Reviewerly/exHarmony huggingface dataset:
Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification

Classification is among the core tasks in machine learning. Existing classification algorithms are typically based on the assumption of at least roughly balanced data classes. When performing tasks involving imbalanced data, such classifiers ignore the minority data in consideration of the overall accuracy. The performance of traditional classification algorithms based on the assumption of balanced data distribution is insufficient because the minority-class samples are often more important than others, such as positive samples, in disease diagnosis. In this study, we propose a cost-sensitive variational autoencoding classifier that combines data-level and algorithm-level methods to solve the problem of imbalanced data classification. Cost-sensitive factors are introduced to assign a high cost to the misclassification of minority data, which biases the classifier toward minority data. We also designed misclassification costs closely related to tasks by embedding domain knowledge. Experimental results show that the proposed method performed the classification of bulk amorphous materials well.
Bringing Real Body as Self-Avatar into Mixed Reality: A Gamified Volcano Experience

In this work we showcase a Mixed Reality experience able to bring users&#x0027; own body as a self-avatar. This is mainly based on an al-gorithm that segment in real time the egocentric full body placing a regular stereo camera in front of a commercial Virtual Reality device. The work behind this paper is divided into: i) Data Acquisition; ii) Algorithm training and iii) Integration into a Mixed Reality environment. To the best of our knowledge, this is the first work that integrates real body as self-avatar both meeting real-time and segmentation quality in real-life conditions beyond benchmarking, We conclude the paper with some preliminary subjective evaluation measuring Presence and Embodiment Factors, that suggests the potential of using your own body as self-avatar in Mixed Reality.
Redirecting Desktop Interface Input to Animate Cross-Reality Avatars

We present and evaluate methods to redirect desktop inputs such as eye gaze and mouse pointing to a VR-embedded avatar. We use these methods to build a novel interface that allows a desktop user to give presentations in remote VR meetings such as conferences or classrooms. Recent work on such VR meetings suggests a substantial number of users continue to use desktop interfaces due to ergonomic or technical factors. Our approach enables desk-top and immersed users to better share virtual worlds, by allowing desktop-based users to have more engaging or present "cross-reality" avatars. The described redirection methods consider mouse pointing and drawing for a presentation, eye-tracked gaze towards audience members, hand tracking for gesturing, and associated avatar motions such as head and torso movement. A study compared different levels of desktop avatar control and headset-based control. Study results suggest that users consider the enhanced desktop avatar to be human-like and lively and draw more attention than a conventionally animated desktop avatar, implying that our interface and methods could be useful for future cross-reality remote learning tools.
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation

LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms. Previous point-based or sparse voxel-based methods are far away from real-time applications since time-consuming neighbor searching or sparse 3D convolution are employed. Recent 2D projection-based methods, including range view and multi-view fusion, can run in real time, but suffer from lower accuracy due to information loss during the 2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> projection. Besides, to improve the performance, previous methods usually adopt test time augmentation (TTA), which further slows down the inference process. To achieve a better speed-accuracy trade-off, we propose Cascade Point-Grid Fusion Network (CPGNet), which ensures both effectiveness and efficiency mainly by the following two techniques: 1) the novel Point-Grid (PG) fusion block extracts semantic features mainly on the 2D projected grid for efficiency, while summarizes both 2D and 3D features on 3D point for minimal information loss; 2) the proposed transformation consistency loss narrows the gap between the single-time model inference and TTA. The experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the CPGNet without ensemble models or TTA is comparable with the state-of-the-art RPVNet, while it runs 4.7 times faster.
Software Development Methodologies: Trending from Traditional to DOSE - An Empirical Study

