Reviewerly/exHarmony huggingface dataset:
Unsupervised Image Classification System for Mobile Devices Based on Incremental Isomap

Unsupervised Machine Learning methods could classify massive images with considerable accuracy, without sensitivity to the selection of labeled data sets compared with supervised learning methods. However, once facing a huge number of images, feature extraction and dimensionality reduction during unsupervised process tend to be more complex and therefore hard to run on mobile devices in real time. In this paper, we propose an unsupervised method based on Incremental Isometric Mapping algorithm integrated with BagNet models for image classification on mobile devices. Then we establish a new image classification system and utilize cloud computing for fast computation so that the system could realize real-time image classification on mobile devices. Finally, we deploy the proposed system on commercial mobile phones and achieve an classification accuracy of 77.9% on photos of users. With the help of the cloud, time cost of the system has declined from 14.55s per image to 1.08s per image.
A Mamdani fuzzy system implementation on Arduino UNO and FPGA

The FPGA is the robust platform to implement fast and complex circuits in the industry. Also, nowadays, Arduino microcontroller is very used because has typical preloaded functions that facilities its use. Intuitively it is known that FPGA is faster due its parallelization possibilities. The purpose of this work is to show in a quantitative way, the difference in performance between Arduino UNO and FPGA Xilinx Spartan 3E 100 CP132 when they are used as embedded Fuzzy system. This may bring arguments to decide whether to use one or another platform for this kind of applications. This paper presents a fuzzy system implementation of a Mamdani type to infer the tip given to a restaurant, where it was measured the time spent in implementing and executing; then the results were compared. These last, offered a minimal numeric difference between systems but highlighted the difference in programming time and processing time.
Analyzing Gun Violence in the United States

This research presents data that has been analyzed regarding gun violence incidents in the United States. The data was evaluated using the Python programming language and its data analytics tools. The findings were visualized using heat maps and choropleth maps of the United States as well as other useful charts and graphs to perform an unbiased analysis on gun violence incidents. There were multiple findings, including how many incidents of gun violence are committed by men as well as how the states with the highest number of incidents are not always states with weak gun laws.
Density Based Clustering Methods for Road Traffic Estimation

Multiple object detection using deep neural networks can lead to transportation vehicles estimate, a necessary requirement for prediction and management of road traffic and parking lot. Highly overlapped objects that look similar and objects that are there at far distances have lesser probability of detection by state-of-art techniques. We propose techniques to estimate the traffic at regions of poor detection probability in the image based on (i) density based clustering and (ii) exclusive object detection in the regions of poor detection. The proposed techniques lead to better estimation in comparison to state-of-art by approximately 12 %. We have utilized RetinaNet and YOLOv3 networks for object detection.
Agile methodology in the educational and methodological activity of university

The article discusses the use of the Agile methodology in providing educational and methodological activities at university. The article presents the current results of the study carried out with the financial support of the Russian Foundation for Basic Research (RFBR) in the framework of the research project No. 19-013-00690 “Economics of educational and methodological activities in high school”. The article proposes the requirements that can be applied to the use of the Agile methodology in providing educational and methodological activities of the University. The possibility and expediency of using this methodology for the successful implementation of these processes has been substantiated. The principles and tools of Agile in adaptation to the educational process of the university are presented. The components of the proposed approach are considered, in particular: the basic principles of the Agile methodology and the possibility of their application in educational and methodological activities; the compatibility of Agile with the existing system of educational and methodological support; areas where Agile technologies and principles can be adapted immediately and without significant problems. The problems on the way of introducing flexible methodologies into the educational and methodological activities of the university are indicated.
Accurate Estimation of Motion Blur Kernel Based on Genetic Algorithms

In this paper, a motion blur kernel estimation method is proposed based on genetic algorithms. Specific individual representation and initialization are developed for optimizing motion blur kernel. Effective fitness function and genetic operators are designed to ensure the convergence of the optimization process. Several traditional methods are compared with the proposed method. Real aerial image data sets are used to test the performance of these methods. It is proved that the proposed method can estimate the blur kernel more effectively and accurately than traditional ones.
A Multi-Criteria Analysis of Benchmark Results With Expert Support for Security Tools

