Reviewerly/exHarmony huggingface dataset:
A five-step drone collaborative planning approach for the management of distributed spatial events and vehicle notification using multi-agent systems and firefly algorithms

In spite of the performance that existing approaches for drone collaborative planning have demonstrated, there is still a need for new solutions which are capable of effectively identifying the right tasks for the right drones at the right times while maximizing the total benefits obtained from the drones' actions. These new solutions should be particularly tested within the context of intelligent transportation systems to assess their impact on mobility and traffic flow. In order to address these issues, we present in this paper a new a five-step solution for drone collaborative planning. Our solution uses a Multi-Agent System as well as a Firefly Algorithm solution to enable drones jointly neutralize ongoing events by considering trust factors and cost/benefit analysis. The solution, which is also capable of issuing appropriate warnings to vehicles to prevent them from incurring any undesirable/dangerous impact due to ongoing events, is using a reward-driven competition to encourage drones to join collaborating teams. Our simulations are showing promising results in terms of processing time, energy consumption, and total reward obtained compared to two other planning approaches relaying on random and priority-based selection of the next locations that drones will visit respectively.
Molecular Structure Optimization Based on Electrons–Nuclei Quantum Dynamics Computation

A new concept of the molecular structure optimization method based on quantum dynamics computations is presented. Nuclei are treated as quantum mechanical particles, as are electrons, and the many-body wave function of the system is optimized by the imaginary time evolution method. The numerical demonstrations with a two-dimensional H2+ system and a H-C-N system exemplify two possible advantages of our proposed method: (1) the optimized nuclear positions can be specified with a small number of observations (quantum measurements) and (2) the global minimum structure of nuclei can be obtained without starting from any sophisticated initial structure and getting stuck in the local minima. This method is considered to be suitable for quantum computers, the development of which will realize its application as a powerful method.
The Cybersecurity (CSEC) Questionnaire: Individual Differences in Unintentional Insider Threat Behaviours

The present study identifies a number of individual difference traits that are related to cyber hygiene behaviour in order to develop a brief questionnaire. The resulting questionnaire identifies basic vulnerabilities in users' practices consisting of updating software, changing passwords, and disclosure of information on social media platforms (SMPs). Using existing scales assessing individual differences (i.e., conscientiousness, extraversion, and openness, regulatory focus), we argue that the cybersecurity scale (CSEC) can be used to identify individuals within an organization that need remedial education and training. Moreover, CSEC scores differentiate between assessment disclosure vulnerability and intrusion vulnerability, allowing for the identification of different kinds of cybersecurity practices.
Investigating the performance of personalized models for software defect prediction

Software defect predictors exploring developer perspective reveal that code changes made by separate developers tend to have different defect patterns. Personalized defect prediction also contributes to this view and gives promising results. We aim to investigate the performance of personalized defect predictors compared to those of traditional models. We conduct an empirical study on six open-source projects for 222 developers. Personalized and traditional defect predictors are built utilizing two algorithms and cross-validation on the historical commit data, and assessed via seven performance measures and statistical tests. Our results show that personalized models (PMs) achieve an increase of up to 24% in recall for 83% of developers, while causing higher false alarm rates for 77% of developers. PMs are better for those developers who contribute to the modules with many prior contributors. Although size metrics contribute to the performance of the majority of the PMs, they significantly differ in terms of information gained from experience, diffusion and history metrics, respectively. The decision of whether a PM should be chosen over a traditional model depends on a set of factors, i.e., selected algorithm, model validation strategy or performance measures, and hence, PM performance significantly differs regarding these factors.
Guiding the evolution of product-line configurations

Abstract A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated for configurations consisting of selected features. Product-line evolution can lead to unintended changes to product behavior. We illustrate that updating configurations after product-line evolution requires decisions of both, domain engineers responsible for product-line evolution as well as application engineers responsible for configurations. The challenge is that domain and application engineers might not be able to interact with each other. We propose a formal foundation and a methodology that enables domain engineers to guide application engineers through configuration evolution by sharing knowledge on product-line evolution and by defining automatic update operations for configurations. As an effect, we enable knowledge transfer between those engineers without the need for interactions. We evaluate our methodology on four large-scale industrial product lines. The results of the qualitative evaluation indicate that our method is flexible enough for real-world product-line evolution. The quantitative evaluation indicates that we detect product behavior changes for up to $$55.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>55.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of the configurations which would not have been detected using existing methods.
On the dissemination of Cyber Threat Intelligence through Hyperledger

