Miracle-dz/UniFAR-FTUs huggingface dataset:
Tactile Design Principles

Vibrotactile feedback has found its way into human-machine interaction in recent years. However, there are no easily accessible and recognized rules for designing this feedback. For example, designers of graphical user interfaces can make use of visual design laws, to improve the design of human-machine interfaces. These laws identify a meta-level that allows to work with simple design principles rather than having to deal with the physiology or the psychological and cognitive stimulus processing of the visual sense. The goal of my future research is to explore and identify a meta-level for the design of tactile feedback in order to provide tactile feedback designers with a tool that they can work with easily and effectively. A pilot study has already been carried out to this end, which has yielded illuminating results. Based on these results, I would like to sharpen the focus of my research work in the coming months and then work on my doctoral thesis.
Paint with Your Mind: Designing EEG-based Interactive Installation for Traditional Chinese Artworks

Museum exhibitions on traditional Chinese paintings are gaining popularity for educational and cultural value. Chinese paintings are characterized by a long history and implicit emotional expression, and it is challenging for non-professional and non-Chinese visitors to understand. To enhance museum visitors’ interest and comprehension of Chinese artworks, we design an EEG-based interactive installation. The installation simulates the process of creation of a work of art, in this case a painting. Visitors can control the generation of lines, colors, and movements of characters by wearing a commercial EEG headset. Our interactive design contributes a novel experience of ’painting with your mind’ and at the same time transform the exhibition into an enjoyable game experience.
Boiling Mind: Amplifying the Audience-Performer Connection through Sonification and Visualization of Heart and Electrodermal Activities

In stage performances, an invisible wall in front of the stage often weakens the connections between the audience and performers. To amplify this performative connection, we present the concept ”Boiling Mind”. Our design concept is based on streaming sensor data related to heart and electrodermal activities from audience members and integrating this data into staging elements, such as visual projections, music, and lighting. Thus, the internal states of the audience directly influence the staging. Artists can have a more direct perception of the inner reactions of audience members and can create physical expressions in response to them. In this paper, we present the wearable sensing system as well as design considerations of mapping heart and electrodermal activity to changes in the staging elements. We evaluated our design and setup over three live performances.
Combined Associations of Serum Ferritin and Body Size Phenotypes With Cardiovascular Risk Profiles: A Chinese Population-Based Study

Background: Serum ferritin (SF) has been correlated with one or more metabolic syndrome features associated with an increased risk for cardiovascular disease (CVD). This study explored the associations between SF and CVD risk factors among different body size phenotypes that were based on metabolic status and body mass index (BMI) categories. Methods: A cross-sectional study was performed using a cohort of 7,549 Chinese adults from the China Health and Nutrition Survey. Participants did not exhibit acute inflammation, were not underweight and were stratified based on their metabolic status and BMI categories. The metabolically at-risk status was defined as having two or more criteria of the Adult Treatment Panel-III metabolic syndrome definition, excluding waist circumference. Results: Compared with individuals without high SF, subjects with high SF had an increased risk of diabetes in the metabolically at-risk normal-weight (MANW) and metabolically at-risk overweight/obesity (MAO) groups. The multivariate-adjusted odds ratios (ORs) were 1.52 [95% confidence interval (Cls): 1.02, 2.28] and 1.63 (95% Cls: 1.27, 2.09), respectively. Adjusted ORs for hyperuricemia from high SF in metabolically healthy normal-weight (MHNW), metabolically healthy overweight/obesity (MHO), MANW, and MAO phenotypes were 1.78 (95% Cls: 1.26, 2.53), 1.42 (95% Cls: 1.03, 1.95), 1.66 (95% Cls: 1.17, 2.36), and 1.42 (95% Cls: 1.17, 1.73), respectively. Similarly, positive correlations of high SF with triglycerides, non-high-density lipoprotein cholesterol, and apolipoprotein B100 were observed in all phenotypes. No association between high SF and elevated low-density lipoprotein cholesterol were observed among participants who were metabolically at-risk, regardless of their BMI categories. However, the ORs for elevated low-density lipoprotein cholesterol from high SF were 1.64 (95% Cls: 1.29, 2.08) in the MHNW group and 1.52 (95% Cls:1.22, 1.91) in the MHO group, significantly. This study demonstrated that the highest ORs were in MAO with a high SF group for all unfavorable CVD risk factors except low-density lipoprotein cholesterol (all p < 0.001). Conclusions: The associations of high SF with the prevalence of CVD risk factors, including diabetes, dyslipidemia, and hyperuricemia, vary in individuals among different body size phenotypes. In the MAO group, subjects with high SF levels exhibited worse CVD risk profiles than individuals without high SF.
Fault Instability and Its Relation to Static Coulomb Failure Stress Change in the 2016 Mw 6.5 Pidie Jaya Earthquake, Aceh, Indonesia

