Reviewerly/exHarmony huggingface dataset:
Application of Optimized Bidirectional Generative Adversarial Network in ICS Intrusion Detection

Aiming at the problem that the traditional intrusion detection method can not effectively deal with the massive and high-dimensional network traffic data of industrial control system (ICS), an ICS intrusion detection strategy based on bidirectional generative adversarial network (BiGAN) is proposed in this paper. In order to improve the applicability of BiGAN model in ICS intrusion detection, the optimal model was obtained through the single variable principle and cross-validation. On this basis, the supervised control and data acquisition (SCADA) standard data set is used for comparative experiments to verify the performance of the optimized model on ICS intrusion detection. The results show that the ICS intrusion detection method based on optimized BiGAN has higher accuracy and shorter detection time than other methods.
Ordinal Outlier Algorithm for Anomaly Detection of High-Dimensional Data Sets

Concerning with the non-linear characteristics of high dimensional data set, we propose a ordinal outlier algorithm for anomaly detection for high-dimensional data sets, which is combined with the idea of Locally Linear Embedding. The core idea of the method is to perform dimensionality reduction in the local space of high-dimensional data set and further use accumulated value of probabilities to reveal outliers. The algorithm retains local separability of data and also has the advantages of PPCA model, which makes it more suitable for processing high dimensional data. Experimental results demonstrate its high feasibility and efficiency and it has power to find more meaningful outliers.
Deep optics

Application-domain-specific cameras that combine customized optics with modern image recovery algorithms are of rapidly growing interest, with widespread applications like ultrathin cameras for internet-of-things or drones, as well as computational cameras for microscopy and scientific imaging. Existing approaches of designing imaging optics are either heuristic or use some proxy metric on the point spread function rather than considering the image quality after post-processing. Without a true end-to-end flow of joint optimization, it remains elusive to find an optimal computational camera for a given visual task. Although this joint design concept has been the core idea of computational photography for a long time, but that only nowadays the computational tools are ready to efficiently interpret a true end-to-end imaging process via machine learning advances.
Visual interpretation of regression error

Abstract Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end‐users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end‐user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach.
A New Context-Based Method for Restoring Occluded Text in Natural Scene Images

Text recognition from natural scene images is an active research area because of its important real world applications, including multimedia search and retrieval, and scene understanding through computer vision. It is often the case that portions of text in images are missed due to occlusion with objects in the background. Therefore, this paper presents a method for restoring occluded text to improve text recognition performance. The proposed method uses the GOOGLE Vision API for obtaining labels for input images. We propose to use PixelLink-E2E methods for detecting text and obtaining recognition results. Using these results, the proposed method generates candidate words based on distance measures employing lexicons created through natural scene text recognition. We extract the semantic similarity between labels and recognition results, which results in a Global Context Score (GCS). Next, we use the Natural Language Processing (NLP) system known as BERT for extracting semantics between candidate words, which results in a Local Context Score (LCS). Global and local context scores are then fused for estimating the ranking for each candidate word. The word that gets the highest ranking is taken as the correction for text which is occluded in the image. Experimental results on a dataset assembled from standard natural scene datasets and our resources show that our approach helps to improve the text recognition performance significantly.
The Impact of Distance Measures in K-Means Clustering Algorithm for Natural Color Images

In image processing, clustering algorithms applied to the segmentation of images. Image segmentation is the practice of clustering a complete image into many meaningful non-overlapped clusters. This is a vital step in computer vision and data analytics because the result of the segmentation process has an impact on other subsequent processes. In image processing, distance is expressed as distance in pixels or shortest path between two data points on the grid, two centers of pixels. Most clustering algorithms utilize distance measures to cluster alike data points (pixels in the case of image) into the same group, while unalike data points are clustered into different groups according to image attributes. The proposed work evaluates the efficiency of the K-means clustering with three distinct distance measures.
Securing Media Information Using Hybrid Transposition Using Fisher Yates Algorithm and RSA Public Key Algorithm Using Pell’s Cubic Equation

