Reviewerly/exHarmony huggingface dataset:
co-STM text categorization method based on Supervised Topic Model

Traditional text classification methods are only trained on homogeneous data with uniform sample distribution and complete category labeling. With the rapid development of computer-related technology, today's data has the characteristics of diverse carrier forms, lack of label data, and unbalanced categories. As a result, traditional text classification technologies can no longer meet the requirements of today's text classification. Aiming at the new characteristics of data, this paper proposes a co-STM (collaborative text classification combined with Supervised Topic Model) collaborative text classification algorithm that integrates SLDA (Supervised LDA) supervised topic model. For category imbalance data, this paper adopts a confidence calculation method based on posterior probability distance and a category-based unlabeled sample selection strategy to select credible unlabeled samples. Comparative experiment results show that the co-STM text classification algorithm can effectively improve the performance of semi-supervised text classification.
CIMask: Segmenting instances by class-specific semantic feature extraction and instance-specific attribute discrimination

• We decompose instance segmentation into class-specific semantic extraction and instance-specific attribute discrimination, the proposed novel method is called CIMask. • To better handle ambiguous samples, a novel center-aware sampling mechanism is introduced for generating ground truth. Instance segmentation is one of the general but challenging tasks in computer vision. In this paper, we present a novel fast single-stage instance segmentation method, called CIMask, which is faster and more accurate than a few recent competitive approaches, including Mask R-CNN baselines. In our approach, the object mask generation is decoupled into class-specific semantic feature extraction and instance-specific attribute discrimination in parallel, responsible for generating powerful feature representation and identifying individuals, respectively. Then, the final masks are generated by distinguishing individuals with the same class-specific semantic. Furthermore, to better deal with the ambiguous samples in natural scenes, a center-aware sampling scheme for ground truth is introduced, which significantly improves the performance without adding extra parameters. Our comprehensive experiments on MS COCO dataset show the effectiveness of the proposed contributions, leading to state-of-the-art single-stage instance segmentation performance. The center-aware sampling scheme obtains a 1% steady performance gain on the Cityscapes dataset.
Medical Blockchains and Privacy in Austria - Technical and Legal Aspects

The utilization of blockchains in the medical domain has been discussed for quite some time, with multiple academic projects targeting various application domains in this field. Still, many countries feature underlying laws and regulations that make this utilization hard to impossible, especially when considering the sensitive nature of medical records. In this work we analyze the specific situation in Austria and analyse the two major regulations that need to be taken into account, the EU-wide GDPR and the Austria-specific ELGA, with respect to blockchain applications in the medical sector in Austria. Furthermore, we outline several additional key issues that need to be taken into consideration, as well as the problem of the most prominent solution, linking to external storage from the blockchain.
Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection

Cross domain weakly supervised object detection (CDWSOD), where we can get access to instance-level annotations in the source domain while only image-level annotations are available in the target domain, adapts object detectors from label-rich to label-poor domains. It usually generates pseudo labels in the target domain and utilize them to finetune the detector pretrained in the source domain. In this paper, we propose a new pseudo-label generation-evaluation framework for CDWSOD task. In particular, an evaluator is introduced for the generated pseudo labels in the target domain and the transferring process involves two players: the detector to generate instance-level pseudo labels and the evaluator to judge the quality of pseudo labels. Only high-quality pseudo labels selected by the evaluator are utilized to finetune the detector. Experiments on three representative datasets demonstrate the effectiveness of our framework in various domains.
Features of Financing Development in Sub-Saharan African Countries

The aim of this paper is to investigate the effects of access to finance and its related activities on innovative growth and development in Sub-Saharan Africa. It was found that the growth effect of finance is sensitive to the choice of proxy. Contrary to studies by Levine (2005), Akinlo and Olufisayo (2009), and other, negative correlations, although surprising, were found between economic growth and all the following control variables: domestic credits to private sector; domestic credit and credits provided other than the private sector; central government credits; bank credit to private sector; and inflation, while broad money stock to GDP ratio as well as FDI are growth-inducing. That explains that a high growth in Sub-Saharan Africa will lead a decrease in the above variables, which seems unrealistic. As it is expected, especially that the credit to the private sector as ratios to GDP and total domestic credit should be conducive for economic growth and development through their innovative activities. The proxies created from major component analysis proved the sensitivity of the effect to the choice of proxy. Thus, these findings suggest that the good or bad effects of access to finance on growth largely depend on the indicator used to proxy for the role played by finance in the economy.
Covid-19 and Cryptocurrency Markets Integration

