Reviewerly/exHarmony huggingface dataset:
An Ontology-Based Diagnosis of Mainstream Service Modeling Languages

This paper presents a diagnosis of mainstream service modeling languages (SoaML, USDL, and ArchiMate) in light of UFO-S, a reference ontology for services. UFO-S is intended as a broad ontology for service phenomena, harmonizing different perspectives on services (e.g., "service as commitment", and "service as capability"), and addressing several phases of the service lifecycle (service offering, service agreement, and service delivery). As result, UFO-S is used as an "analysis theory" to identify choices in these languages concerning their focus and coverage of service phenomena. We identify a number of possible improvements concerning the representation of service participant (roles), the description of service offerings, service agreements and service delivery.
Classification for Rectal CEUS Images Based on Combining Features by Transfer Learning

It is very important to diagnose patients with rectal cancer, which can provide reference for the follow-up treatment. The gold standard for rectal cancer diagnosis is biopsy, but biopsy is invasive and risky. With the development of contrast-enhanced ultrasound (CEUS) technology, CEUS has become a reliable modality to diagnose rectal cancer. The degree of contrast enhancement can reflect the distribution of micro vessels inside the tumor. CEUS images are classified into three grades according to the inhomogeneity of enhancement inside rectal cancer. In this paper, we use deep learning and transfer learning to classify CEUS images. Features of rectal CEUS images were extracted by AlexNet, VGG16 and Resnet50. The extracted features were combined and normalized. A three-layer fully connected neural network was trained to classify the features of rectal CEUS images. The combination of features extracted by VGG16 and ResNet50 achieve 87.91% accuracy and AUC is 0.978.
Reduction of Steganalytical Methods Set with Determined Reliability

The technology that allows to exclude from computing the steganalytical methods based on the same regularities is described. It consists of mathematical model and algorithm. The mathematical apparatus of regression analysis is used.
SQL Injection Attack Detection using ResNet

SQL Injection being the top web vulnerabilities, is defined as a code injection technique to gain access over sensitive data. According to OWASP — 2017, it is recorded as one of the top ten web vulnerabilities. Detecting these web vulnerabilities will help to protect sensitive, confidential data. ResNet based SQL injection detection method automatically learns and detects SQL injections attacks. The Tokenizer is used for tokenizing the words, gives word count and vectorize the tokens by converting each token into integer sequence or vector where the coefficient of every token is binary. Processed samples are trained using ResNet algorithm. The user interface is designed for a test case, which expects a user to enter a malicious or normal query. The trained ResNet model is effectively able to identify whether the input request is a malicious or normal request. The results of experiments demonstrate that ResNet can effectively identify different types of SQLIA.
A Comparative Study on CBIR Using Color Features and Different Distance Method

Image retrieval system has been effectively and efficiently used tool for large image databases. In CBIR system, the input is query image and output is retrieved similar images. CBIR is the method to retrieve images on visual content. Several retrieval approaches have been developed to increase the performance of CBIR system. In this paper, we present a comparative analysis of CBIR using color features. We also discuss about color features such as Color statistics, Color moment, Dominant color descriptor (DCD), HSV histogram, Color Correlogram, and Color String. In this research, we find distance between two images using different similarity metrics, namely Jaccard distance (JD), Hamming distance (HD), Euclidean distance (ED), Cheby–Chev (CD), and Manhattan distance (MD). Support Vector Machine (SVM) is used for classification of categories. The comparative results on Corel database with 1000 images. The experimental result is on precision for calculating system performances.
Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars

Abstract Jarosite is an indicator of ephemeral, acidic fluids in Martian history, which often occurs with some other secondary minerals based on Mars in situ and remote sensing detections. However, detailed Raman and visible and near‐infrared (VNIR) spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars still lack, which are essential for jarosite identifications and hence a better understanding of Martian aqueous history. In this study, we prepared 44 binary mixtures of jarosite with four other secondary minerals (alunite, gypsum, kaolinite, and montmorillonite), and characterized their detailed Raman and VNIR spectral features. We find the Raman spectral patterns of jarosite, alunite and gypsum are distinctive and ready for their identification in mixtures, although mixtures with montmorillonite and kaolinite show high fluorescence background and their Raman spectral features are obscured by strong and sharp peaks of jarosite. The normalizing strengths of their characteristic Raman spectral features (e.g., ν 1 for jarosite) change with jarosite mass fractions, and mineral distribution maps and volume percentages of endmembers are acquired using Raman imaging method. The band depths of absorption features in VNIR spectra are also correlated with jarosite mass proportions, and four models are proposed to evaluate jarosite mass fractions in the mixtures using a partial least squares algorithm. These detailed spectral properties of mixtures and models would provide valuable information for in situ Raman investigations on Mars (e.g., ExoMars and Mars 2020) and orbital VNIR remote sensing detections (e.g., Compact Reconnaissance Imaging Spectrometer for Mars).
Remarks on Quaternion Neural Networks with Application to Trajectory Control of a Robot Manipulator

