively an older algorithm to SGO, the efficiency and efficacy of PSO for function optimization are well established. In this paper, an effort is made to explore an effective alternate model in hybridizing PSO and SGO. In the proposed model, a transitional concept is used. An alternate switching between PSO and SGO is carried out after a fixed iteration. An exhaustive simulation is done on several benchmark functions and a comparative analysis is presented at the end. The results reveal that the proposed approach is a better alternative to obtain effective results in comparatively less iterations than stand-alone models.
Research on dynamic mining of cloud data based on fuzzy analytic hierarchy process

In order to improve the detection and mining ability of cloud data block chain in distributed wireless communication network, a dynamic mining method of cloud data in distributed wireless communication network based on fuzzy analytic hierarchy process (AHP) is proposed. The heterogeneous storage structure model of cloud data in distributed wireless communication network is constructed. Fuzzy distributed detection method is used to detect the dynamic incremental characteristics of cloud data in distributed wireless communication network, and the distribution feature of association rules in distributed wireless communication network cloud data is extracted, and the block fuzzy feature matching fusion clustering method is used to cluster the cloud data packet chain in distributed wireless communication network. The cloud data packet block chain detection and recognition in distributed wireless communication network are carried out in the block fuzzy feature matching fusion clustering center, and the dynamic mining of cloud data in distributed wireless communication network is realized by combining fuzzy analytic hierarchy process (AHP). The simulation results show that the accuracy of cloud data dynamic mining in distributed wireless communication network is high, the precision of data mining is high, and the recognition ability is good.
New Italian Grammar in Practice

New Italian Grammar in Practice Author: Susanna Nocchi Alma Edizioni Italian grammar for English speakers. Also available for Chinese speakers. (Level A1 - B2) Free Pick up at School *Due to major USPS delays, Speakitaly NYC is not responsible for any lost or damaged packages once they are shipped and the tracking number is provided.