Reviewerly/exHarmony huggingface dataset:
Content Based Image Retrieval Using Two Color Feature Extraction

Abstract Content Based Image Retrieval (CBIR) is a process to search for an image based on the content or features that are inside. Nowadays, many image retrieval applications have been made to meet the needs, so this application can provide convenience in terms of the introduction and search for an image. In this research, we used 10 different objects as image retrieval consists of Bicycle, Cow, Flower, Frangipani, Grape, Horse, Lovebird, Orange, Strawberry, Tree. These objects can be expressed in 10 classes. Our aim using these objects is viewed from the color of every object and the object of a different kind. From that point of view we built a CBIR system by utilizing the main features of the object (image). The main feature is the color feature. In this research, the main process is the extraction of color features with the color histogram and color moments. So in this research it will produce feature extraction by measuring the similarity in the library image. This measurement is done by calculating the closest distance using the euclidean distance method. The data library used in this research is 10 pieces of data, the test data is 50 pieces with 5 pieces for each of these classes. After testing using data and methods described above, the results of accuracy are obtained that the application of the color moments method gets better results than the color histogram method.
Combating Illegal Drugs Trafficking Using the Internet by Means of the Profiling Method

The paper actualizes the issues of combating drug trafficking using the Internet on the example of Ukraine. Given the significant difference in the approach to this issue among Ukrainian and foreign scientists, this allowed to carry out a multifaceted analysis. The purpose of the article is to conduct a comprehensive study (criminological and forensic) on the use of the Internet in the commission of crimes related to drug trafficking, on the example of Ukraine. Profiling was used as the main method. The novelty of this work is the extrapolation of the method of profiling cybercriminals to persons who commit illicit drug distribution using the Internet in Ukraine as a special category of cybercriminals. The specific objectives of the study are: to study the characteristics of an offender who commits crimes related to drug trafficking via the Internet using the method of profiling; study of typical investigative situations that arise when committing crimes related to drug trafficking on the Internet; development of comprehensive recommendations for combating this group of crimes, taking into account both the identity of an offender and investigative situations that most often arise in practice. Based on the study, the article conducted (using secondary data) profiling of people who distribute drugs using the Internet in Ukraine. The profiling also includes a comprehensive portrait of such a criminal, which includes both socio-demographic and psychological characteristics of this category of criminals, studied their motivation, conducted a study of the victimological aspect and features of the study of evidence in this category of crimes.
Context-Based Sentiment Analysis on Amazon Product Customer Feedback Data

A sentiment is a mindset, opinion, or decision that a sensation triggers. Sentiment Analysis is the procedure of using text analytics to mine different sources of data for opinions. It is also referred to as opinion mining and explores the emotions of a person in relation to certain products. The internet is indeed rich with resources relating to sentiment analysis such as feedback of a product on an e-commerce website and tweets sent by an individual on twitter social networking site. Context-based sentiment analysis is an exciting tool that continues to evolve as Artificial Intelligence and Machine Learning become more sophisticated. It is a precise science that can be used to aid new product development as well as for marketing and public relations. Analysis will be able to process product data at scale in an efficient way. Customer behavior can be analyzed and it can be found out what products are in demand and which products are not doing well. This model plans to perceive the properties of products and stores which play a pivotal role in growing sales. Word Sense Disambiguation is used to distinguish two similar sounding words from each other. Customer behavior can be analyzed and it can be found out what products are in demand and which products are not doing well. Through this, it is proposed to find out sentiments involved in each product whether they be positive or negative, based on which sentiment analysis will be carried out. Naïve Bayes and Support Vector Machine Algorithms have been used for classification. The model will be robust with a high precision and straightforward addition of attributes from various sources to the set.
Application of Deep Learning Neural Network in Online Supply Chain Financial Credit Risk Assessment

Under the background of "Internet +", in order to solve the problem of deeply mining credit risk behind online supply chain financial big data, this paper proposes an online supply chain financial credit risk assessment method based on deep belief network (DBN). First, a deep belief network evaluation model composed of Restricted Boltzmann Machine (RBM) and classifier SOFTMAX is established, and the performance evaluation test of three kinds of data sets is carried out by using this model. Using factor analysis to select 8 indicators from 21 indicators, and then input them into RBM for conversion to form a more scientific evaluation index, and finally input them into SOFTMAX for evaluation. This method of online supply chain financial credit risk assessment based on DBN is applied to an example for verification. The results show that the evaluation accuracy of this method is 96.04%, which has higher evaluation accuracy and better rationality compared with SVM method and Logistic method.
Artificial neural networks and adaptive neuro-fuzzy inference systems for parameter identification of dynamic systems

