Reviewerly/exHarmony huggingface dataset:
Visual analytics for spatio-temporal air quality data

Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatiotemporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations.
BioVoice: A multipurpose tool for voice analysis

This paper introduces BioVoice, a user-friendly software tool for the acoustical analysis of the human voice. It estimates more than 20 acoustical parameters with advanced and robust analysis techniques specifically developed for different vocal emissions, from the newborn to the adult and the singer. BioVoice performs both time and frequency analyses, detecting the number, length, and percentage of voiced and unvoiced segments and computing fundamental frequency (F0), formant frequencies (F1-F3), noise level, and jitter. The software tool computes the melodic shape of F0 out of 12 basic shapes and allows performing perceptual analysis in newborn and child voice. In the singing voice case, formants up to F5 are computed as well as the quality ratio and parameters concerning vibrato and its regularity. Colour figures and Excel tables show F0, the spectrogram with formants, voiced segments, and quality ratio. Examples of voice analysis in adults, children, newborns, and singers are presented. They show the specific capabilities and the high performance of BioVoice also as compared to another existing software tool. BioVoice is a free user-friendly software tool for voice analysis that implements new estimation techniques. Basic parameters are computed as well as new ones specifically developed for newborn cry and singing voice analysis, not available with current software tools. BioVoice is capable to deal with low to high pitched voices implementing dedicated tools. Thanks to its simple and intuitive interface, colour figures and Excel tables, it is a valuable tool suitable also for the inexperienced user.
Nonlinear Magnon Scattering Mechanism for Microwave Pumping in Magnetic Films

Magnon scattering enables non-linear microwave devices, such as frequency selective limiters and signal to noise enhancers. It may also impact information transfer within spintronic devices. Here, a quantitative understanding of magnon processes in thin films is developed using micromagnetic simulations, in combination with newly developed analytic theory and experimental data. A technique for calculating the number of magnons at each frequency and wavevector as a function of external input such as power and frequency is identified. It is shown that, near the nonlinear threshold, the dominant parametrically excited magnon pairs are those with minimal group velocity and the correct energy. These results complement Brillouin Light Scattering experiments and indicate a path for wavevector-modulated magnon production based only on simulated results and/or analytic theory, a desirable goal for information transfer and communication.
PointSkelCNN: Deep Learning‐Based 3D Human Skeleton Extraction from Point Clouds

Abstract A 3D human skeleton plays important roles in human shape reconstruction and human animation. Remarkable advances have been achieved recently in 3D human skeleton estimation from color and depth images via a powerful deep convolutional neural network. However, applying deep learning frameworks to 3D human skeleton extraction from point clouds remains challenging because of the sparsity of point clouds and the high nonlinearity of human skeleton regression. In this study, we develop a deep learning‐based approach for 3D human skeleton extraction from point clouds. We convert 3D human skeleton extraction into offset vector regression and human body segmentation via deep learning‐based point cloud contraction. Furthermore, a disambiguation strategy is adopted to improve the robustness of joint points regression. Experiments on the public human pose dataset UBC3V and the human point cloud skeleton dataset 3DHumanSkeleton compiled by the authors show that the proposed approach outperforms the state‐of‐the‐art methods.
Augmented Reality in Medical Teaching-Learning Process Content: A Systematic Review

Educators have attempted to innovate the teaching-learning process through computational tools. Some medical educational applications have been created with Augmented Reality resources. Serious Games are one of these kinds of applications, aiming as purpose education or training tasks for users considering entertainment too. Augmented Reality games require appropriate devices for satisfactory experience, as well a usability evaluation to ensure the application's efficiency. With this in mind, this study presents a systematic review to identify approaches that use Mixed or Augmented Reality games in the teaching-learning process of medical contents, and that execute at least one preliminary evaluation with users. Only six selected studies attend the eligibility criteria. We notice a lack of Mixed or Augmented Reality serious games in many medical areas, such as hematology. This work also verified that the evaluation process of these games no adopts a standard protocol or well-known instruments available in the literature for usability evaluation, revealing an opportunity for new studies.
The Effect of Virtual Reality on Knowledge Transfer and Retention in Collaborative Group-Based Learning for Neuroanatomy Students