This paper examines the impact of COVID-19COVID-19 on the integration and dynamic linkages of the cryptocurrenciesCryptocurrencies (Bitcoin, Ethereum, Litecoin, XRP and Stellar). ARDL bound test approach and Granger causality tests are used in this study for the period from 17 April 2019 to 15 September 2020. We found evidence of no cointegration among the cryptocurrencies in both pre- and during COVID-19. Thus, the cryptocurrencies market offers an ample opportunity for the potential benefits from portfolio diversificationPortfolio diversification and hedgingHedging strategies even during the COVID-19 pandemic. In addition, Granger causalityGranger causality tests show that Bitcoin is the most influential cryptocurrency in the short-run. The findings of this study may have implications for crypto-investors, international investors and fund managers who want to diversify their investments in cryptocurrencies.
Effects and Mitigation of Out-of-vocabulary in Universal Language Models

One of the most important recent natural language processing (NLP) trends is transfer learning - using representations from language models implemented through a neural network to perform other tasks. While transfer learning is a promising and robust method, downstream task performance in transfer learning depends on the robustness of the backbone model's vocabulary, which in turn represents both the positive and negative characteristics of the corpus used to train it. With subword tokenization, out-of-vocabulary (OOV) is generally assumed to be a solved problem. Still, in languages with a large alphabet such as Chinese, Japanese, and Korean (CJK), this assumption does not hold. In our work, we demonstrate the adverse effects of OOV in the context of transfer learning in CJK languages, then propose a novel approach to maximize the utility of a pre-trained model suffering from OOV. Additionally, we further investigate the correlation of OOV to task performance and explore if and how mitigation can salvage a model with high OOV.
Hard Samples Rectification for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being vulnerable to the hard positive and negative samples in the target unlabelled dataset. Our HSR contains two parts, an inter-camera mining method that helps recognize a person under different views (hard positive) and a part-based homogeneity technique that makes the model discriminate different persons but with similar appearance (hard negative). By rectifying those two hard cases, the re-ID model can learn effectively and achieve promising results on two large-scale benchmarks.
Assessing Gender Bias in Predictive Algorithms using eXplainable AI

Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial).
Multi Group Marine Predator Algorithm

The Marine Predator algorithm is a nature-inspired optimization algorithm with a simple search mechanism and suffers from a lack of population diversity reduction in the late iteration. In order to enhance population diversity and avoid falling into local optima, we propose a multi-group marine predator algorithm. (MGMPA). In MGMPA, the population is divided into exploitation, equilibrium and exploration populations, and the number of each subpopulation is dynamically controlled. The useful information of populations and individuals is fully utilized through a weighted maximum likelihood estimation strategy. The effectiveness of MGMPA was verified by using several test functions of CEC2104.
Research on Rural Landscape Planning and Design based on BIM

Abstract Garden landscape is the soft decoration of living environment. By creating a comfortable and pleasant environment, landscape design can not only improve the microclimate, but also improve people’s spiritual life. With the rapid development of rural areas, rural landscape planning and design has become an important direction of rural sustainable development in the future, which has also become a new starting point of building a beautiful China. However, how to plan and construct the relevant landscape design has become an important research topic. With the increasing status of BIM in the construction industry, BIM has been applied to landscape planning. This paper first analyzes the difference between BIM and traditional landscape design software. Then, this paper puts forward a detailed rural landscape planning and design scheme, which can better improve the living standards of rural people.
LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions whilst Leveraging Human Perceptual Judgements

Building facial analysis systems that generalize to extreme variations in lighting and facial expressions is a challenging problem that can potentially be alleviated using natural-looking synthetic data. Towards that, we propose LEGAN, a novel synthesis framework that leverages perceptual quality judgments for jointly manipulating lighting and expressions in face images, without requiring paired training data. LEGAN disentangles the lighting and expression subspaces and performs transformations in the feature space before upscaling to the desired output image. The fidelity of the synthetic image is further refined by integrating a perceptual quality estimation model, trained with face images rendered using multiple synthesis methods and their crowd-sourced naturalness ratings, into the LEGAN framework as an auxiliary discriminator. Using objective metrics like FID and LPIPS, LEGAN is shown to generate higher quality face images when compared with popular GAN models like StarGAN and StarGAN-v2 for lighting and expression synthesis. We also conduct a perceptual study using images synthesized by LEGAN and other GAN models and show the correlation between our quality estimation and visual fidelity. Finally, we demonstrate the effectiveness of LEGAN as training data augmenter for expression recognition and face verification tasks.
Differentially Oblivious Data Analysis With Intel SGX: Design, Optimization, and Evaluation