Quality along with on time, within budget and rapid delivery of software is always a major concern of every organization. Several Software Development Methodologies exist for the successful delivery of software. These methodologies are categorized into Traditional and Agile Methods that further include Waterfall, iterative, spiral, evolutionary, eXtreme, Scrum, Kanban, RAD and many more models under them. Existence of many development models raises the need for appropriate selection of models or methodology to deliver successful software. The underlying review considers Waterfall, Iterative, RAD, Scrum models followed by DevOps culture for software development. Their tabular comparative assessment and graphical review based on recent Google and Stack Overflow Trends is performed that accelerates the development trend towards DevOps based Software Engineering (DOSE). DevOps employs alternative automation tools at different stages of software development to achieve the target of success along with quality software delivery. Though traditional models follow old strategies of software development but based on renowned researchers survey work and the popularity of these models in software industries makes them a part of this quality survey and assessment. By virtue of this quality survey, researchers/students and software developers will find it easy to understand the modus operandi of development methodologies. It will also help these target audiences to decide at ease about appropriate and efficient model selection for software development. Other models under these development methods or DevOps can also be considered and compared as a part of future/further research.
Using Direct Volume Rendering for Augmented Reality in Resource-constrained Platforms

Rendering a large volume is a challenging task on mobile and Augmented Reality (AR) devices due to lack of memory space and device limitations. Therefore, we implemented an Empty Space Skipping (ESS) optimization algorithm to render the high-quality large models on HoloLens. We designed and developed a system to visualize the computerized tomography (CT) scan data and Digital Imaging and Communications in Medicine (DICOM) files on Microsoft HoloLens 2. We used the Unity3D game engine to develop the system. As a result, we achieved about 10 times more frames per second (fps) on a high-quality model than the non-optimized version.
Atomicity and Regularity Principles Do Not Ensure Full Resistance of ECC Designs against Single-Trace Attacks

Elliptic curve cryptography (ECC) is one of the commonly used standard methods for encrypting and signing messages which is especially applicable to resource-constrained devices such as sensor nodes that are networked in the Internet of Things. The same holds true for wearable sensors. In these fields of application, confidentiality and data integrity are of utmost importance as human lives depend on them. In this paper, we discuss the resistance of our fast dual-field ECDSA accelerator against side-channel analysis attacks. We present our implementation of a design supporting four different NIST elliptic curves to allow the reader to understand the discussion of the resistance aspects. For two different target platforms-ASIC and FPGA-we show that the application of atomic patterns, which is considered to ensure resistance against simple side-channel analysis attacks in the literature, is not sufficient to prevent either simple SCA or horizontal address-bit DPA attacks. We also evaluated an approach which is based on the activity of the field multiplier to increase the inherent resistance of the design against attacks performed.
Recommendation System Algorithms on Location-Based Social Networks: Comparative Study

Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has gained considerable attention in research using techniques and methods based on users’ geosocial activities. In this study, we present a comparative analysis of three matrix factorization (MF) algorithms, namely, singular value decomposition (SVD), singular value decomposition plus (SVD++), and nonnegative matrix factorization (NMF). The primary task of the implemented recommender system was to predict restaurant ratings for each user and make a recommendation based on this prediction. This experiment used two performance metrics for evaluation, namely, root mean square error (RMSE) and mean absolute error (MAE). The RMSEs confirmed the efficacy of SVD with a lower error rate, whereas SVD++ had a lower error rate in terms of MAE.
Drivers’ Visual Distraction Detection Using Facial Landmarks and Head Pose

Drivers’ distraction has been widely studied in the field of naturalistic driving studies. However, it is difficult to use traditional variables, such as speed, acceleration, and yaw rate to detect drivers’ distraction in real time. Emerging technologies have obtained features from human faces, such as eye gaze, to detect drivers’ visual distraction. However, eye gaze is hard to detect in naturalistic driving situations, because of low-resolution cameras, drivers wearing sunglasses, and so forth. Instead, head pose is easier to detect, and has correlation with eye gaze direction. In this study, city-wide videos are collected using onboard cameras from over 289 drivers representing 423 events. Head pose (pitch, yaw, and roll rates) are derived and fed into a convolutional neural network to detect drivers’ distraction. The experiment results show that the proposed model can achieve recall value of 0.938 and area under the receiver operating characteristic curve value of 0.931, with variables from five time slices (1.25 s) used as input. The study proves that head pose can be used to detect drivers’ distraction. The study offers insights for detecting drivers’ distraction and can be used for the development of advanced driver assistance systems.
Radar pulse repetition interval modulation recognition with combined net and domain-adaptive few-shot learning