Information is the source of investment as well as income. Protecting information has become one of the primary objectives of security algorithms. In the field of multimedia, there is scope for extracting sensitive data without any loss of information which implies that there is no trace of an intrusion. Multimedia data must be confidential, and its integrity and identity must be preserved as it encounters authentication, data encryption, data decryption, digital watermarking and other similar activities. Providing multimedia security ensures content-based security. This paper introduces an enhanced algorithm for the transmission of images between the sender and recipient by addressing the most relevant security aspects of image transformation as well as transmission.
Moving to OOP: An Active Observation Approach for a Novel Composite Visual Servoing Configuration

The imaging quality of a camera has an important influence on the results of visual servoing. To improve the imaging quality, a novel active-observation-based composite visual servoing (AOBCVS) combined with eye-in-hand and eye-to-hand configuration can be adopted, which consists of a mobile observer and a manipulator. One main problem of this configuration that should be solved is to endow the mobile observer with active observation ability. In order to obtain the optimal observation effect of the two markers (one marker can be seen as a target, and the other marker can be attached to a manipulator), a two-level scheme is designed for a mobile robot; in the decision level, a vehicle can determine the optimal observation position (OOP) by optimizing an observation measure function (OMF), and in the control level, the vehicle is driven to the OOP and a pan-tilt camera mounted on it keeps the two markers in the view. The scheme of observer has such advantages: 1) a satisfactory solution for the field-of-view problem; 2) a comprehensive OMF; and 3) an ability to adjust the camera position in real time based on the current observation effect. Finally, we provide the simulation and experimental results to validate the effectiveness of the composite configuration.
A Survey of Domain Adaptation for Machine Translation

Neural machine translation (NMT) is a deep learning based approach for machine translation, which outperforms traditional statistical machine translation (SMT) and yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although a high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for MT. Because of the current dominance of NMT in MT research, we give a brief review of domain adaptation for SMT, but put most of our effort into the survey of domain adaptation for NMT. We hope that this paper will be both a starting point and a source of new ideas for researchers and engineers who are interested in domain adaptation for MT.
Comparative approach for global solar estimation in four typical Senegalese climatic zones

This work constitutes an approach for the estimation of global solar radiation on 4 climatic zones of Senegal. These are the sites at Dakar, Saint Louis, Tambacounda and Ziguinchor. For this study, data from the Helioclim-3 database of the Meteosat-2 satellite are used as model inputs. For the validation process estimated data and meteorological observations were incorporated into the hourly values. The Clear Sky models of Bird & Hulstrom, Lacis & Hansen and Davies & Hay are simulated. They are based on the determination of the transmission coefficients of the various atmospheric constituents. These coefficients require the availability of current meteorological parameters (relative humidity, ambient temperature, atmospheric pressure) and geographical parameters of the site (latitude, longitude and altitude) and also of the solar position. A comparative study of the results obtained by simulation of the three models showed that the Bird & Hulstrom model gives a better estimate of the global component of solar irradiation. The errors between the values of the meteorological observations and those calculated are small for all 4 sites. The mean square errors are small between 10% and 20% and mean relative errors between 0.1% and 12%. The Bird & Hulstrom model was therefore finally chosen for the establishment of global hourly solar radiation over all four zones. The algorithm structure was built using R software.
Handbook of Graphical Models

Graphical models represent probability distributions as a graph with edges denoting conditional dependence relationships between random variables. These models have been studied and developed in co...
Dengue Virus Infected Leukocyte Classification on Microscopic Images with Image Histogram Based Support Vector Machine

