Reviewerly/exHarmony huggingface dataset:
Expert Assessment of Synonymic Rows in RuWordNet

This article explores the principles of synsets in the RuWordNet thesaurus and synonyms in the classical dictionaries of Russian synonyms (N = 10) to identify discrepancies and improve the principles of organising synsets in RuWordNet. The relevance of the study is determined by the demand for WordNet resources in natural language processing tasks. The authors selected 102 RuWordNet thesaurus synsets, including nouns (N = 34), adjectives (N = 34) and verbs (N = 34). The meanings of the lexemes were correlated according to the data given in Russian language thesauri (N = 2). The comparative method and an independent expert assessment of RuWordNet revealed a number of discrepancies and inaccuracies in the representation of synsets concerning polysemy, hypo-hyperonymic relationships, lexical meanings of words and parts-of-speech synonymy. On the basis of this study, the authors recommend the elimination of individual shortcomings in the construction of the RuWord-Net synsets, in particular the polysemy and parts-of-speech synonymy.
Similarity Analysis of Control Software Using Graph Mining

The control software of large scale industrial systems such as machines and plants in the domain of automated Production Systems (aPS) is oftentimes developed using the method clone and own. However, cloning is one of the reason for high maintenance cost in software lifecycle and thus, the need for clone detection is arising. In large scale control software, clone detection is a tedious work, which cannot be performed manually. In order to alleviate the problem of huge number of clones in control software, structural clone detection can be performed. Detecting structural clones can help in better understanding of large scale and complex software, detecting commonly-used design patterns, and software evolution. In this work, the software structure is represented as a call graph depicting software artefacts and their direct dependencies. These call graphs are compared based on graph mining approach to detect similarities between two software structures. The proposed method is adapted and applied to two industrial use cases with different size and complexity. The obtained similar fragments in the software structures are evaluated and verified through manual analysis. The results show that the proposed method is promising approach to capture the similarities between two software structures.
A Framework for Inconsistency Detection Across Heterogeneous Models in Industry 4.0

Manufacturing systems nowadays get more interconnected and flexible. Developing such a system appeals for closer interdisciplinary collaboration. Various models are used by different engineers to shape specific views on the system, but might also introduce contradictions, i.e. inconsistencies, leading to engineering delays or failures. This study proposes a knowledge-based framework to detect and avoid inconsistency across models representing different views of the same system. A prototype of the framework is implemented and evaluated.
Faster region-based convolutional neural network method for estimating parameters from Newton’s rings

Our study investigated an object-detection method based on the faster region-based convolutional neural network (faster R-CNN). The method was designed to determine the center of either a concentric circle or concentric ellipse. Specifically, the central spot of the image (as the object region) can be marked by the bounding box when the circular or elliptical image is used as input data for the faster R-CNN model. The center point of the bounding box can then be calculated according to the coordinates of the upper left and lower right corners, that is, the center position of the concentric circle or concentric ellipse. It is important to determine the center coordinates when taking optical measurements, as the curvature radius of optical components can thus be obtained. The effectiveness of this method is demonstrated through simulation images. Furthermore, we can obtain the center coordinates of the actual Newton’s rings image using the above method; according to the coordinate transformation method, the curvature radius can be estimated based on the center.
Thai Handwritten Recognition on Text Block-Based from Thai Archive Manuscripts

Automatic transcription of ancient handwritten manuscripts can be a challenging task when compared with a transcription of contemporary handwriting. Characters and words can have unusual and varying shapes, with significant variation between writers, and sufficient labelled data from which to train machine learning algorithms can be difficult to access. This paper describes ancient Thai handwriting transcription on block-based from archive manuscripts, using a hybrid deep neural network with both convolutional (CNN) and recurrent (RNN) layers, trained using Connectionist Temporal Classification (CTC) loss. Six architecture variations are compared. Data augmentation was applied to synthetically increase the number of training samples, resulting in improved learning. Thai archive manuscripts were collected from the Thai National Library. The character error rate (CER) in the best architecture was found to be 11.9 percent.
Semi-supervised Flexible Joint Distribution Adaptation

