Miracle-dz/UniFAR-FTUs huggingface dataset:
SonicFace

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.
Modeling for Litigation: Mixing Qualitative and Quantitative Approaches

A contractor to the channel tunnel project instigated litigation to claim for the costs of disruption and delay. We used several interacting models to make the case for the claim more persuasive, coherent, and verifiable. Mixing qualitative modeling large cognitive maps with influence models and with system dynamics simulation modeling improved the quality of the claim. Quality, in this case, being that the modeling process was understandable to the client to the extent that it could argue the claim in court, that every aspect was expected to be transparent to the judge, and that it was robust and defensible management science. Cycling between modeling approaches gave benefits that could not have been attained by either hard or soft modeling in isolation. The claim ultimately was settled satisfactorily out of court with the client acknowledging that the modeling played a significant role.
Producer Responsibility and Recycling Solar Photovoltaic Modules

Rapid expansion of the solar photovoltaic (PV) industry is quickly causing solar to play a growing importance in the energy mix of the world. Over the full life cycle, although to a smaller degree than traditional energy sources, PV also creates solid waste. This paper examines the potential need for PV recycling policies by analyzing existing recycling protocols for the five major types of commercialized PV materials. The amount of recoverable semiconductor material and glass in a 1Â m2 area solar module for the five types of cells is quantified both physically and the profit potential of recycling is determined. The cost of landfill disposal of the whole solar module, including the glass and semiconductor was also determined for each type of solar module. It was found that the economic motivation to recycle most PV modules is unfavorable without appropriate policies. Results are discussed on the need to regulate for appropriate energy and environmental policy in the PV manufacturing industry particularly for PV containing hazardous materials. The results demonstrate the need to encourage producer responsibility not only in the PV manufacturing sector but also in the entire energy industry.
Effects of filter concatenation for directly modulated transmission lasers at 2.5 and 10 Gb/s

We report computer simulation results of the effects on optical signal quality of passage through a cascade of wavelength division multiplexing (WDM) filters-multiplexers and demultiplexers-for directly modulated lasers with different chirp characteristics. In particular, lasers with transient or adiabatic chirp characteristics at 2.5 Gb/s and 10 Gb/s are investigated, and we find clear differences between the laser types with respect to filter concatenation effects. Filters with an optical bandwidth suitable for a 200-GHz channel-spacing system are considered, and we evaluate the system behavior as a function of laser frequency offset for a fixed number of filters. The reference network architecture used for the simulations is an optically transparent metropolitan scale network in which the WDM signals may be demultiplexed and then multiplexed again at multiple optical network elements. The signal quality is evaluated in terms of a distortion-induced eye-closure penalty as well as the excess attenuation or loss suffered. We find that transient chirp-dominated lasers show a generally symmetric distortion penalty response to laser frequency offset, whereas the response for adiabatic chirp dominated lasers is highly asymmetric. Furthermore, the extinction ratio for the latter class of lasers can be improved, in some cases, by the appropriate offset between laser and filter center frequencies.
ASSESSMENT OF THE IMPACT OF LAND USE CHANGES ON NET PRIMARY PRODUCTIVITY USING HUMAN APPROPRIATION OF NET PRIMARY PRODUCTIVITY IN ANJI, CHINA

The land use changes (LUC) have altered the vegetation net primary productivity (NPP). Human appropriation of net primary productivity resulting from LUC (HANPPluc) can be used to quantify the consequence of LUC on vegetation NPP. HANPPluc is defined as the difference between the potential and actual NPP availability in ecosystems. Based on the Thornthwaite Memorial model, CASA model, and multiple linear regression, this paper estimated the potential and actual NPP, explored the temporal and spatial changes of HANPPluc, and analyzed the impact of LUC on vegetation NPP in Anji, China. There was a significant increasing trend in annual HANPPluc from 1984 to 2014. The total mean value of HANPPluc was 155 Gg C year, while HANPPluc per unit was 82 ± 54 g C m year. The growth of Moso bamboo (Phyllostachys pubescens) forests area caused an increase of 33.5 Gg C year in NPP. However, the urbanization was the main reason for the decrease of NPP, which resulted in a decrease of 97.9 Gg C year in NPP. This study quantified the impact of LUC on vegetation NPP, and provided key data for urban landscape planning, Moso bamboo management, and regional ecological carbon cycle models.
Emotions recognition in human computer interaction: An audit

