Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Influence of Sound Source Visualization on the Ventriloquism Effect in an Auralized Virtual Reality Environment

The ventriloquism effect (VQE) describes the illusion that the puppeteer’s voice seems to come out of the puppet’s mouth. This effect can even be observed in virtual reality (VR) when a spatial discrepancy between the auditory and visual component occurs. However, previous studies have never fully investigated the impact of visual quality on VQE. Therefore, we conducted an exploratory experiment to investigate the influence of the visual appearance of a loudspeaker on the VQE in VR. Our evaluation yields significant differences in the vertical plane which leads to the assumption that the less realistic model had a stronger VQE than the realistic one.
An RSSI-based weighting with accelerometers for real-time indoor positioning

The robot's positioning is a key factor for autonomous robotics. The RSSI from Wi-Fi access points is a famous technique because the Wi-Fi access points have been installed. However, the RSSI values depend on real-time envelopment. This paper proposes to use accelerometers in robots to reduce error from RSSI. This system consists remote robots that installs the ESP8266 and accelerometer MPU-6050. The proposes is to weigh between RSSI from the transceiver module and RSSI that are predicted from accelerometers. The results are shown that the localization error is reduced when compared with only RSSI technique. By using the weighting technique, the cumulative error from the predicted position is reduced when the position that calculates with RSSI technique is correct.
Channel Estimation using Block Sparse Joint Orthogonal Matching Pursuit in Massive MIMO Systems

The channel estimation of the muti-user massive MIMO systems is a crucial task which enables us to leverage their high degrees of freedom. Due to the large number of base station antennas and consequently the huge number of channel paths, the massive MIMO channel estimation becomes more challenging. In this paper, we suggest a sparsity-based algorithm to estimate the channels more efficiently. To this end, we would offer a problem modelling to exploit the spatial correlation among different antennas of the BS as well as the inter-user similarity of the channel supports. An iterative thresholding technique has been suggested to approximate the channel matrix. The simulation results confirm that the proposed method has outstanding performance compared to its counterparts.
Data Analytics based Statistical Analysis of Air Pollution in the Major Cities of Karnataka

Air pollution is the discharge of contaminations into the air that is damaging to human wellbeing and the planet. Air contamination is one of the big issues to deal with for any nation. In South Asia, they rank it as the sixth most risky killer. People won’t realize the damaging outcomes of hassle if he/she has not experienced it. Pollutants comprising particulate matter (PM) or nitrogen dioxide (NO2) can cause respiratory ailments or cardiovascular sicknesses. World Health Organization tested that 11.6% of worldwide sudden demise may follow back to air pollution. The Air Quality Data is capable to perceive the patterns and identify correlating elements on key ranges of Air Pollution. To our knowledge, this analysis offers to deal with distinguishing a pollution source as starting from an individual area considering collected records. This gives a helpful strategy for air quality administration, and the outcome may be noteworthy to ecological and monetary issues. In this article, the air quality information of the Indian state, Karnataka were analyzed to find standards or styles which might deliver our perception into how extreme the trouble can be measured.
Attract Students Attention using Gamification

Gamification is non-game context which is adopting game context. In education perspective, gamification able to attract students attention. In this paper, we observe implementation of gamification in learning process. Problem revealed is student disinterest on particular subject. Subject of research are fourth grade primary school students in Primary School of Percobaan, Pakem. Gamification penetration is expected able to shift quality of interaction which impacts on learning process. Students desire pleasure experience. In this research, we aim to generate pleasure experiences. Result indicates gamification able to attain students attention. Learning experience is becoming more engaging. However, it requires to be strengthened in future works. Pedagogy aspects should be considered adopted.
Infrared Target Tracking Algorithm Based on Bernoulli Filter and Support Vector Machine

Infrared small target tracking algorithm is an important method to obtain the target states effectively. Support Vector Machine (SVM), a classic method in machine learning methods, is effective for target recognition. The Bernoulli filter is the optimal single-target filter for target tracking within the Bayesian framework. However, there is no related research on the joint application of the SVM and Bernoulli filter for infrared small target tracking. In this paper, the SVM and Bernoulli filter are jointly applied to the tracking and recognition of small targets in complex backgrounds. The research shows that, with the SVM, Bernoulli filter can indeed be applied to the actual multi-frame infrared target tracking scene to achieve effective tracking and recognition of infrared small targets.
Speaking with avatars - influence of social interaction on movement behavior in interactive hearing experiments

