Reviewerly/exHarmony huggingface dataset:
3D User Interface for a Multi-user Augmented Reality Mission Planning Application

In this paper, we present a 3D User Interface design for a shared augmented reality setup to support strategic, tactical, and training platforms. An accurate 3D holographic object registration is necessary to enable a shared augmented reality experience using multiple Microsoft HoloLens devices. We developed a sensor data fusion framework which uses both external positional sensor data and Microsoft HoloLens to reduce augmented reality registration errors in our shared augmented reality application. Our shared augmented reality 3D User Interface design implemented in the application, together with our sensor data fusion framework, enables the use of multiple Microsoft HoloLens to support a mission planning scenario.
Extended State based Planning Approach for Deep Space Exploration

This paper deals with a new modeling method and a powerful planning approach for deep space probes. The key technique is based on the concept of extended state model. Furthermore, extended state model planning (ESMP) algorithm is designed to be fully compliant with the model, and flaw selection strategies are proposed to avoid redundant work. Finally, we discuss the time complexity of ESMP, and computational results comparing ESMP with the algorithm in Europa are presented.
Personalized Hybrid Book Recommender

Personalized Recommendation Systems (RS) provide end users with suggestions about items that are likely to be of their interest based on users' details such as demographics, location, time, and emotion. In this article, a Personalized Hybrid Book Recommender (PHyBR) is presented, which integrates personality traits with users' demographic data and geographical location to improve the quality of recommendations. The Ten Item Personality Inventory (TIPI) was used to determine users' personality traits. PHyBR was evaluated using two metrics, that are, Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA). Both metrics revealed PHyBR outperforms the baseline models (without considering personality traits and geographical location factor) in terms of the recommendation accuracies. This study shows that users who are in the same geographical contexts intend to have similar preferences. Therefore, users' personality details along with their geographical locations can be used to provide improved personalized recommendations.
SEMOGRAPH Information System as a Platform for Network-Based Linguistic Research: A Case Study of Verbal Behavior of Social Network Users

Contemporary state of the art of scientific development is characterized by a great influence of advanced technologies and high-tech solutions; linguistic science is also affected by new challenges. Language researchers face the problem of processing and analyzing large linguistic databases, applying advanced technology products and resorting to computer-integrated methods. In this context, computerized systems of processing linguistic data are becoming especially relevant. In the paper, the authors present the SEMOGRAPH Information System—a unique tool specially designed for conducting linguistic research and analyzing their data. SEMOGRAPH gives a possibility to carry out text data analysis, create and tag various corpora, conduct psycholinguistic, sociolinguistic and other experiments, elaborate classifiers and thesauruses, work out linguistic models, and solve other tasks connected with processing linguistic data. The potentials of SEMOGRAPH are demonstrated on the example of multiparameter analysis of social features, psychological characteristics, and speech behavior of social network users.
Efficient heterogeneous proximity preserving network embedding model

We study the problem of representation learning in heterogeneous information networks. Its unique challenges come from the existence of multiple types of vertices and edges. Existing proximity-based network embedding techniques ignore the type information when evaluating the proximity and limits their usage in heterogeneous scenario. In this paper, we propose a heterogeneous proximity preserving network embedding model via meta path guided random walk, which is capable of capturing the high-order proximity between vertices specified by the given path. To improve the learning efficiency, we introduce a sampling based learning strategy which can incrementally learn representations. We conduct experiments on two real world heterogeneous information networks. Experimental results on several mining tasks prove the effectiveness of our approach over many competitive baselines. The model is very efficient and is able to learn embeddings for large networks both in offline and online scenarios. Besides, for expert system, our approach can be applied to improve the representation of knowledge entities by depicting the knowledge base as a heterogeneous information network.
Optimal whole-body motion planning of humanoids in cluttered environments