Human computer interaction, most widely known as its acronym HCI. It is concerned with understanding how people make use of devices and systems that assimilate or embed computation, and how such devices and systems can be more useful and more usable. Two channels have been distinguished in human interaction. One is implicit and another is explicit. Explicit channel defines about anything or nothing on the other hand implicit message conveys the message about speaker themselves. Both linguistic and technology takes lots of efforts to understand explicit message. Emotions are associated with implicit messages. Emotions can be analyzing through vocal signals, facial expressions and multimodal information. This paper aim is to deliver a brief guidebook of approaches, application area and comparative studies used to analyze the emotions over past decades researches. After reviewing the results of emotions recognition, it is found that happiness has the highest accuracy rate than other emotions.
WORD2VEC HYPERPARAMETERS TUNING FOR EFFICIENT DEEP NEURAL NETWORK CLASSIFICATION

Word embedding is an emerging topic in recent years. The application of deep learning in word embedding is expanding and being used by top corporations like Google and Facebook in various applications such as search and recommendation engines. Some several techniques and libraries have been published, like Word2Vec, FastText and GloVe. Word embedding usually defines the word by its accompanying words. Researchers often choose the default hyperparameters mentioned in the original paper for word embedding by Mikolov, such as Word2Vec. The default hyperparameters have different effects on different downstream tasks. One simply cannot use the same hyperparameters for word embedding for downstream classification tasks and semantic analogy downstream tasks. The default and recommended hyperparameters work the best with specific downstream tasks of analogy and semantic meaning. Complex downstream tasks such as classification might require different hyperparameters. This research aims to study hyperparameters' impact on the downstream task of classification. This will help the researchers to identify the combination of the word embedding hyperparameters that will fit their downstream tasks. In this research, different deep neural network (DNN) classifiers were used for the downstream classification tasks such as Recurrent Neural Network, Convolution Neural Network and Long Short-Term Memory Neural Network. The DNN networks were fed by different word embedding vector spaces trained using different hyperparameters. The results show that by navigating the hyperparameters space, the downstream task of classification accuracy can increase by 4.8%. The results also show that LSTM networks are more resilient to changes in the hyperparameters. However, CNN with a specific combination of word embedding hyperparameters can achieve the highest accuracy in the classification downstream task.
Human Activity Recognition using Long Short-Term Approach

Human Activity Recognition is one of the most popular area of research for machine learning and deep learning enthusiasts. It is a classification problem which aims to learn predicting the class of activities one is performing. This paper takes Long short-term memory approach for this time series classification problem. We try to classify six activities which are standing, sitting, jogging, walking, walking upstairs and walking downstairs. We build and train the model using Google’s TensorFlow library. Our experiment was done with dataset by WISDM (Wireless Sensor data mining) dataset. LSTM approach shows that it performs better than traditional algorithm with 97% accuracy.
DISTRIBUTION OF DIFFERENT K FRACTIONS IN INCEPTISOLS OF EASTERN UTTAR PRADESH

The present investigation was done to study distribution of different K fractions in Inceptisol of Eastern U.P. The soils were low to medium in available K content. High correlation between exchangeable and nonexchangeable K content were observed. It is concluded that nonexchangeable K can be major contributor of crops in soils and that some measure of this fraction must be included in soil fertility evaluation. Low to medium available K in this study and low reserve K indicate the need to relock into K fertility management for obtaining higher productivity of intensive cropping in these soils.
Recent advances in direct solar thermal power generation

The recent energy crisis and environmental burden are becoming increasingly urgent and drawing enormous attention to solar-energy utilization. Direct solar thermal power generation technologies, such as thermoelectric, thermionic, magnetohydrodynamic, and alkali-metal thermoelectric methods, are among the most attractive ways to provide electric energy from solar heat. On the one hand, these methods have the potential to be more efficient than traditional ways since they can convert heat to electricity directly without experiencing the conventional intermediate mechanical energy conversion process; on the other hand, these electricity generators are generally silent, reliable, and scalable, making them very suitable to serve as a distributed power generation system for certain specialized fields, such as military and space applications. A lot of effort has been devoted to investigate the energy conversion theory and practical applications thus far. This paper is intended to present a thorough review on recent advances in developing the thermoelectric, thermionic, magnetohydrodynamic, and alkali-metal thermoelectric technologies for direct solar thermal power generation. Both the fundamental issues and latest application research are illustrated and critical issues are discussed. The paper concludes with a description of future developments expected in the subjects covered.
Deep Convolutional Bidirectional LSTM Model for identifying Ventricular Tachyarrhythmia using ECG Signal Variability