Herein, we applied the fault instability criterion and integrated it with the static Coulomb stress change (ΔCFS) to infer the mechanism of the 2016 Mw 6.5 Pidie Jaya earthquake and its aftershock distribution. Several possible causative faults have been proposed; however, the existence of a nearby occurrence, the 1967 mb 6.1 event, created obscurity. Hence, we applied the fault instability analysis to the Pidie Jaya earthquake 1) to corroborate the Pidie Jaya causative fault analysis and 2) to analyze the correlation between ΔCFS distribution imparted by the mainshock and the fault instability of the reactivated fault planes derived from the focal solution of the Pidie Jaya aftershocks. We performed the fault instability analysis for two possible source faults: the Samalanga-Sipopok Fault and the newly inferred Panteraja Fault. Although the maximum instability value of the Samalanga-Sipopok Fault is higher, the dip value of the Panteraja Fault coincides with its optimum instability. Therefore, we concluded that Panteraja was the causative fault plane. Furthermore, a link between the 1967 mb 6.1 event and the 2016 Mw 6.5 earthquake is discussed. To analyze the correlation between the fault instability and the ΔCFS, we resolved the ΔCFS of the Pidie Jaya mainshock on its aftershock planes and compared the ΔCFS results with the fault instability calculation on each aftershock plane. We discussed the possibility of conjugate failure as shown by the aftershock fault instability. Related to the ΔCFS and fault instability comparison, we found that not all the aftershocks have positive ΔCFSs, but their instability value is high. Thus, we suggest that the fault plane instability plays a role in events that do not occur in positive ΔCFS areas. Apart from these, we also showed that the off-Great Sumatran Fault (Panteraja and Samalanga-Sipopok Faults) are unstable in the Sumatra regional stress setting, thereby making it more susceptible to slip movement.
A Learning-Based Spectrum Access Stackelberg Game: Friendly Jammer-Assisted Communication Confrontation

Defensive and offensive capabilities are both significant in communication confrontation games. By exploiting the above two capabilities, a new confrontation mechanism in the spectrum domain between two opposing teams denoted as the blue team (BT) and red team (RT), is designed. The basic idea is that by sacrificing parts of ally performance to severely deteriorate the opponent side communications. Specifically, a friendly and smart jammer (assuming in the BT) is deployed to weaken opponent (i.e., members in the RT) communications without causing great damages to other BT members, while the smart RT members try to evade the jamming and alleviate mutual interference. The interactions among the friendly jammer and other nodes are modeled as a Stackelberg game, with each player seeking for their respective utility maximization. We prove that each sub-game is an exact potential game. To efficiently search for the equilibrium solutions, a parallel log-linear learning algorithm is proposed, based on which each user intelligently decides their spectrum access policies. Numerical results demonstrate that: 1) RT communications are effectively suppressed; meanwhile, mutual interference among ally BT communication pairs is significantly alleviated; 2) the proposed algorithm achieves a close-to-optimal solution; 3) compared with the current state of solutions, i.e., random selection, stochastic learning automata, our algorithm performs better in terms of both utility and convergence.
Prediction of Telecommunication Network State Based on Knowledge Graphs

The article focuses on developing a new prediction method for telecommunications network state based on knowledge graphs. The article analyses the prediction methods supported by current network monitoring and management systems. The proposed prediction method is based on analysis of behavior of each end-user device that allows reach high accuracy of prediction. Knowledge graphs of the networks are build using domain knowledge and statistical data. They contain knowledge about the possible states of the elements of the networks along with the knowledge about possible transitions between them that can be presented in the form of prediction rules. These rules allow predict the state of the network for the defined timestamp in the future. In the case study the practical task of internet traffic prediction for a segment of cable TV operator network is considered. Conclusions are formulated, and the areas of further research are defined.
An Extensive Survey of Machine Learning Based Approaches on Automated Pathology Detection in Chest X-Rays

Radiography is one of the most common and eminent medical imaging technologies in the world to date. Chest radiography is a very powerful and successful way of diagnosing thoracic diseases of humans. With the latest advancements and development in computer hardware, computer vision and especially with the publicly available large-scale datasets, machine learning based approaches on automated pathology detection in chest radiography have become increasingly popular among researchers. Our study conducts an extensive survey on existing machine learning approaches, its datasets and techniques on pathology detection in Chest X-Rays. The paper presents popular and publicly available labelled Chest X-Rays datasets with its specifications and discusses about the labellers, labelling methodologies used by them in a comprehensive discussion. Then, popular effective Image Processing techniques for Chest X-Rays images are presented. Then the paper further discusses about the current machine learning architectures used and portraits the effectiveness of Deep Convolutional Neural Networks for the purpose. Finally, the paper concludes with a discussion with gaps in current literature, unexplored areas and possible future with them in Machine Learning based automated pathology detection on Chest X-Rays.
Flexible Generalized Spatial Modulation for Visible Light Communications

