Reviewerly/exHarmony huggingface dataset:
Zone-Based Federated Learning in Indoor Positioning

With the advancement of artificial intelligence (AI), indoor positioning systems have been getting more attention in smart cities and smart homes for various purposes. However, protecting the privacy of users is one of the significant challenges in all smart environments. In very recent years, federated learning (FL) has been introduced as a solution for a privacy-aware indoor positioning system. However, there is still a risk of information leakage when transferring the model gradients between the clients and the central server in the federated learning process. In this paper, to avoid revealing the location information of users, we propose a zone-based federated learning method that employs FL to determine the zone of the users instead of their exact location. Therefore, even with the leakage of model gradients, the users’ exact location cannot be determined; thus, their privacy is preserved. We verified the suggested method utilizing two real datasets and compared the results with the non-FL mode. Also, the accuracy of the proposed method is evaluated in various number of communication rounds, number of clients, and number of data for each client. The results on both datasets indicate that while the proposed method preserves the privacy of clients, the accuracy of that is very close to the accuracy of the non-FL mode.
DevRanker: An Effective Approach to Rank Developers for Bug Report Assignment

Bug assignment, which routes software projects’ bug reports to the appropriate fixers, is an important part of the development of software and its maintenance. Manual bug assignment is a time-consuming process that delays debugging. So various machine learning and information retrieval approaches have been used in order to automate the process of bug assigning. However, Most previous deep learning-based studies have focused on developers assigned to bug reports and have not specifically considered developers’ collaboration and interaction to resolve bug reports. Here, we propose a novel approach for automatic bug assignment based on Bidirectional Encoder Representations from Transformers (BERT) and Preference Neural Network (PNN). First, we preprocess the textual data in the bug reports. Second, we use BERT as a word embedding technique to get vector representation of bug reports. Third, we calculate the developers’ suitability score based on different developers’ activity features for each bug report. Finally, PNN is used to rank developers for each bug report. Experiments are performed on open-source projects, namely Eclipse UI, Birt, JDT and SWT, and top-k accuracy is measured as an evaluation metric. The results obtained from our experiments can prove that our approach could markedly improve the performance of automatic bug assignment.
Integration of ontology reasoning-based monocular cues in deep learning modeling for single image depth estimation in urban driving scenarios

Humans are able to estimate the depth of objects in their environment even using only one eye through the use of what are known as monocular cues. In this paper, we aim to integrate human knowledge and human-like reasoning used for monocular depth estimation within deep neural networks. The idea is to support the network in order to help it learn in an explicit and fast way the essential cues for the target task. For this purpose, we investigate the possibility of directly integrating geometric, semantic, and contextual information into the monocular depth estimation process. We propose exploiting an ontology model in a deep learning context to represent the urban environment as a structured set of concepts linked with semantic relationships. Monocular cues information are extracted through reasoning performed on the proposed ontology and are fed together with the RGB image in a multistream way into the deep neural network for depth estimation. Our approach is validated and evaluated on widespread benchmark datasets: KITTI, CityScapes, and AppolloScape. The obtained results show that the proposed method improves upon the state-of-the-art monocular depth estimation deep models and shows promising results regarding cross-evaluation, mainly for unseen driving scenarios.
Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data

With the continuous increase in the computational power and resources of modern high-performance computing (HPC) systems, large-scale ensemble simulations have become widely used in various fields of science and engineering, and especially in meteoro-logical and climate science. It is widely known that the simulation outputs are large time-varying, multivariate, and multivalued datasets which pose a particular challenge to the visualization and analysis tasks. In this work, we focused on the widely used Parallel Coordinates Plot (PCP) to analyze the interrelations between different parameters, such as variables, among the members. However, PCP may suffer from visual cluttering and drawing performance with the increase on the data size to be analyzed, that is, the number of polylines. To overcome this problem, we present an extension to the PCP by adding Bézier curves connecting the angular distribution plots representing the mean and variance of the inclination of the line segments between parallel axes. The proposed Angular-based Parallel Coordinates Plot (APCP) is capable of presenting a simplified overview of the entire ensemble data set while maintaining the correlation information between the adjacent variables. To verify its effectiveness, we developed a visual analytics prototype system and evaluated by using a meteorological ensemble simulation output from the supercomputer Fugaku.
The Internet of Senses: A Position Paper on the Challenges and Opportunities of Multisensory Immersive Experiences for the Metaverse