Automatically identifying pulse repetition interval (PRI) patterns is an important task for electronic support measure (ESM) systems in a complex electromagnetic environment. Existing learning-based approaches need handcrafted features to increase recognition accuracy and are unable to deal with the potential mismatch between the training set and testing samples. In this paper, we design a multiscale convolution block with a vectorized embedding and squeeze-and-excitation mechanism for feature extraction. Accordingly, we also consider the problem of domain shift, proposing a nonlinear, metric-based relation module to make the network quickly adapt to the new environment with an extremely low number of target training samples. Experiments on the PRI simulation datasets not only show the superiority of our network architecture but also prove the effectiveness of the relation module and our training method on domain generalization.
Text Mining and Pre-Processing Methods for Social Media Data Extraction and Processing

A huge amount of unstructured data is generated from social media platforms like Twitter. Volume of tweets and the velocity with which they are generated on various topics presents extensive challenges in data analytics and processing techniques. Linguistic flexibility for writing tweets presents many challenges in preprocessing and natural language processing tasks. Addressing these challenges, this chapter aims to select, modify, and apply information retrieval and preprocessing steps for retrieving, storing, organizing, and cleaning real-time large-scale unstructured Twitter data. The work focuses on reviewing the previous research and applying suitable preprocessing methods to improve the quality of data by removing unessential data. It is also observed that using tweeter APIs and access tokens provides easy access to real-time tweets. Preprocessing methods are fundamental steps of text analytics and NLP tasks to process unstructured data. Analyzing suitable preprocessing methods like tokenization, removal of stop word, stemming, and lemmatization are applied to normalize the extracted Twitter data.
LevelEd SR: A Substitutional Reality Level Design Workflow

Virtual reality (VR) and augmented reality (AR) have continued to increase in popularity over the past decade. However, there are still issues with how much space is required for room-scale VR and experiences are still lacking from haptic feedback. We present LevelEd SR, a substitutional reality level design workflow that combines AR and VR systems and is built for consumer devices. The system enables passive haptics through the inclusion of physical objects from within a space into a virtual world. A validation study (17 participants) has produced quantitative data that suggests players benefit from passive haptics in entertainment VR games with an improved game experience and increased levels of presence. Including objects, such as real-world furniture that is paired with a digital proxy in the virtual world, also opens up more spaces to be used for room-scale VR. We evaluated the workflow and found that participants were accepting of the system, rating it positively using the System Usability Scale questionnaire and would want to use it again to experience substitutional reality.
Adapting Fleming-Type Learning Style Classifications to Deaf Student Behavior

This study presents the development of a novel integrated data fusion and assimilation technique to classify learning experiences and patterns among deaf students using Fleming’s model together with Thai Sign Language. Data were collected from students with hearing disabilities (Grades 7–9) studying at special schools in Khon Kaen and Udon Thani, Thailand. This research used six classification algorithms with data being resynthesized and improved via the application of feature selection, and the imbalanced data corrected using the synthetic minority oversampling technique. The collection of data from deaf students was evaluated using a 10-fold validation. This revealed that the multi-layer perceptron algorithm yields the highest accuracy. These research results are intended for application in further studies involving imbalanced data problems.
Evaluation multi label feature selection for text classification using weighted borda count approach

Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and effective preprocess to enhance the learning process. Choosing a Multi-Label Feature Selection (MLFS) algorithm is the most basic, critical, and sensitive choice in ML classification operations. If this choice is based on a criterion, it cannot be attributed to always being sound. Choosing the best algorithm must be evaluated using several different criteria to be examined from different aspects. In this article, we turn the issue into an election and use the Weighted Borda Count method for voting. We do the voting in three stages continuously so that a subset of different features does the voting. In the second stage, voting of different methods is done with six criteria, and each criterion selects the methods in order of priority from the beginning to the end. Voting steps 1 and 2 are performed on eighteen text datasets used. Finally, in the final voting stage, the methods are evaluated and voted on by different text datasets. The final result of the voting in the third stage shows the desired MLFS methods based on their performance from beginning to end. According to the experiments performed and the results obtained, it can be seen that the selection of the algorithm based on several different criteria and considering the overall performance of the algorithm will be better than the selection based on one criterion.
Replication of design theories: Reflections on function, outcome, and impact

