Reviewerly/exHarmony huggingface dataset:
Comprehensive Global Path Planning for Lunar Rovers

Aiming at the large-scale autonomous detection of the lunar rover, this paper proposes a comprehensive global path planning algorithm based on the lunar digital elevation map. Firstly, a planning environment analysis method was designed based on the obtained lunar digital elevation map. Based on the extracted terrain information and solar conditions, a comprehensive smoothness map and an effective solar energy map are generated, which provided a reference for the subsequent comprehensive global path planning. Then, the MFA* algorithm is proposed. A multi-cost heuristic function is designed to adjust the focus of the algorithm to generate paths in different regions based on terrain and lighting information. And improved the search mechanism of the A* algorithm to shorten the search time of the algorithm, which is suitable for the fast search of the large-scale path on the lunar surface. Finally, the area of the Sinus Iridum was selected as the simulation scenario to verify the effectiveness of the algorithm.
Optical Flow Based on Lucas-Kanade Method for Motion Estimation

Optical flow is considered an important step in many computer vision applications. Some optical flow estimation approaches are mainly based on correlation, gradient and frequency information respectively. Among algorithms most known for computing Optical Flow vectors are Lucas-Kanade and Horn-Schunck. In this paper, Optical Flow Based Lucas-Kanade method is implemented for detecting moving objects. First, optical flow computation between consecutive frames of an image sequence is calculated. Then, the resultant optical flow vectors obtained is coded to color to have a visualized motion estimation. Finally, a segmentation step is performed on the resulted motion estimation to highlight objects of interest. Simulation results on some image sequences show that the estimated flow accurates the motion for each and every pixel. A quantitive evaluation is performed on the Middlebury dataset for optical flow. Results obtained on the Middleburry dataset show that the estimated optical flow is promising and can be applied as a preliminary task to many treatments higher level in computer vision such as recognition or tracking.
Users' perception of navigating bibliographic families from IFLA-LRM perspective

Purpose This paper focuses on the way users navigate bibliographic families not only when a user has no specific document in mind but also when he/she has a specific predefined need in mind. Design/methodology/approach To this end, the Epic of Kings was selected as a test-bed for the study and both situations were studied based on International Federation of Library Associations and Institutions-Library Reference Model (IFLA-LRM), but the potential users (participants of this study) were not directly exposed to the entities of the model. Card sorting, interview and distributing questionnaire constituted the data-gathering process. Findings Almost all of the participants in this study, when they had no specific resource in mind, generated a top-down view of the family, and in this view, all of them disregarded the item entity and lots of them disregarded the manifestations also. Yet on the other side, when they were asked to assume themselves in certain situations (in need of a specific work with a predefined expression and format), they viewed the bibliographic family from a bottom-up approach. Originality/value Most of the studies in this area regard the navigation process of users as a top-down approach and the Functional Requirements for Bibliographic Records (FRBR) family as a model suitable for hierarchical top-down visualization of bibliographic families. Yet this study poses the bottom-up approach of users regarding the family.
A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings

Measuring the semantic similarity of texts has a vital role in various tasks from the field of natural language processing. In this paper, we describe a set of experiments we carried out to evaluate and compare the performance of different approaches for measuring the semantic similarity of short texts. We perform a comparison of four models based on word embeddings: two variants of Word2Vec (one based on Word2Vec trained on a specific dataset and the second extending it with embeddings of word senses), FastText, and TF-IDF. Since these models provide word vectors, we experiment with various methods that calculate the semantic similarity of short texts based on word vectors. More precisely, for each of these models, we test five methods for aggregating word embeddings into text embedding. We introduced three methods by making variations of two commonly used similarity measures. One method is an extension of the cosine similarity based on centroids, and the other two methods are variations of the Okapi BM25 function. We evaluate all approaches on the two publicly available datasets: SICK and Lee in terms of the Pearson and Spearman correlation. The results indicate that extended methods perform better from the original in most of the cases.
RETRACTED: Application of artificial intelligence technology and embedded digital image in interior design