This paper presents a quaternion neural network-based controller for a robot manipulator that can be used to investigate the possibility of using quaternion neural networks in practical applications. The quaternion neural network, which synthesises the control input for tracking an end-effector of the robot manipulator to the desired trajectory, assumes the role of an adaptive-type servo controller in a control system. Two types of network, such as feed-forward quaternion neural network and a recurrent quaternion neural network, were used to design servo-level controller and their performances were compared. Numerical simulations for controlling a three-link robot manipulator are performed to evaluate the characteristics of the proposed controllers and to demonstrate the feasibility as well as the effectiveness of the proposed controllers.
Cloud Cognitive Services Based on Machine Learning Methods in Architecture of Modern Knowledge Management Solutions

Cognitive Services are cloud computing services available to help developers build intelligent applications based on Machine Learning (ML) methods with pre-trained models as a service. Machine Learning platforms are one of the fastest growing services of the cloud because ML and Artificial Intelligence (AI) platforms are available through diverse delivery models such as cognitive computing, automated machine learning, model management. Cognitive Computing is delivered as a set of APIs. Due to the nature of the technologies involved in ML ecosystems and Knowledge Hierarchy—Data, Information, Knowledge, Wisdom (DIKW) Pyramid, there is a natural overlap of a technologies and Knowledge Management (KM) processes. The modern architecture of software solutions can be developed with the use of a wide technology stack, including cloud computing technologies and Cognitive Services (CS). We can use a wide range of ML tools at all levels of the DIKW pyramid. In this paper, we propose a new CS based approach to build an architecture of Knowledge Management system. We have analyzed the possibilities of using CS at all levels of the DIKW pyramid. We discussed some of the relevant aspects of Cloud CS and ML in Knowledge Management context and possibilities implementation of Cognitive Services on knowledge processing.
Filter Selection Methods for Multiclass Classification

Feature selection is used in choosing relevant features that contribute to the predictive power of a machine learning model. Irrelevant features tend to decrease model accuracy and cause overfitting. Feature selection is the solution to dimensionality problems, especially that data nowadays are unstructured. There are three types of feature selection techniques; filter, wrapper and embedded. Filter methods uses statistical scoring and ranks features by the score. Wrapper methods uses a model to select features and evaluates according to model accuracy. Embedded methods combine the properties of both previous algorithms and selects features in the process of training. This study is focused on the filter types, specifically Chi-square, Information Gain and Relief. On the other hand, multiclass classification is a task that involves classifying instances into three or more classes. This paper aims to compare the performance of the Support Vector Machine (SVM) multiclass classifier when entered with feature subsets generated from three different filter feature selection methods. The dataset is a clothing review text data taken from Kaggle. It contains multiple classes with 23486 review instances. Since filter selection methods utilize ranking, CHI, IG, and Relief were able to select and rank almost the same number of features. When the data was fed into SVM, CHI garnered 66.84% accuracy, IG got 32.90% accuracy while Relief obtained 29.69% accuracy. Experiments on SVM showed that among the generated data subset from the three filter selection methods, the subset using CHI yielded higher accuracy, precision, recall and F1 score compared to others.
Region-aware Random Erasing

Object detection task, as a prevailing direction of computer vision, involves many challenges. One of its most general problems is overfitting. Random Erasing is a state-of-art Data Augmentation method for avoiding overfitting. However, it aims at classification task. When it is used to train object detection models, it sometimes discards the objects, then the bounding boxes correspond to some noise regions. To solve this shortcoming of Random Erasing, this paper proposes Range-aware Random Erasing data augment method. In training stage, Range-aware Random Erasing randomly occludes a part of foreground and a part of background rather than occludes a part of a whole image. By using this approach, we can not only enlarge our training dataset to reduce overfitting without discarding objects, but also reduce the impact of background information. By combing Region-aware Random Erasing with Tiny-YOLOv3 on two public datasets, Widerface and ILSVRC2015-VID, nice performance improvements in mAP are showed.
Declarative Data Analytics: A Survey

