Reviewerly/exHarmony huggingface dataset:
Smart Agro- Supply Chain Management

In this digital era, technological importance has been excellent support for making decisions in agriculture. The development of agriculture has been under development for the past few years due to a lack of technology usage and environmental changes. The aim of this paper is to reach farmers marketing through technology. The study used a statistical survey design technique to collect data from farmers for their awareness of e-Commerce. E-Agriculture is a platform to support the marketing of agricultural products. The main objective of our project is to establish a bridge between a farmer and a customer. The price will be fixed by the farmers, and there is no intermediate between farmers and the customers. So the customers also get their products (vegetables/fruits/grains, etc…) at the actual price, and also, the farmers get the right estimate from a customer.
A Concept Supporting a Resilient, Fault-tolerant and Decentralised Search

Centroid terms are single, descriptive words that semantically and topically characterise text documents and thus can act as their very compact representation in automated text processing tasks that strongly rely on the semantic similarity of texts. Algorithms to classify and cluster them make use of this information. In this book, the novel, brain- and physicsinspired concept of centroid terms is introduced and deeply discussed. Furthermore, their unique properties and practical usage in major natural language processing and text mining tasks are covered. In this regard, a new graph-based method for their fast calculation is presented as well. In contrast to methods relying on the bag-of-words model, the derived centroid distance measure can uncover
An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.
Using particle swarm optimisation and the silhouette metric to estimate the number of clusters, select features, and perform clustering

© Springer International Publishing AG 2017. One of the most difficult problems in clustering, the task of grouping similar instances in a dataset, is automatically determining the number of clusters that should be created. When a dataset has a large number of attributes (features), this task becomes even more difficult due to the relationship between the number of features and the number of clusters produced. One method of addressing this is feature selection, the process of selecting a subset of features to be used. Evolutionary computation techniques have been used very effectively for solving clustering problems, but have seen little use for simultaneously performing the three tasks of clustering, feature selection, and determining the number of clusters. Furthermore, only a small number of existing methods exist, but they have a number of limitations that affect their performance and scalability. In this work, we introduce a number of novel techniques for improving the performance of these three tasks using particle swarm optimisation and statistical techniques. We conduct a series of experiments across a range of datasets with clustering problems of varying difficulty. The results show our proposed methods achieve significantly better clustering performance than existing methods, while only using a small number of features and automatically determining the number of clusters more accurately.
Face Recognition Based on Viola-Jones Face Detection Method and Local Binary Pattern (LBP)

In this paper, the system consists of many steps, the first step includes the histogram equalization, detection, feature extraction, and classification. At first, the data set of a face image is segmented into four segments, after that Local Binary Pattern (LBP) algorithm is performed to extract features for each segment. The best feature vectors for all persons are stored in a new dataset in the next stage in order to be used in the testing phase. Finally, the accuracy rate of performance is evaluated to prove its robustness. Experiments show satisfying results and more accuracy achieved by the paper.
Analisis Tingkat Kejahatan pada Anak Dibawah Umur Menggunakan Metode FP-Growth

Crimes in minors are a series of negligence by parents who endanger or pose a dangerous threat to the child. The purpose of this study is to implement Data Mining, Association rule, and the FP-Growth Algorithm in cases of juvenile crime so that it can extract knowledge and important and interesting information from the database. The data source used is raw data that has not been processed and is a crime data on minors which are summarized in the form of reports from the West Sumatra Regional Police. The results of this study are in the form of software by analyzing data collected using the FP-Growth Algorithm and using the concept of FP-Tree development in searching for Frequent Itemset, for testing the results carried out with applications that have been designed namely the Php programming language. The results of testing are obtained from associations of crime cases that often occur in minors. So it can be seen that data mining using the FP-Growth Algorithm can be used to analyze cases of crime in minors as a material consideration for the police in order to know the ins and outs of crime in children so that it can assist the investigation process.
A Novel Architecture for Low Power Adiabatic Cipher

AES stands for Advanced Encryption Standard. It is widely used in today’s various security applications. S-box method is the most common and important in today’s data security and embedded applications.. This S-box consumes a considerable percentage of power of the whole system. S-box is very prone to differential power attacks(DPA). DPA is the most threatening types of attacks in cryptographic systems. In this paper, we have implemented one positive polarity Reed Muller type S-box is implemented using adiabatic logic. Efficient charge recovery logic(ECRL)is used here. FinFET based ECRL is used to implement the S-box has been observed and calculated .The performance of ECRL based S-box is compared with conventional CMOS based S-box. The statistical parameters for DPA cipher design are also analyzed.
An equalisation control method for network big data transmission based on parallel computing

