Reviewerly/exHarmony huggingface dataset:
The Detailed Data on the Neural Compute Stick Acceleration Performance

The four trends such as real-time, intelligence security and privacy have spawned the rise of edge computing and front-end intelligence. The Neural Computing stick(NCS) can move calculations from the cloud to the terminal, and provide dedicated deep neural network acceleration for terminal AI devices. However, there is currently no detailed data comparison of the NCS acceleration performance. This paper compares the computational speed of convolutional neural networks on the Raspberry Pi in the presence or absence of the NCS. In addition, we further compare the calculated speeds of the CPU, GPU, and NCS through the vehicle model. The result shows that the NCS can achieve 4 to 6 times acceleration in neural networks.
Object Detection Algorithm based on Dense Connection

The way that information propagates in neural networks is of great importance. In this paper, we propose a connectivity pattern: dense connection, aiming to solve object detection algorithm YOLO-Tiny with less convolutional layers, low feature utilization rate, low precision and poor detection of small objects. We integrate dense connection into YOLO-Tiny, increasing its convolutional layers and improving the feature extraction network. Improved network extracts feature maps and fuses the feature maps by using the Dense Block module. Detection network completes the classification and location at different scales with different anchor boxes. We tested improved network on the Pascal VOC dataset. The experimental results show that our network has improved accuracy by 15% compared with the original algorithm. Although the detection speed has increased, it can still meet the requirements of real-time detection. Compared with the YOLO-Tiny model, our model size only increases by 9.8. MB, compared to the YOLO model, the model size is about 1/5 of the original.
Improved grey wolf optimization based on the Quantum-behaved mechanism

Grey Wolf optimization algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It has strong local search ability but weak global search ability. An improved grey wolf optimization is proposed, which combining quantum behavior strategy with grey wolf optimization algorithm, to prevent the algorithm from falling into local optimum. 5 benchmark functions are used to compare with other improved grey wolf optimization algorithms, which proves that the proposed algorithm has better optimization performance.
Use of Mobile Applications as Bilingual Educational Tools for Kindergarten and Primary School Children in Native Kukama-Kukamiria Communities in Iquitos - Peru

This work presents an experience related to the conception, development and validation of a mobile applicative created to be used as an educational tool guided to the basic teaching of mathematics and language for initial education (and primary) of Bilingual Educational Institutions of indigenous communities Kukama-Kukamiria. The Kukama-Kukamiria is one of the 48 official indigenous languages recognized in the Peru, and one of the 44 languages indigenous amazon Peruvians, most of these languages have serious extinction problems, manifested it with less and less number of speaking. In Peru the educational indicators related to the indigenous communities are mostly faulty, caused it for diverse factors among those that highlight the lack of educational contents and even the limited number and absence of educational. An applicative mobile offline has been developed, for mobile devices with S.O Android (cell phones and tablets), with basic content for initial education (and first grades of the primary one). This applicative was conceptualized as a tool playfull, interactive, based on sounds of the images. In this process they participated educational and members of the communities Kukama-Kukamiria of Iquitos. As part of the validation processes, a comparison test has been developed among the employment of the applicative and the use of the traditional teaching that it is used in the communities Kukama-Kukamiria. Favorable results have been achieved regarding the use of the applicative like tools for teaching in the classroom, leave it has demonstrated that the use of this applicative overcomes a series of limitations of the service educational Peruvians in native communities Kukama– Kukamiria.
STORAGE, ACCESS AND PROTECTION OF FULL GENOME SEQUENCING DATA IN RUSSIA AND FOREIGN COUNTRIES: PRACTICAL ASPECT