This study investigated to what extent social interaction influences the motion behavior of normal-hearing listeners in interactive hearing experiments involving audiovisual virtual reality. To answer this question, an experiment with eleven participants was performed, using two different levels of virtualization, two levels of interactivity, and two different noise levels. The task of the participants was either to communicate with two real or two virtual interlocutors (conditions ’real’ and ’active’) or to listen passively to a conversation between three virtual characters (condition ’passive’). During the experiment, the gaze, head and body motion of the participants was recorded. An analysis of variances showed that the gaze and saccade behavior does not differ between ’real’ and ’active’ conditions. Behavior was found to be different between ’active’ and ’passive’ conditions. For the head motion related parameters, such a significant effect was not found. A classifier was trained to distinguish between conditions based on motion and pose features. Performance was higher for the comparison between ’active’ and ’passive’ conditions than for the comparison between ’real’ and ’active’ conditions, indicating that the measured difference in motion behavior was not sufficient to distinguish between the ’real’ and the ’active’ condition.
An improved Vandermonde Decoding PE for Distributed Storage System

An improved Vandermonde decoding PE for distributed storage system is proposed in this paper. The PE first give a decoding algorithm based on matrix partition and Lagrange interpolation method to split the complex matrix invention to small Vandermonde matrix’s parallel parameter calculation. PE architecture is constructed by hardware working manner in Galois field of GF (28). The PE is designed to work for both encoding and decoding with same module but different data and operation. The PE number is configurable to have best performance in different erasure codes “r” situations backward compatible by the control of data block distribution and Vandermonde erasure codes data selection. The hardware architecture is fully verified at RTL level and synthesized with GF 12nm technology lib and simulation results showed 60.6% to 79% speed advantages compared to Gaussian elimination.
Integrating Continuous and Teleporting VR Locomotion into a Seamless "HyperJump" Paradigm

Virtual reality comes with many promises. It allows us to explore the world in ways that were not possible before, like flying untethered or teleporting, experiencing a dream-like state while fully awake, or having a world that responds to our every whim through the blinking of our eyes or gesture of our hands. To fulfill many potentials it bears, it should allow users to navigate freely through the virtual world they create.
Learning to Predict Software Testability

Software testability is the propensity of code to reveal its existing faults, particularly during automated testing. Testing success depends on the testability of the program under test. On the other hand, testing success relies on the coverage of the test data provided by a given test data generation algorithm. However, little empirical evidence has been shown to clarify whether and how software testability affects test coverage. In this article, we propose a method to shed light on this subject. Our proposed framework uses the coverage of Software Under Test (SUT), provided by different automatically generated test suites, to build machine learning models, determining the testability of programs based on many source code metrics. The resultant models can predict the code coverage provided by a given test data generation algorithm before running the algorithm, reducing the cost of additional testing. The predicted coverage is used as a concrete proxy to quantify source code testability. Experiments show an acceptable accuracy of 81.94% in measuring and predicting software testability.
Climate Change: Resetting Plant-Insect Interactions

Elevated CO 2 and temperature are altering the interactions between plants and insects with important implications for food security and natural ecosystems. Ecologically, the acceleration of plant phenology by warming is generating mismatches between plants and insect pollinators. Similarly, shifting the rate of plant development relative to insect development can amplify or minimize the consequences of herbivory. Warming also enables some insects to increase the number of generations per year, thus increasing damage to plant communities. The suitability of plant tissues as food for insects also is modulated by global change. Elevated CO 2 typically increases the concentration of leaf carbohydrates and in combination with elevated temperature decreases nitrogen content. Together, these changes lower nutritional value, causing herbivores to consume more foliage to meet their nutritional needs. While the responses of primary metabolites in plants to global change are reasonably well understood, how elevated CO 2 and temperature affect plant defensive compounds (allelochemicals) is considerably less predictable. Recent studies indicate that exposure to elevated CO 2 suppresses the plant defense hormone jasmonic acid (JA) while stimulating production of salicylic acid (SA). By differentially affecting defense compounds, these changes in plant hormones potentially increase susceptibility to chewing insects and enhance resistance to pathogens. Exposure to elevated temperature, in contrast, stimulates JA, ethylene, and SA, enhancing defenses. A deeper understanding of how elevated CO 2 and temperature, singly and in combination, modulate plant hormones promises to increase our understanding of how these elements of global change will affect the positive and negative interactions between plants and insects. Functional analysis of rice NPR1 -like genes reveals that OsNPR1/NH1 is the rice orthologue conferring disease resistance with enhanced herbivore susceptibility.
Functional properties of sonicated and non-sonicated extracted leaf protein concentrate from Diplazium esculentum