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.
Secure virtual machine allocation against attacks using support value based game policy

Summary Cloud computing, as a supreme computing stage, presents new security challenges. At the point when virtual machines (VMs) are sent in a cloud domain, VM placement strategies can essentially influence the general security dangers of the whole cloud. As of late, the attacks are explicitly intended to co‐locate with target VMs in the cloud. The VM placement without considering the security risks may put the clients, or even the whole cloud, in threat. Therefore, here we prompt an exhaustive approach to appraise the cloud environment security risk from network, host, and VM. In like manner, secure VM allocation against co‐resident attacks using support value based game policy is proposed that lessens the general security dangers of a cloud, while decides the multi‐objective measures such as efficiency, coverage, and the maximum link utilization. Then, the support value is calculated for the given multi‐objective values. At last, the game theory is used to allocate the best VM. The experimental results prove that the proposed method outperforms than the existing methods in regards of efficiency, coverage maximum link utilization, execution time, throughput, latency, workload balance, power consumption, and standard deviation.
A High-Performance Systolic Array Accelerator Dedicated for CNN

The rapid development of artificial intelligence has made the convolutional neural network (CNN) more important. The traditional computing architecture based on CPU can't meet the requirements of the practical applications. Therefore, the development of a new hardware computing platform for CNN becomes more urgent. This paper proposes a systolic array accelerator dedicated to CNN. As CNN model requires a lot of simple logic operations, we optimize the convolution calculation module with the systolic multiply-accumulate (MAC) array. We design three kinds of convolution calculation mappings, which can deal with convolution with different sizes. Our accelerator realizes efficient reuse of local storage area data, which reduces data movement and improves computing performance. To balance storage bandwidth and computational speed, the convolutional layer is subdivided into granular tasks and executed to mask the time of accessing external storage. This accelerator also supports Winograd convolution of 3x3 weight kernels. The heterogeneous system consisting of the accelerator and the self-developed digital signal processor SWIFT (SWIFT DSP) is verified on the FPGA platform. The experimental results show that our accelerator outperforms traditional accelerators under the same condition.
Mobile Robot Path Planning Based on Improved Reinforcement Learning Optimization

The constant parameter is usually set in adaptive function with traditional mobile robot path planning problem. Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path recently. In order to effectively solve mobile robot path planning problem in obstacle avoidance environment, a path planning model and search algorithm based on improved reinforcement learning are proposed. The incentive model of reinforcement learning mechanism is introduced with search selection strategy, modifying dynamic reward function parameter setting. The group intelligent search iterative process of global position selection and local position selection is exploited to combine particle behavior with reinforcement learning algorithm, dynamically adjusting the empirical parameter of the reward function by strengthening the data training experiment of Q-learning. to determine the constant parameters for simulation experiment, once the distance between the robot and the obstacle is less than a certain thresholds value, the 0-1 random number is used to randomly adjust the moving direction, avoiding the occurrence of mobile robot path matching deadlock. The study case shows that the proposed algorithm is proved to be better efficient and effective, thereby improving the search intensity and accuracy of the mobile robot path planning problem. And the experimental simulation shows that the proposed model and algorithm effectively solve mobile robot path planning problem that the parameter selection and the actual scene cannot be adapted in real time in traditional path planning problem.
Identification of Seven Low-Resource North-Eastern Languages: An Experimental Study

This paper describes an experimental study on the identification of seven north-eastern (NE) low-resource languages (LRL) of India namely, Assamese, Bengali, Hindi, Manipuri, Mizo, Nagamese, and Nepali using 55-dimensional hybrid features (HF) and hierarchical technique. However, the development of language identification (LID) systems could be leveraged only with the availability of specially curated speech data in LRL and it is a really challenging task to build a system on such under-resourced languages. We have collected around 42 h of speech data (including 35 h for training and 7 h for testing) for analysis on the above-said seven LRL. The process of designing speech database in LRL has been generic enough to be used for other languages as well. We have compared our proposed methodology with baseline system on collected speech data. From the experimental study, it has been observed that our proposed system is outperformed over baseline system and results are encouraging for researchers in low-resource languages. This initial study unveils the importance of HF for NE-LRL.
Path Planning for a Mobile Robot in Unknown Dynamic Environments Using Integrated Environment Representation and Reinforcement Learning