The relevance of the chosen topic is due to the need to resolve legal problems in the field of observance of human and civil rights and freedoms when storing, accessing and protecting full genome sequencing data. The purpose of this study is the formation of conceptual criteria on the basis of which a new model of regulatory regulation of this sphere of public relations will be built. To achieve this goal, the tasks of studying the regulatory legal acts in force in Russia and a number of foreign countries were solved. General scientific, private-scientific and special methods of scientific knowledge (system-structural, formal-legal) were used. In order to formulate conceptual criteria of practical importance for storing access and protecting genome-wide sequencing data in Russia and foreign countries, it was proposed to develop clarifying characteristics or gradation of human and civil rights and freedoms in the context of realization of public state interests. It is also necessary to unify the content of the conceptual apparatus of normative acts taking into account the peculiarities of genetic information, work out the procedure for accessing data, and provide for a system of its depersonification. For the first time, the authors substantiate the need to transform the content of the human rights declared by the state to life, freedom, personal and family secrets, and others with the development of new technologies in the field of DNA scanning. The basic criteria that are of practical importance for the storage, access and protection of genome-wide sequencing data indicate the need to improve normative concepts, establish categories of persons with the right to access such data, normatively fix the conditions for observing an anonymous survey, and also refuse to get acquainted with the results , to develop mechanisms for the depersonification of the obtained genetic information).
An Optimizing Preprocessing Algorithm for Enhanced Web Content

In the Web generation, a large amount of structured and unstructured data is present on the Internet. To extract meaningful information from the Web is an extremely difficult task. Preprocessing helps to understand a user query in the IR system. Knowledge extraction is a fundamental method for mining particular knowledge from information retrieval (IR). One of the major challenges determines in current web search vocabulary mismatch problem during the preprocessing. In IR system determine the limitation in web search the search query string is that the relationships between the query expressions and the expanded terms are limited. The query expressions relate to search term fetching information from IR. The expanded terms by adding those terms are most similar to the words of the search string. In this article, we mainly focus on behind user’s search string in Web. We identify the best features within this context for term selection in supervised learning-based model. In this proposed system the main consideration of preprocessing techniques like Tokenization, Stemming, and spell checking and to determine the correct words from the user search string, for the enhanced results for the user.
Action Recognition Using 3D CNN and LSTM for Video Analytics

With the advent of growing digital technology, large amount of video data is being generated, making video analytics a promising technology. Human activity recognition in videos is currently receiving increased attention and activity recognition systems are a large field of research and development with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while increasing safety (Guo and Lai in Pattern Recognit 47:3343–3361, 2014, [1]). The existing system for action recognition involves using Convolutional Neural Networks (CNN). Videos are taken as a sequence of frames and frame-level CNN sequence features generated are fed to Long Short-Term Memory (LSTM) model for video recognition. However, the abovementioned methodology takes frame-level CNN sequence features as input for LSTM, which may fail to capture the rich motion information from adjacent frames or multiple clips. It is important to consider adjacent frames that allow for salient features, instead of mapping an entire frame into a static representation. Thereby, to mitigate this drawback, a new methodology is proposed wherein initially, saliency-aware methods are applied to generate saliency-aware videos. Then, an end-to-end pipeline is designed by integrating 3D CNN with LSTM, followed by a time series pooling layer and a softmax layer to predict the activities in video.
Chaos Based Network Initialization Approach for Feed Forward Artificial Neural Networks

Weight initialization of sigmoidal feed forward artificial neural network (SFFANN) and the Convolutional neural networks (CNN) has been a known factor which affects the learning abilities of the neural network. The uniform random weight initialization approach has been quite often used as the conventional network weight initial technique, due to its simplicity. However, various researches have shown that the random technique may not be the ideal choice of weight initialization for these neural networks. In this work, we analyze two separate chaotic functions and explore the possibilities of these being used as the weight initialization methods against the conventional random initialization technique for SFANNs as well as for the CNNs. For the SFFANNs, this analysis were done over 8 function approximation problems chosen for experimentation. The mean test error values along with a two sample t -test results strongly suggest that the Chebyshev chaotic map based weight initialization technique outperforms the conventional random initialization technique for most of the problems under consideration and hence may be used as an alternative weight initialization technique for the SFFANNs. For the CNN experiment, the MNIST dataset was used for analyzing the performance of the random and the Chebyshev based initialization scheme. Results strongly support the use of the Chebyshev chaotic map based initialization scheme as an alternate to the conventional random initialization.
Cloning Vulnerability Detection in Driver Layer of IoT Devices