For the traditional feature transfer method, there are problems that the projection transformation is too rigid and data manifold structure cannot be captured inadequately. In this paper, we propose a feature extraction method with the ability of knowledge transfer named Semi-supervised Flexible Joint Distribution Adaptation (SFJDA). Firstly, we introduce a flexible transformation constraint instead of the traditional linear projection into Joint Distribution Adaptation (JDA) to relax this constraint and extract shared feature between source and target domains. Secondly, Manifold Alignment (MA) is introduced to mine geometric information of the source and target domains. Finally, Linear Discriminant Analysis (LDA) and its kernel form are integrated into the objective function to keep class separability during label refinement procedure. Experimental results on 36 groups of image datasets in the classification task validate the feasibility and effectiveness of the proposed algorithm.
Simultaneous Transparent and Non-Transparent Object Segmentation With Multispectral Scenes

For an autonomous mobile system such as an autonomous robot that moves throughout a city, semantic segmentation is important. Performing semantic segmentation under diverse conditions, in turn, requires 1) a robust ability to recognize objects in low-visibility environments, such as at night and 2) the ability to recognize objects that transmit visible light, such as glass and acrylic used in doors and windows. To satisfy these requirements, using RGB images and infrared images simultaneously is considered effective. Visibility and infrared transmission characteristics are different for different objects; therefore, merely entering them into the conventional semantic segmentation framework is not applicable. For example, when a pedestrian is present behind a glass, the visible image captures the pedestrian rather than the glass and the infrared image captures the glass. In this research, we propose a new semantic segmentation method having a three-stream structure, focusing on the difference in the transmission characteristics. This method extracts not only valid features for ordinary non-transparent objects but also features effective for the recognition of transparent objects by utilizing differences in objects to be imaged owing to transmission characteristics. Furthermore, we constructed a new dataset called “coaxials” for the visible and infrared coaxial dataset and demonstrated that we can obtain better segmentation performance compared with the conventional method.
Keyword Extraction from Bengali News

Keyword extraction is a process where a text is given to the computer and the computer returns a set of keywords that recommend topical words and phrases from the content of documents. Keyword extraction methods are being applied to many areas especially when we extract keywords in the area of information retrieval. This has a particular interest because people retrieve significant information based on keywords. Many techniques have been developed for English text(s). But, a very few attempts have been made for Bengali keyword extraction as well as for context learning. This paper presents a graph-based keyword extraction algorithm over eight different datasets collected from news portal. Moreover, we have also shown whether the study of co-occurrences allows keeping a record of the formation of each Bengali text. However, it is more tedious to manipulate and often leads to messy visualizations.
Pollux: Interactive Cluster-First Projections of High-Dimensional Data

Semantic interaction is a technique relying upon the interactive semantic exploration of data. When an analyst manipulates data items within a visualization, an underlying model learns from the intent underlying these interactions, updating the parameters of the model controlling the visualization. In this work, we propose, implement, and evaluate a model which defines clusters within this data projection, then projects these clusters into a two-dimensional space using a “proximity≈similarity” metaphor. These clusters act as targets against which data values can be manipulated, providing explicit user-driven cluster membership assignments to train the underlying models. Using this cluster-first approach can improve the speed and efficiency of laying out a projection of high-dimensional data, with the tradeoff of distorting the global projection space.
Deep Dive into Faces: Pose &amp; Illumination Invariant Multi-Face Emotion Recognition System