Blockchain is widely used nowadays and it is expected to be more exploited in the near future. Blockchain tends to be more used due to its security layer beyond any active component. Therefore, companies are relying on cutting expenses through converging to the blockchain network and to stop relying on traditional security components due to their high costs and security concerns. The Blockchain technology provides several advantages therefore more applications will be leveraging Distributed Ledger Technologies (DLTs) in the upcoming future. The current work is focused around tackling the use of Blockchain for Cyber Threat Intelligence (CTI) sharing providing a new means to disseminate securely threats data between Security Operations Center (SOC) teams.
Deep Gaussian process based multi-speaker speech synthesis with latent speaker representation

This paper proposes deep Gaussian process (DGP)-based frameworks for multi-speaker speech synthesis and speaker representation learning. A DGP has a deep architecture of Bayesian kernel regression, and it has been reported that DGP-based single speaker speech synthesis outperforms deep neural network (DNN)-based ones in the framework of statistical parametric speech synthesis. By extending this method to multiple speakers, it is expected that higher speech quality can be achieved with a smaller number of training utterances from each speaker. To apply DGPs to multi-speaker speech synthesis, we propose two methods: one using DGP with one-hot speaker codes, and the other using a deep Gaussian process latent variable model (DGPLVM). The DGP with one-hot speaker codes uses additional GP layers to transform speaker codes into latent speaker representations. The DGPLVM directly models the distribution of latent speaker representations and learns it jointly with acoustic model parameters. In this method, acoustic speaker similarity is expressed in terms of the similarity of the speaker representations, and thus, the voices of similar speakers are efficiently modeled. We experimentally evaluated the performance of the proposed methods in comparison with those of conventional DNN and variational autoencoder (VAE)-based frameworks, in terms of acoustic feature distortion and subjective speech quality. The experimental results demonstrate that (1) the proposed DGP-based and DGPLVM-based methods improve subjective speech quality compared with a feed-forward DNN-based method, and (2) even when the amount of training data for target speakers is limited, the DGPLVM-based method outperforms other methods, including the VAE-based one. Additionally, (3) by using a speaker representation randomly sampled from the learned speaker space, the DGPLVM-based method can generate voices of non-existent speakers.
Augmented Reality in Outdoor Settings

New technologies such as Augmented Reality (AR) have already been exploited in the promotion of unique and well-known cultural assets. Nevertheless, different types of heritage assets can be found scattered in many different places, in both urban and rural landscapes, where AR applications may not function properly due to problems concerning the effectiveness of GPS or image recognition methods. It is an important issue that limits the exploitation of AR technology with a view to hidden cultural heritage promotion. The current study presents a hybrid image recognition technique used in an AR application and its evaluation in the outdoor settings. The hybrid technique is based on an image-based marker and its evaluation mainly aimed to study if and to what extent it is accepted by the users, if it raises aesthetic issues, and if it affects the use and acceptance of the AR in culture field. The evaluation was based on a qualitative research and the results showed that to most participants the used hybrid technique does not create an aesthetical issue. Additionally, all participants found the AR app useful and easy to use and they consider that the AR technology can significantly contribute to the promotion of cultural heritage.
Architectural Jewels of Lublin

Lublin is a city located in the eastern part of Poland, which is an important place of cultural heritage, being the venue where the Polish-Lithuanian Union was signed 450 years ago in 2019. This article presents “Architectural Jewels of Lublin,” a computerized serious board game for two players. The aim of the game is to collect points for recognizing models of the city's historic architectural objects and their correct position on the board. They represent the landmarks of the historic Old Town quarter. Another point of the game is to answer questions about the cultural heritage of Lublin. 3D models of historic buildings were initially designed manually and then 3D printed in FFF (Fused Filament Fabrication) technology. The correct location of the object on the board is identified by sensors working in the RFID (Radio-Frequency IDentification) technology supported by two microcontrollers of an Arduino platform, which were connected to the software managing the whole game shown on a tablet monitor. The game is used both to promote Lublin at numerous cyclical cultural and science popularization events, and during conferences and seminars organized for circles representing cultural heritage from Poland and abroad. It is aimed at presenting a way to integrate many different contemporary digital technologies that can serve education in the area of cultural heritage. The game, in contrast to popular games using VR and AR technologies, combines in an interesting way physical and digital space using modern computer technologies. The research carried out on the participants of the game has shown its high effectiveness in raising the historical awareness of its participants, as well as the players’ positive attitude toward the game.
RLP-AGMC: Robust label propagation for saliency detection based on an adaptive graph with multiview connections