Dengue virus detection using blood smear with staining method known as immunocytochemistry streptavidin biotin peroxidase complex has early detection problem caused by the low in number of leukocyte in early day of infection. In order to help the detection of virus infection, this research develop automated system to count and classify infected leukocyte from the microscopic image using image histogram based support vector machine. Image processing is for the detection of the cells by using Gram-Schmidt orthogonalization for converting image into grayscale high contrast grayscale image, median filter for image smoothing, Otsu threshold for image segmentation, image morphology and circularity filter for cleaning up unwanted noise after segmentation. Furthermore, support vector machine for the infected cell classification using image histogram of the cells as feature vector. The result of the research is the image histogram based suport vector machine can classify the infected leukocyte with 83.94% accuracy.
Bing-CSF-IDF+: A Semantics-Driven Recommender System for News

This work proposes the Bing-CSF-IDF+ recommender – a content-based recommender that makes use of semantic relationships, and combines the best features of our earlier introduced Bing-SF-IDF+ and CF-IDF+ systems. First, we make use of concepts and concept relationships from a domain ontology. Next, Bing-CSF-IDF+ employs the synsets and synset relationships from a semantic lexicon that have not been previously captured by the domain ontology. Last, named entities and their frequencies as provided by Bing – not present in the semantic lexicon and domain ontology – are utilized. Our experiments show that Bing-CSF-IDF+ significantly outperforms Bing-SF-IDF+ and CF-IDF+ on $$F_1$$ -scores and Kappa statistics based on a news data set.
OffStreamNG: Partial Stream Hybrid Graph Edge Partitioning Based on Neighborhood Expansion and Greedy Heuristic

Recently, graph edge partitioning has shown better partitioning quality than the vertex graph partitioning for the skewed degree distribution of real-world graph data. Graph edge partitioning can be classified as stream and offline. The stream edge partitioning approach supports a big graph partitioning; however, it has lower partitioning quality, is affected by stream order, and it has taken much time to make partitioning compared with the offline edge partitioning. Conversely, the offline edge partitioning approach has better partitioning quality than stream edge partitioning; however, it does not support big graph partitioning. In this study, we propose partial stream hybrid graph edge partitioning OffStreamNG, which leverages the advantage of both offline and stream edge partitioning approaches by interconnecting via saved partition state layer. The OffStreamNG holds vertex and load states as partition state, while the offline component is partitioning using neighborhood expansion heuristic. And it is transferring this partition state to the online component of Greedy heuristic with minor modification of both algorithms. Experimental results show that OffStreamNG achieves attractive results in terms of replication factor, load balance, and total partitioning time.
Image noise recognition algorithm based on BP neural network

As an important research subject in the field of digital image processing, noise recognition of noisy images is still faced with many problems of low accuracy in recognition algorithm, such as noise value of misjudgment and miscalculation recognized by the recognition algorithm, truth-value misjudgment and miscalculation, etc. By analyzing BP neural network, a recognition algorithm based on BP neural network is designed to solve the above problems. Firstly, the cause of image noise and two classical recognition algorithms are analyzed. Secondly, the input value and network structure of BP neural network identification algorithm are designed. Then, the training method and decision criteria of BP neural network recognition algorithm are described. Finally, Matlab software is used to simulate and identify the noise points of the image containing salt and pepper noise. Simulation results show that the BP neural network identification algorithm has a very low noise leakage number and False Alarming Ratio(FAR), and a good identification effect for salt and pepper noise.
Autoscaling recovery actions for container‐based clusters

Summary In cloud computing, resizing component resources is often limited by the available resources offered by a provider. After reaching a resource limit, a component cannot acquire more resources, which can badly affect the load situation. This article presents multiple predictable recovery actions of a self‐healing model for an identified anomalous behavior (eg, overload, underload) to auto‐scale compute resources in a containerized cluster environment according to various workload conditions. The efficacy of the model is demonstrated through an evaluation with different auto‐scaling strategies based on the number of created/terminated containers, container migration, resource utilization, and response time. The results show that the proposed model provides promising overall performance under dynamic workloads compared to other auto‐scaling strategies.
Chinese Text Keyword Extraction Based on Doc2vec And TextRank