With the spread of the Internet of Things (IoT), the IoT operating systems have correspondingly increased and brought more potential security risks. For instance, it is not hard to find that many driver layer codes in IoT operating systems could come directly from open source projects, where the vulnerabilities would also be propagated. These vulnerabilities could leak sensitive information and even lead to arbitrary code execution. However, existing clone detecting tools have limitations, especially for clones with minor modifications. In this paper, we propose a method that can detect not only exact clones, but also clones with additions, deletions, and partial modifications. The proposed method uses code patches and program slicing to get precisely fingerprint of the restructured clones. Then the fingerprint matching is achieved through a greedy-based optimization algorithm. Afterwards, the detecting tool called RCVD is implemented based on the proposed method. Finally, the experimental results indicate that the method has a significant effect on detecting restructured cloning vulnerabilities. By this means, the Orange Pi and WisCam have been detected dozens of clone-caused vulnerabilities in the code of driver.
The SOC is Dead, Long Live the SOC!

Abstract The traditional tiered security operation centre approach is flawed, writes Matthew Smith, Global Head of Cyber Security, St. James's Palace - simply because we are human. We must leverage the benefits of machine learning, user behaviour analytics and security automation to deliver effective detection and response in 2020 and beyond.
Adapting Machine Learning Techniques for Credit Card Fraud Detection

Credit card fraud is a form of financial frauds growing every year and causing losses to financial multinationals as well as government sector. Traditional methods like manually detecting credit card frauds are feasible only for small datasets, but with the rise of big data, these methods are of no worth, hence has been proved as an unrealistic and time-consuming solution. Therefore, the finance industry is looking for help from data mining and machine learning methodologies in order to handle problems. But constructing rule for fraud detection algorithms is difficult because of imbalance nature of credit card dataset. Several challenges while mining credit card dataset with the experimental solution are presented through this paper. Undersampling and SMOTE oversampling techniques are applied to balance the real-time credit card transaction dataset, and then several supervised machine learning algorithms are compared to classify credit card frauds precisely. Credit card fraud detection model using logistic regression is pragmatic as the best classifier when compare to decision tree, support vector machine, and K-nearest neighbor. Confusion matrix and several other matrices are used during experiments to evaluate the performances of the credit card fraud detection model.
Attention-based Multi-task Learning for Sensor Analytics

Sensors and internet of things (IoTs) are ubiquitous in our modern day-to-day living. Applications range from smart home devices that control cooking ranges to mobile phones, wearable devices that serve as fitness trackers and personalized coaches. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from individual sensors. In this work we show that multi-task learning (MTL) is naturally suited for sensor data learning, and propose a novel multi-task learning approach with attention mechanism, M-Att, that jointly trains classification/regression models from multiple related tasks where data on each task is generated from one or more sensors. The temporal and non-linear relationships underlying the captured data are modeled using a combination of both convolution neural network (CNN) and long-short term memory (LSTM) models. And the attention mechanism seeks to learn shared feature representations across multiple tasks for improving the overall generalizability of the machine learning model. We evaluate our proposed method in both classification and regression settings on an activity recognition dataset and environment monitoring dataset. Comparing the proposed approach to other competitive single-task learning and multi-task learning approaches we demonstrate the high performance of our proposed model with promising results.
A Compact co-Firefly Algorithm for Matching Ontologies

Ontologies can provide the shared knowledge for the intelligent systems to implement their collaboration. However, the heterogeneous entities seriously hampers two ontologies’ communication. It is necessary to determine the entity mappings between two ontologies, so-called ontology matching. Recently, Swarm Intelligent Algorithm (SIA) becomes the suitable methodology to determine the ontology alignment. Being inspired by the succeed of Firefly Algorithm (FA) in many domains, in this paper, a Compact co-Firefly Algorithm (CCFA) is proposed to optimize the ontology alignment. The experiment uses the benchmark of Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparing results show that CCFA-based matcher significantly outperforms other SIA-based matchers in terms of recall, precision and f-measure at 5% significance level.
Fuzzy Particle Swarm Page Rank Clustering Algorithm