The benchmarking of security tools is endeavored to determine which tools are more suitable to detect system vulnerabilities or intrusions. The analysis process is usually oversimplified by employing just a single metric out of the large set of those available. Accordingly, the decision may be biased by not considering relevant information provided by neglected metrics. This article proposes a novel approach to take into account several metrics, different scenarios, and the advice of multiple experts. The proposal relies on experts quantifying the relative importance of each pair of metrics towards the requirements of a given scenario. Their judgments are aggregated using group decision making techniques, and pondered according to the familiarity of experts with the metrics and scenario, to compute a set of weights accounting for the relative importance of each metric. Then, weight-based multi-criteria-decision-making techniques can be used to rank the benchmarked tools. The usefulness of this approach is showed by analyzing two different sets of vulnerability and intrusion detection tools from the perspective of multiple/single metrics and different scenarios.
Image Captioning with Pretrained Language Generators

We present a novel framework for image captioning combining scene graph models with pre-trained language models. This is in contrast to previous works, which largely rely on an encoder-decoder like architecture. In our experiments we use a two-stage pipeline: a) generating scene graphs from an image and b) using pre-trained language generators to obtain captions. Using scene graphs leads to more grounded captions and helps in exploiting the visual context between different objects in an image. By fine-tuning pretrained language models, we are able to reuse the vast, compressed knowledge in these models for image captioning.
Online Agricultural Extension Based on Internet Communication Principles

The important role agricultural growth plays in mitigating poverty, economic growth stimulation as well as development in emerging economies like Kenya cannot be over stated. Available literature shows that agriculture is a key driver of economic growth and development strategies in attaining sustainable poverty reduction. ICT acts as an enabler when addressing food security with such factors as legal framework, policy, knowledge, markets, technology, and research among others. Information and skills gaps have kept most subsistence farming systems to much less productive and lucrative than they could be. Further, extension services are always often faced with the problem of underfunding, weak link between farmers and agricultural research agencies as well as lack acceptable contact to farmers. The problem is exacerbated by lack of coordination along the agricultural value chain from farm inputs to food processing, which increases the cost of production and lowers farmers’ revenue. The aim of this paper is to investigate the perception stakeholders in agricultural value chain have on online agricultural extension services as well as the challenges associated with the same.
A Study on the E-C Consecutive Interpreting in Ocean Security Conference Guided by Compression Theory

This conference interpretation is the interpretation of the discussion on the phenomena taking place in the South China Sea. In this paper, the author uses an authentic corpus from transcripts of a conference on ocean security. There are five levels of compression on conference interpreting. They are syllabic compression, lexical compression, syntactic compression, semantic compression and situational compression. In this paper, in syllabic compression, the author aims to compress the syllables of words and phrases. In lexical compression, the author compresses unnecessary expressions of words and phrases. In syntactic compression, the author compresses sentences and phrases syntactically to make the text structure more concise. In semantic compression, the author compresses repeated expressions. In situational compression, the author compresses unnecessary expressions in the speech.
Experimental Cryptanalysis of No-equilibrium Chaotic System Based Random Number Generator

Chaotic systems have to be carefully designed in critical applications. Therefore, these systems need high-quality cryptanalysis methods to make sure they work securely. In this paper, a three dimensional no-equilibrium chaotic system to produce random number bits is analyzed with giving numerical and experimental results. Generating random numbers/bits is vital in security systems since they require unpredictable values to keep the key securely for the attackers. The master-slave synchronization method is used to present the security weakness of the "novel" no-equilibrium chaotic system with hidden attractors. The no equilibrium chaotic system is analyzed with a scalar time series. The simulation and experimental results demonstrate that random number bits generated through the target chaotic system can be estimated because its randomness is based on deterministic sources.
Hardness of Segment Cover, Contiguous SAT and Visibility with Uncertain Obstacles

We define the problem segment cover as follows. We are given a set of pairs of sub-intervals of the unit interval. The problem asks if there is a choice of a single interval from each pair such that the union of the chosen intervals covers the entire unit interval. This problem arises naturally while attempting to compute visibility between a point and a line segment in the plane in the presence of uncertain obstacles. Segment cover is equivalent to a restricted version of SAT which we call contiguous SAT. Consider a SAT with the following restrictions. An input formula is in CNF form and an ordering of the clauses is given in which clauses containing any fixed literal appear contiguously. We call this restricted problem contiguous SAT. Our main result is that the problems segment cover and contiguous SAT are NP-hard. We also discuss hardness of approximation for these problems.
Loop Destroying Algorithm for Generating Creative Solutions Applying in Health Care Area