Abstract The replication of existing research studies and theories is considered a foundational pillar of knowledge accumulation and an important instrument of discourse across research disciplines. Although replication has a long tradition in natural and behavioural science research, the design science research (DSR) community is yet to adopt it, especially the replication of design theories. However, it is unclear how the DSR community could benefit from the replication of design theories. Similarly, the goal of design theories is to obtain utility instead of truth raises questions regarding the transferability of replication into the DSR domain. Against this background, this study reflects on the function, outcome, and impact of replications, to understand whether the replication of design theories is possible and necessary. The study proposes that replication can be an important catalyst for reuse and knowledge accumulation in DSR because it provides evidence on the boundaries of design theory. Specifically, replication can increase or decrease the level of confidence and projectability associated with design theory.
Skeleton-Based Human Motion Prediction With Privileged Supervision

Existing supervised methods have achieved impressive performance in forecasting skeleton-based human motion. However, they often rely on action class labels in both training and inference phases. In practice, it could be a burden to request action class labels in the inference phase, and even for the training phase, the collected labels could be incomplete for sequences with a mixture of multiple actions. In this article, we take action class labels as a kind of privileged supervision that only exists in the training phase. We design a new architecture that includes a motion classification as an auxiliary task with motion prediction. To deal with potential missing labels of motion sequence, we propose a new classification loss function to exploit their relationships with those observed labels and a perceptual loss to measure the difference between ground truth sequence and generated sequence in the classification task. Experimental results on the most challenging Human 3.6M dataset and the Carnegie Mellon University (CMU) dataset demonstrate the effectiveness of the proposed algorithm to exploit action class labels for improved modeling of human dynamics.
Body Warping Versus Change Blindness Remapping: A Comparison of Two Approaches to Repurposing Haptic Proxies for Virtual Reality

Haptic retargeting using body warpingThe user's virtual and physical hands are co-located.The movement of the physical and virtual hands are offset.The misaligned physical and virtual objects are grasped simultaneously. Change blindness remappingThe virtual object and physical proxy are not co-located.When the user looks away the virtual object is aligned with the physical proxy.Co-location is achieved and the user can interact.Figure 1: Illustration of haptic retargeting based on body warping (left) and change blindness remapping (right) adapted from [33].
Sentiment Analysis of Multilingual Texts Using Machine Learning Methods

Sentiment analysis is a topical task of evaluating the content of texts, articles and statements in order to study public opinion and the relationship between the moods of users and their phrases. At the same time, the analysis of multilingual texts is more difficult. Therefore, in this work a study was carried out with datasets in different languages, including the use of automatic translation. Various machine learning methods, several neural network architectures, and the VADER analyzer were applied. Also, NER was combined with other techniques in the work to determine the sentiment of individual entities. The authors evaluated the methods on various datasets using the F-measure. The obtained metric values show the best results for a neural network.
EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs.
Design of Multipliers using Reversible Logic and Toffoli Gates

The power dissipation in the electronic products needs to be lowered to conserve the battery life and reliable operations. To reduce power dissipation in various levels such as algorithmic level, architectural level and circuit level, the researchers have been concentrating. To stay away from energy dispersal in a circuit, it is planned utilizing reversible processing. Reversible figuring is an interaction where the info data can be created back from its yield data. The early explores have been focused on the actual reversibility, the main kind of reversibility. Actual reversibility is an interaction which should result in no expansion in actual entropy. To accomplish this, an actual machine is required which burns-through zero energy while registering. To fulfil this imperative, the actual machine ought to be non-dissipative and ought to preserve the actual entropy. Consequently the early explores presumed that no actual gadgets can be reversible and theoretical rationale tasks ought to be reversible. Thus it specifies second sort of reversibility, sensible reversibility, in which the data entropy should be moderated. The design is synthesized using reversible gates which are optimized for minimum number of Toffoli gates. The proposed designs are compared with the other designs based on the number of Toffoli gates. Based on the comparison, it can be concluded that the design uses a maximum of 72&#x0025;less Toffoli gates and a minimum of 1&#x0025; less Toffoli gates than the designs available in the literature.