ABSTRACT Leaf protein concentrate of edible fern Diplazium esculentum was extracted using sonication and non-sonication methods and its functional properties were evaluated. The leaf protein concentrate contained 34.28 and 9.89% protein for sonicated and non-sonicated extractions, respectively. Sonication yielded (36.36%) better emulsion activity over non-sonication (31.10%). Foaming capacity was also found better in sonication (7.27%) over non-sonication (6.99%), and oil absorption capacity also improved in sonication (7.55 g of oil/g) over its counterpart (7.41 g of oil/g). Contact angle experiments with the leaf protein concentrate evidenced the enhancement of more hydrophilicity of the sonicated sample. Thermal properties of the leaf protein concentrate with sonication evidenced more stability (55.58 and 95.95°C) over the non-sonicated (46.75 and 75.60°C). Fourier transform infrared spectral analysis with the leaf protein concentrate of Diplazium esculentum was used to assess the secondary structural data such as α-helix and β-sheets. The various percentages of secondary structures of protein in sonicated and non-sonicated samples were 49.06 and 69.16% for α-helix, 33.59% and 17.02% for β-sheet and 42.02 and 35.68% for turn, respectively. The results of the present investigation showed credible evidence to support that sonication method of extraction of leaf protein concentrate was better in terms of protein yield and functional properties as compared to the non-sonication. The sonicated extracted leaf protein concentrate has the potential for developing value added products.
Artists' labour market and gender: Evidence from German visual artists

Using comprehensive data from German visual artists, we provide strong empirical evidence of a gender gap in revenues. We ﬁnd that female artists have signiﬁcantly lower revenues from the art market and are about ten percentage points less likely to remain in the top category over three years. This gap persists in the most prominent art forms and is more pronounced for younger artists. Only 30 to 40 percent of these gaps can be explained by diﬀerences in observable characteristics. We also ﬁnd diﬀerences in the networking behaviour of the artists of diﬀerent genders: females are connecting more, whereas males tend to create tighter links, suggesting the importance of the latter for the art market.
Development and Characterization of 68 Microsatellite Markers of Black Amur Bream Megalobrama terminalis by Next-Generation Sequencing

This study developed and characterized 68 novel polymorphic microsatellite markers from black Amur bream Megalobrama terminalis by next-generation sequencing. Variability was tested on 36 individuals collected from Qiantang River, Zhejiang Province, China. The number of alleles per locus ranged from 2 to 13. Observed heterozygosity ranged from 0.028 to 0.944, whereas the expected heterozygosity ranged from 0.028 to 0.887. Polymorphism Information Content ranged from 0.027 to 0.862. Moreover, 53 microsatellites were in agreement with Hardy–Weinberg equilibrium. Twenty-eight pairwise tests in 33 microsatellite loci indicated linkage disequilibrium. These microsatellites are a valuable tool for further genetics studies of this species.
Numerical Method and Parallelization for the Computation of Synchrotron Radiation

The purpose of this work is to develop and parallelize an accurate and efficient numerical method for the computation of synchrotron radiation from relativistic electrons in the near field. The high-brilliance electron beam and coherent short-wavelength light source provide a powerful method to understand the microscopic structure and dynamics of materials. Such a method supports a wide range of applications including matter physics, structural biology, and medicine development. To understand the interaction between the beam and synchrotron radiation an accurate and efficient numerical simulation is needed. With millions of electrons, the computational cost of the field would be large. Thus, multilevel parallelism and performance portability are desired since modern supercomputers are getting more complex and heterogeneous. The performance model and the performance analysis are presented. CCS CONCEPTS • Applied computing → Physics; • Computing methodologies→ Sharedmemory algorithms; Parallel programming languages; Massively parallel and high-performance simulations.
Study on the Removal of Odorous Gases from Composting Process using Local Bio-Media of Vietnam

Odor pollution is an increasing problem in Vietnam as a tropical country during the urbanization and industrialization. The odor from sewage systems, farms of poultry, pig, and beef, food processing companies, composting factory, and landfills is a severe problem in many nearby residential areas. In this study, two lab-scale biofiltration systems where pristine local bio-media and cultured bio-media with specially formulated microorganisms were employed in biofilters and bio-trickling filter for controls of odor (i.e., hydrogen sulfide and ammonia) from composting process were fabricated and operated. The odorous gas flow was created by composting solid waste collected from an agricultural market (i.e., mainly vegetable), containing low concentrations of 1.32 ± 0.32 mgNH3 m-3 and 5.20 ± 0.28 mgH2S m-3 under stable condition. For the biofilter model, commercial compost and cow manure were used as substrates and packed into the models. For the bio-trickling filter model, K3 bio-media with biofilm developed by contacting activated sludge was used as packing material. The results showed that adding specially formulated microorganisms could reduce adaption time and lead to slightly better odor control performance. Among the substrates, cow manure provided the highest odorous gas removal efficiency of ≥ 90% during the stable phase with the elimination capacity of 0.0492 gNH3 m-3 h-1 and 0.225 gH2S m-3 h-1. The study results show a high potential of cow manure biofilter for control of H2S and NH3 gases in the practical application under Vietnam’s condition.
Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012)

