Miracle-dz/UniFAR-FTUs huggingface dataset:
Studying the Relationship between Social Position and the Meaning of Work using Siamese BERT Networks and Generalized K-Means Clustering

It is often taken for granted that work means the same thing to everyone. However, at a time of near-unprecedented political polarization and in the midst of a recession in which tens of millions of people have lost their jobs, it is imperative that we develop a greater understanding of not only what work means to different people, but also how the meaning of work systematically relates to workers’ positions within society and economic risks they face. To uncover the distribution of expectations about work across the occupational structure, I analyze workers’ review of their employers, ﬁrst developing rich document embeddings and then clustering occupations. First, I conduct an experiment to contrast the ability of a multilayer, bidirectional LSTM network and four variations of Siamese BERT networks to classify reviews into one of 49 occupations. I ﬁnd that using my data to ﬁne-tune a Sentence-BERT network pre-trained for NLI and STS-B performs best. Next, I use this model to encode the reviews before applying a novel clustering algorithm that preserves information about workers’ stances toward their experiences as evidence the one-to ﬁve-star ratings they provide. I am thus able to test whether there is evidence of consensus about “bad jobs” while exploring the distribution of expectations about work over the occupational structure.
A COMPACT TRI-BAND FLEXIBLE MIMO ANTENNA BASED ON LIQUID CRYSTAL POLYMER FOR WEARABLE APPLICATIONS

In this paper, a compact tri-band flexible MIMO antenna based on liquid crystal polymer (LCP) is designed to operate in WLAN and WiMAX bands. The antenna consists of two identical antenna elements. The isolation structure includes a ground slot, two I-shaped branches, and a parasitic strip. The measured results show that the impedance bandwidth (S11 < −10 dB) covers three frequency bands of 2.38–2.55 GHz, 3.37–3.60 GHz, and 4.92–5.37 GHz, and the S21 of working bands is basically better than −19 dB. Moreover, the flexibility of the MIMO antenna is analyzed at different bent cases. The specific absorption ratio (SAR) values are obtained by simulating the model of antenna approaching human body. The simulated results show that the SAR value of the antenna meets the European Union (EU) standard. The proposed antenna demonstrates the characteristics of satisfactory radiation, high isolation, sound gain in working bands and flexibility, which has good application prospects in the wearable field.
A Case For Refocusing Upstream: The Political Economy Of Illness

“You know," he said, “sometimes it feels like this. There I am standing by the shore of a swiftly flowing river and I hear the cry of a drowning man. So I jump into the river, put my arms around him, pull him to shore and apply artificial respiration. Just when he begins to breathe, there is another cry for help. So I jump into the river, reach him, pull him to shore, apply artificial respiration, and then just as he begins to breathe, another cry for help. So back in the river again, reaching, pulling, applying, breathing and then another yell. Again and again, without end, goes the sequence. You know, I am so busy jumping in, pulling them to shore, applying artificial respiration, that I have no time to see who the hell is upstream pushing them all in. ”1
Development ofWindPowerPredictionModels for PawanDanavi Wind Farm in Sri Lanka

*is paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. *e monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. *e correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. *e impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.
Unstable Pulse Discharge in Mixing Layer of Gaseous Reactants

The paper describes results of experimental and computational efforts on the filamentary transversal pulse discharge dynamics in ambient conditions, different gases (mixtures), and high-speed airflow. The effect of fast turbulent expansion of the post-discharge channel is studied experimentally in order to enhance mixing processes of fuel and oxidizer and reduce the time of ignition. The idea of extra benefit possessed with nonequilibrium, unsteady, and non-homogeneous discharge is verified numerically.
A CONCEPTUAL FRAMEWORK FOR THE SEISMIC RESILIENCE ASSESSMENT OF COMPLEX URBAN SYSTEMS

Cities are complex systems made up of interdependent networks formed of different but interdependent physical and social urban components. The resilience of an urban system depends on its ability to resist, absorb, accommodate itself to, and recover from the effects of a disaster (such as an earthquake) in a timely and efficient manner. Due to ever-increasing population growth trends and urbanization in risk-prone areas, planned pre-disaster mitigation actions, as well as smart and strategic urban planning, are of key importance. In this paper a conceptual framework for the holistic seismic resilience assessment of complex urban systems, taking into account all the urban components which have been identified as having an important impact on such an assessment, is presented. The urban systems are first sub-divided into four basic components, each of which is defined by its own quantitative attributes. This concept consists of three different parts: (i) a probabilistic fragility analysis for each individual physical element (i.e. a building or an infrastructure element), (ii) a composite index methodology for the measurement of community disaster resilience, and (iii) a complex network approach (based on graph theory) for the assessment of the resilience of urban systems as a whole. The presented framework is the authors' first attempt to upgrade existing quantitative resilience assessment approaches by introducing some new and important aspects (e.g. the inclusion of urban open space and its potential, the introduction of multiple figures-of-merit as a measure for the system performance, and the consideration of the time-dependency of the system's resilience).
Spammer Detection using Deep Learning Methods

