Reviewerly/exHarmony huggingface dataset:
Two approaches to compilation of bilingual multi-word terminology lists from lexical resources

Abstract In this paper, we present two approaches and the implemented system for bilingual terminology extraction that rely on an aligned bilingual domain corpus, a terminology extractor for a target language, and a tool for chunk alignment. The two approaches differ in the way terminology for the source language is obtained: the first relies on an existing domain terminology lexicon, while the second one uses a term extraction tool. For both approaches, four experiments were performed with two parameters being varied. In the experiments presented in this paper, the source language was English, and the target language Serbian, and a selected domain was Library and Information Science, for which an aligned corpus exists, as well as a bilingual terminological dictionary. For term extraction, we used the FlexiTerm tool for the source language and a shallow parser for the target language, while for word alignment we used GIZA++. The evaluation results show that for the first approach the F 1 score varies from 29.43% to 51.15%, while for the second it varies from 61.03% to 71.03%. On the basis of the evaluation results, we developed a binary classifier that decides whether a candidate pair, composed of aligned source and target terms, is valid. We trained and evaluated different classifiers on a list of manually labeled candidate pairs obtained after the implementation of our extraction system. The best results in a fivefold cross-validation setting were achieved with the Radial Basis Function Support Vector Machine classifier, giving a F 1 score of 82.09% and accuracy of 78.49%.
An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

This chapter presents an assessment of falls and everyday situations in people by sensors dataset collected in fall simulation. This evaluation was performed through the use of intelligent techniques and models based on feature selection techniques and fuzzy neural networks. Therefore, this work can be seen as an auxiliary approach of presenting a vision of knowledge extraction for the construction of actions, prevention, and training to functional that will work in areas correlated to health impacts of people who may have difficulties or injuries due to the impact suffered in a fall. The results obtained were compared with state of the art for the theme and the version of the hybrid model that acts on the most relevant dataset dimensions identifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people.
Approximating C<sub>free</sub> Space Topology by Constructing Vietoris-Rips Complex

We present a new way of constructing sparse roadmaps using point clouds that approximates and measures the underlying topology of the C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">free</sub> space. The main advantage of the constructed roadmap is its homotopy equivalence to the η-offset of the C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">free</sub> space. Though only used to plan paths as a regular roadmap in this work, because the roadmap preserves the topology of the underlying sampled space, the information can be used to plan paths beyond the simple connection of graph vertices. To construct the roadmap, we first sample the configuration space so that the resulting graph is a n-skeleton graph that constructs a Vietoris-Rips (VR) complex. Then, we perform a series of topological collapses to remove vertices from the graph while still preserving its topological properties. The resulting roadmaps are used to plan paths for different robots and the experimental results show that the proposed topological approach is faster and more feasible in complex high-dimensional spaces.
Analysis of Y00 Protocol Under Quantum Generalization of a Fast Correlation Attack: Toward Information-Theoretic Security

In our previous work, it was demonstrated that the attacker could not pin-down the correct keys to start the Y00 protocol with a probability of one under the assistance of unlimitedly long known-plaintext attacks and optimal quantum measurements on the attacker's quantum memory. However, there were several assumptions that the Y00 system utilized linear-feedback shift registers as pseudo-random-number generators, and a fast correlation attack was disabled by irregular mapping on the Y00 systems. This study generalizes such an attack to remove the assumptions of the previous work. The framework of the security analyses of this study reiterates two well-known results from the past: (1) Y00 systems would be cryptanalyzed when the system is not designed well; (2) the system is possibly information-theoretically secure when the system is designed well, although the attacker's confidence in the correct key increases over time but the success probability of key recovery does not reach unity in finite time; (3) the breach probability of the shared keys increases with time. Hence, a key-refreshment procedure for the Y00 protocol is provided herein. Such security analyses are important not only in key refreshment but also in initial key agreement.
Improvements of the Balance Discovery Attack on Lightning Network Payment Channels