The so-called metaverse will allow users to blend real and virtual worlds in a seamless manner, thus drastically changing how humans interact, socialize, and even work. While progress in extended reality headsets, wireless communications, blockchain technologies and quantum communications are paving the way for the metaverse, many of the emerging applications are still focusing on only two human senses: hearing and vision. In this position paper, we argue that in order to truly make immersive experiences, additional senses need to be stimulated. Moreover, affective computing and brain-computer interface principles need to be incorporated to make the interaction (be it human-human, human-avatar, avatar-avatar) more natural, and sustainable solutions need to be developed, so that we do not destroy our real world while trying to build a virtual version of it. This future internet has been termed “the Internet of Senses (IoS)”. Here, we highlight some of the challenges in developing the IoS, as well as some of the opportunities that multisensory immersive experiences will bring to the manufacturing, retail, entertainment, and healthcare domains. We conclude with a brief description of a testbed under development in Canada to test emerging IoS and metaverse technologies and applications.
Adaptive Importance Sampling for Equivariant Group-Convolution Computation

This paper introduces an adaptive importance sampling scheme for the computation of group-based convolutions, a key step in the implementation of equivariant neural networks. By leveraging information geometry to define the parameters update rule for inferring the optimal sampling distribution, we show promising results for our approach by working with the two-dimensional rotation group SO(2) and von Mises distributions. Finally, we position our AIS scheme with respect to quantum algorithms for computing Monte Carlo estimations.
Reduced state of the field and classicality of quantum Gaussian evolution

The notion of classicality of quantum evolution of light is an object of both conceptual and practical importance. The main goal of this work is to derive the exact conditions for the classicality of quantum Gaussian evolution, i.e., the evolution of Gaussian states of light and their convex combinations, a model which is of great significance in quantum optics and information. According to our findings, quantum Gaussian evolution should be considered classical if the Hamiltonian and Lindblad operators generating it correspond to passive optical transformations. This is illustrated with several explicit examples, ranging from Gaussian thermal operations to entanglement-maximizing dissipative engineering. Our results are obtained using the recently introduced mesoscopic formalism of the reduced state of the field, which was originally devised as as a description of macroscopic quantum fields. Here, to make the framework suitable for our goal, we redevelop it as a tool for probing classicality, which constitutes our second main contribution.
Graph Attention Memory for Visual Navigation

Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy due to the long-term memory problem. To address this issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module, and control module. The memory construction module builds the topological graph based on supervised learning by taking the exploration prior. Then, guided attention features to reach the goal are extracted with the graph attention module. Finally, the deep reinforcement learning based control module makes decisions based on visual observations and guided attention features. In addition, the convergence of the proposed GAM module for recurrent attention operation is analyzed in this paper. We evaluate GAM-based navigation system in two complex 3D ViZDoom environments. Experimental results show that the GAM-based navigation system outperforms all baselines in both success rate and navigation efficiency, and significantly improves the generalization.
Multi-population Cooperative Particle Swarm Optimization with Covariance Guidance

The particle swarm optimization algorithm has been widely utilized to address a wide range of different engineering optimization problems due to its few parameters and simple structure. The conventional particle swarm optimization algorithm takes in information from two sources: Global optimal particle and individual optimal particle. However, learning from these two sources alone is inefficient to solve complex high-dimensional problems. Therefore, this paper proposes a multi-population cooperative particle swarm optimization (COVPSO) algorithm with covariance guidance strategy, through the use of a covariance guidance strategy to guide population evolution direction. In COVPSO, the population is divided based on the Euclidean distance from the particle to the global optimal particle, and the population is divided into the elite group, exploratory group, and inferior group. As a result of grouping the population and adopting different strategies, the elite group has good exploitation ability, while the exploration group has good exploration ability, and the inferior groups by introducing a differential mutation operator to improve global exploration ability. Therefore, the population has a good balance between exploration and exploitation. This study utilizes ten benchmark functions and five PSO variants broadly used in the literature to verify the merits of COVPSO to demonstrate its efficiency. The findings of the experiments show that COVPSO has a faster convergence rate and a more precise solution.
Mapless Path Planning of Multi-robot Systems in Complex Environments via Deep Reinforcement Learning