Adaptive spatial modulation (ASM) varies the modulation size across the transmit light-emitting diodes (LEDs) to improve the overall spectral efficiency of visible light communication (VLC) systems. This article proposes a novel ASM scheme, referred to as flexible generalized spatial modulation (FGSM), for VLC systems. The proposed FGSM system changes the modulation sizes over the LEDs and the number of active LEDs, to improve the average symbol error rate (SER) and spectral efficiency compared to ASM with a fixed overall number of LEDs. The modulation sizes are selected in order to optimize the average SER under a predefined spectral efficiency value. A closed-form expression of approximate SER is derived along with decoding complexity calculations for the proposed system. Numerical results are provided to confirm the superiority of the proposed system and to support theoretical derivations.
The value of endoscopic sinus surgery in chronic rhinosinusitis

Chronic rhinosinusitis (CRS) is a disease with significant impacts at both a societal and personal level. There has been an increase in emphasis on patient‐centered care and patient outcomes, with value becoming a commonplace concept in health care systems. This review seeks to better define the value that endoscopic sinus surgery (ESS) provides in the treatment of CRS.
A Versatile Inverse Kinematics Formulation for Retargeting Motions Onto Robots With Kinematic Loops

Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this letter, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops. We support the precise control of the position and orientation of several end-effectors, and the Center of Mass (CoM) of slowly walking robots. Our formulation safeguards against a disassembly when IK targets are moved outside the workspace of the robot, and we introduce a regularizer that smoothly circumvents kinematic singularities where velocities go to infinity. With several validation examples and three physical robots, we demonstrate the versatility and efficacy of our IK on overactuated systems with loops, and for the retargeting of an expressive motion onto a bipedal robot.
Can deep learning beat numerical weather prediction?

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Guided Dual Network Based Transfer with an Embedded Loss for Lane Detection in Nighttime Scene

Lane detection in the daytime has achieved great success in recent years. Compared with the daytime, lane detection at nighttime is still more difficult because the light is very weak. The previous methods use the planet of additional annotated data at nighttime environment to train the network. However, the annotation of images at nighttime is not only very expensive, but also very hard to annotate. Consequently, in this paper, we propose a dual network, which takes two images of the same scene in daytime and nighttime as input and detects lanes simultaneously. Specifically, we first adopt the transfer to generate nighttime images from the daytime images in an unsupervised manner. Note that in this way, we do not require annotate any data. Then, we send the daytime and nighttime images of the same scene to a dual network, in which an embedded loss is used to guide nighttime branch to learning discriminative information from the daytime branch to improve the detection of nighttime lanes. To strength the feature extraction of lane detection, we also propose streak-aware pooling to aggregate local contextual information. Extensive experiments on the nighttime scene of the CULane benchmark demonstrates the effectiveness of our method.
Market Segmentation on the Johannesburg Stock Exchange II

Abstract Given the reclassification of the Johannesburg Stock Exchange (JSE) sector indices that occurred in March 2000, this paper updates the factor analytic procedure conducted by van Rensburg and Slaney (1997). It is found that the new Financial-Industrial (CI21) and Resources (CI11) indices may be used as observable proxies for the first two principal components extracted from the covariance matrix of JSE returns. Consequently, it is suggested that these indices replace the Industrial and All-Gold index in future applications of the two factor arbitrage pricing theory (APT) model. Prior research is extended by considering the implications of the dichotomy in the return generating processes underlying JSE financial- industrial and resource stocks for the estimation of security betas. It is mathematically demonstrated that this dichotomy implies that the cross-sectional correlation matrix of the market model's residual errors is not diagonal. As a result, conventionally conducted market model regressions are characterised by the problem of omitted variable bias and downwardly biased t statistics. A remedial procedure is proposed, which may serve as a general correction for omitted variable bias in ordinary least squares regression analysis when using panel data. Finally, it is pointed out that the All-Share Index, conventionally employed as the market proxy in South African beta estimation, is not mean-variance efficient given the opportunity for offshore investment. This implies that the capital asset pricing model, as conventionally specified by South African academics does not hold on the JSE.
Distributed Collaborative Sensemaking: Tracing a Gradual Process

