Reviewerly/exHarmony huggingface dataset:
Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model

Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.
Explanation-Guided Training for Cross-Domain Few-Shot Classification

Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the predictions of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The source code is available https://github.com/SunJiamei/few-shot-lrp-guided.
Violence Detection in Videos Based on Fusing Visual and Audio Information

Determining whether given video frames contain violent content is a basic problem in violence detection. Visual and audio information are useful for detecting violence included in a video, and are usually complementary; however, violence detection studies focusing on fusing visual and audio information are relatively rare. Therefore, we explored methods for fusing visual and audio information. We proposed a neural network containing three modules for fusing multimodal information: 1) attention module for utilizing weighted features to generate effective features based on the mutual guidance between visual and audio information; 2) fusion module for integrating features by fusing visual and audio information based on the bilinear pooling mechanism; and 3) mutual Learning module for enabling the model to learn visual information from another neural network with a different architecture. Experimental results indicated that the proposed neural network outperforms existing state-of-the-art methods on the XD-Violence dataset.
Artificial Intelligence Based Smart Facial Expression Recognition Remote Control System

In this digital era, face based acknowledgment plays an indispensable role in almost all the real-time applications. This paper presents an outward appearance based innovation, which mechanizes the controller of different frameworks like TV, sound frameworks, lighting frameworks and projectors. This research work presents a man-made reasoning based structure, which performs constant acknowledgment of outward appearance and consequently changes the TV slot. Here, the front end work is completed by MATLAB programming to handle the picture and the yield from MATLAB is sent by means of sequential correspondence and executed in Arduino IDE for empowering the controller. The actualized framework plans to help the working, dump and aged individuals, who perceive the articulations and convert them into an ideal information by means of Arduino IDE and the yield is seen through virtual terminal. The equipment arrangement is actualized by utilizing the Arduino UNO. MATLAB programming helps to measure the picture and distinguish the disposition of the client before sending the outcome to Arduino. Further, Arduino gets the yield of the MATLAB and changes the air of the family room to be dependent on the client's outward appearance.
A Consortium Blockchain-Based Access Control Framework With Dynamic Orderer Node Selection for 5G-Enabled Industrial IoT

5G-enabled Industrial Internet of Things (IIoT) deployment will bring more severe security and privacy challenges, which puts forward higher requirements for access control. Blockchain-based access control method has become a promising security technology, but it still faces high latency in consensus process and weak adaptability to dynamic changes in network environment. This article proposes a novel access control framework for 5G-enabled IIoT based on consortium blockchain. We design three types of chaincodes for the framework named policy management chaincode (PMC), access control chaincode (ACC), and credit evaluation chaincode (CEC). The PMC and ACC are deployed on the same data channel to implement the management of access control policies and the authorization of access. The CEC deployed on another channel is used to add behavior records collected from IIoT devices and calculate the credit value of IIoT domain. Specifically, we design a two-step credit-based Raft consensus mechanism, which can select the orderer nodes dynamically to achieve fast and reliable consensus based on historical behavior records stored in the ledger. Furthermore, we implement the proposed framework on a real-world testbed and compare it with the framework based on practical Byzantine fault tolerance consensus. The experiment results show that our proposed framework can maintain lower consensus cost time with 100 ms level and achieves four to five times throughput with lower hardware resource consumption and communication consumption. Besides, our design also improves the security and robustness of the access control process.
Building a best-in-class automated de-identification tool for electronic health records through ensemble learning

The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.
Feels like Team Spirit: Biometric and Strategic Interdependence in Asymmetric Multiplayer VR Games

Virtual reality (VR) multiplayer games increasingly use asymmetry (e.g., differences in a person’s capability or the user interface) and resulting interdependence between players to create engagement even when one player has no access to a head-mounted display (HMD). Previous work shows this enhances player experience (PX). Until now, it remains unclear whether and how an asymmetric game design with interdependences creates comparably enjoyable PX for both an HMD and a non-HMD player. In this work, we designed and implemented an asymmetric VR game (different in its user interface) with two types of interdependence: strategic (difference in game information/player capability) and biometric (difference in player’s biometric influence). Our mixed-methods user study (N=30) shows that asymmetries positively impact PX for both player roles, that interdependence strongly affects players’ perception of agency, and that biometric feedback—while subjective—is a valuable game mechanic.
XRStudio: A Virtual Production and Live Streaming System for Immersive Instructional Experiences