The Lighting Network (LN) is a network of micropayment channels that runs on top of Bitcoin. The balances of payment channels are not broadcasted to the LN network to preserve the privacy of the nodes participating in the network. A balance disclosure attack (BDA) has been proven to be successful in determining the balance of large amounts of channels in the network. In this paper we propose an improved algorithm for the BDA as well as a new type of attack that leverages the differences between LN client software implementations. Our improved algorithm extends the original BDA by performing payments from both sides of the channel. The new attack uses malformed payments to shutdown payment channels an adversary is not part of.
MA<sup>2</sup>RA - Manual Assembly Augmented Reality Assistant

Cyber-physical systems and automation play a major role in Industry 4.0. However, due to its flexibility and high responsiveness to short-term changes in the assembly of complex and varied products, manual work cannot be neglected. Cognitive assistance systems relieve the mental stress of a worker and thus help prevent mistakes during the assembly process. Systems utilizing augmented reality (AR) technologies are suitable for manual operations. In order to motivate the development needs of industrial augmented reality (IAR) applications, a prototype is described below. This paper focuses on the conceptual development and the implementation of a prototype assistance system. By using AR glasses and a data glove, an inexperienced worker is guided through a demonstrative assembly process where each assembly step is documented by the system. Finally, a user test is performed to evaluate the prototype.
Neural Network Based Heterogeneous Sensor Fusion for Robot Motion Planning

This paper presents a neural network based heterogeneous sensor fusion approach towards real-time traversability estimation of mobile robots using sensor data. Even though significant advances have been made for autonomous navigation in structured terrain conditions, obtaining reliable traversability estimates for tracked vehicle navigation in challenging terrain conditions is still an open research problem. In this regard, we propose a neural network architecture capable of fusing depth images along with roll and pitch measurements on board the robot to perform traversability estimation. The proposed architecture is trained in a variety of simulated structured and unstructured environments. As such, the proposed architecture is capable of extracting the relevant features from the sensor measurements in a data driven manner as compared to existing heuristic based approaches that fail to generalize for different environmental conditions. The reliability of the traversability estimates provided by the trained architecture was validated in indoor and outdoor conditions using real sensor data. In addition, the feasibility of using the traversability estimates in incremental path planning was also demonstrated through simulation. For both applications the proposed approach provided compelling results. Inferences based on the results of the experiments along with directions for future research are also outlined.
Identifying the Central Figure of a Scientific Paper

Publishers are increasingly using graphical abstracts to facilitate scientific search, especially across disciplinary boundaries. They are presented on various media, easily shared and information rich. However, very small amount of scientific publications are equipped with graphical abstracts. What can we do with the vast majority of papers with no selected graphical abstract? In this paper, we first hypothesize that scientific papers actually include a "central figure" that serve as a graphical abstract. These figures convey the key results and provide a visual identity for the paper. Using survey data collected from 6,263 authors regarding 8,353 papers over 15 years, we find that over 87% of papers are considered to contain a central figure, and that these central figures are primarily used to summarize important results, explain the key methods, or provide additional discussion. We then train a model to automatically recognize the central figure, achieving top-3 accuracy of 78% and exact match accuracy of 34%. We find that the primary boost in accuracy comes from figure captions that resemble the abstract. We make all our data and results publicly available at https://github.com/viziometrics/centraul_figure. Our goal is to automate central figure identification to improve search engine performance and to help scientists connect ideas across the literature.
Evolving Customer Experience Management in Internet Service Provider Company using Text Analytics

Customer experience is of crucial significance to the constant growth of a business. It is necessary to ensure great customer experience, thus maintaining customer loyalty and satisfaction. An approach that intended to develop and improve customer experience is called Customer Experience Management (CEM). CEM is a strategy practiced to track, supervise, and arrange all synergy to help a business focal point on the needs of its customers. This research uses sentiment analysis and topic modeling to analyze the experience of Internet Service Provider customers. The output of this research expected to drive the strategies change in CEM. This research uses data taken from customer tweets on Twitter. It is considering that the data on social media is enormous and unstructured. Therefore, classification using Naive Bayes Classifier applied to assist and expedite in the sentiment analysis process. The classification for sentiment analysis using NBC gained accuracy above 82%. Hence, the classification models using NBC achieve excellent capability for sentiment analysis. To determining topics that often discussed by customers, this research uses the Latent Dirichlet Allocation models for Topic Modeling.
Sentiment Analysis on Product Reviews