Aiming at the impact of semantic relations between texts on text keyword extraction, a method of keyword extraction based on Doc2vec and TextRank algorithms was proposed. Doc2vec is used to represent the vocabulary vector of the text set. Based on the correlation between text and vocabulary and the difference between vocabulary semantics, the transition probability matrix of TextRank is improved, and keyword extraction is performed. Experimental results show that this method is superior to traditional keyword extraction in the accuracy of keyword extraction, and proves that using Doc2vec for word vector representation improves the performance of the TextRank algorithm.
Deep Support Vector Machines for Speech Emotion Recognition

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: zero crossing rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.
Schopenhauer Diagrams for Conceptual Analysis

In his Berlin Lectures of the 1820s, the German philosopher Arthur Schopenhauer (1788–1860) used spatial logic diagrams for philosophy of language. These logic diagrams were applied to many areas of semantics and pragmatics, such as theories of concept formation, concept development, translation theory, clarification of conceptual disputes, etc. In this paper we first introduce the basic principles of Schopenhauer’s philosophy of language and his diagrammatic method. Since Schopenhauer often gives little information about how the individual diagrams are to be understood, we then make the attempt to reconstruct, specify and further develop one diagram type for the field of conceptual analysis.
Polynomial Based Kalman Filter Result Fitting to Data

A Kalman based trajectory estimate always has an associated uncertainty characterized by its covariance. A way to reduce this uncertainty consists in the adaptive selection of parameters during the filtering process. Different informed strategies for automatically tuning the parameters in Kalman smoothers were already proposed. We propose here a different semi-blind post processing approach, which is faster and more robust. Starting from the conjecture that the trajectory is polynomial in cartesian coordinates, our method supposes to fit the data obtained at the output of Kalman filter to a polynomial. We propose a new polynomial fitting method based on wavelets in two steps: denoising and polynomial part extraction and we compare favorably its performance with the performance of classical polynomial fitting method in an experiment using real data.
Assessing similarity in handwritten texts

Today, people rely almost full time on digital texts. It is not surprising that handwriting earned a special status, and solutions to mimic real handwriting became attractive. A particular field called handwriting synthesis generates renderings of text which resemble natural writing but are synthesized from actual handwriting samples. The main idea behind samples’ current solutions is to collect enough samples to capture a given subject’s writing style, and therefore be able to reproduce it in new texts, with natural variability. Nevertheless, the question remains of how much input variability is enough to represent specific handwriting. In this paper, we address sample acquisition for handwriting synthesis. We conducted a study comparing written text similarity between two sets of samples, one using augmented pangrams (with a total of 473 characters) and the other using general texts (with 1586 characters). Our results show that the samples collected with pangrams are statistically equivalent in variation with samples collected using general texts, with many benefits, particularly the shorter time needed to collect the samples. We also made our data collection publicly available, providing a valuable original resource for future research.
The Controller Design of the Water-Aerial Vehicle Based on Variable Gain PID

Combining the advantages of unmanned aerial vehicle (UAV) and unmanned surface vehicle (USV), water-aerial vehicles satisfy the requirements of operation in water-air amphibious environment. Due to the medium characteristics of air and water are different, it is difficult to maintain stability of the amphibious vehicle in the operation stage of transition. In order to solve this problem, the mathematical models of the amphibious vehicle are established in different motion stages, and one stability controller is designed based on the variable gain PID. The simulation results show that the proposed control method is feasible and meets the requirements of the water-air amphibious vehicle.
Characteristics of Expertise Locator System in Academia: A Systematic Literature Review

The ever-increasing need to find experts in academia contributes to the rise of Expertise Locator System (ELS) in academia. ELS can exist in the form of an expert search system or expert recommender system. Albeit the systems' common purpose of finding an expert, the existing ELS are different from each other in characteristics. There is a consideration, reason, and purpose why an ELS is created with specific characteristics. It is therefore essential to assess and review the characteristics of the system, before creating an ELS. By systematically reviewing recent articles using the Kitchenham method, this research identifies various type of characteristics, so that in the future, ELS in academia can be developed with these characteristics. This study has also identified and proposed seven new characteristics of ELS.