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate.
Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer

The high-dimensionality, complexity, and irregularity of electronic health records (EHR) data create significant challenges for both simplified and comprehensive health assessments, prohibiting an efficient extraction of actionable insights by clinicians. If we can provide human decision-makers with a simplified set of interpretable composite indices (i.e., combining information about groups of related measures into single representative values), it will facilitate effective clinical decision-making. In this study, we built a structured deep embedding model aimed at reducing the dimensionality of the input variables by grouping related measurements as determined by domain experts (e.g., clinicians). Our results suggest that composite indices representing liver function may consistently be the most important factor in the early detection of pancreatic cancer (PC). We propose our model as a basis for leveraging deep learning toward developing composite indices from EHR for predicting health outcomes, including but not limited to various cancers, with clinically meaningful interpretations.
Safe Navigation of a Non-Holonomic Robot with Low Computational-power in a 2D Dynamic Environment

This paper proposes a navigation strategy for a low-computational power robot in a 2D complex environment with dynamic obstacles. In this work, a non-holonomic robot is considered. The Hybrid A* algorithm is fused with a reactive control algorithm to provide safe navigation. The Hybrid A* is a graph search-based algorithm implemented to give a near-optimal path. The reactive control is based on sliding mode control and uses the information from the onboard sensors to avoid static and dynamic obstacles. Computer simulations are performed to validate the proposed navigation algorithm.
Classification of human movements by using Kinect sensor

In recent years, studies have been carried out to classify human movements in many areas such as health and safety. To classify human movements, image processing methods have also started to be used in recent years. With the help of learning-based algorithms, human posture can be defined in the images obtained by various imaging methods. The predecessor methods of these classification algorithms are machine learning and deep learning. In addition, in recent years, the use of sensors that can detect human joints in perceiving human posture has also increased. The Kinect sensor, developed by Microsoft, is one of the most frequently used sensors because it is not wearable and can detect joints with infrared rays and transfer this information directly to the computer via USB connection. This study used a dataset called CAD60 that included real-time human posture information and images obtained using a Microsoft Kinect sensor, which is available in the literature. This dataset contains data that includes different movements/postures of different people. Within the scope of this study, the performances of these algorithms were obtained by using classification algorithms with the MATLAB program and these performances were compared. The classification algorithms have been used to try to improve the results by using different architectures. When raw data is used, classification accuracy is obtained as 72.60% with one of the machine learning methods, the Cosine K-Nearest Neighbor method. With the feature selection method, this success value has been increased to 74.18%. In addition, when classified by the Support Vector Machines method after the feature extraction process using the Long Short Term Memory method from the deep network architectures, which is the method proposed in this study, the accuracy rate was increased to 98.95%. The best method of classifying human posture was investigated by using different methods and a method was proposed by comparing it with the literature.
A forensic analysis of streaming platforms on Android OS

This work builds on existing research in streamed video reconstruction on the Android OS, which previously demonstrated that caching occurs in most cases for the Chrome and Firefox Web browsers. Prior work also outlined that streaming application caching behaviour is dependent on both the implementation of the service, as well as the actions taken by the user, with contrasting results between replaying videos and viewing live content. We conduct a forensic investigation for the Twitch, Facebook, Reddit, Instagram and Periscope Android applications, with a focus on the application specific folders in the/data/data directory. Applications were populated with data by creating accounts and viewing a mixture of live and replay (recorded) video streams, with a focus on attempting to recover video fragments or identifiers for particular streams/videos. As users may take action to hinder forensic endeavours, additional videos were viewed to identify baseline caching and overwriting behaviour on each application. Additionally, Android's ‘Cache clear’ operation was evaluated for its anti-forensic potential. While Android seems to produce different behaviour for live and recorded streams, which is consistent with prior work, our findings suggest that Android applications typically retain few, or no, video artefacts, which contrasts with their browser based counterparts. Cache clearing also appears to be a powerful, and trivial, anti-forensics step for clearing locally cached media in each application. We suggest that, going forward, new applications should be tested on a variety of platforms, as it appears that they do not necessarily leave behind consistent forensic traces across versions.
Interactive Perceptual Companion: Prototyping a Digital Perception for HAI