Sentiment Analysis is a process of analyzing and categorizing the emotion or sentiment over any given review or text piece in order to know what the reviewer wants to express in the form of positive, negative or neutral. Today, people are highly interested in buying things online from any e-commerce site or they search for a product review in order to know the quality and one's perception toward that product before buying. The product provider also gets to know about the user's opinion over a product. This can help the company to improve its marketing strategy and quality of product in their favor. Sentiment analysis uses various semantic approaches like on these online reviews to extract as much feature it can and categorize the type of opinion. Some techniques also help in rating the product value based on user's opinion. This paper is a literature survey including various authors and their sentiment techniques on product or online review.
Construction of Quantum Codes From Matrix-Product Codes

Recently, the construction of matrix-product codes with certain self-orthogonality is an effective way to obtain good quantum codes. One of the most frequently used means in this process is to construct quasi-unitary matrices as the defining matrices of matrix-product codes. Further, if the defining matrix is NSC, then the corresponding matrix-product code has maximal minimum distance. Inspired by these idea, we propose a constructive method about NSC quasi-unitary matrices over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 2 with q being a prime power, and construct k × k NSC quasi-unitary matrices for any k <; q by certain properties of the polynomial ring F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ], extending or improving the known results. Through such defining matrices, we obtain new classes of quantum codes from matrix-product codes, many of which show better parameters than previously known.
Developing a Secure, Smart Microgrid Energy Market using Distributed Ledger Technologies

The ability for the smart microgrid to allow for the independent generation and distribution of electrical energy makes it an attractive solution towards enabling universal access to electricity within developing economies. Distributed Ledger Technologies (DLTs) are being considered as an enabling technology for the secure energy trade market however the high processing, energy and data exchange requirements may make them unsuitable for the Industrial Internet of Things technologies used in the implementation of the microgrid and the limited connectivity infrastructure in developing technologies. This work serves to assess the suitability of DLTs for IIoT edge node operation and as a solution for the microgrid energy market by considering node transaction times, operating temperature, power consumption, processor and memory useage, in addition to mining effort and end user costs.
On the probability amplitude of quantum entanglement and the Pauli matrices

Abstract The probability amplitudes for quantum entanglement, also known as Bell sates, are utilized to arrive explicitly at the identity matrix I and the $$\sigma_{x}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>σ</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:math> , $$\sigma_{y}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>σ</mml:mi><mml:mi>y</mml:mi></mml:msub></mml:math> , and $$\sigma_{z}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>σ</mml:mi><mml:mi>z</mml:mi></mml:msub></mml:math> Pauli matrices, via a straight-forward $$2 \times 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> matrix representation that utilizes the vector direct product . It is also indicated that this approach is completely equivalent to the utilization of the Kronecker product $$\otimes$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>⊗</mml:mo></mml:math> to multiply the relevant ket vectors. Furthermore, it is shown that the polarization rotation matrix R , operating on the various versions of the probability amplitude for quantum entanglement, yields four identities elegantly interconnecting these probability amplitudes.
Regression and progression in stochastic domains

Reasoning about degrees of belief in uncertain dynamic worlds is fundamental to many applications, such as robotics and planning, where actions modify state properties and sensors provide measurements, both of which are prone to noise. With the exception of limited cases such as Gaussian processes over linear phenomena, belief state evolution can be complex and hard to reason with in a general way, especially when the agent has to deal with categorical assertions, incomplete information such as disjunctive knowledge, as well as probabilistic knowledge. Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive, allowing for such belief states to be expressed naturally as constraints. While that proposal is powerful, the task of how to plan effectively is not addressed. In fact, at a more fundamental level, the task of projection, that of reasoning about beliefs effectively after acting and sensing, is left entirely open. To aid planning algorithms, we study the projection problem in this work. In the reasoning about actions literature, there are two main solutions to projection: regression and progression. Both of these have proven enormously useful for the design of logical agents, essentially paving the way for cognitive robotics. Roughly, regression reduces a query about the future to a query about the initial state. Progression, on the other hand, changes the initial state according to the effects of each action and then checks whether the formula holds in the updated state. In this work, we show how both of these generalize in the presence of degrees of belief, noisy acting and sensing. Our results allow for both discrete and continuous probability distributions to be used in the specification of beliefs and dynamics.
Restoration of Historical Document Images Using Convolutional Neural Networks