There is increased interest in using virtual reality in education, but it often remains an isolated experience that is difficult to integrate into current instructional experiences. In this work, we adapt virtual production techniques from filmmaking to enable mixed reality capture of instructors so that they appear to be standing directly in the virtual scene. We also capitalize on the growing popularity of live streaming software for video conferencing and live production. With XRStudio, we develop a pipeline for giving lectures in VR, enabling live compositing using a variety of presets and real-time output to traditional video and more immersive formats. We present interviews with media designers experienced in film and MOOC production that informed our design. Through walkthrough demonstrations of XRStudio with instructors experienced with VR, we learn how it could be used in a variety of domains. In end-to-end evaluations with students, we analyze and compare differences of traditional video vs. more immersive lectures with XRStudio.
Quantitative determination of auxiliary information for mapping soil heavy metals and soil contamination risk assessment

Heavy metal contamination of soil poses a threat to food chains and human health, particularly in mining areas. This study aims to advance the accuracy of mapping heavy metals in soil by incorporating auxiliary information and to assess the soil contamination risk to local residents in a mining area. Pearson correlation coefficients of environmental factors containing each of the eight heavy metals (Zn, As, Cd, Cr, Cu, Ni, Pb, Hg) are utilized to quantitatively assign the auxiliary variables for each heavy metal to reduce false interference and advance prediction accuracy. Ordinary kriging and cokriging are compared to estimate the heavy metal distributions and thus confirm whether and what auxiliary variables work on the distributions of heavy metals. Distribution maps of heavy metals are ramped based on the contamination levels of the soil quality standards of China,and then overlapped with the land-cover types of the study areas. The results show: (1) the distributions of Zn, Cd, Cu, Ni, Pb, and Hg are related to the soil's properties, the distribution of As appears to be associated to Pb and Cu, and Cd prediction accuracy is unaffected by the corporation of auxiliary variables in this study. (2) As, Cd, Hg, Pb, and Zn contaminates most soils in the areas studied, reaching at least Grade I levels near the residential field and Hg reaching the highest level of pollution based on the soil quality standards of China.
Dance and Choreography in HCI: A Two-Decade Retrospective

Designing computational support for dance is an emerging area of HCI research, incorporating the cultural, experiential, and embodied characteristics of the third-wave shift. The challenges of recognising the abstract qualities of body movement, and of mediating between the diverse parties involved in the idiosyncratic creative process, present important questions to HCI researchers: how can we effectively integrate computing with dance, to understand and cultivate the felt dimension of creativity, and to aid the dance-making process? In this work, we systematically review the past twenty years of dance literature in HCI. We discuss our findings, propose directions for future HCI works in dance, and distil lessons for related disciplines.
Teaching by Analogy: From Theory to Practice

ABSTRACT Analogy is a powerful tool for fostering conceptual understanding and transfer in STEM and other fields. Well‐constructed analogical comparisons focus attention on the causal‐relational structure of STEM concepts, and provide a powerful capability to draw inferences based on a well‐understood source domain that can be applied to a novel target domain. However, analogy must be applied with consideration to students' prior knowledge and cognitive resources. We briefly review theoretical and empirical support for incorporating analogy into education, and recommend five general principles to guide its application so as to maximize the potential benefits. For analogies to be effective, instructors should use well‐understood source analogs and explain correspondences fully; use visuospatial and verbal supports to emphasize shared structure among analogs; discuss the alignment between semantic and formal representations; reduce extraneous cognitive load imposed by analogical comparison; and encourage generation of inferences when students have some proficiency with the material. These principles can be applied flexibly to topics in a wide variety of domains.
Enhanced Round-Robin Algorithm in the Cloud Computing Environment for Optimal Task Scheduling

Recently, there has been significant growth in the popularity of cloud computing systems. One of the main issues in building cloud computing systems is task scheduling. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms.
An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system

This paper proposes an efficient path planning technique for the autonomous collision-free navigation of wheeled mobile robots with simple hardware based on an adaptive neuro-fuzzy inference system (ANFIS). The distance between the robot and obstacles is measured using three ultrasonic sensors that are installed on the left, front, and right of the robot. These distances from the sensors form the inputs to the ANFIS-utility function block that calculates an obstacle avoidance steering angle for the robot. The obstacle avoidance behavior of the robot is modeled under six scenarios of facing an obstacle. The instantaneous position of the robot and the target are available from Global Positioning System (GPS) modules. A simulation mobile robot in V-REP has been integrated into the ANFIS controller coded in MATLAB. The simulation results show that the proposed ANFIS-utility function-based path planning technique surpasses some of the related algorithms in terms of finding near-optimal paths.
Data Network Effects: Key Conditions, Shared Data, and the Data Value Duality

Clough and Wu (2020) provide an interesting and thought-provoking response to our article (Gregory, Henfridsson, Kaganer, & Kyriakou, 2020) on the role of Artificial Intelligence (AI) and data netw...
A GDPR-Compliant Framework for IoT-Based Personal Health Records Using Blockchain