There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values.
An area-efficient ECC architecture over GF(<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1139" altimg="si6.svg"><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:msup></mml:math>) for resource-constrained applications

Elliptic Curve Cryptography (ECC), an asymmetric-key cryptographic system, received extensive research and industry recognition owing to its reliable security with relatively smaller key sizes. It is most effective with resource-constrained applications. The very intensive function in ECC is point-multiplication. In this article, we propose a novel architecture for elliptic curve point-multiplication based on modified Lopez–Dahab–Montgomery(LDM) point-multiplication algorithm to achieve a reduction in hardware complexity and time complexity. The proposed architectural data-path is pipelined and organized to realize finite-field operations i.e., FF-multiplication and FF-square operations, in parallel to reduce the critical-path delay. The FF-operations are scheduled to perform each iteration of point-addition(PA) and point-doubling(PD) operations in eight clock-cycles without data-dependency. The elliptic curve-point-multiplication(ECPM) implementation over the National Institute of Standards and Technology(NIST) recommended binary curve GF(2163) shows that the proposed design achieves point-multiplication in 3.71μs utilizing 8529 LUTs on Xilinx Virtex-7 FPGA. In addition the design achieves an improvement of 36%, 32%, and 39% area-time efficiency when compared to the best existing designs in the literature when implemented over GF(2163), GF(2233) and GF(2283) respectively.
New Techniques for Zero-Knowledge: Leveraging Inefficient Provers to Reduce Assumptions, Interaction, and Trust

We present a transformation from NIZK with inefficient provers in the uniform random string (URS) model to ZAPs (two message witness indistinguishable proofs) with inefficient provers. While such a transformation was known for the case where the prover is efficient, the security proof breaks down if the prover is inefficient. Our transformation is obtained via new applications of Nisan-Wigderson designs, a combinatorial object originally introduced in the derandomization literature. We observe that our transformation is applicable both in the setting of super-polynomial provers/poly-time adversaries, as well as a new fine-grained setting, where the prover is polynomial time and the verifier/simulator/zero knowledge distinguisher are in a lower complexity class, such as $$\mathsf {NC}^1$$ . We also present $$\mathsf {NC}^1$$ -fine-grained NIZK in the URS model for all of $$\mathsf {NP}$$ from the worst-case assumption $$\oplus L/\mathrm {poly}\not \subseteq \mathsf {NC}^1$$ . Our techniques yield the following applications:
A cognitive map integrated intuitionistic fuzzy decision-making procedure for provider selection in project management

Organizations make use of project management methodologies, which provide an effective manner to achieve managerial goals, maintain the strength of the companies in increasing competition. Efficiency in planning, budgeting, and scheduling are provided so that high quality outputs are obtained through these processes. Agile project management methodology, which has been emerged from unpredictability of customer requirements and changeable business environment, is apt to cope with the failures of traditional project management tools. Besides, lean six-sigma project management methodology has become a combination of lean and six-sigma, which were opponent methodologies previously. This paper aims to determine the most suitable outsourcing provider alternative by presenting a novel cognitive maps-based intuitionistic fuzzy decision making procedure. Interrelationships among evaluation criteria are weighted employing intuitionistic fuzzy cognitive map technique because of the causal links among evaluation criteria, vagueness, fuzziness, and hesitation in data. Moreover, the most appropriate provider alternative for both agile and lean six-sigma project management methodologies is identified by utilizing intuitionistic fuzzy TOPSIS method, which aims for minimizing the closeness to the ideal solution while maximizing the distance from the anti-ideal solution in hesitative environment. The case study is carried out in a bank that performs in Turkish banking sector.
The Method Based On Dijkstra Of Multi-directional Ship’s Path Planning