One of the advancements in humanization of robots is its ability to recognize human emotions. Facial expression plays a key role in identifying human emotions relative to other cues. In this research, an intelligent network capable of real-time emotion recognition from multiple faces using deep learning technique is presented. The proposed network is based on Convolution Neural Network (CNN) in which three blocks of Convolution layers for feature extraction and two blocks of Dense layers for classification are used. The novelty of this method lies in recognizing emotions from multiple faces simultaneously in real time and its invariance to head pose, illumination and age factor. Most of reported work in literature for multiple faces is for frontal face without illumination variation. The proposed emotion recognition system is deployed on Raspberry Pi3 B+ for human robot interaction applications and achieved an average accuracy of 95.8% in real time.
HT-GSOM: Dynamic Self-organizing Map with Transience for Human Activity Recognition

Recognition of complex human activities is a prominent area of research in intelligent video surveillance. The current state-of-the-art techniques are largely based on supervised deep learning algorithms. The inability to learn from unlabeled video streams is a key shortcoming in supervised techniques in most current applications where large volumes of unlabeled video data are utilized. Furthermore, the dominant focus on persistence in traditional machine learning algorithms has induced two limitations; the influence of outdated information in memory- guided decision making, and overfitting of acquired knowledge on specific past events, weakening the plasticity of the learning system. To address the above requirements, we propose a new adaptation of the Growing Self Organizing Map (GSOM), formed in a hierarchical two-stream learning pipeline to accommodate unlabeled video data for human activity recognition, which facilitates plasticity by implementing a transience property, without losing the stability of the learning system. The proposed model is evaluated using two benchmark video datasets, confirming its validity and usability for human activity recognition.
Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence

In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.
Deep Embedded Cluster Tree

The idea of combining the high representational power of deep learning techniques with clustering methods has gained much interest in recent years. Optimizing representation and clustering simultaneously has been shown to have an advantage over optimizing them separately. However, so far all proposed methods have been using a flat clustering strategy, with the true number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to know the true number of clusters during optimization. Instead, the level of detail to be analyzed can be chosen afterward and for each sub-tree separately. An optional data-augmentation-based extension allows DeepECT to ignore prior-known invariances of the dataset, such as affine transformations in image data. We evaluate and show the advantages of DeepECT in extensive experiments.
Towards an Integration of Information Security Management, Risk Management and Enterprise Architecture Management – A Literature Review

Organizations are faced with an increased number of security-related challenges. Our research interest is on information security matters with our proposition being that enterprise architecture management (EAM) can support risk management (RM) and information security management (ISM) for instance by providing a plethora of information about an organization's information assets. We conducted a literature review, which underlines our proposition. The pivotal question we aim to answer is how EAM, RM and ISM efforts can be integrated for "the greater good", i.e., to achieve a facilitation of RM and ISM through the adoption of EAM. As a result, we present an integrated conceptual model which places our findings in the context of the well-established concepts defined in ISO-27001, ISO-31000 and ISO-42010.
An Interactive Indoor Drone Assistant

With the rapid advance of sophisticated control algorithms, the capabilities of drones to stabilise, fly and manoeuvre autonomously have dramatically improved, enabling us to pay greater attention to entire missions and the interaction of a drone with humans and with its environment during the course of such a mission. In this paper, we present an indoor office drone assistant that is tasked to run errands and carry out simple tasks at our laboratory, while given instructions from and interacting with humans in the space. To accomplish its mission, the system has to be able to understand verbal instructions from humans, and perform subject to constraints from control and hardware limitations, uncertain localisation information, unpredictable and uncertain obstacles and environmental factors. We combine and evaluate the dialogue, navigation, flight control, depth perception and collision avoidance components. We discuss performance and limitations of our assistant at the component as well as the mission level. A 78% mission success rate was obtained over the course of 27 missions.
Identity-Based Aggregate Signature Scheme in Vehicle Ad-hoc Network

Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.
Advancements in Reversible Data Hiding in Encrypted Images Using Public Key Cryptography