An up-to-date personal health record (PHR) system is crucial for people's health. Achieving a reliable PHR system in the e-Health and m-Health era is still a challenge concerning data integration from different EHRs, data interoperability, and enforcing that access to data is fully under the patient's control. We address these challenges by proposing an electronic health wallet (EHW) system that uses emergent decentralized technologies like blockchain and IPFS and adopts health data interoperability standards and technologies like FHIR's APIs. The EHW stands on a GDPR-compliant framework for IoT-based PHR systems that ensures both data privacy and interoperability. The proposed conceptual framework and system architecture provide a comprehensive solution for a patient-centered IoT-based PHR system that preserves data privacy and satisfies the data interoperability needs. By encouraging patients to share their data in a controlled way, also enables health big data analytics by utilizing the IoT data in a privacy-preserving fashion.
Fractional Stockwell transform: Theory and applications

The Stockwell transform (ST), which is an invertible time-frequency spectral localization technique, uniquely combines elements of wavelet transform and short-time Fourier transform. However, its signal analysis ability is restricted in the time-frequency plane. In this paper, the novel fractional Stockwell transform (FRST) is proposed to address this problem, based on the ST and fractional Fourier transform (FRFT). It displays the time and fractional-frequency information jointly in the time-fractional-frequency plane. More importantly, it has explicit physical interpretation and usefulness for practical applications. First, the theories of continuous FRST are presented in detail, including its definition, basic properties and time-fractional-frequency analysis. Next, the discrete version for the transform, together with its reconstruction formula is considered. Further, the FRST in the realm of almost periodic functions is explored to expand its theory in the persistent signal space. Lastly, we investigate and discuss several applications based on FRST, including the time-frequency of chirp signals and the detection of disturbed chirp signals. Simulations are presented to verify the rationality and effectiveness of the FRST.
DistanciAR: Authoring Site-Specific Augmented Reality Experiences for Remote Environments

Most augmented reality (AR) authoring tools only support the author’s current environment, but designers often need to create site-specific experiences for a different environment. We propose DistanciAR, a novel tablet-based workflow for remote AR authoring. Our baseline solution involves three steps. A remote environment is captured by a camera with LiDAR; then, the author creates an AR experience from a different location using AR interactions; finally, a remote viewer consumes the AR content on site. A formative study revealed understanding and navigating the remote space as key challenges with this solution. We improved the authoring interface by adding two novel modes: Dollhouse, which renders a bird’s-eye view, and Peek, which creates photorealistic composite images using captured images. A second study compared this improved system with the baseline, and participants reported that the new modes made it easier to understand and navigate the remote scene.
Augmented reality for maritime navigation data visualisation: a systematic review, issues and perspectives

Abstract This study investigates the use of augmented reality technology (AR) in the field of maritime navigation and how researchers and designers have addressed AR data visualisation. The paper presents a systematic review analysing the publication type, the AR device, which information elements are visualised and how, the validation method and technological readiness. Eleven AR maritime solutions identified from scientific papers are studied and discussed in relation to previous navigation tools. It is found that primitive information such as course, compass degrees, boat speed and geographic coordinates continue to be fundamental information to be represented even with AR maritime solutions.
Lower is Better? The Effects of Local Latencies on Competitive First-Person Shooter Game Players

Video game play is among the most popular forms of entertainment in the world and eSports is a multi-billion dollar industry. Esports gamers, and competitive gamers more broadly, want fast game systems to maximize their chances of winning. In general, the faster the game system, the lower the latency between a player’s action and the intended outcome. But how much small reductions in local latencies benefit competitive players is not known. This paper presents results from a 43-person user study that evaluates the impact of system latencies for high-end gaming systems (below 125 ms) on experienced Counter-strike: Global Offensive (CS:GO) players. Analysis of the results show pronounced benefits to CS:GO player performance (accuracy and score) for even small reductions in latency, with subjective opinions on Quality of Experience following suit.
3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame

Facial expression spotting is the preliminary step for micro- and macro-expression analysis. The task of reliably spotting such expressions in video sequences is currently unsolved. Current best systems depend upon optical flow methods to extract regional motion features, before categorisation of that motion into a specific class of facial movement. Optical flow is susceptible to drift error, which introduces a serious problem for motions with long-term dependencies, such as high frame-rate macro-expression. We propose a purely deep learning solution which, rather than tracking frame differential motion, compares via a convolutional model, each frame with two temporally local reference frames. Reference frames are sampled according to calculated micro- and macro-expression duration. As baseline for MEGC2021 using leave-one-subject-out evaluation method, we show that our solution performed better in a high frame-rate (200 fps) SAMM long videos dataset (SAMM-LV) than a low frame-rate (30 fps) (CAS(ME)2) dataset. We introduce a new unseen dataset for MEGC2022 challenge (MEGC2022-testSet) and achieves F1-Score of 0.1531 as baseline result.