Reviewerly/exHarmony huggingface dataset:
Adjusting Relative Translation Gains According to Space Size in Redirected Walking for Mixed Reality Mutual Space Generation

We propose the concept of relative translation gains, a novel Redirected Walking (RDW) method to create a mutual movable space between the Augmented Reality (AR) host's reference space and the Virtual Reality (VR) client's space. Previous RDW methods have focused on maximizing the movable space at the expense of aligning the coordinates between the AR and VR side, and could only be applied to collaborative scenarios involving sequential tasks. Our method solves these problems by adjusting the remote client's walking speed for each axis of a VR space to modify the movable area without coordinate distortion. We estimate the relative translation gain threshold, defined as the extent to which the walking speed can be altered without creating a perceived difference in distance. In order to reflect features of the reference space in generating the mutual space, we then examine how changing its size affects the threshold value. Our study showed that for remote clients connected to the larger reference space, relative translation gains can be increased to utilize a VR space bigger than their real space. Our method can be applied to create optimal mutual spaces for a wider variety of asymmetric Mixed Reality (MR) remote collaboration systems.
Breast cancer risk prediction in African women using Random Forest Classifier

One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate.In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique.A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model.We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features.Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.
Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users’ privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.
EAGLE: Large-Scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery

Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed significant progress in object detection in ground imagery, but it is still in its infancy in airborne imagery, mostly due to the scarcity of diverse and large-scale datasets. Despite being a useful tool for different applications, current airborne datasets only partially reflect the challenges of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale dataset for multi-class vehicle detection with object orientation information in aerial imagery. It features high-resolution aerial images composed of different real-world situations with a wide variety of camera sensor, resolution, flight altitude, weather, illumination, haze, shadow, time, city, country, occlusion, and camera angle. The annotation was done by airborne imagery experts with small-and large-vehicle classes. EAGLE contains 215,986 instances annotated with oriented bounding boxes defined by four points and orientation, making it by far the largest dataset to date in this task. It also supports researches on the haze and shadow removal as well as super-resolution and in-painting applications. We define three tasks: detection by (1) horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented bounding boxes. We carried out several experiments to evaluate several state-of-the-art methods in object detection on our dataset to form a baseline. Experiments show that the EAGLE dataset accurately reflects real-world situations and correspondingly challenging applications. The dataset will be made publicly available.
Semi-Supervised Speech Recognition Via Graph-Based Temporal Classification

Semi-supervised learning has demonstrated promising results in automatic speech recognition (ASR) by self-training using a seed ASR model with pseudo-labels generated for unlabeled data. The effectiveness of this approach largely relies on the pseudo-label accuracy, for which typically only the 1-best ASR hypothesis is used. However, alternative ASR hypotheses of an N-best list can provide more accurate labels for an unlabeled speech utterance and also reflect uncertainties of the seed ASR model. In this paper, we propose a generalized form of the connectionist temporal classification (CTC) objective that accepts a graph representation of the training labels. The newly proposed graph-based temporal classification (GTC) objective is applied for self-training with WFST-based supervision, which is generated from an N-best list of pseudo-labels. In this setup, GTC is used to learn not only a temporal alignment, similarly to CTC, but also a label alignment to obtain the optimal pseudo-label sequence from the weighted graph. Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores, considerably outperforming standard pseudo-labeling, with ASR results approaching an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Improving One-Shot NAS with Shrinking-and-Expanding Supernet

Training a supernet using a copy of shared weights has become a popular approach to speed up neural architecture search (NAS). However, it is difficult for supernet to accurately evaluate on a large-scale search space due to high weight coupling in weight-sharing setting. To address this, we present a shrinking-and-expanding supernet that decouples the shared parameters by reducing the degree of weight sharing, avoiding unstable and inaccurate performance estimation as in previous methods. Specifically, we propose a new shrinking strategy that progressively simplifies the original search space by discarding unpromising operators in a smart way. Based on this, we further present an expanding strategy by appropriately increasing parameters of the shrunk supernet. We provide comprehensive evidences showing that, in weight-sharing supernet, the proposed method SE-NAS brings more accurate and more stable performance estimation. Experimental results on ImageNet dataset indicate that SE-NAS achieves higher Top-1 accuracy than its counterparts under the same complexity constraint and search space. The ablation study is presented to further understand SE-NAS.
Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19