A privacy-preserving data analytics system enables a cloud user to perform the distributed job in a secure manner such that the data privacy can be guaranteed during the cloud-outsourced computation. However, many SGX-based solutions are vulnerable to some side-channel attacks, including the access pattern leakage from both network and memory. Several data-oblivious algorithms with full obliviousness have been proposed in the literature, but they are impractical to be used in the cloud due to the expensive computational overhead. In this article, we propose a DPSpark system with the security defined in a notion of <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -differentially private obliviousness ( <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math></inline-formula> -DPO), which relaxes full obliviousness to enable an efficiency improvement. Based on this definition, we present a perturbation-shuffle-analysis (PSA) computing architecture and design several typical differentially oblivious operators. In further, we optimize the system efficiency by reducing the number of oblivious shuffles and choosing an appropriate privacy budget. Finally, we benchmark the system in different parameters. The experimental results show that DPSpark significantly outperforms two state-of-the-art solutions, only with 10.1-85.4 percent additional overhead performing an SGX-based data analysis application.
Interpretation of Lesional Detection via Counterfactual Generation

To interpret the decision of Deep Neural Networks (DNNs), explainable artificial intelligence research has been widely investigated. Especially, visualizing the attribution maps is known as one of the efficient ways to provide explanations for the trained networks. Applying existing visualization methods on medical images has significant issues in that the medical images commonly have inherent imbalanced data poses and scarcity. To tackle such issues and provide more accurate explanations in medical images, in this paper, we propose a new explainable framework, Counterfactual Generative Network (CGN). We embed counterfactual lesion prediction of DNNs to our explainable framework as prior conditions and guide to generate various counterfactual lesional images from normal input sources, or vice versa. By doing so, CGN can represent detailed attribution maps and generate corresponding normal images from leisonal inputs. Extensive experiments are conducted on the two chest X-ray datasets to verify the effectiveness of our method.
Understanding Users Through Three Types of Personas

Personas is a suggested method to extend IT professionals' understanding of users and users’ needs. A common advantage expressed is that personas extend the IT professionals’ empathy for the users, but a disadvantage is that personas are typically defined at the start of a software project and gradually are forgotten, since there is little reference to the personas through the software development project. In this paper we report experiences of coaching IT professionals in defining agile user stories based on personas, called: Persona User Stories (PUS). The aim of these workshops, was to extend the usage of personas and thereby extend the IT professionals’ understanding of their users. In a research project with three companies, we coached teams of IT professionals in three-hour workshops with 76 participants in total. The workshops were conducted at each company using personas already defined by the IT professionals. The persona descriptions were based on three types of information: (a) assumptions, (b) secondary research, and (c) data specific to a project. Our findings show that personas based on assumptions result in the participants questioning the description of the personas and having difficulties in understanding the personas. For making the persona user stories (PUS), the participants used themselves more often as a reference when working with the assumption based personas, than the participants using the other two types of personas.
Co-watching 360-Films in Nursing Homes

This work investigates experiences and practical aspects of co-located and co-watched 360-videos in head mounted displays by groups of older people at nursing homes. In a study involving 19 residents at two different nursing homes, co-watching screenings were arranged with 360-videos produced in the local area by filmmakers. Data was collected through non-participant observation and semi-structured interviews with the participants. Input from nurses and facilitators were also collected. We found this to be a much appreciated, feasible, and enjoyable immersive experience improving short-term well-being, expressed through (e.g.) new conversations, pride in participation, and spontaneous movements. However, the value of co-watching was mainly captured for residents who already knew each other, and we found limited indications of virtual co-presence. We further recognized the value of the videos themselves and the desire for new 360-video experiences. But also, a need for better headsets suitable for older people and shared use at nursing homes to avoid social isolation due to the introduction of VR technology.
A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines

Structure-from-Motion (SfM) using the frames of a video sequence can be a challenging task because there is a lot of redundant information, the computational time increases quadratically with the number of frames, there would be low-quality images (e.g., blurred frames) that can decrease the final quality of the reconstruction, etc. To overcome all these issues, we present a novel deep-learning architecture that is meant for speeding up SfM by selecting frames using predicted sub-sampling frequency. This architecture is general and can learn/distill the knowledge of any algorithm for selecting frames from a video for generating high-quality reconstructions. One key advantage is that we can run our architecture in real-time saving computations while keeping high-quality results.
Tether: A Study on Bubble-Networks

Tether is a stablecoin, namely a cryptocurrency associated with an underlying security. Tether provides one of the most relevant ways to buy bitcoins and has been the centre of many controversies. In fact, it has been hypothesized that new tethers are issued without the underlying reserves, and that new massive Tether emissions are the basis of strong speculative movements on the Bitcoin, with consequent bubble effects. In the course of this article, we conduct a Social Network Analysis focused on the Tether transaction graph to identify the main actors that play a leading role on the network and characterize the transaction flow between them. From our analysis, we conclude that 1) the Tether transaction network does not enjoy the Smallworld property, with the robustness and reliability it carries with it; 2) cryptopcurrency exchanges are the nodes with the greatest centrality; 3) even Assortativity is not found, as the subjects who move Tether on a large scale do not give continuity to their presence and operations, therefore do not get a chance to consolidate stable links between them; and 4) among the exchanges, Bitfinex, which has co-ownership and co-administration relationships with the Tether issuer, can be mostly associated with the Rich-gets-Richer property.
Weighted Average Precision: Adversarial Example Detection for Visual Perception Of Autonomous Vehicles

Recent works have shown that neural networks are vulnerable to carefully crafted adversarial examples (AE). By adding small perturbations to original images, AEs are able to deceive victim models, and result in incorrect outputs. Research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving applications. However, existing studies either use simplifying assumptions on the outputs of object detectors or ignore the tracking system in the perception pipeline. In this paper, we first propose a novel similarity distance metric for object detection outputs in autonomous driving applications. Then, we bridge the gap between the current AE detection research and the real-world autonomous systems by providing a temporal AE detection algorithm, which takes the impact of tracking system into consideration. We perform evaluations on Berkeley Deep Drive and CityScapes datasets, by using different white-box and black-box attacks, which show that our approach outperforms the mean-average-precision and mean intersection over-union based AE detection baselines by significantly increasing the detection accuracy.
Augmented Reality Application for Chemical Engineering Unit Operations

Understanding unit operations is an essential part in chemical engineering course. An important example is the continuous distillation column, which operation is often seen as a black box, where the incoming feed will undergo separation process inside the column to produce desired products. Despite having learned the concepts on how they work, students may find it difficult to comprehend and visualize what is going on inside a distillation column and how to connect various theories involved in the design and calculations. By developing a virtual visualization tool, such as augmented reality (AR), students can better visualize the process, such as fluid flow profiles and different components that make up a distillation column. Although the idea of incorporating AR for higher education learning is not entirely new, this is the first initiative to implement virtual technology for chemical engineering curriculum in Singapore, which serves as a novel pedagogical approach to complement the conventional pen-and-paper teaching method. Besides enhancing the students’ learning experience, it is believed that the AR application would provide a way to improve the students’ motivation and interest to learn the subject as well as a complementary tool for laboratory demonstration, as it is practically safe and time-saving.
Active Tethered Hook: Heavy Load Movement using Hooks that Move Actively with Micro UAVs and Winch System

In this paper, multiple micro UAVs (Unmanned Aerial Vehicles) are used to carry small hooks tethered to a heavy load, providing movement of the load within a three-dimensional workspace. We propose an active tethered hook. By moving and attaching the hook to an elevated location and winding the wire with a winch mechanism attached to a load holder, a heavy object can be moved that far exceeds the payload capability of a micro UAV. An RGB-D camera is mounted on the load holder to detect a suitable position to hook at the elevated position, measure the relative position between the micro UAV and the target and perform position control based on the feedback. We verified the hook attachment using this method and examined the hook selection method according to the conditions. Through experiments, it is confirmed that the detection of a rod-shaped object using the point cloud and the position measurement of the micro UAV using a marker were executed accurately. We also confirmed and demonstrated that it is possible to attach the hook to a target in autonomous flight.