Aiming at the problem of the shortest path's optimization for direct navigation of ships, the Dijkstra algorithm search is improved. The environment of the ship's navigation area was modeled by rasterization and swelling. Then the four-direction search, eight-direction search and arbitrary-angle search based on the Dijkstra algorithm were improved, and the three search methods were compared. Simulation experiments and research results show that the improved arbitrary angle search method can best meet the requirement of the shortest direct flight path.
Distinguishing Oracle Variants Based on the Isomorphism and Symmetry Invariances of Oracle-Bone Inscriptions

Oracle bone inscriptions are the earliest mature writing system discovered in China.Oracle is usually published in the form of glyphs and pictures, and the related oracle font database environment is closed and limited.Due to the lack of standardization of ancient oracle bone inscriptions, the structure and glyph of the same characters are not unified, and a large number of different shapes and complicated characters coexist.Even ancient character devotees need to complete the basic recognition of oracle with the help of professional reference books and experts.In addition, in the field of traditional literature, distinguishing variant characters of oracle-bone inscriptions needs strong expert knowledge and lacks efficiency.The recognition process consists of two stages.According to the characteristics of oraclebone inscriptions, such as left-right symmetry, up-down symmetry and positive and negative coexistence, the corresponding data enhancement methods are used to get sufficient training samples in the first stage.In the second stage, the recognition candidate set under each threshold condition in the first stage are used to select the recognition candidate set which is empty under the specific threshold condition, and introduce the prior knowledge to distinguish the oracle variants.In the first stage, computer related methods are used to identify oracle variants.In the second stage, on the basis of the first stage, a set of recognition results that cannot be determined roughly is selected, and a priori knowledge is introduced to integrate multi-domain methods to identify oracle variants.The method proposed in this paper has achieved great results in the recognition of oracle variant characters.
Automated Anomaly Detection in CPS Log Files

When Cyber-Physical Systems (CPS) work incorrectly we would like to know the reason for this behavior. Experts inspect log files of CPS to get an idea about what went wrong. The large amount of information, which is stored in those log files, and the complexity of CPS pose a challenge to experts that try to manually detect anomalies in the system’s behavior. We propose to automate anomaly detection in CPS log files by applying a clustering approach to find time spans, in which the regarded system behaves abnormal. With our approach, we aim to significantly reduce the time and effort that is needed by experts to discover anomalies in the log files without having to build a model of the system first. The results from our evaluation show that our generic approach can effectively find anomalies for different types of CPS.
Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks

This paper proposes a new hybrid method to predict global horizontal irradiance (GHI) at temporal horizons of 1, 2, 3 and 4 hours, combining the satellite visible images and meteorological information. First, the satellite visible images are preprocessed to remove the diurnal effects caused by the solar zenith angle. Then the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network (CNN). After that, the GHI forecasting model is developed which is based on the combined use of meteorological information and cloud cover factors. The sensitivity of the prediction accuracy to a variety of CNN structures with different widths, depths, and pooling methods is also explored in the paper. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed. The forecasting skills of the proposed method for different time horizons are demonstrated by comparing with several benchmark models.
Using bidirectional LSTM with BERT for Chinese punctuation prediction

Punctuation prediction is an important step in the post processing of ASR systems. Lack of punctuation text is usually difficult to read and understand. In this paper, we propose a method based on Chinese punctuation prediction by combining the Bidirectional Long Short-Term Memory (BLSTM) and the Bidirectional Encoder Representations from Transformers (BERT), which makes the use of BERT as text encoding layers for learning contextualized word representations for improving the performance of BLSTM network. Compared with the previous punctuation prediction methods based on Recurrent Neural Network (RNN), our method improves the performance of punctuation prediction with the powerful ability of capturing semantics and long-distance dependencies in Chinese unsegmented text. Our experimental results on Chinese news datasets that our BERT-BLSTM based method outperforms the baseline by up to 31.07% absolute in overall micro-F1.
Privacy-preserving solutions in the Industrial Internet of Things

Industrial Internet of Things (IIoT) is a relatively new area of research that utilises multidisciplinary and holistic approaches to develop smart solutions for complex problems in industrial environments. Designing applications for the IIoT is a non trivial issue and requires to address, among many others, technology concerns, the protection of personal data, and the privacy of individuals. In this review paper, we identify privacy-preserving solutions that have been proposed in the literature to safeguard the privacy of individuals being part, or interacting with, the IIoT environment. As such, it considers two main categories of the analysed protocols, i.e., the privacy-preserving data management and processing solutions, and the privacy-preserving authentication methods.
Industrial Training Platform Using Augmented Reality for Instrumentation Commissioning