Previous studies on public opinion expression in communication, political science and discourse analysis are restricted to a censorship-/counter-censorship frame and focus their analysis on events with political agendas. This study explores netizens’ discursive practice by focusing the analysis on netizens’ language use in context per se, rather than from a censorship/counter-censorship viewpoint. It adopts a discursive pragmatic approach to examine a ‘mundane’ trending topic regarding a dispute between two public figures rather than ‘major’ events with acute social and political agendas. This study present evidence that Weibo users criticise public figures through indirect discursive strategies, including parody of name, constructed dialogues and rhetorical questions. It also highlights two prominent sentiments in Weibo public spheres during the COVID-19 pandemic – cyber nationalism and binary opposition between China and the rest of the world. The online backlash against Fang demonstrates how easily netizens can change their views towards a certain event.
End-To-End Speaker Diarization as Post-Processing

This paper investigates the utilization of an end-to-end diarization model as post-processing of conventional clustering-based diarization. Clustering-based diarization methods partition frames into clusters of the number of speakers; thus, they typically cannot handle overlapping speech because each frame is assigned to one speaker. On the other hand, some end-to-end diarization methods can handle overlapping speech by treating the problem as multi-label classification. Although some methods can treat a flexible number of speakers, they do not perform well when the number of speakers is large. To compensate for each other’s weakness, we propose to use a two-speaker end-to-end diarization method as post-processing of the results obtained by a clustering-based method. We iteratively select two speakers from the results and update the results of the two speakers to improve the overlapped region. Experimental results show that the proposed algorithm consistently improved the performance of the state-of-the-art methods across CALLHOME, AMI, and DIHARD II datasets.
Chasing Play on TikTok from Populations with Disabilities to Inspire Playful and Inclusive Technology Design

There is an open call for technology to be more playful [5, 79] and for tech design to be more inclusive of people with disabilities [80]. In the era of COVID19, it is often unsafe for the public in general and people with disabilities, in particular, to engage in in-person design exercises using traditional methods. This presents a missed opportunity as these populations are already sharing playful content rich with tacit design knowledge that can be used to inspire the design of playful everyday technology. We present our process of scraping play potentials [4] from TikTok from content creators with disabilities to generate design concepts that may inspire future technology design. We share 7 emerging themes from the scraped content, a catalog of design concepts that may inspire designers, and discuss the relevance of the emerging themes and possible implications for the design concepts.
A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.
A CRNN-based attention-seq2seq model with fusion feature for automatic Labanotation generation

Labanotation is a widely-used notation system for dance recording. Numerous methods for automatic Labanotation generation from motion capture data have been proposed to save time and human labor. However, dance performance variations, data noises, and difficulties in recognizing long continuous motion sequences limit the performance of existed methods. In this paper, we propose a CRNN-based attention-seq2seq model with fusion features for a robust and effective Labanotation generation.First, we fuse bone feature and Lie group feature to extract not only the information of bones between adjacent joints but also the relative geometry relationships between connected bones. Then, in the proposed seq2seq model, we employ the Convolutional Recurrent Neural Networks (CRNN) to learn the spatial–temporal representation of motion capture data and the attention mechanism to learn good alignments between input motion feature sequences and output symbol sequences. Extensive experiments on real-world datasets show that the proposed method obtains considerable recognition accuracy (90.65% on the LabanSeq16 dataset and 93.29% on the LabanSeq48 dataset), which outperforms state-of-the-art approaches on the task of automatic Labanotation generation.
CAFE: Knowledge graph completion using neighborhood-aware features

Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations. Because KGs are often constructed automatically by means of information extraction processes, they may miss information that was either not present in the original source or not successfully extracted. As a result, KGs might lack useful and valuable information. Current approaches that aim to complete missing information in KGs have two main drawbacks. First, some have a dependence on embedded representations, which impose a very expensive preprocessing step and need to be recomputed again as the KG grows. Second, others are based on long random paths that may not cover all relevant information, whereas exhaustively analyzing all possible paths between entities is very time-consuming. In this paper, we present an approach to complete KGs based on evaluating candidate triples using a set of neighborhood-based features. Our approach exploits the highly connected nature of KGs by analyzing the entities and relations surrounding any given pair of entities, while avoiding full recomputations as new entities are added. Our results indicate that our proposal is able to identify correct triples with a higher effectiveness than other state-of-the-art approaches, achieving higher average F1 scores in all tested datasets. Therefore, we conclude that the information present in the vicinities of the two entities within a candidate triple can be leveraged to determine whether that triple is missing from the KG or not.
PriParkRec: Privacy-Preserving Decentralized Parking Recommendation Service