In order to solve the problem of high output bit error rate and poor balance of data transmission of network big data, an equalisation control method for network big data transmission based on parallel computing is proposed in this paper. In the method, firstly, the information flow in the network channel model is reconstructed through the coherent correlation detection method, and then the channel impulse response of network is calculated. Secondly, according to the channel impulse response, big data output channels are improved and scheduled through the link sequential allocation method. Finally, frequency multiplication processing is performed to the big data output channels through the frequency domain expansion method, and adaptive equalisation allocation is performed according to the parallel computing conditions for channel output to achieve channel equalisation. A simulation experiment shows that the method proposed in this paper can provide relatively good channel equalisation in network bit data transmission and relatively strong adaptive allocation ability of output parallel computing, which reduces communication bit error rate of network.
Content Based Image Retrieval based on Domain Knowledge Acquisition

Enormous growth has been noted in digital technology over the years and this has led to massive growth in data mainly images and videos, hence with the volume of the data has also increased. Moreover, searching and retrieval of the accurate image from the huge dataset has become eminent as well as one of the challenging issue. In order to solve mainly two technique were used i.e. based on text and content. Moreover, content based is technique that is more efficient. In past several technique were used but they lack from the efficiency. Hence, in this paper we have proposed a methodology based named as DKA (Domain Knowledge Acquisition), DKA focuses on two main goals first is learning the similarities across the varied domain and other is learning on complex objects. The main advantage of DKA is the whether the object is whether from same domain or the other the retrieval process is very smooth. Moreover In order to evaluate the performance we have retrieved the top 5 response of query image and later it is compared with the several state-of-art technique and the existing method based on the precision parameter.
Load Balancing With Max-Min of Summations

Cloud computing is a computing tool for human-kind. In recent years, it is using to generate IT services, appliances for higher activities computing and outsourcing in a cost-efficient and flexible way. In modern times, a variety of types of bandwidth eater are growing speedily Cloud computing is growing phenomenal gradually to supply the different kinds of cloud services and applications to the internet-based customer. Cloud computing utilizes Internet applications to execute the large-scale jobs. The most important objective of cloud compu-ting is to allocate and calculate different services transparently throughout a scalable network of machines. Load balancing is one of the significant issues in Cloud Computing. Loads should be divided as CPU load, the capacity of memory and system load which is the measurement of work that a computation system performs. Load balancing is a modern method where the load is being shared amongst several machines of a distributed system to enhance the utilization of various applications and response time of multiple tasks and prevent overloading situation and under loading situation. In or approach, we developed an algorithm, LBMMS, which combines all least completion time. For this study, LBMMS presents the proficient deployment of various resources in cloud computing.
The QR Algorithm

In the exercise it is implicitly assumed that u*u ≠ 0 and therefore u ≠ 0.
Parsing and Creating JSON Objects with mJson

Many third-party APIs are available for parsing and creating JSON objects. Chapter 8 explores one of the simplest of these APIs: mJson.
Community Model - A New Data Fusion Filter Paradigm

Data fusion is an important area in different fields of applications as it provides a detailed analysis of the data store which helps in analytics and decision support system.Among the numerous Data Fusion models available in literature, the six pioneer pieces of work in terms of Data Fusion Models are -Waterfall Model, Multisensor Integration Fusion Model, Behavioral Knowledge-based Data Fusion Model, Omnibus Model, Dasarathy Model and JDL Model.In this paper, we have given a comparative study of the above models based on basic features like signalling overhead, power consumption, fault tolerance, survivability and robustness facilities subsequently.Here, a new improved model, named Community Model has been proposed for better throughput in terms of Data Transmission Time (DTT).This model can be applied to any number of filter levels irrespective of application areas.
Rotation and Projection Transformed Joint Feature Descriptors of Human Skeletal Action Recognition

Abstract：In robotics, human skeletal action recognition with joint-based representations are widely used in human-robot collaboration.Mining temporal and spatial information in action sequence is a challenging problem.Insufficient information extraction tends to bring the algorithm less discriminability.In view of this, a high-performance joint-based descriptor called rotational and projective skeleton signature (RPSS) is proposed.First of all, build person-centric coordinate system; then learn average limbs (skeleton segments) lengths from the training set; Secondly, rotate the skeleton by a set of angles, meanwhile project the rotated skeletons on a certain plane to get the sequence-features; Finally, use histogram of oriented gradient (HOG) to exact spatio-tempral information to generate a final RPSS description.The RPSS descriptors were tested on a number of public datasets and compared with classical and learning based description algorithms.The results show that it has decent discriminative power, and the experiments adding different degrees of noises and recording average calculation time show it also has strong robustness and real-time performance.
Notes on Contributors