Augmented reality (AR) platforms are poised to have great potential for industries when it comes to complex processes like maintenance or instrument commissioning in the Oil and Gas industry. The human-centered technology displays context-specific 3-D information in a real environment related to a specifically targeted object. Immersive experiences are expected to boost task efficiency, the quality of training, and maintenance purposes. This research shows an AR system that allows users to train in the calibration of HART instrumentation, using the Unity 3D platform and Meta 2 glasses. This application offers a platform that has advantages for users since it can train in the operation of expensive industrial equipment in an error-prone system. The validation of the system was carried out through an evaluation module, which allows the user to determine the level of training and autonomously build their own learning, obtaining positive responses that show that around 82% of the population evaluated.
Multi-view Gaussian processes with posterior consistency

Gaussian processes (GPs) are powerful tools for regression and classification tasks, and have been used widely in various fields of machine learning. However, GPs are barely applied directly to the scenario of multi-view learning so far. In this paper, we propose two kinds of multi-view GPs frameworks called MVGP1 and MVGP2 with posterior consistency, which combine multiple views by regularizing the marginal likelihood. Utilizing the posterior distribution consistency criterion, the MVGP1 is first presented. Then to further improve classification accuracy, we present the MVGP2, in which we introduce a trade-off parameter to govern the relative importance of the likelihood term compared with the regularization term. The MVGP1 significantly reduces the training time of the multi-view GPs with acceptable performance on the accuracy, and the MVGP2 achieves state-of-the-art classification performance. Experimental results on multiple real-world classification data sets show the effectiveness of the proposed frameworks.
Evolutionary gestures

column Public Access Share on Evolutionary gestures: when a gesture is not quite legacy biased Authors: Adam S. Williams Colorado State University Colorado State UniversityView Profile , Francisco R. Ortega Colorado State University Colorado State UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 5September - October 2020 pp 50–53https://doi.org/10.1145/3412499Published:01 September 2020Publication History 4citation725DownloadsMetricsTotal Citations4Total Downloads725Last 12 Months173Last 6 weeks29 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
A parallel whale optimization algorithm and its implementation on FPGA

Whale optimization Algorithm (WOA), as a novel nature-inspired swarm optimization algorithm, has demonstrated superior performance in solving optimization problems. However, the performance deteriorates when applied to large-scale complex problems due to rapidly increasing running time required for huge computational tasks. Based on interactions within population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. Field Programmable Gate Array (FPGA) is an acceleration device of high parallelism and programmability. Meanwhile, Open Computing Language (OpenCL) provides a general architecture for heterogeneous development. In this paper, an efficient implementation of parallel WOA on FPGA is proposed named FPWOA. Experiment studies are conducted by performing WOA on CPU and FPWOA on FPGA respectively to solve ten well known benchmark functions. Numerical results show that our approach achieves a favourable speedup while maintaining optimization performance.
Detection by Attack: Detecting Adversarial Samples by Undercover Attack

The safety of artificial intelligence systems has aroused great concern due to the vulnerability of deep neural networks. Studies show that malicious modifications to the inputs of a network classifier, can fool the classifier and lead to wrong predictions. These modified inputs are called adversarial samples. In order to resolve this challenge, this paper proposes a novel and effective framework called Detection by Attack (DBA) to detect adversarial samples by Undercover Attack. DBA works by converting the difficult adversarial detection problem into a simpler attack problem, which is inspired by the espionage technique. It appears to be attacking the system, but it is actually defending the system. Reviewing the literature shows that this paper is the first attempt to introduce a detection method that can effectively detect adversarial samples in both images and texts. Experimental results show that the DBA scheme yields state-of-the-art detection performances in both detector-unaware ( $$95.66\%$$ detection accuracy on average) and detector-aware ( $$2.10\%$$ attack success rate) scenarios. Furthermore, DBA is robust to the perturbation size and confidence of adversarial samples. The code is available at https://github.com/Mrzhouqifei/DBA.