The digital technologies of Artificial Intelligence (AI) has been redefined. The modern technology economy has paved the way. Artificial Intelligence (AI) methods have gained significant researchers' awareness and have been used efficiently to solve engineering issues. Artificial intelligence for large and complex challenges, can thus take a lot of time to simulate and random search. Instructions AI researchers' close attention, and both were successfully solve technical problems. AI algorithms as well because of the random search function takes a lot of time with large and complex problems. Therefore, the development of efficient algorithms to find solutions in real-world applications of limited resources, time and money to potential demand. Artificial Neural Network (ANN) is proposed architecture of an embedded controller for monitoring a computer image and the image data, which in the case of various kinds of control, especially in the illumination device using the application management AI digitally controlled. Processing system with real-time monitoring and management of digital images of the proposed. Artificial neural network algorithm used to make identification of the luminance values in the area. Embedded microcontroller to complete part of a lighting control system several lighting and imaging sensor measurement and a work area of the control room. The proposed lighting system power consumption measured and compared with the power consumption.
Entanglement monotones from complementarity relations

Abstract Bohr’s complementarity and Schrödinger’s entanglement are two prominent physical characters of quantum systems. In this article, we formally connect them. It is known that complementarity relations for wave-particle duality are saturated only for pure, single-quanton, quantum states. For mixed states, the wave-particle quantifiers never saturate a complementarity relation and can even reach zero for a maximally mixed state. To fully characterize a quanton, it is not enough to consider its wave-particle aspect; we have also to regard its quantum correlations with other systems. Here we prove that for any complete complementarity relation involving predictability and visibility measures that satisfy the criteria established in the literature, the corresponding quantum correlations are entanglement monotones. Therefore, we formally connect entanglement monotones with complementarity relations without appealing to a particular measure.
Design and Experimental Validation of an Augmented Reality System With Wireless Integration for Context Aware Enhanced Show Experience in Auditoriums

The development of multiple cultural and social related activities, such as shows related with the performing arts, conferences or presentations rely on facilities such as auditoriums, theatres and conference sites, which are progressively including multiple technological features in order to enhance user experience. There are still however situations in which user experience is limited owing to lack of environment adaption, such as people with disabilities. In this sense, the adoption of Context Aware paradigms within auditoriums can provide adequate functionalities in order to comply with specific needs. This work is aimed at demonstrating the feasibility in enhancing user experience (e.g., improving the autonomy of disabled people) within auditorium and theatre environments, by means of an Augmented Reality (AR) device (HoloLens smart glasses) with wireless system integration. To carry out the demonstration, different elements to build AR applications are described and tested. First, an intensive measurement campaign was performed in a real auditorium in the city of Pamplona (Baluarte Congress Center) in order to evaluate the feasibility of using Wi-Fi enabled AR devices in a complex wireless propagation scenario. The results show that these environments exhibit high levels of interference, owing to the co-existence and non-coordinated operation of multiple wireless communication systems, such as on site and temporary Wi-Fi access points, wireless microphones or communications systems used by performers, staff and users. Deterministic wireless channel estimation based in volumetric 3D Ray Launching have been obtained for the complete scenario volume, in order to assess quality of service metrics. For illustration purposes, a user-friendly application to help hearing impaired people was developed and its main features were tested in the auditorium. Such an application provides users with a 3D virtual space to visualize useful multimedia content like subtitles or additional information about the show, as well as an integrated call button.
True Random Number Generator (TRNG) for Secure Communications in the Era of IoT

True Random number Generator (TRNG) is critical for secure communications. In this work, we explain in details regarding our recent solution on TRNG using random telegraph noise (RTN) including the benefits and the disadvantages. Security check is performed using the NIST randomness tests for both the RTN-based TRNG and various conventional pseudo random umber generator. The newly-proposed design shows excellent randomness, power consumption, low design complexity, small area and high speed, making it a suitable candidate for future cryptographically secured applications within the internet of things.
On Pseudorandom Encodings

We initiate a study of pseudorandom encodings: efficiently computable and decodable encoding functions that map messages from a given distribution to a random-looking distribution. For instance, every distribution that can be perfectly and efficiently compressed admits such a pseudorandom encoding. Pseudorandom encodings are motivated by a variety of cryptographic applications, including password-authenticated key exchange, "honey encryption" and steganography. The main question we ask is whether every efficiently samplable distribution admits a pseudorandom encoding. Under different cryptographic assumptions, we obtain positive and negative answers for different flavors of pseudorandom encodings, and relate this question to problems in other areas of cryptography. In particular, by establishing a two-way relation between pseudorandom encoding schemes and efficient invertible sampling algorithms, we reveal a connection between adaptively secure multiparty computation for randomized functionalities and questions in the domain of steganography.
Internet communications: time phenomenon and communicative activity