Abstract Due to high degrees-of-freedom of humanoids and induced redundancy, there are multiple ways of performing a given manipulation task. Finding optimal ways of performing tasks is one desirable property of any motion planning framework. This includes optimizing both the path with respect to a certain objective function and also the final pre-grasp or goal position. Additionally, a variety of constraints need to be satisfied such as stability, self-collision and collision with objects in the environment and also kinematic loop-closure formed during the task. In this paper, an asymptotically optimal sampling based approach for generating motion plans is presented. A novel constraint solver extension to the bidirectional Fast Marching Trees (BFMT*) algorithm in the form of a way-point generator is proposed such that it can be applied for whole-body motion planning of humanoids. Moreover, a comparison of the performance of the proposed extension of BFMT* and the state-of-art RRT* based motion planner is shown. A gradient based inverse kinematics solver has also been implemented in combination with an optimization technique to generate goal configurations in order to ensure optimality in the pre-grasp position. The efficacy of the proposed approach is evaluated in a simulation environment on Hubo+ robot model. The results show a significant improvement in path costs, as well as overall optimality of given tasks for the proposed approach. Additionally, rigorous analysis over the choice of planning algorithms considered in this paper is present for the considered scenarios.
Secure and efficient arithmetic-based multi-secret image sharing scheme using universal share

Visual secret sharing (VSS) scheme is an encryption technique to hide secret message into two or more meaningless images, called as shares. Shares are stack together to decrypt the secret message using human visual system. To share many secrets, multi-secret image sharing schemes are often used. Diversity in sharing multiple secrets is using the universal share. Company organizer shares multiple secret images using this common share. Computation cost is the major drawback of such scheme. This study presents computationally efficient arithmetic-based multi-secret image sharing scheme using the universal share. Binary arithmetic-based scheme utilizes distinct value selecting function while encoding and decoding of secret images. Distinct value selecting function enhances randomness property of shares and fulfills threshold property. Thus, the proposed method provides computationally efficient method while satisfying threshold security criteria.
Improving Traceability Links Recovery in Process Models Through an Ontological Expansion of Requirements

Often, when requirements are written, parts of the domain knowledge are assumed by the domain experts and not formalized in writing, but nevertheless used to build software artifacts. This issue, known as tacit knowledge, affects the performance of Traceability Links Recovery. Through this work we propose LORE, a novel approach that uses Natural Language Processing techniques along with an Ontological Requirements Expansion process to minimize the impact of tacit knowledge on TLR over process models. We evaluated our approach through a real-world industrial case study, comparing its outcomes against those of a baseline. Results show that our approach retrieves improved results for all the measured performance indicators. We studied why this is the case, and identified some issues that affect LORE, leaving room for improvement opportunities. We make an open-source implementation of LORE publicly available in order to facilitate its adoption in future studies.
Improved Sum of Residues Modular Multiplication Algorithm

Modular reduction of large values is a core operation in most common public-key cryptosystems that involves intensive computations in finite fields. Within such schemes, efficiency is a critical issue for the effectiveness of practical implementation of modular reduction. Recently, Residue Number Systems have drawn attention in cryptography application as they provide a good means for extreme long integer arithmetic and their carry-free operations make parallel implementation feasible. In this paper, we present an algorithm to calculate the precise value of “ X mod p ” directly in the RNS representation of an integer. The pipe-lined, non-pipe-lined, and parallel hardware architectures are proposed and implemented on XILINX FPGAs.
Quantifying and understanding the differences in visual activities with contrast subsequences

Understanding differences and similarities between scanpaths has been one of the primary goals for eye tracking research. Sequences of areas of interest mapped from fixations are a major focus for many analytic techniques since these sequences directly relate to the semantic meaning of the visual input. Many studies analyze complete sequences while overlooking the micro-transitions in subsequences. In this paper, we propose a method which extracts subsequences as features and finds contrasting patterns between different viewer groups. The contrast patterns help domain experts to quantify variations between visual activities and understand reasoning processes for complex visual tasks. Experiments were conducted with 39 expert and novice radiographers using nine radiology images corresponding to nine levels of task complexity. Identified contrast patterns, validated by an expert, prove that the method effectively reveals visual reasoning processes that are otherwise hidden.
Measuring impropriety in complex and real representations

So-called improper signals, i.e., signals which are correlated with their complex conjugates, can occur in many signal processing applications such as communication systems, medical imaging, audio and speech processing, analysis of oceanographic data, and many more. Being aware of potential impropriety can be crucial whenever we model signals as complex random quantities since an appropriate treatment of improper signals, e.g., by widely linear filtering, can significantly improve the system performance. After a brief introduction into the fundamentals of improper signals, this article focuses on the problem of quantifying the impropriety of complex random vectors and gives a survey of various impropriety measures in both the composite real representation and the augmented complex formulation. Unlike in previous publications, these two frameworks are presented side by side to reveal the differences and common points between them. Moreover, their applicability is compared in several practical examples. As additional aspects, we consider the problem of testing for impropriety based on measurement data, and the differential entropy of Gaussian vectors as an impropriety measure in information theoretic studies. The article includes a tutorial-style introduction, a collection of important formulae, a comparison of various mathematical approaches, as well as some new reformulations.
SeTA