Creativity can be achieved by using creative computing theory and knowledge combination method. Generally speaking, Creativity can only be achieved by brains, which belong to human beings and other intelligent creatures. Machine creativity can rarely be presented. deep learning and reinforcement learning are regarded as having chances to achieve machine creativity and auto-processing. Trained agencies can process complicated real-time issues by being trained in the virtual environment facing with different states. Parameters of the agency are changed all the time until the model is under fitted. However, the creative decision could be achieved to some extent, novel ideas can rarely be output by machines. Therefore, based on the human creativity generation mechanism, loop destroying can break the thinking loop to produce novel ideas. Some machine learning theories such as Pearson correlation, are used for data processing and model establishment. To state the algorithm more explicitly, an example of the health care solution generation is explained at the end of the paper.
Estimation of User-Specific Visual Attention Considering Individual Tendency toward Gazed Objects

An estimation method of user-specific visual attention considering individual tendency toward gazed objects is presented in this paper. For realization the user-specific visual attention for images, it can be effective to use the past gaze tendency of a target user and gaze data obtained from other users. However, the collaborative use of these information is difficult since there may be no gaze data obtained from other users. Then the proposed method focuses on the saliency map and enables the collaborative use of those information for estimation of user-specific visual attention. It is confirmed that the estimation accuracy of the proposed method improved by approximately 20% compared to the state-of-the-art saliency estimation method by performing the experiment.
A proposal of Web accesses method considering tolerable delay for each content

When a user browses a web page, user can wait until web page is displayed. This time is called tolerable delay time. A conventional method to maximize the number of web accesses of web page is displayed within tolerable delay time has been proposed. In general some web pages can convert ordinary web pages (ordinary contents) that you usually use to simple web pages (simple contents) reduced contents such as images. By converting from an ordinary content to simple content, the number of web accesses satisfying the tolerable delay time may be increased. However, conventional method assumed that type of content is only the ordinary content. Moreover, tolerable delay time of each content was the fixed value. In this paper, we propose a method to maximize the number of web accesses by converting ordinary contents to simple contents when the waiting time for access does not satisfied tolerable delay time. In addition, we analyze the characteristic of our proposal method by changing tolerable delay time of content.
The development of a pocket book as a learning resource at vocational high school

Abstract The change of education curriculum in Indonesia affects school subjects, including those taught in vocational high school’s culinary department. The addition of the fusion food material in the learning syllabus demands teachers to seek various learning resources. This study aimed to develop a pocket book on fusion food as a learning resource for vocational high school students. This was a R&amp;D (research and development) which specifically used the 4D development model (define, design, develop, and disseminate). The research participants involved experts on learning material and media, as well as Grade 12 culinary students from state vocational high school SMKN 1 Kalasan. The data collection technique included observation, interviews, document studies, and a survey, while the data analysis technique was descriptive qualitative. The findings show that the feasibility of the fusion food pocket book is deemed very feasible as a learning resource with the average score of 88.9% from the material experts, 99.1% from the media experts, and 82.3% from the students.
Enhanced Security Architecture for Visual Cryptography Based on Image Secret Sharing

This paper provides an approach to a new encryption architecture using double layer encryption standards for the existing secret sharing methodology. The image encryption standard in this scheme deals with both gray scale and color images and provides the experimental results. The paper deals with the transmission of multimedia such as images over insecure and secure networks, secret sharing helps to mask the image from the attacker by breaking it down to shares which are not at all related in the sense of content to the original image and provide the security of only reconstructing the original image when the client has all the shares.
Multi-Agent Deep Reinforcement Learning Based Pricing Strategy for Competing Cloud Platforms in the Evolutionary Market

In the cloud market, there exist multiple cloud providers competing against each other in order to attract cloud users and make profits. Each provider needs an appropriate pricing strategy. In this paper, we analyze how a cloud provider sets the price effectively when competing against other cloud providers. The price charged by the cloud provider is affected by many factors such as the prices of its opponents, the price set in the previous round, the choice of cloud users and the marginal cost of the cloud provider. Therefore, we model this problem as a Partially Observable Markov Game where cloud providers compete against each other in the evolutionary market. In more detail, in this game, the amount of cloud users is increasing before it eventually becomes saturated and the marginal value of users is also changing. Then we use a gradient-based multi-agent reinforcement learning algorithm to generate the pricing strategy for the cloud service provider. Finally, we evaluate our pricing strategy against other typical pricing strategies by conducting extensive experiments. The experimental results show that our pricing strategy can not only adapt the price according to the opponents' prices, but also adapt the price according to the changes of the cloud users' valuations on the cloud resource, and therefore it can outperform other pricing strategies. Furthermore, we also find that when training the pricing strategy against the pricing strategy generated by our algorithm, our pricing strategy can still win in terms of the long-term profits. The experimental results can provide useful insights for designing practical pricing strategies.
URNAI: A Multi-Game Toolkit for Experimenting Deep Reinforcement Learning Algorithms