An electrocardiogram (ECG) signal is used widely to detect ventricular tachyarrhythmia (VTA) and to diagnose heart disease. Deep learning models and large ECG data have made the diagnosis of VTA an attractive task to demonstrate the power of artificial intelligence in clinical applications. One of the life-threatening complications of VTA is cardiac arrest (CA). The VTA is divided into two categories: ventricular fibrillation (VF) and ventricular tachycardia (VT). Abnormal electrical activity in the ventricle causes VT, which leads to CA, whereas the chaotic electrical activity in the ventricle leads to VF. To improve the clinical diagnostic system and to help cardiologists, it is essential to identify the risk of VTA at an early stage. The goal of this paper is to develop an end-to-end (E2E) deep learning model that uses a convolution neural network (CNN) and a bidirectional long-short term memory network (BiLSTM) to classify VT and VF arrhythmias from multiple ECG databases. The CNN extracts features from ECG signals, and BiLSTM learns information. The ECG signals are acquired from the MIT-BIH malignant ventricular arrhythmia database (VFDB) and the Creighton University VTA database (CUDB). These ECG signals indicate that heart rate variability is a fast and dynamic event. Before the method's implementation, ECG signals are windowed at a fixed size according to annotation information and then normalized within each window. In terms of accuracy and sensitivity, the proposed CNN-BiLSTM deep learning model outperforms existing state-of-the-art methods. These results made it possible to obtain a relatively higher average accuracy (AC) of 99.37%, precision (PE) of 97.12%, a sensitivity (SE) of 98.15%, and F-score (FS) of 98.43%, and an overall accuracy of 99.07%, respectively.
RUM Based Calcium Pulse Modeling

It is believed that plants have developed very sensitive mechanisms through which they can perceive and respond to stimuli, like subtle touch. Signaling molecules and phytohormones including intracellular calcium have been implicated in touch responses. The modeling of calcium pulse helps us enable elucidation of the mechanisms through which plants perceive mechanostimuli. Kern el based machine learning methods have been demonstrated as powerful modeling tools. In this paper, a new machine learning method, the Relevance Units Machine (RUM), is used to fit calcium pulses.
Organizational Culture Restraining in-House Knowledge Transfer between Project Managers a Case Study

Even though the importance of knowledge transfer within project-oriented organizations is frequently emphasized in the recent project management literature, in practice, knowledge transfer only takes place to a rather limited extent. Based on an in-depth case study, findings from a company, in which several initiatives to promote knowledge transfer between five full-time project managers were taken, are presented. However, the initiatives did not bring the expected outcome. The organizational subculture encompassing project managers may explain reluctance in involvement in knowledge transfer and in sharing of lessons learned. Based on Schein's notion (1987) six basic underlying assumptions in the organizational culture that limit knowledge transfer are identified. Further, the authors propose an interaction paradigm underlying project managers' interactions.
A Fan-Shaped Compact Water Antenna With Wide Bandwidth and Optical Transparence

A novel wideband and compact water antenna with optically transparent characteristic is presented in this communication. This antenna mainly consists of a fan-shaped cylindrical radiator, a top disk, a cylindrical base, and a water reflector. Based on two resonant modes, this antenna can achieve wideband characteristic. Meanwhile, when combined with fan-shaped radiator and top disk loading, the overall dimensions can be greatly reduced without compromising impedance matching. The ground made of distilled water is used for full transparency. The proposed antenna covers frequency range from 1.16 to 2.79 GHz for $S_{11} < -10$ dB. As far as we know, the proposed antenna shows the widest relative bandwidth (82.5%) in the field of transparent antennas, without sacrificing size and radiation performance.
A General Small-Angle X-ray Scattering-Based Screening Protocol for Studying Physical Stability of Protein Formulations

A fundamental step in developing a protein drug is the selection of a stable storage formulation that ensures efficacy of the drug and inhibits physiochemical degradation or aggregation. Here, we designed and evaluated a general workflow for screening of protein formulations based on small-angle X-ray scattering (SAXS). Our SAXS pipeline combines automated sample handling, temperature control, and fast data analysis and provides protein particle interaction information. SAXS, together with different methods including turbidity analysis, dynamic light scattering (DLS), and SDS-PAGE measurements, were used to obtain different parameters to provide high throughput screenings. Using a set of model proteins and biopharmaceuticals, we show that SAXS is complementary to dynamic light scattering (DLS), which is widely used in biopharmaceutical research and industry. We found that, compared to DLS, SAXS can provide a more sensitive measure for protein particle interactions, such as protein aggregation and repulsion. Moreover, we show that SAXS is compatible with a broader range of buffers, excipients, and protein concentrations and that in situ SAXS provides a sensitive measure for long-term protein stability. This workflow can enable future high-throughput analysis of proteins and biopharmaceuticals and can be integrated with well-established complementary physicochemical analysis pipelines in (biopharmaceutical) research and industry.
Public Appeals as a Budgetary Weapon