This research presents an exploration of a post-anthropocentric perspective towards the design of human-agent interaction (HAI), observed through the development of a wearable device prototype. The device argues for an alternative stance towards the orthodox human-centered perspectives in HAI executed through human-computer interaction (HCI). Taking a research through design approach the device presents a speculative scenario of agential interaction in the form of a perceptual companion interface. It overlaps with post-anthropocentric philosophical discourse around the phenomenon of agential perception present in contemporary imaginings of HCI. The prototype intends to act as a catalyst for imagining post-anthropocentric agential interactions within HAI.
Does Media Format Matter? Investigating the Toxicity, Sentiment and Topic of Audio Versus Text Social Media Messages

Audio messaging and voice-based interactions are growing in popularity. Lexical features of a manually-curated dataset of real-world audio tweets, as well as text and video/image tweets from the same user accounts, are analyzed to explore how user-generated audio differs from text. The toxicity, sentiment, topic and length of audio tweet transcripts are compared with their accompanying text, date-matched text tweets from the same users and date-matched video/image tweets and their accompanying text. Audio tweets were significantly less toxic than both text tweets and text that accompanied the audio tweet, as well as significantly lower sentiment than their accompanying text. The topics and word counts of audio, text and video/image tweets also differed. These findings are then used to derive design implications for audio and conversational agent interaction. This research contributes preliminary insights about audio social media messages that may help researchers and designers of audio- and agent-based interaction better understand and design for different media formats.
Cybersecurity Challenges and Needs in The Context of Digital Development in Zimbabwe

This research was on the cybersecurity challenges and needs of grassroot users of cyberspace in Zimbabwe. A qualitative research methodology was used and was guided by the Interpretivist philosophy. Judgmental sampling was used to select the participants in the study based on their knowledge on cybersecurity matters. The sample was drawn from people from Murewa District of Zimbabwe. A descriptive research design was used to answer the research questions. Both unstructured and structured interviews as well as observations were used to collect data on the cybersecurity needs and challenges of grassroot users of cyberspace. The findings revealed that the challenges that grassroot users are facing include identity theft, poor internet connectivity and infrastructure problems. The research also exposed the need for stronger physical security of ICT assets and cybersecurity legislation. The researcher recommended the use of solar systems as an alternative source of energy and continuous review and alignment of cybersecurity legislation in line with the changing cyber threat landscape.
Simulation of Homogenous Fish Schools in the Presence of Food and Predators using Reinforcement Learning

We utilized Deep Reinforcement Learning to incor-porate schooling, foraging, and predator avoidance behaviors into a single fish behavior model. We used Proximal Policy Optimization (PPO) with Intrinsic Curiosity Reward (ICR) to make fish agents learn in our Unity Environment. We created an interactive control system on Unity that allows users to visualize and manipulate the simulation using only a mouse and keyboard. We compared our model with three variations: one without schooling reward, one without foraging reward, and one without predator avoidance reward. Our original model (schooling, foraging, and predator avoidance) clearly illustrated the unification of all three behaviors.
Analysis of the Effect of Social Skills and Disposition of Digital Literacy on Mathematical Literacy Ability

&lt;p style="text-align: justify;"&gt;This study aims to analyze the characteristics of social skills, digital literacy disposition, and mathematical literacy abilities of 49 students; to analyze the theoretical model of the direct and indirect influence of digital literacy skills and social skills on mathematical literacy skills. The number of samples is 49 of the 12th-grade students at state high school #1 Southern Konawe. The research instruments consisted of a mathematical literacy ability test, a social skills questionnaire, and a digital literacy disposition questionnaire. The data were analyzed by descriptive statistics and structural equational models (SEM) with a Smart PLS 3.0 application. The results showed that students' mathematical literacy skills tended to be better on content and context indicators but they are weak on competency and process indicators. Students' social skills tend to be better on indicators of peer-relationship skills and self-management skills, but are also weak on indicators of academic skills and compliance skills. Mastery of the digital literacy disposition tends to be better on the internet searching, content evaluation, and information sharing indicators but is still weak on knowledge assembly. Furthermore, it was found that (a) social skills have a direct effect on digital literacy disposition but do not directly affect mathematical literacy skills; (b) the digital literacy disposition has a direct effect on mathematical literacy skills.&lt;/p&gt;