This study develops a new path planning method which utilizes integrated environment representation and reinforcement learning to control a mobile robot with non-holonomic constraints in unknown dynamic environments. With the control algorithm presented, no approximating the shapes of the obstacles or even any information about the obstacles' velocities is needed. Our novel approach enables to find the optimal path to the target efficiently and avoid collisions in a cluttered environment with steady and moving obstacles. We carry out extensive computer simulations to show the outstanding performance of our approach.
Shearlet-Based Light Field Reconstruction of Scenes with Non-Lambertian Properties

Light fields representing Lambertian scenes are characterized by specific epipolar plane images, which support in Fourier domain is limited by the minimum and maximum scene depth. This feature of the light field has been taken into account when designing the shearlet-based light field reconstruction algorithm. However, this feature does not hold for the case of non-Lambertian scenes. Therefore, the original algorithm performs sub-optimally for such scenes. In this work, we extend the shearlet based reconstruction algorithm to enable light fields reconstruction for scenes composed of surfaces with non-Lambertian properties. Instead of assuming Lambertianity and the corresponding Fourier-domain support, we estimate the real Fourier-domain support of the given light field of a non-Lambertian scene. Based on the estimated support, we adapt the Shearlet frame to properly span that support. The results demonstrate that the new method yields a faithful reconstruction of densely sampled light fields of non-Lambertian scenes from a small number of captured views.
Convolutional Autoencoder for Discriminating Handwriting Styles

We describe in this paper how we use a CNN-autoencoder for unsupervised anomaly detection, here adapted to detect anomalies in handwritten text samples. There is no distinction between a learning and test dataset, the text snippet containing the anomaly is the only data available. Since character recognition is not aimed at, we expect that the autoencoder should learn the styling characteristics of the writing. Algorithms used for graphology commonly measure the extends of the vertical strokes over or under the base line, the slope of the strokes or the closure of letters such as the “o”. Given the text baseline which is detected by image processing, the image of the sample is arbitrarily split in tiles for the training / recognition task. We also use a partial Radon projection to convert these tiles in a more abstracted representation for the learning/detection tasks. The Radon projection is computed over a [π /2] range which captures the most style variations in handwritten text. Discrimination takes place directly while training, and by shifting image tiles from batch to batch as the training epochs go on, outliers are discarded and localised.
Report on a Hackathon for Car Navigation Using Traffic Risk Data

Car drivers select their routes based on the information obtained about accidents and traffic congestion along the route. In recent years, nowcasting and forecasting of various traffic risk events is being performed by using diverse sensor data. However, there is no clarity as yet on what and how to communicate to the driver in case there are traffic risks on the route. In this paper, we have developed an environment that enables non UI experts to quickly create car navigation prototypes by using traffic risk data. This paper includes our report on a hackathon that we held using this environment. The hackathon theme was "Develop a new car navigation system equipped with a mechanism that makes the driver aware of traffic risks and helps them determine the most appropriate driving routes." Twenty three researchers and professionals from the field of traffic engineering participated. Our results have brought certain common problems to the awareness of the experts. The information obtained from this report will be very beneficial for our community to determine the direction of collaboration.
Visual Localization under Appearance Change: A Filtering Approach

A major focus of current research on place recognition is visual localization for autonomous driving. In this scenario, as cameras will be operating continuously, it is realistic to expect videos as an input to visual localization algorithms, as opposed to the single-image querying approach used in other place recognition works. In this paper, we show that exploiting temporal continuity in the testing sequence significantly improves visual localization — qualitatively and quantitatively. Although intuitive, this idea has not been fully explored in recent works. Our main contribution is a novel Monte Carlo-based visual localization technique that can efficiently reason over the image sequence. Also, we propose an image retrieval pipeline which relies on local features and an encoding technique to represent an image as a single vector. The experimental results show that our proposed method achieves better results than state-of-the-art approaches for the task on visual localization under significant appearance change. Our synthetic dataset is made available at: http://tiny.cc/jd73bz
Forest Fire Fighting Using Heterogeneous Ensemble of Unmanned Aerial Vehicles

This work presents a novel approach to forest fight fighting using a heterogeneous ensemble of unmanned aerial vehicles of different types, which can perform patrol, confirmation, monitoring, and extinguishing missions. Their coordinated teamwork allows suppressing of the forest fires in the places not easily accessible by humans due to involving vehicles specially equipped with fire-extinguishing tools. The functions and missions of the ensemble as well as its architecture are considered. The coordination model based on the case-scenario control system and joint coordination framework of the heterogeneous ensemble is presented. The proposed model provide the required credibility and efficiency of fire response.