The article focuses on a theoretical overview of time phenomenon in Internet communications. Advanced technologies and virtual interactions make interaction and communicative competence priories in Internet communications. Time is seen as both a biological and a social phenomenon and the notion of time competence embraces the ability to establish and maintain social contacts within the indicated timeline and capability to extend and restrict the timeframe of communications. Based on a theoretical analysis of reflection and regulation of communicative activity in Internet communications the authors have devised a training programme “Enhancement of time competence in Internet communications”. This five module programme is aimed to university and high school students. The article also presents the results of implementing this programme.
Neural Machine Translation for translating into Croatian and Serbian

In this work, we systematically investigate different set-ups for training of neural machine translation (NMT) systems for translation into Croatian and Serbian, two closely related South Slavic languages. We explore English and German as source languages, different sizes and types of training corpora, as well as bilingual and multilingual systems. We also explore translation of English IMDb user movie reviews, a domain/genre where only monolingual data are available. First, our results confirm that multilingual systems with joint target languages perform better. Furthermore, translation performance from English is much better than from German, partly because German is morphologically more complex and partly because the corpus consists mostly of parallel human translations instead of original text and its human translation. The translation from German should be further investigated systematically. For translating user reviews, creating synthetic in-domain parallel data through back- and forward-translation and adding them to a small out-of-domain parallel corpus can yield performance comparable with a system trained on a full out-of-domain corpus. However, it is still not clear what is the optimal size of synthetic in-domain data, especially for forward-translated data where the target language is machine translated. More detailed research including manual evaluation and analysis is needed in this direction.
Real-time identified chaotic plants using neural enhanced learning machine technique

Purpose This paper aims to propose a new neural-based enhanced extreme learning machine (EELM) algorithm, used as an online adaptive estimation model, regarding undetermined system dynamics and containing internal/external perturbations. Design/methodology/approach The EELM structure bases on the single layer feed-forward neural (SLFN) model in which the hidden weighting coefficients are initiated in random and the weighting outputs of the SLFN are online modified using an online adaptive rule implemented from Lyapunov stability concept. Findings Four different benchmark uncertain chaotic system tests have been satisfactorily investigated for demonstrating the superiority of proposed EELM technique. Originality/value Authors confirm that this manuscript is original.
Approaching SMM4H 2020 with Ensembles of BERT Flavours

This paper describes our solutions submitted to the Social Media Mining for Health Applications (#SMM4H) Shared Task 2020. We participated in the following tasks: Task 1 aimed at classifying if a tweet reports medications or not, Task 2 (only for the English dataset) aimed at discriminating if a tweet mentions adverse effects or not, and Task 5 aimed at recognizing if a tweet mentions birth defects or not. Our work focused on studying different neural network architectures based on various flavors of bidirectional Transformers (i.e., BERT), in the context of the previously mentioned classification tasks. For Task 1, we achieved an F1-score (70.5%) above the mean performance of the best scores made by all teams, whereas for Task 2, we obtained an F1-score of 37%. Also, we achieved a micro-averaged F1-score of 62% for Task 5.
Feature Ranking for Hierarchical Multi-Label Classification with Tree Ensemble Methods

In this work, we address the task of feature ranking for hierarchical multi-label classification (HMLC).The task of HMLC concerns problems with multiple binary variables, organized into a hierarchy of target attributes.The goal is to train a model to learn and accurately predict all of them, simultaneously.This task is receiving increasing attention from the research community, due to its wide application potential in text document classification and functional genomics.Here, we propose a group of feature ranking methods based on three established ensemble methods of predictive clustering trees: Bagging, Random Forests and Extra Trees.Predictive clustering trees are a generalization of decision trees, towards predicting structured outputs.Furthermore, we propose to use three scoring functions for calculating the feature importance values: Symbolic, Genie3 and Random Forest.We test the proposed methods on 30 benchmark HMLC datasets, show that Symbolic and Genie3 scores return relevant rankings, that all three scores outperform the HMLC-Relief ranking method and are computed in very timeefficient manner.For each scoring function, we find the most appropriate ensemble method and compare the scores to find the best one.
Generation and Detection of Media Clones