This Pictorial discusses the outcomes of a distributed embodied ideation workshop. In this workshop, students of a Bachelor program on Management by Design explained their thesis by visualizing and externalizing the problem space using everyday objects. The use of objects to represent and understand complex design problems is well documented in the fields of Design and Human-Computer Interaction, but is often researched in collocated settings. What happens when the design tools used to externalize and represent complex design problems are everyday household objects, and the discussion happens in a distributed, asynchronous manner? How is shared meaning negotiated and established? In this Pictorial, we discuss six pictures that were taken of the visual representations that the students constructed, and reflect upon the distributed, asynchronous process that was employed to create, develop, and agree upon meaning.
Syndrome-Aided Gradient Descent Bit Flipping Algorithm for LDPC Decoding

Gradient descent bit flipping (GDBF) algorithm is an important low-complexity method for decoding LDPC codes. In particular, the multi-bit GDBF decoding has the best decoding performance among BF algorithms if a suitable threshold is prescribed. However, extensive computer searches are needed to find such a threshold for a specific LDPC code. In this paper, we propose a syndrome-aided GDBF (SAGDBF) algorithm to address the problem. The thresholds in our algorithm are adaptively adjusted according to the syndrome values in the iterative process. Simulation results for different LDPC codes suggest that the error performances of the proposed SAGDBF algorithm are close to and even better than the original multi-bit GDBF algorithm at a relatively small number of iterations. In addition, the complexity increased in our proposed algorithm is negligible compared with the original multi-bit GDBF algorithm. Hence, the proposed algorithm is suitable for practical purposes.
A Kelvin Probe Force Microscopy Study of the Photogeneration of Surface Charges in All‐Thiophene Photovoltaic Blends

Light‐induced generation of charges into an electron acceptor–donor phase‐segregated blend is studied. The blend is made of highly ordered nanoscopic crystals of 3″‐methyl‐4″‐hexyl‐2,2′:5′,2″:5″,2‴:5‴,2″″‐quinquethiophene‐1″,1″‐dioxide embedded into a regioregular poly(3‐hexylthiophene) matrix, acting as acceptor and donor materials, respectively. Kelvin probe force microscopy investigations reveal a tendency for the acceptor nanocrystals to capture the generated electrons whereas the donor matrix becomes more positively charged. The presence of particular positively charged defects, i.e., nanocrystals, is also observed within the film. The charging and discharging of both materials is studied in real time, as well as the effect of different acceptor–donor ratios. Upon prolonged thermal annealing at high temperatures the chemical structure of the blend is altered, leading to the disappearance of charge separation upon light irradiation. The obtained results allow a better understanding of the correlation between the nanoscopic structure of the photoactive material and solar‐cell performance.
Identifying Ridesharing Risk, Response, and Challenges in the Emergence of Novel Coronavirus Using Interactions in Uber Drivers Forum

The outbreak and emergence of the novel coronavirus (COVID-19) pandemic affected every aspect of human activity, especially the transportation sector. Many cities adopted unprecedented lockdown strategies that resulted in significant nonessential mobility restrictions; hence, transportation network companies (TNCs) have experienced major shifts in their operation. Millions of people alone in the USA have filed for unemployment in the early stage of the COVID-19 outbreak, many belonging to self-employed groups such as Uber/Lyft drivers. Due to unprecedented scenarios, both drivers and passengers experienced overwhelming challenges that might elongate the recovery process. The goal of this study is to understand the risk, response, and challenges associated with ridesharing (TNCs, drivers, and passengers) during the COVID-19 pandemic situation. As such, large-scale crowdsourced data were collected from online ridesharing forums (i.e., Uber Drivers) since the emergence of COVID-19 (January 25–May 10, 2020). Word bigrams, word frequency heatmaps, and topic models are among the different natural language processing and text-mining techniques used to preprocess the data and classify risk perception, risk-taking, or risk-averting behaviors associated with ridesharing during a major disease outbreak. Results indicate higher levels of concern about economic disruption, availability of stimulus checks, new employment opportunities, hospitalization, pandemic, personal hygiene, and staying at home. In addition, unprecedented challenges due to unemployment and the risk and uncertainties in the required personal protective actions against spreading the disease due to sharing are among the major interactions. The proposed text-based data analytics of the ridesharing risk communication dynamics during this pandemic will help to identify unobserved factors inadvertently affecting the TNCs as well as the users (drivers and passengers) and identify more efficient strategies and alternatives for the forthcoming “new normal” of the current pandemic and the ones in the future. The study will also guide us toward understanding how efficiently online social interaction outlets can be designed and implemented more effectively during a major crisis and how to leverage such platforms for providing guidelines during emergencies to minimize transmission of disease due to shared travel.