With the evolving needs of cloud-based information exchange, the challenge of a reliable and secure communication channel has also been the center of attention recently. Although cryptography has been a traditional tool for secure communication; yet it also carries a possibility of attacks aimed to gain access to the sensitive information. As an alternative, the use of reversible data hiding (RDH) technique has been proposed. RDH scheme involves concealment of the data in the cover image, both of which are retrieved in a lossless manner at the receiver end. Over an insecure channel in cloud communication, the cover content is encrypted; thus resulting in Reversible Data Hiding in Encrypted Images (RDHEI). RDHEI traditionally uses symmetric key cryptography involving a drawback of key management overhead. Public key cryptography (PKC) offers an alternate and efficient solution for cloud-based environments in which the homomorphic properties of public key are utilized. This paper provides an overview of the recent advancements in research in the context RDHEI based on PKC; discussing several proposed schemes addressing the core challenges of data expansion and complex computations using public key encryption. In addition to this, the shortcomings of the discussed schemes are highlighted along with possible remedies that will also guide new directions for future work.
A Secure Identity-Based Three-Party Authenticated Key Agreement Protocol Using Bilinear Pairings

The modern Internet technology era requires secure key agreement protocols, which are playing a significant role in the field of cryptography and network security. These protocols are mainly designed to establish a common session key between different parties. It can be easily found that various key agreement protocols are designed in the literature. However, many of these protocols are either proven insecure or have a burden of communication and computational cost. Therefore, a more secure key agreement protocol is needed. This paper exhibits an identity-based three-party authenticated key agreement (ID-3PAKA) protocol is devised, which securely and efficiently negotiates a common secret session key among three parties over the Internet. This protocol is based on the elliptic curve cryptography (ECC). It uses the idea of identity-based encryption (IBE) with bilinear pairings. The security of the proposed work is based on the hardness assumption of the discrete logarithm problem (DL) and elliptic curve. Further, we show that the proposed protocol ensures the known security properties of the session key.
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient

Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of deterministic nature. This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy Gradient when trained on a latent image embedding. In addition, an extension of DDPG is derived using a value function as critic, making use of a learned deep dynamics model to compute the policy gradient. This approach leads to a symbiotic relationship between the deep reinforcement learning algorithm and the latent trajectory optimizer. The trajectory optimizer benefits from the critic learned by the RL algorithm and the latter from the enhanced exploration generated by the planner. The developed methods are evaluated on two continuous control tasks, one in simulation and one in the real world. In particular, a Baxter robot is trained to perform an insertion task, while only receiving sparse rewards and images as observations from the environment.
Programmable Generator of Pseudo-White Noise for Jamming Applications

This paper presents a novel method of pseudo-white noise jamming signal digital generation. Noise signal is generated only in parts of frequency spectrum based on Box-Müller algorithm. Thus necessary emission power of barrage jamming is decreased while jamming effectiveness is increased comparing to classical sweep jamming. It is analyzed how the jamming signal frequency spectrum is managed. The method is simpler for the application than classical barrage jamming, especially when jamming frequency bandwidth is limited.
Automatic differential equations identification by self-configuring genetic programming algorithm

Abstract The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated.
Data Augmentation For CNN-Based 3D Action Recognition on Small-Scale Datasets

Video-based human action recognition recently plays a vital role in many industrial applications thanks to the popularity of depth sensors. A large number of conventional approaches, which have combined handcrafted features and traditional classifiers, cannot deal with various challenges in the field such as the complexity of human actions in the realistic environment. In order to improve recognition performance by exploiting more high-level discriminative features, an efficient skeleton-based action recognition method using deep convolutional neural networks (CNNs) is studied with an image encoder to transform skeleton coordinate data to image-formed data. Since deep learning techniques are fundamentally designed for efficiently working with large datasets, the network overfitting usually occurs if training CNNs on small-scale datasets. To address this issue, a novel data augmentation technique is proposed for both the informative enrichment and overfitting prevention, wherein a skeleton sequence is depicted by manifold action images based on randomly adding some skeleton frames during the data transformation and preparation for the training set. Experimental results on several small-scale challenging datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of action recognition accuracy.