Insufficient parking space and traffic congestion are important usual suspects in our urban life. Organisation and managing available parking spaces have raised a lot of awareness. Traditional centralized parking approaches are with an insidious single point of failure and unsuitable for large organization because the manager is overburdened with authority and responsibility. The malicious service provider enables to (i) track the precise mobility patterns of citizens. (ii) collect the data to infer privacy-sensitive information, e.g., where the parking requester (i.e., driver) socialize, work, and live; (iii) analyze trajectories to monitor the location of the driver for entertainment (e.g., discovering one-night stands). To design a decentralized smart and practical parking platform without compromising the privacy of the entities is an urgent issue for the smart city. Blockchain (or distributed public ledger technology) is a near-ideal decentralized and distributed solution for centralized parking-space-recommendation systems. However, most of the existing parking recommendation solutions on top of blockchain ignore to protect the privacy of drivers during parking spot detection and matching phases while cannot maintain the property of anonymity of the drivers. In this paper, an efficient and privacy-preserving parking-space recommendation service platform, named as PriParkRec, along with the proof-of-concept solution to protect the requester's privacy in PriParkRec while maintaining most of the benefits of current parking-space sharing service, i.e., accountability, (anonymous) authentication, (anonymous) payment, and reputation ratings. Indeed, PriParkRec service relies on well-known privacy-enhancing cryptographic building blocks (e.g., oblivious PRF, private set intersection, anonymous credentials, anonymous payment, and smart-contract, etc), these building blocks are carefully integrated together to achieve privacy and security goals to revolutionize the existing car-parking service system coming with the private blockchain and industrial internet of things (IIoTs) technologies.
Approaches towards Blockchain Innovation: A Survey and Future Directions

A blockchain is a decentralized peer to peer platform which provides security services based on some key concepts, namely authentication, confidentiality, integrity and authorization. It is the process of recording and keeping track of the resources without the intervention of a centralized authority. This paper provides an overview of blockchains, the structure of blockchains, consensus algorithms, etc., It compares the algorithms based on their utility and limitations. Though blockchains provide secure communication, there are some minimal data leaks which are discussed. Various security issues in blockchains are discussed such as denial of service attacks, etc., In addition to security, some other blockchain challenges are presented like scalability, reliability, interoperability, privacy and consensus mechanisms for integration with AI, IoT and edge computing. This paper also explains about the importance of blockchains in the fields of smart healthcare, smart grid, and smart financial systems. Overall, this paper gives the glimpse of various protocols, algorithms, applications, challenges and opportunities that are found in the blockchain domain.
Standardizing Reporting of Participant Compensation in HCI: A Systematic Literature Review and Recommendations for the Field

The user study is a fundamental method used in HCI. In designing user studies, we often use compensation strategies to incentivize recruitment. However, compensation can also lead to ethical issues, such as coercion. The CHI community has yet to establish best practices for participant compensation. Through a systematic review of manuscripts at CHI and other associated publication venues, we found high levels of variation in the compensation strategies used within the community and how we report on this aspect of the study methods. A qualitative analysis of justifications offered for compensation sheds light into how some researchers are currently contextualizing this practice. This paper provides a description of current compensation strategies and information that can inform the design of compensation strategies in future studies. The findings may be helpful to generate productive discourse in the HCI community towards the development of best practices for participant compensation in user studies.
Bagging-based positive-unlabeled learning algorithm with Bayesian hyperparameter optimization for three-dimensional mineral potential mapping

Mineralization is a rare event. Hence, the geosciences datasets used for three-dimensional (3D) mineral potential mapping (MPM) are often imbalanced, consisting of scarce positive samples and abundant unlabeled data. Compared with selecting positive samples, it is challenging to select reliable negative samples in 3D MPM. However, the application of supervised machine learning algorithms in 3D MPM requires balanced positive and negative samples. Consequently, semi-supervised machine learning algorithms, which are only trained on positive samples, are widely used in 3D mineral prospecting. In this study, the bagging-based positive-unlabeled learning (PUL) algorithm that utilizes positive samples and unlabeled data in the training process was developed and applied to produce a 3D gold (Au) potential map in the Wulong Au district, China. This study employed Bayesian hyperparameter optimization to tune the hyperparameters of the bagging-based PUL algorithm. The performance of the bagging-based PUL algorithm was further compared with that of the widely used random forest, weights-of-evidence, one-class support vector machine, and continuous weighting approach in 3D MPM. The results demonstrated that the bagging-based PUL algorithm outperformed the aforementioned widely used predictive methods. The 3D mineral targets obtained by the bagging-based PUL algorithm can be beneficial for subsurface Au exploration in the Wulong Au district of China. • Using the bagging-based positive-unlabeled learning algorithm for addressing the imbalanced issue for 3D MPM. • Bayesian optimization algorithm is employed to improve the automaticity of our algorithm. • A case study from the Wulong Au district of China is presented.