Fuzzy C-Means (FCM) is the most commonly used clustering algorithm despite being sensitive to initial choice of the cluster centroids. To overcome this shortcoming, the present study proposes a clustering algorithm called Fuzzy Particle Swarm Page Rank Clustering (Fuzzy-PSOPRC) which embeds the concept of fuzzy clustering into particle swarm optimization. Fuzzy-PSOPRC gives more precise cluster centroids which groups data objects into better clusters. The proposed algorithm has been tested and evaluated on benchmark datasets. Experimental results show that the proposed clustering algorithm achieves better results in comparison with other clustering techniques in terms of accuracy and F1 measure.
Marketing intangibles: the case of library services in higher education institutions

Purpose This paper aims to investigate the adoption of marketing techniques in academic libraries in Ghana, and how they are applied in the promotion of their services to patrons. Design/methodology/approach Adopting the qualitative research approach, a total of 21 professional and para-professional library staff from three private universities participated in the study. The main data collection instrument was the use of semi-structured interviews. Data gathered was analysed, discussed and presented thematically. Findings The findings revealed that the study's participants are fully aware of the benefits of inculcating marketing activities into their daily routines and were making efforts at making their users aware of their products and services with the use of various strategies including the deployment of social media tools. Some of the challenges that were discovered to hinder the success of marketing in the libraries were lack of funds, lack of knowledge of marketing techniques by staff and a lack of marketing policy to guide marketing in the libraries. Research limitations/implications The findings of this study imply that, even though carried on a few academic libraries, the same can apply to most academic libraries as the characteristics are generally the same. Practical implications To boost marketing initiatives in the selected university libraries, the following recommendations informed by the findings of the study are made: creation of marketing policies, increase use of social networking sites for marketing, organization of seminars and workshops on marketing, diversifying sources of funding for libraries and conducting user surveys. Social implications The implementation of the recommendations has the potential to increase the level of interactions between academic libraries and their patrons as well as impact positively on the information-seeking behaviour of library patrons. Originality/value Apart from the application of works of other scholars, which have been duly cited and acknowledged, the entirety of this study or research output is the result of the handiworks of the researchers. Again, to the best of the authors’ knowledge, this work has not been a production of existing research nor is earlier published in any journal.
DETECTION OF LEUKEMIA IN MICROSCOPIC WHITE BLOOD CELL IMAGES USING GAUSSIAN FEATURE CONVOLUTIONAL VISUAL RECOGNITION ALGORITHM

Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.
A Tale of Two Headers: A Formal Analysis of Inconsistent Click-Jacking Protection on the Web

Click-jacking protection on the modern Web is commonly enforced via client-side security mechanisms for framing control, like the X-Frame-Options header (XFO) and Content Security Policy (CSP). Though these client-side security mechanisms are certainly useful and successful, delegating protection to web browsers opens room for inconsistencies in the security guarantees offered to users of different browsers. In particular, inconsistencies might arise due to the lack of support for CSP and the different implementations of the underspecified XFO header. In this paper, we formally study the problem of inconsistencies in framing control policies across different browsers and we implement an automated policy analyzer based on our theory, which we use to assess the state of click-jacking protection on the Web. Our analysis shows that 10% of the (distinct) framing control policies in the wild are inconsistent and most often do not provide any level of protection to at least one browser. We thus propose recommendations for web developers and browser vendors to mitigate this issue. Finally, we design and implement a server-side proxy to retrofit security in web applications.
Technological aspects of modelling and research of smart grid

In this paper design of communicational devices and approbation of Smart Grid technologies are considered. As well the characteristics of the functional properties of Smart Grid technology are presented. Model of the power system in complex program MATLAB are developed.