There have been remarkable improvements in the salient object detection in the recent years. During the past few years, graph-based saliency detection algorithms have been proposed and made advances. Nevertheless, most of the state-of-the-art graph-based approaches are usually designed with low-level features, misleading assumption, fixed predefined graph structure and weak affinity matrix, which determine that they are not robust enough to handle images with complex or cluttered background. In this paper, we propose a robust label propagation-based mechanism for salient object detection throughout an adaptive graph to tackle above issues. Low-level features as well as deep features are integrated into the proposed framework to measure the similarity between different nodes. In addition, a robust mechanism is presented to calculate seeds based on the distribution of salient regions, which can achieve desirable results even if the object is in contact with the image boundary and the image scene is complex. Then, an adaptive graph with multiview connections is constructed based on different cues to learn the graph affinity matrix, which can better capture the characteristics between spatially adjacent and distant regions. Finally, a novel RLP-AGMC model, i.e. robust label propagation throughout an adaptive graph with multiview connections, is put forward to calculate saliency maps in combination with the obtained seed vectors. Comprehensive experiments on six public datasets demonstrate the proposed method outperforms fourteen existing state-of-the-art methods in terms of various evaluation metrics.
Scalable and Explainable Outfit Generation

We present an end-to-end system for learning outfit recommendations. The core problem we address is how a customer can receive clothing/accessory recommendations based on a current outfit and what type of item the customer wishes to add to the outfit. Using a repository of coherent and stylish outfits, we leverage self-attention to learn a mapping from the current outfit and the customer-requested category to a visual descriptor output. This output is then fed into nearest-neighbor-based visual search, which, during training, is learned via triplet loss and mini-batch retrievals. At inference time, we use a beam search with a desired outfit composition to generate outfits at scale. Moreover, the attention networks provide a diagnostic look into the recommendation process, serving as a fashion-based sanity check.
Librarians participation in professional development workshops using Zoom in Nigeria

The purpose of this study is to investigate the extent to which librarians in Nigeria engaged in professional development workshops during the COVID-19 era. The study adopted a survey method using an online questionnaire. Factors such as saving money, the free nature of workshops, eliminating travel risk, in the comfort of the home, and providing an opportunity for all were mentioned as the benefits of participating in online workshops using Zoom. Buying data bundle, lack of computer/Android phone/smartphone, ignorance or lack of awareness of up-coming workshops, lack of time, power outage, nonchalant attitude towards technology, and network failures were identified as challenges of participation. The Zoom platform can be adopted for organizing workshops and meetings, and for teaching and learning in the post COVID-19 era.
MirrorNeRF: One-shot Neural Portrait Radiance Field from Multi-mirror Catadioptric Imaging

Photo-realistic neural reconstruction and rendering of the human portrait are critical for numerous VR/AR applications. Still, existing solutions inherently rely on multi-view capture settings, and the one-shot solution to get rid of the tedious multi-view synchronization and calibration remains extremely challenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera, which is the first to combine neural radiance field with catadioptric imaging so as to enable one-shot photo-realistic human portrait reconstruction and rendering, in a low-cost and casual capture setting. More specifically, we propose a light-weight catadioptric system design with a sphere mirror array to enable diverse ray sampling in the continuous 3D space as well as an effective online calibration for the camera and the mirror array. Our catadioptric imaging system can be easily deployed with a low budget and the casual capture ability for convenient daily usages. We introduce a novel neural warping radiance field representation to learn a continuous displacement field that implicitly compensates for the misalignment due to our flexible system setting. We further propose a density regularization scheme to leverage the inherent geometry information from the catadioptric data in a self-supervision manner, which not only improves the training efficiency but also provides more effective density supervision for higher rendering quality. Extensive experiments demonstrate the effectiveness and robustness of our scheme to achieve one-shot photo-realistic and high-quality appearance free-viewpoint rendering for human portrait scenes.
Sampling-Based Trajectory Repairing for Autonomous Vehicles