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.
Assessing a Decision Support Tool for SOC Analysts

It is difficult to discern real-world consequences of attacks on an enterprise when investigating network-centric data alone. In recent years, many tools have been developed to help understand attacks using visualisation, but few aim to predict real-world consequences. We have developed a visualisation tool that aims to improve decision support during attacks in Security Operation Centres (SOCs). Our tool visualises propagation of risks from sensor alert data to Business Process (BP) tasks. This is an important capability gap present in many SOCs today, as most threat detection tools are technology-centric. In this article, we present a user study that assesses our tool’s usability and ability to support the analyst. Ten analysts from seven SOCs performed carefully designed tasks related to understanding risks and recovery decision-making. The study was conducted in laboratory conditions with simulated attacks and used a mixed-method approach to collect data from questionnaires, eye tracking, and semi-structured interviews. Our findings suggest that relating business tasks to network asset in visualisations can help analysts prioritise response strategies. Finally, our article also provides an in-depth discussion on user studies conducted with SOC analysts more generally, including lessons learned, recommendations and a critique of our own study.
Research on recognition of dangerous driving behavior based on support vector machine

Research on dangerous driving behavior recognition is beneficial to regulate the driving behavior of drivers. As the existing algorithms are sensitive to noise, and abnormal data often affects the process of identifying dangerous driving behaviors. This paper proposes a novel driving behavior research method. Such method establishes a driving behavior recognition model based on Support Vector Machine (SVM) and oversampling. The experimental results show that the proposed model demonstrates a higher recognition rate.
Chatbot Assistant for English as a Second Language Learners

This work demonstrates an experimental implementation of a helper bot using IBM Watson. It is primarily aimed at people who know English as a second language. With the help of IBM Watson Assistant tool, the chatbot uses APIs like Google Translate API, Text to Speech API, SimpleWIki and Musixmatch API, to provide features like rich responses, translation to regional languages, text to speech conversion facilities, useful information in simpler English, and displaying music lyrics for music in regional languages. This is particularly helpful for those who are newly learning English and are more comfortable in their regional language.
The Role of Critical Success Factors of Knowledge Stations in the Development of Local Communities in Jordan: A Managerial Perspective

Knowledge Stations (KSs) have emerged as one of the Information and Communication Technology (ICT) key initiatives designed to enhance the quality of life of those living in remote and local communities through improving their digital information literacy to enhance their educational and vocational wellbeing. This study aimed to fill a research gap by exploring KSs’ critical success factors (CSFs) that are vital for KSs to achieve their mission by answering the following two questions: what are KSs' CSFs?, and how can these factors enhance KSs’ role in the knowledge-based development of local communities (K-B DLC)? The findings suggested significant support for thirty-five CSFs divided into four groups related to KSs departments, KSs themselves, LCs, and Hosting Organizations (HOs). The findings also suggested significant support for ways that CSFs can enhance the services provided by knowledge stations as demonstrated via the study’s theoretical model. In addition, KSs as an ICT initiative, are integral in developing the knowledge society in Jordan, but face a range of inhibitors such as insufficient funds, the absence of trainers’ incentives, and a lack of cooperation with higher education institutions such as universities.
Unsupervised Active Pre-Training for Reinforcement Learning

We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for ActivePre-Training. APT learns a representation and a policy initialization by actively searching for novel states in reward-free environments. We use the contrastive learning framework for learning the representation from collected transitions. The key novel idea is to collect data during pre-training by maximizing a particle based entropy computed in the learned latent representation space. By doing particle based entropy maximization, we alleviate the need for challenging density modeling and are thus able to scale our approach to image observations. APT successfully learns meaningful representations as well as policy initializations without using any reward. We empirically evaluate APT on the Atari game suite and DMControl suite by exposing task-specific reward to agent after a long unsupervised pre-training phase. On Atari games, APT achieves human-level performance on 12 games and obtains highly competitive performance compared to canonical fully supervised RL algorithms. On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult for training from scratch. Importantly, the pre-trained models can be fine-tuned to solve different tasks as long as the environment does not change. Finally, we also pre-train multi-environment encoders on data from multiple environments and show generalization to a broad set of RL tasks.