In the last decade, several game environments have been popularized as testbeds for experimenting reinforcement learning algorithms, an area of research that has shown great potential for artificial intelligence based solutions. These game environments range from the simplest ones like CartPole to the most complex ones such as StarCraft II. However, in order to experiment an algorithm in each of these environments, researchers need to prepare all the settings for each one, a task that is very time consuming since it entails integrating the game environment to their software and treating the game environment variables. So, this paper introduces URNAI, a new multi-game toolkit that enables researchers to easily experiment with deep reinforcement learning algorithms in several game environments. To do this, URNAI implements layers that integrate existing reinforcement learning libraries and existing game environments, simplifying the setup and management of several reinforcement learning components, such as algorithms, state spaces, action spaces, reward functions, and so on. Moreover, URNAI provides a framework prepared for GPU supercomputing, which allows much faster experiment cycles. The first toolkit results are very promising.
Performance Analysis of Various Edge Detection Techniques in X-Ray Imaging

Abstract Edge detection is an image processing method used for discover the limitations of objects within the image. It works by sensing incoherence in illumination. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision and machine vision. In these four techniques such as Sobel Operator, Robert Operator, Prewitt Operator, and Canny Operator are used for detection of edge detection in which the canny operator is the most effective manner of finding the edge detection using X-rays. The key aim of this task is to sense human minor leg bone fracture from X-Ray imageries. The suggested structure has two steps namely pre-processing segmentation and fracture detection. Canny method produces very effective image. This paper compares the various edge detection techniques for detecting the bone fracture of lower leg bone and finds effective. The performance of the four segmentation techniques are compared based on the execution time and accuracy. The execution speed of Canny operator is 32.5 seconds and accuracy is 88.7%.
Semi-supervised Riemannian Dimensionality Reduction and Classification Using a Manifold-based Random Walker Graph

Efficient classification of manifold-based data demands dimensionality reduction. However, the optimal lower dimension is mostly unknown. Also, supervised classification demands a collection of expert-annotated samples. This collection is tedious, costly, and error prone. Thus, semi-supervised classifications are motivated. In this paper, we propose a principled method to determine the optimal lower dimension and to reduce dimension of manifold-based data for their semi-supervised classification. This method relies on a manifold-based random walker graph and enhances the discriminative power of the classifier in the reduced dimensional space. The reduced dimensional data can be classified by any method or the same graph after adapting edge weights of the graph to them. Thus, the proposed dimensionality reduction is independent of the classifier type. This method is evaluated on segmenting tissues on fat-water (2-channel) magnetic resonance images.
A WEB-BASED ONLINE STORE APPLICATION FOR FABRIC MATERIAL SALES

Abstract Internet can make business run easier to have information about products and service available to their potential customers. The use of information system such as online store is needed to facilitate customers in getting fabric material product information, convenience, and efficiency in making purchase. This study aimed at solving customers problem that must manually come to an offline store to make a purchase. Data was collected by interviewing a company that focus on fabric material sales. The result of this study is the website application for online store, made using Yii PHP Framework and MySQL database is uses to store information.
A Smart Background Scheduler for Storage Systems

In today's enterprise storage systems, supported data services such as snapshot delete or drive rebuild can result in tremendous performance overhead if executed inline along with heavy foreground IO, often leading to missing Service Level Objectives (SLOs). Typical storage system applications such as Virtual Desktop Infrastructure (VDI) or web services follow a repetitive high/low workload pattern that can be learned and forecasted. We propose a priority-based background scheduler that learns this pattern and allows storage systems to maintain peak performance and meet service level objectives (SLOs) while supporting a number of data services. When foreground IO demand intensifies, system resources are dedicated to service foreground IO requests and any background processing that can be deferred are recorded to be processed in future idle cycles as long as our forecaster predicts that the storage pool has remaining capacity. The smart background scheduler adopts a resource partitioning model that allows both foreground and background IO to execute together as long as foreground IOs are not impacted, harnessing any free cycles to clear background debt. Using traces from VDI and web services applications, we show how our technique can out-perform a static policy that sets fixed limits on the deferred background debt and reduces SLO violations from 54.6% (when using a fixed background debt watermark), to only 6.2 % when dynamically adjusted by our smart background scheduler.