Historical documents are priceless, irreplaceable and an integral part of national and world history. Handwritten documents can be a letter, notes, journal, memoir etc. or any formally written manuscript implicating a historical event or cultural heritage. These types of documents are often fragile and degraded. They have limited timespan because of materials used, and need to be digitized, restored and preserved in digital format to ensure their longevity. In this research work, we have proposed a novel methodology using a single convolutional neural network to denoise and restore old handwritten documents. The proposed model demonstrates excellent performance in restoration and preservation of severely degraded old handwritten document images of around 70 years old.
I-CARS: An Interactive Context-Aware Recommender System

Context-aware recommendation has attracted significant attentions over online sites due to its smart context adaption in improving recommendation quality. However, the user's instant contexts do not follow his/her regular user behaviour patterns, thus have not been well captured for advanced personalization of recommendation generation. In this work, we propose an Interactive Context-Aware Recommender System (I-CARS), which allows users to interact and present their needs, so the system can personalize and refine user preferences. I-CARS iteratively asks a question to a user to trigger feedback in term of her recent contexts and incorporates the response to recommend items most likely satisfying his/her instant interests. Specifically, we first propose a Personalized Weighted Context-Aware Matrix Factorization (PW-CAMF) that enables the personalization of important contexts for each user. Then we propose two question selection strategies that exploit user preferences through feedback. We have conducted comprehensive experiments over two real datasets. The experimental results prove the effectiveness of our I-CARS system compare to existing competitors.
Weakly Supervised Instance Segmentation Based on Two-Stage Transfer Learning

Weakly supervised instance segmentation, which could greatly decrease financial and time cost, is one of fundamental computer vision tasks.State-of-the-art methods mainly concentrate on improving the quality of generated pixel level labels, namely masks, using complex traditional segmentation methods, and ignore the effect of the quality of generated masks.Namely, the masks of small object instances tend to be invalid, which would degrades the performance of instance segmentation.In this paper, we propose a twostage transfer learning framework for weakly supervised instance segmentation.We explicitly discriminate the invalid and valid generated masks, and just utilize the valid masks for training to avoid the interference of invalid ones.We use a network-based transfer learning strategy to effectively utilize all useful information, including category labels and bounding-box information of all objects and valid generated masks.Besides, we further use a feature-mapping-based transfer learning strategy to improve the performance of small object instance segmentation.We demonstrate the effectiveness of the proposed method on the PASCAL VOC 2012, and the experimental results show that our proposed method is effective and outperforms state-of-the-art methods.
Hybrid Cloud – Architecture for Administration Shells with RAMI4.0 Using Actor4j

In this paper, the actor-oriented Java framework Actor4j is used to implement administration shells in a RAMI4.0 context to enable the implementation of hybrid cloud solutions. A novel architecture is conceptualized around the usage of the Actor Model, especially in the area of edge and cloud computing. The administration shell can be either implemented by an asset that is OPC-UA ready or be mapped to a gateway in the device (service) cloud. The administration shell implemented with the Actor Model (predominantly for the public and private cloud) consists of four modules: the components module (describing the corresponding system), the publish-subscribe module, the additional logic module (conditions, alarms), and the interaction module (behavior). The main idea is to reuse an OPC-UA compliant implementation for the internal actor logic. At the device cloud level, IT/OT entities can be composed together in a domain specific point of view. This is implemented over three abstractions levels (data acquisition, data analytics and control & monitoring). The proposed Actor Model fits very well for the implementation of large scale administrations shells.
Data Augmentation Method for Object Detection in Underwater Environments

This paper proposes a novel data augmentation method for enhancing the performance of deep-learning-based object detection in underwater environments. With deep-learning-based methods, system performance is highly dependent on the learning dataset. In extreme conditions such as underwater environments, however, it is difficult to obtain sufficient image data. To make this easier, we propose an image generation method that augments learning data by applying the special conditions of underwater environments. The proposed method generates virtual underwater images by using the optical properties of water from images taken above ground. The generated data can be used in place of underwater data, thus reducing the training effort. Experimental results show that the proposed method is effective.