There are many uses for virtual reality (VR) in education, and there is a consensus about its contribution in the teaching and learning processes. However, the majority of the studies assess the effectiveness of an individual learning in VR, and there is a need to explore more on the effects of VR using different levels of immersion and collaboration. This paper presents an experiment to investigate knowledge transfer in a group-based learning game. We introduce a VR serious game to support teaching and learning processes in neuroanatomy health education. A between-subjects experiment was conducted with 23 students to jointly assess learning, knowledge retention, and sense of presence. As a control condition, grouped students assembled a physical model of the human brain, while in the experimental condition, a virtual brain was assembled. In each group, one participant assembled the brain, while the others observed and verbally collaborated in a group-based learning strategy. Results shown high mean scores in the virtual condition. When comparing the knowledge test performance before and immediately after the experiment, we found significant difference only for the virtual condition. The same can be observed for retention. Because of the promising results achieved and motivated by the need of more engaging new tools for remote learning - fully used in quarantine conditions, such as the current one because of the Covid-19 pandemic - we conducted a pilot user study to evaluate the learning effect of a remote version of our collaborative VR game.
Demo: A Blockchain Based Protocol for Federated Learning

In this demo, we demonstrate a novel blockchain based protocol for federated learning. We present the system architecture and describe the blockchain based protocol that seamlessly provides secure communication in federated learning with physically distributed data sets.
Manual PCB assembly using Augmented Reality towards Total Quality

Despite the fourth wave of industry automation, the so-called Industry 4.0, manual assembly still remains as an important step to many products and is responsible for employing a lot of workers. But manual assembly is prone to errors, is time consuming and boring. One of the enabling technologies of Industry 4.0, Augmented Reality (AR) plays a major role on helping manual assembly by providing on-demand guidance. The technology can also support monitoring features that are compliant to Total Quality Management (TQM) requirements which seeks to minimize errors and maximize quality. This paper reviews the research on using AR to aid manual assembly and presents requirements and the architecture design for an AR system applied to the context of manual assembly of Printed Circuit Boards (PCB). Four device configurations and SDKs were evaluated to be applied in industrial processes of manual assembly of PCB. The paper explores particulars of the task, product and workers towards a novel AR system to fulfill high quality product expectations. This study shows that an AR-Based assembly need not to be expensive in order to be effective, it is shown that every assembly configuration has to be carefully assessed in order to achieve a proper system configuration.
Disguising Code to Help Students Understand Code Similarity

To act with academic integrity in programming, students need to understand the concept of code similarity and the aspects that contribute to it, because undue similarity is often used as a first step in detecting plagiarism or collusion. However, if students are told at all about code similarity, it is typically in very general terms, which might not be clear to all students. This paper proposes CSD, a tool to independently educate students about code similarity through the application of semantic-preserving disguises. Given Java or Python code, CSD will disguise the code without changing its semantics. CSD can apply 60 types of disguise covering five code components: comments, white space, identifier names, data types, and constants. The tool will be integrated into a submission system, so that each time a student submits their code, they will be shown a version of the code with some disguises applied, along with a related explanation. This is expected to motivate further use of the tool while educating students about the futility of trying to copy and disguise code.
Efficient Online Scheduling for Coflow-Aware Machine Learning Clusters