ABSTRACT Public appeals improve presidential success in budgeting and lawmaking, but the evidence remains scarce at the state level. This study tests the influence of public appeals on gubernatorial success in the budgeting process. It posits that chief executives who choose to appeal to the public will be able to put additional pressure on the lawmakers and reduce the chances of their proposals being cut. The proposition is tested in a longitudinal study of all states from 2007 to 2019. Public discussions of budget proposals are extracted from gubernatorial speeches using computational text analysis. The findings provide strong support for the main hypothesis, which holds across different econometric specifications and operationalizations of success. This study complements the traditional view of state budgeting as a process of bargaining and negotiations between the executive and legislative branches and indicates that public involvement may have a systematic influence on the outcome.
Interdigitated contact semiconductor laser as optoelectronic 'AND' gate

Semiconductor lasers with interdigitated contacts have been successfully fabricated that are capable of achieving new functionalities. By differential current pumping, two sets of light–current characteristics can be formed with threshold current linearly dependent on the length ratio of unpumped to pumped regions. The device can function as an optoelectronic AND gate.
Landscape Integrity Eclipses Local Effects of Floral Resource Availability on Bumble Bee (Bombus Spp.) Abundance in a Water-Limited Island Ecosystem

ContextHabitat loss threatens to exacerbate climate change impacts on pollinator communities, particularly in Mediterranean-type ecosystems where late season floral resource availability is limited by seasonal drought. While gardens have been found to supplement floral resources in water-limited urban landscapes, less is known about the role of natural habitat diversity in sustaining late season floral resources in more intact landscapes. ObjectivesWe investigated the importance of habitat integrity and diversity for bumble bees in a water-limited ecosystem, observing bumble bee community response to seasonal drought across gradients of disturbance and soil moisture.MethodsWe applied hierarchical models to estimate the effects of local site conditions versus landscape scale estimates of matrix habitat on bumble bee abundance. Floral resources, soil moisture, and other environmental variables were sampled along randomly distributed belt transects. Geospatial estimates of matrix habitat were derived from terrestrial ecosystem data. Bumble bees were sampled with blue vane traps.ResultsIn the late season we found that modified wet areas supported more floral resources and bumble bee workers as compared to dry semi-natural environments. Wetlands also supported more late season floral resources and bumble bee workers, though the latter effect was not significant. Despite higher levels of late season floral resources in modified wet environments, modified matrix habitat was negatively associated, and natural matrix positively associated, with workers in June and late-flying queens in July and August. We also detected differences in bumble bee community composition in disturbed versus undisturbed environments.ConclusionsThough wet modified habitats sustained the highest levels of late season floral resource availability and worker abundances in our study, bumble bee diversity and abundance were limited primarily by the availability of natural matrix habitat at the landscape scale. The conservation of natural habitat integrity and diversity can help support critical nesting and foraging habitat, and should be prioritized in efforts to foster the resilience of pollinator communities.
Factors Influencing Community Participation of Youth Affairs Committee Members in Mon State Myanmar

This research explains factors (usage of social media, social factors, political & factors) most effect on community participation of youth affairs committee members. This research work was conducted in Myanmar before the Military Coup. The CEC (Chief Executive Committees) members were actively participation in Military coup protest. Moreover, most of the committee members exponent the democratic values to community in this times. The study area was selected Mon State because of the media age of Mon state is 26.7 years. Moreover, there were three major ethnic group – Mon , Kayan and Burma lived in Mon state. This condition shows that youth person settled diverse opinions and thoughts in Mon state. The 131 youth affairs committee members were proportionally selected from each layer of Mon state youth affair committee for collect data. Linear regression and descriptive methods was employed to analyze collected data. It was found that social factors and political & legal factors have positively significant effect on community participation of youth leaders. The policy makers of Mon state should provide the opportunities for youth to engage in community development works.
An Algebraic Approach to Find Some Topological Indices of Derived Graphs of the Benzene Ring

Topological indices play a vital role in understanding the chemical and structural properties of the chemical compounds and nanostructures. By finding the M-polynomial of a graph representing a chemical compound, one can obtain the closed forms of some of the commonly known degree-based topological indices of the compound, such as the Zagreb index, general Randic ́ Index and harmonic index. In this article, we obtain the expression for the M-polynomial of the derived graphs of the Benzene ring embedded in the P-type surface network in 2D, namely the line graph, the subdivision graph, and the line graph of its subdivision. Furthermore, some of the degree-based topological indices are obtained for these graphs using their M-polynomials.