High temperatures and severe drought contributed to extensive tree mortality from fires and bark beetles during the 2000s in parts of the western continental United States. Several states in this region have greenhouse gas (GHG) emission targets and would benefit from information on the amount of carbon stored in tree biomass killed by disturbance. We quantified mean annual tree mortality from fires, bark beetles, and timber harvest from 2003–2012 for each state in this region. We estimated tree mortality from fires and beetles using tree aboveground carbon (AGC) stock and disturbance data sets derived largely from remote sensing. We quantified tree mortality from harvest using data from US Forest Service reports. In both cases, we used Monte Carlo analyses to track uncertainty associated with parameter error and temporal variability. Regional tree mortality from harvest, beetles, and fires (MORTHþBþF) together averaged 45.8 ± 16.0 Tg AGC yr 1 (±95% confidence interval), indicating a mortality rate of 1.10 ± 0.38% yr . Harvest accounted for the largest percentage of MORTHþBþF (∼50%), followed by beetles (∼32%), and fires (∼18%). Tree mortality from harvest was concentrated in Washington and Oregon, where harvest accounted for ∼80% of MORTHþBþF in each state. Tree mortality from beetles occurred widely at low levels across the region, yet beetles had pronounced impacts in Colorado and Montana, where they accounted for ∼80% of MORTHþBþF. Tree mortality from fires was highest in California, though fires accounted for the largest percentage of MORTHþBþF in Arizona and New Mexico (∼50%). Drought and human activities shaped regional variation in tree mortality, highlighting opportunities and challenges to managing GHG emissions from forests. Rising temperatures and greater risk of drought will likely increase tree mortality from fires and bark beetles during coming decades in this region. Thus, sustained monitoring and mapping of tree mortality is necessary to inform forest and GHG management.
Summary Review of Empirical Literature on Defining the Event , Measuring Abnormal Returns , and Reporting the Results

The study investigates the information content of insider trading transactions employing different signal definitions. Using directors' open market dealings during the period 1999-2000 for FTSE100 companies and new extensions of the event study methodology, the evidence suggests directors have, and trade on, price-sensitive private information and, thus, attain short-term abnormal returns. The study finds that the market perceives such transactions as price sensitive signals. Selling signals are assimilated by the market later than buying signals but create greater market reaction, particularly when the signal is reinforced by the trades of more than one director.
Organizational Inclusion and Leadership in Times of Global Crisis

The globe is facing a never before seen pandemic situation because of Covid-19 Virtual workspaces have become a reality today With the emergence of newer ways of working, it is necessary to ensure that employees contribute and feel involved in the workspace This paper attempts to explore the ways in which practitioners and policymakers of inclusion perceive it at the workplace in the current COVID-19 pandemic, outline the role of leaders in fostering inclusion, and empirically test how organizational inclusion (OI) impacts organizational outcomes during the disturbing times The participants were employees working in the service sector companies in India A mixed-method approach would be adopted for data collection The content analysis technique would be used for the qualitative data analysis and Partial Least Square – Structural Equation Modelling (PLS-SEM) to analyze the quantitative data The paper provides insights on how inclusion is perceived differently by each individual, and though organizations have policies in place, getting them into practice is yet to be accomplished The findings of the study indicate the benefits of having an inclusive work environment along with leadership commitment during disruption The study is a novel attempt to empirically examine the way of leveraging a diverse workforce through inclusion to benefit the organization in the times of global crisis It also adds to the existing body of knowledge on how inclusion and role of a leader are experienced by the employer and employee in Indian companies, which is a niche area of research © 2021, University of Wollongong All rights reserved
Training of women self-help groups against reproductive tract infections - A pilot study

Reproductive tract infections (RTIs) are silent epidemics and put a heavy toll on women in developing countries. Women, because of their ignorance and socio-cultural inhibitions, hardly seek treatment for RTIs. Women selfhelp groups (SHGs) if adequately trained, can play an active role in creating awareness and promote healthy behaviours among women. The present study aimed to evaluate the Effectiveness of STAR (Self-help group Training to Alleviate Reproductive tract infection) program on knowledge, practices, skills and health-seeking behaviours on RTIs among self-help group women in the reproductive age group. A quasi-experimental (non-equivalent control group) study with repeated follow-up was done among 63 women belonging to the selected SHGunits. Women in the intervention group (n=33) underwent a training program on RTIs and had regular monthly follow-up for 6months, and the comparison group (n=30) only had a routine follow-up. Post-test observations were conducted at the end of three months and six months. The data were analyzed using repeated-measures ANOVA using SPSS package version 16. There was a signi icant difference in the knowledge, practice, skill and health-seeking behaviour scores at different points of time (P<0.001). However, the between-group pairwise comparison revealed that the training program was effective in improving the mean knowledge (P=0.001), skill (P=0.014) and health-seeking behaviour (P=0.034) scores and had no effect on the practice score (P=0.74). The STAR programwas found to be effective in improving the knowledge, skill and health-seeking behaviours of the self-help group women.