Availability of large scale tagged datasets is a must in the field of deep learning applied to the eye tracking challenge. In this paper, the potential of Supervised-Descent-Method (SDM) as a semiautomatic labelling tool for eye tracking images is shown. The objective of the paper is to evidence how the human effort needed for manually labelling large eye tracking datasets can be radically reduced by the use of cascaded regressors. Different applications are provided in the fields of high and low resolution systems. An iris/pupil center labelling is shown as example for low resolution images while a pupil contour points detection is demonstrated in high resolution. In both cases manual annotation requirements are drastically reduced.
A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction

In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image as an input, the TML raises each pixel value to the power of a weight and then multiplies them, thereby extracting the higher-order local auto-correlation from the input image. The TML can also be used to extract co-occurrence from the feature map of a convolutional network. The training of the TML is formulated based on backpropagation with constraints to the weights, enabling us to learn discriminative multiplication patterns in an end-to-end manner. In the experiments, the characteristics of the TML are investigated by visualizing learned kernels and the corresponding output features. The applicability of the TML for classification and neural network interpretation is also evaluated using public datasets.
Kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters

This paper proposes kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters which learn the variable weights and adjust the cluster weights automatically. We develop the new objective functions that are obtained from the proposed algorithms to achieve the desirable partition by minimizing the dissimilarity measures with kernelization of the metric. Correspondingly, two additional steps are introduced to k-means algorithms, so that, not only the performance is improved, but also the efficiency remains. More specifically, the proposed algorithms learn two types of weights at each iteration where variable weights identify relevant variables and cluster weights to confine the occurrence of the large variance cluster. Finally, the experiments on ten UCI benchmark datasets corroborate the superiority of the proposed algorithms.
A hybrid deep neural network model for query intent classification

Query intents describe user information needs for searching on the web. How to capture the query intents is a crucial research topic in information retrieval. Search engine users always employ insufficient or unclear words as queries, thus making query intents ambiguous and uncertain to be interpre ted by search engines. Query intent classification can deal with the problem by clarifying user queries and interpreting information needs for improving user satisfaction. Two main challenges have been addressed to classify query intents: one is how to effectively represent short and ambiguous queries; the other is how to generate a set of appropriate categories for matching diverse queries. In the paper, we propose a hybrid deep neural network model for query intent classification to meet the challenges. Our model adopts two state-of-the-art neural network models to comprehensively represent queries as feature vectors. We then employ query logs to automatically generate intermediate categories for fine-grained query intent clarification. Experimental results show that our method can outperform other baseline models, and effectively improve the performance in query intent classification.
Paving Ontological Foundation for Social Engineering Analysis

System security analysis has been focusing on technology-based attacks, while paying less attention on social perspectives. As a result, social engineering are becoming more and more serious threats to socio-technical systems, in which human plays important roles. However, due to the interdisciplinary nature of social engineering, there is a lack of consensus on its definition, hindering the further development of this research field. In this paper, we propose a comprehensive and fundamental ontology of social engineering, with the purpose of prompting the fast development of this field. In particular, we first review and compare existing social engineering taxonomies in order to summarize the core concepts and boundaries of social engineering, as well as identify corresponding research challenges. We then define a comprehensive social engineering ontology, which is embedded with extensive knowledge from psychology and sociology, providing a full picture of social engineering. The ontology is built on top of existing security ontologies in order to align social engineering analysis with typical security analysis as much as possible. By formalizing such ontology using Description Logic, we provide unambiguous definitions for core concepts of social engineering, serving as a fundamental terminology to facilitate research within this field. Finally, our ontology is evaluated based on a collection of existing social engineering attacks, the results of which indicate good expressiveness of our ontology.
The Frontier of SGD and Its Variants in Machine Learning