Ensuring the safety of autonomous vehicles is a challenging task, especially if the planned trajectories do not consider all traffic rules or they are physically infeasible. Since replanning the complete trajectory is often computationally expensive, efficient methods are necessary for resolving such situations. One solution is to deform or repair an initially-planned trajectory, which we call trajectory repairing. Our approach first detects the part of an invalid trajectory that can stay unchanged. Afterward, we use a hierarchical structure and our novel sampling-based algorithm informed closed-loop rapidly-exploring random trees (informed CL-RRTs) to efficiently repair the remaining part of the trajectory. We evaluate our approach with different traffic scenarios from the CommonRoad benchmark suite. The computational efficiency is demonstrated by comparing the computation times with those required when replanning the complete trajectory.
Data about fall events and ordinary daily activities from a sensorized smart floor

A smart floor with 16 embedded pressure sensors was used to record 420 simulated fall events performed by 60 volunteers. Each participant performed seven fall events selected from the guidelines defined in a previous study. Raw data were grouped and well organized in CSV format. The data was collected for the development of a non-intrusive fall detection solution based on the smart floor. Indeed, the collected data can be used to further improve the current solution by proposing new fall detection techniques for the correct identification of accidental fall events on the smart floor. The gathered fall simulation data is associated with participants' demographic characteristics, useful for future expansions of the smart floor solution beyond the fall detection problem.
Game-based Learning using Augmented Reality

Game-based learning with Augmented Reality (AR) is fast gaining popularity as an educational tool for primary school students due to its proven effectiveness, particularly for lower age groups. The present ubiquity of smart devices also contributes to the rapid adoption of such technology in classrooms. The gamification element and instantaneous feedback are vital to gain and maintain younger students' focus and interest. This study investigates the effects of using such tool on students' learning of the Solar system; a topic that is part of the Malaysian upper primary school's Science syllabus. The study was conducted remotely involving 20 respondents from various public primary schools in Malaysia. Pre-test and post-test results are presented in this paper. The result validates the effectiveness of the developed game-based learning app, i.e., SoLAR Kid, in improving students' understanding of the topic. From our experiment, we observed a clear mean difference between pre-test and post-test scores.
A temporally dynamic examination of research method usage in the Chinese library and information science community

In this paper, we discuss how research methods are used in the Chinese library and information science (LIS) community, a fundamental question in the pursuit of a global overview of this interdisciplinary research domain. We used manual coding to identify research methods mentioned in 2,421 Chinese-language papers written by 53 prestigious Chinese LIS researchers across different generations. For all selected publications, we manually identified and classified methods using a modified version of an established classification scheme for LIS research methods. Moreover, we examined how research methods are used by researchers belonging to different age groups over time. We identified a significant shift in the use of methods among examined publications: quantitative methods are increasingly used over time, driven both by the emergence of newer generations of researchers and by senior researchers gradually adopting newer methods. These findings reflect a major, and heretofore undiscussed, shift in research style in the LIS community in China. Our approach facilitates a new, more dynamic discussion of how research methods are adopted in a research community and will greatly contribute to future studies on this topic.
DRL4IR: 2nd Workshop on Deep Reinforcement Learning for Information Retrieval

Modern information retrieval (IR) consists of a series of processes, including query expansion, candidate item recall, item ranking, item re-ranking, etc. The final ranked item list will be exposed to the user, which will accordingly provide feedback through some expected actions such as browsing and click. Such a whole process can be formulated as a decision-making process where the agent is the IR system while the environment is the specific user. This decision-making process can be one-step or sequential, depending on the scenarios or the ways of problem formulation. Since 2013, Deep reinforcement learning (DRL) has been a fast-developing technique for decision-making tasks. The high capacity of deep learning models is incorporated in the reinforcement learning framework so that the agent may successfully handle complex decision-making. In recent years, there have been a bunch of publications attempting to leverage DRL techniques for different IR tasks such as ad hoc retrieval, learning to rank and interactive recommendation. Nonetheless, the fundamental theory, the principle of RL methods or the recognized experimental protocols of decision-making in IR, has not been well developed, making it challenging to evaluate the correctness of a proposed method or judge whether the reported experimental performance is valid. We propose the second DRL4IR workshop at SIGIR 2021, which provides a venue to gather the academia researchers and industry practitioners to present the recent progress of DRL techniques for IR. More importantly, people in this workshop are expected to discuss more about the fundamental principles of formulating a decision-making IR task, the underlying theory as well as the practical effectiveness of the experiment protocol design, which would foster further research on novel methodologies, innovative experimental findings and new applications of DRL for information retrieval. DRL4IR organized at SIGIR'20 was one of the most popular workshops and attracted over 200 conference attendees. In this year, we will pay more attention to fundamental research topics and recent applications, and expect about 300 participants.