Other| June 01 2021 Notes on Contributors Poetics Today (2021) 42 (2): 321–324. https://doi.org/10.1215/03335372-8883304 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Notes on Contributors. Poetics Today 1 June 2021; 42 (2): 321–324. doi: https://doi.org/10.1215/03335372-8883304 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsPoetics Today Search Advanced Search Liv Moeslund Ahlgren is an MA student in linguistics at Aarhus University. Her fields of interest include conversation analysis, fieldwork ethics, hate speech, and language and gender.Tore Rye Andersen, associate professor of comparative literature at Aarhus University, is the author of books on the contemporary American novel, serialization, and Indiana Jones. He has published widely on American and British fiction and on the mediality and materiality of literature in such journals as Narrative, Critique, English Studies, and Convergence and in several anthologies.Naomi S. Baron is professor emerita of linguistics at American University in Washington, DC. Her books include Always On: Language in an Online and Mobile World (2008) and Words Onscreen: The Fate of Reading in a Digital World (2015). She coordinates an international project for disseminating research findings about reading mediums (print, digital, or audio) to educators.Dorothee Birke, associate professor in English Literature... Issue Section: Contributors You do not currently have access to this content.
A Novel Clustering Algorithm Introducing New Denoising Technique

Breast cancer is one of common cancers in the developing countries. Detection at an early stage is very crucial for better chance of treatment. The techniques used to detect breast cancer are complex and time consuming. Computerized extraction of tumor areas from mammogram images is challenging due to shape and density of breast tumors which can sometimes surrounded by mucous (mucin). One of the challenges is to detect boundaries which can be blurred under noise factor. In this paper, we are introducing a clustering technique combined with specific structural features operations. A new noise elimination algorithm eases the noise problem and enhance the segmentation process using discrete cosine transform. Followed is the segmentation phase where classifying breast tumor from normal tumor are performed using a combined DCT and fuzzy c means algorithm. The contributions of the research are utilizing new filtering technique for noise removal. We also use Fuzzy C mean clustering algorithm using DCT information to determine the initial number of clusters. The tumor extracted segments are then transferred to the frequency domain using DCT and is used to for classifications. A test algorithm is implemented to classify new mammograms. Experimental results for all the proposed algorithms are extensively performed. The noise removal algorithm are proven robust. The experimental results of the search algorithm depicted different match and mismatch cases. 93% of the cases were a match case and predicted correctly. 5% were light cases and could not be detected from the images.
Job Recommendation System Implementation in Python vs. C++

Implementing a machine learning algorithm gives you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for the transformations on those structures. There are numerous micro-decisions required when implementing a machine learning algorithm, like Select programming language, Select Algorithm, Select Problem, Research Algorithm, Unit Test and these decisions are often missing from the formal algorithm descriptions. The notion of implementing a job recommendation (a classic machine learning problem) system using to two algorithms namely, KNN [3] and logistic regression [3] in more than one programming language (C++ and python) is introduced and we bring here the analysis and comparison of performance of each. We specifically focus on building a model for predictions of jobs in the field of computer sciences but they can be applied to a wide range of other areas as well. This paper can be used by implementers to deduce which language will best suite their needs to achieve accuracy along with efficiency We are using more than one algorithm to establish the fact that our finding is not just singularly applicable.
THE ISSUE OF THE VIOLENCE IN THE DIGITAL RHETORIC DIMENSION

This article deals with the analysis of the essence of the rhetorical representation of the topic of violence in the mass media, mainly, actualizing the problem of truth and objectivity.Drawing on the historical experience, it can be stated that humanity has developed along with the topic of violence (slavery, wars, revolutions, gender and racial discrimination, etc.).With the advent of digital (or media) space, there was rethinking of ordinary and, at first glance, understandable things that possibly have already become obvious for our existence.The digital space adds a new meaning, a new dimension to "old" problems, and assigns new tasks to the politics, economics, psychology, culture, philosophy and other scientific fields for studying this phenomenon and the processes around it.The topic of violence is considered in a new way due to the mass media, by transmitting information, informing the public about events that build our picture of the world, our attitude towards it, and due to manipulation, media themselves construct content with violence in respect of their consumers.An analysis of the topic of violence and its interaction with the mass media (and the media in general) indicates that there is an accentuation of the problems of "will", "gender", "reality and unreality" and "aggression", and that interest in issues related to human psychology grows.Thus, the topic of violence is raised anew due to new mechanisms of rhetorical representations, manipulative and pragmatic intentions of content "creators".Violence began to be spoken of not only as the content that creates the media space, but also as the practice of "creators" towards the public.Due to rhetoric and its methods and techniques, content creators achieve their goal: they evoke a particular emotional feeling in a person and build a particular attitude towards one or another object.In ancient times, discourse was centered on "text" and "speech", but today it expands beyond rhetoric, acquiring new dimensions in the form of videos, photos, banners, audio files, etc.This research is a socio-philosophical review of the topic of violence, digital rhetoric and its mechanisms of effect, and rests on an analysis of the topic of the truth of violence.
Genetic programming for region detection, feature extraction, feature construction and classification in image data

© Springer International Publishing Switzerland 2016. Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.
An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.
Child exploitation site is shut down

An analysis of Bitcoin payments allowed law enforcement investigators to locate a major child exploitation website, shut it down and make hundreds of arrests.