Distributed machine learning (DML) is an increasingly important workload. In a DML job, each communication phase can comprise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coflow</i> , and there are dependencies among its coflows. Thus, efficient coflow scheduling becomes critical for DML jobs. However, the majority of existing solutions focus on scheduling single-stage coflows with no dependencies. While there are a few studies schedule dependent coflows of multi-stage jobs, they suffer from either practical or theoretical issues. Motivated by this situation, we study how to schedule dependent coflows of multiple DML jobs to minimize the total JCT in a shared cluster. We present a formal mathematical formulation for this problem and prove its NP-hardness. To solve this problem without job size information, we present an online coflow-aware optimization framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parrot</i> . The core idea in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parrot</i> is to infer the job with the shortest remaining processing time (SRPT) each time and dynamically control the inferred job's bandwidth based on how confident it is an SRPT job while being mindful of not starving any other job. Specifically, in the design of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parrot</i> , we present a least per-coflow attained service (LPCAS) policy to infer the SRPT job. We further propose a dynamic job weight assignment mechanism and a linear program (LP) based weighted bandwidth scaling strategy for sharing bandwidth among DML jobs. We have proved that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parrot</i> algorithm has a non-trivial competitive ratio. The results from large-scale trace-driven simulations further demonstrate that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Parrot</i> can reduce the total JCT by up to 58.4 percent, compared to the state-of-the-art Aalo solution.
Video-based person re-identification by intra-frame and inter-frame graph neural network

In the past few years, video-based person re-identification (Re-ID) have attracted growing research attention. The crucial problem for this task is how to learn robust video feature representation, which can weaken the influence of factors such as occlusion, illumination, and background etc. A great deal of previous works utilize spatio-temporal information to represent pedestrian video, but the correlations between parts of human body are ignored. In order to take advantage of the relationship among different parts, we propose a novel Intra-frame and Inter-frame Graph Neural Network (I2GNN) to solve the video-based person Re-ID task. Specifically, (1) the features from each part are treated as graph nodes from each frame; (2) the intra-frame edges are established by the correlation between different parts; (3) the inter-frame edges are constructed between the same parts across adjacent frames. I2GNN learns video representations by employing the adjacent matrix of the graph and input features to conduct graph convolution, and then adopts projection metric learning on Grassman manifold to measure the similarities between learned pedestrian features. Moreover, this paper proposes a novel occlusion-invariant term to make the part features close to their center, which can relive several uncontrolled complicated factors, such as occlusion and pose invariance. Besides, we have carried out extensive experiments on four widely used datasets: MARS, DukeMTMC-VideoReID, PRID2011, and iLIDS-VID. The experimental results demonstrate that our proposed I2GNN model is more competitive than other state-of-the-art methods.
A Deep Learning Model For The Automatic Detection Of Malignancy In Effusion Cytology

The excessive accumulation of fluid between layers of pleura covering lungs is known as pleural effusion. Pleural effusion may be due to various infections, inflammations or malignancy. The cytologists visually examine the microscopic slide to detect the malignant cells. The process is time-consuming, and interpretation of reactive cells and cells with ambiguous levels of atypia may differ between pathologists. Considerable research is happening towards the automation of fluid cytology reporting. We propose an integrated approach based on deep learning, where the network learns directly to detect the malignant cells in effusion cytology images. Architecture U-Net is used to learn the malignant and benign cells from the images and to detect the images that contain malignant cells. The model gives a precision of 0.96, recall of 0.96, and specificity of 0.97. The AUC of the ROC curve is 0.97. The model can be used as a screening tool and has a malignant cell detection rate of 0.96 with a low false alarm rate of 0.03.
HBPFP-DC: A parallel frequent itemset mining using Spark

The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. Facing big data applications, parallel and distributed solutions are widely studied. However, the frequent itemset mining process is a continuous iteration process. As an in-memory parallel execution model in which all data will be loaded into memory, Spark is especially beneficial for iterative calculations. In the study, we propose a HBPFP-DC (High Balanced Parallel Fp-Growth Considering Data Correlation) algorithm on the Spark platform. HBPFP-DC uses a newly defined node computation workload estimation model to realize the balanced grouping of the calculation tasks among computing nodes, so that each computing node can achieve a completely asynchronous frequent itemset mining only relying on their respective local projection datasets. And, in order to improve the 'compression factor' of the tree structure to boost mining efficiency, we consider the correlation among items when performing the above grouping process. Thereby, network and computing consumption by dividing similar items in the same group are significantly decreased. Finally, extensive experiments demonstrate that our proposed solution is efficient and scalable.
Learnable Cost Volume Using the Cayley Representation

Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors. However, the standard inner product in the commonly-used cost volume may limit the representation capacity of flow models because it neglects the correlation among different channel dimensions and weighs each dimension equally. To address this issue, we propose a learnable cost volume (LCV) using an elliptical inner product, which generalizes the standard inner product by a positive definite kernel matrix. To guarantee its positive definiteness, we perform spectral decomposition on the kernel matrix and re-parameterize it via the Cayley representation. The proposed LCV is a lightweight module and can be easily plugged into existing models to replace the vanilla cost volume. Experimental results show that the LCV module not only improves the accuracy of state-of-the-art models on standard benchmarks, but also promotes their robustness against illumination change, noises, and adversarial perturbations of the input signals.
A Lightweight Pseudo-Random Number Generator Based on a Robust Chaotic Map

In this paper, we propose a new simple and fast algorithm for entropy extraction and pseudo-random numbers generation from a robust chaotic map. This method could be used as an entropy source in some cryptographic applications. Based on robust chaos and simple operations, the randomness of the generated sequences might be accepted for many security applications. We run a statistical randomness test-suite on some randomly generated sequences to evaluate their randomness.
The Use of ISO/IEC 27005: 2018 for Strengthening Information Security Management (A Case Study at Data and Information Center of Ministry of Defence)

The country's vital objects that provide data and information are vulnerable to threats. Information in the form of valuable assets needs to be protected from threats and vulnerabilities. Data and information must be guaranteed confidentiality, integrity, authenticity, and availability. Data and Information Center of Ministry of Defence, better known as Pusdatin Kemhan. Business processes owned by this organization are very complex. Pusdatin Kemhan requires strengthening in managing information security because the assets carried are important and high-value assets that are needed by leaders who assist in making decisions to support national defense and security. The implementation of information security and the existence of risk management are still not well managed. This study will provide an information security management design method based on risk management based on ISO / IEC 27005: 2018. The results of this study are the information security management policy at the Center for Data Processing and Information Technology.
Evaluation of different pre-processing methods of X-ray micro computed tomography images

Adequate images of real tablets provide the possibility to validate in silico software tools like F-CAD. The goal of this work was to evaluate the influence of different pre-processing methods of X-ray micro-computed tomography images of 12 different tablet batches. The images were processed with the open source software ImageJ. Different bleach- and lightning corrections were applied. The influence of a filtering process and two different contrast enhancement methods were evaluated. The images were segmented with the k-means algorithm and F-CAD determined the recovery rates. All results were analysed in terms of their desirability. Suitable pathways were found for each binary mixture. The mean recovery rates for all batches were approximately 100%. However, it was not possible to find a suitable program procedure to cover all binary mixtures simultaneously. Nevertheless, it was possible to confirm the possibility of a suitable validation method for in silico tools.
Extension Officers’ Adoption of Modern Information Communication Technologies to Interact with Farmers of Trinidad

The Ministry Extension struggled to provide adequate services to Trinidadian farmers because of a reliance on top-down methods. Information Communication Technologies (ICTs) have been touted as an effective and efficient way to serve farmers in the Caribbean. This study sought to describe the factors affecting Ministry Extension officers’ decisions to adopt text messaging via Short Messaging Service (SMS) to communicate with farmers. The Diffusion of Innovation theory guided this study, and data were collected from a census of Extension officers in the Ministry Extension service. Results showed most Extension officers used voice calls and SMS to communicate with farmers. Many Extension officers who did not use ICTs were willing to use multimedia messaging, SMS, electronic mail (email), voice calls, and social media to interact with farmers. Extension officers were accustomed to using text messaging, saw the benefit of using SMS to communicate with farmers, and were confident in their ability to use SMS for farmer interactions. However, results indicated a lacking policy environment for SMS use, and limited support existed from peers and supervisors to use SMS to interact with farmers. Findings showed most Extension officers perceived farmers can use mobile telephones to make calls, send text messages, and use multimedia messaging. This implies Extension officers were aware of the potential for using a variety of ICTs to engage with farmers. However, internal policies and administrative support are critical to their adoption of ICTs for farmer interactions in Extension.
Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study

Purpose Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home. Design/methodology/approach A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed. Findings The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement. Originality/value This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.