Abstract A Numerical optimization is a classical field in operation research and computer science, which has been widely used in areas such as physics and economics. Although optimization algorithms have achieved great success for plenty of applications, handling the big data in the best fashion possible is a very inspiring and demanding challenge in the artificial intelligence era. Stochastic gradient descent (SGD) is pretty simple but surprisingly, highly effective in machine learning models, such as support vector machine (SVM) and deep neural network (DNN). Theoretically, the performance of SGD for convex optimization is well understood. But, for the non-convex setting, which is very common for the machine learning problems, to obtain the theoretical guarantee for SGD and its variants is still a standing problem. In the paper, we do a survey about the SGD and its variants such as Momentum, ADAM and SVRG, differentiate their algorithms and applications and present some recent breakthrough and open problems.
A New Dynamic Neighbourhood-Based Semantic Dissimilarity Measure for Ontology

The semantic web is a global initiative which employs ontologies to offer rich, semantic-based knowledge representation. Concepts in these ontologies are explored to find (dis)similarities between them using (dis)similarity measures. Despite the existence of numerous (dis)similarity measures, none have dynamically determined the quantum of information required to discover (dis)similarities between concepts. In this article, a new, efficient, feature-based semantic dissimilarity measure is proposed where the prime novelty lies in the dynamic selection of the semantic neighourhood (features) of the concepts. The neighbourhood is dynamically selected in accordance with the local density of the concept and the density of the ontology determined by the proposed density coefficient. Further, the proposed measure also scales down the dissimilarity value in accordance with the depth of the concept pair, using the novel Depth Coefficient.
Work Design and Job Rotation in Software Engineering: Results from an Industrial Study

Context: Job rotation is a managerial practice to be applied in the organizational environment to reduce job monotony, boredom, and exhaustion resulting from job simplification, specialization, and repetition. Previous studies have identified and discussed the use of project-to-project rotations in software practice, gathering empirical evidence from qualitative and field studies and pointing out set of work-related factors that can be positively or negatively affected by this practice. Goal: We aim to collect and discuss the use of job rotation in software organizations in order to identify the potential benefits and limitations of this practice supported by the statement of existing theories of work design. Method: Using a survey-based research design, we collected and analyzed quantitative data from software engineers about how software development work is designed and organized, as well as the potential effects of job rotations on this work design. We investigated 21 work design constructs, along with job burnout, role conflict, role ambiguity, and two constructs related to job rotation. Results: We identified one new benefit and six new limitations of job rotation, not observed in previous studies and added new discussions to the existing body of knowledge concerning the use of job rotation in software engineering practice. Conclusion: We believe that these results represent another important step towards the construction of a consistent and comprehensive body of evidence that can guide future research and also inform practice about the potential positive and negative effects of job rotation in software development companies.
Interculturality in Blended Learning: Challenges of Electronic Communication

The presented paper focuses on modern trends in blended learning. The global world can be described by two aspects, i.e., interculturality (meeting cultures through the Internet and in reality) and electronic transfer of data and information. Both these phenomena present a challenge and opportunity for blended learning. The paper describes a case study on how blended learning course of intercultural business communication was introduced at the University of Hradec Kralove. It presents the results of students’ satisfaction with the new method of teaching and learning. The students showed their satisfaction with blended learning methodology implemented in the course, however, the paper also highlights possible threats for the learning process, such as data fatigue caused by too much screen time. The respondents expressed their satisfaction with the variety of learning methods but they themselves highlighted the fact that they spend a lot of time in front of the screen and using mobile devices, even if better learning results were intended, is not necessarily beneficial for the participants of the learning process. This paper is trying to stress the urge for the balance in blended learning because the vast majority of scholars neglect this aspect which can potentially bring problems.
Automatic Question Generation based on MOOC Video Subtitles and Knowledge Graph

With the popularity of MOOCs (Massive Open Online Courses), videos have gradually replaced textbooks as the most common educational resources. Quiz is an important teaching tool in online learning process [1], and manually constructing quiz questions is time-consuming and labor-intensive, so there is a pressing need for automatic question generation from video subtitles. The challenge of this problem is that video subtitles are different from textbooks considering their characteristics such as redundancy, colloquialism, and no sentence division. In this paper, we firstly use external wiki knowledge graph (WIKIDATA [2]) to extract facts of interest from video subtitles, and then propose a novel template-based method to generate quiz questions from knowledge graph. Compared to traditional template-based method [4], we make improvements on the accuracy and comprehensibility of the question. Final experiment on SimpleQuestions dataset [3] shows that the proposed method outperforms other competitive methods by +6% in terms of BLEU on the task of question generations from knowledge graph [4].