The explosive growth in activities on social media and e-commerce platforms have led to signiﬁcant challenges for platforms to manage fake reviews and spammers. As such, developing algorithms that can ﬁlter fake reviews and identify spammers have attracted enormous interest from the artiﬁcial intelligence community. Researchers have developed various models to tackle this problem, including models that are based solely on textual features (NLP models) and models that examine relationships between users (GNN). This project combined the advantages of both methods through implementing a state-of-art natural language processing model within a graph-neural-network architecture, and proved that this enhanced method can outperform each of the above architectures individually.
An Assessment of the Impact of Auto-Mechanic Activities on Groundwater in Diobu, Port Harcourt, Nigeria

-An investigation to assess the impact of auto-mechanic activities on the groundwater in Mile 2 Diobu was conducted with the aim to ascertain the level of pollution as well as the portability of the groundwater for human consumption. A total of four boreholes were analyzed for their physicochemical properties (temperature, pH and electrical conductivity) and heavy metal content (Pb, Cd, Fe, Cr, and Zn). Groundwater samples were collected from three functional boreholes within the mechanic village and a control groundwater sample was collected far away from the workshop area. The results of the analysis compared with the WHO guideline values for drinking water quality parameters revealed that all the parameters assessed were within the permissible limit set by the organization except the pH which was low and indicated that the groundwater within the area was slightly acidic as the pH ranged from 6.0 – 6.5. It was therefore concluded from the study that the automechanic activities carried out in the area has not significantly impacted on the groundwater resource. However, the low pH indicated that the water requires minor treatment before human consumption. The recommendations of the study include continuous monitoring of the concentration of Fe in the groundwater resource as well as other contaminants; appropriate regulatory authorities should put in place legislature to manage, regulate and control disposal of the wastes generated from auto-mechanic activities; education of workshop owners on dangers of indiscriminate waste disposal.
Depolymerization of Cellulolytic Enzyme Lignin for the Production of Monomeric Phenols over Raney Ni and Acidic Zeolite Catalysts

In the present study, cellulolytic enzyme lignin, which was isolated from enzymatic hydrolysis residues of bamboo, could be efficiently depolymerized into oily products with a yield of over 60 wt % using a range of acidic zeolites and/or Raney Ni catalysts. The degraded products are mainly composed of phenolic monomers, which can be used as versatile chemicals or the precursor for biofuel production. The yields of monophenols were 12.9 wt % and no more than 5.0 wt % when catalyzed by Raney Ni or acidic zeolites, respectively. However, a yield of monophenols as high as 21.0–27.9% was obtained using a Raney Ni combination with acidic zeolite catalysts. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis revealed that the depolymerization level of the oily fraction catalyzed by a combination of catalysts was more complete in comparison to those catalyzed by an independent catalyst. The results indicated that the catalytic activity of a fully heterogeneous catalyst combinatio.
Trends and Patterns in Distance Education (2014–2019): A Synthesis of Scholarly Publications and a Visualization of the Intellectual Landscape

The field of distance education (DE) is dynamic and constantly evolving; it reflects and adapts according to changes in socio-cultural, demographic, political, and technological domains. Thus, there is a need to understand past and present activities in the field, in order to better inform future research. The main purpose of this study was to examine DE research through data mining and analytics approaches, using social network analysis (SNA) and text mining to conduct a bibliographic analysis. The findings highlighted three main strands of DE research: (a) issues related to open education; (b) the design, support, and quality assurance of online DE; and (c) the implementation and use of educational technology, media, and digital tools. SNA of the bibliometric data identified pivotal theoretical contributions, including that the fields of distance education and educational technology converge. The article concludes with recommendations for future research directions.
Broadening Low-Frequency Band Gap of Double-Panel Structure Using Locally Resonant Sonic Crystal Comprised of Slot-Type Helmholtz Resonators

An approach is presented to form and broaden the low-frequency band gap of the double panel structure (DPS) by using a locally resonant sonic crystal (LRSC) in this work. The LRSC is made of cylindrical Helmholtz resonators arranged on square lattice. Their designs are similar to a slot-type resonator, but have different depths of slot. Elongating the slit neck inward and distributing the depths of slots produce a broad local resonant band gap at low frequencies: an average insertion loss (IL) of 10.9 dB covering 520 Hz to 1160 Hz with a LRSC of 12 cm width. Next, the effect of porous material filled into the resonators on the local resonant band gap is evaluated. It is shown that filling of porous material into the resonators decreases the height and width of the local resonant band gap. Finally, the transmission losses (TLs) through the DPS with LRSC are calculated as a function of the incident angle of the sound wave for LRSC embedded in porous material and not. The results show that the porous material can be significantly reduce the incident angle dependency of TL through the DPS with LRSC.
SEISMIC PERFORMANCE OF BRIDGE PIERS MADE WITH RECYCLED CONCRETE AGGREGATE-AN EXPERIMENTAL INVESTIGATION

Utilizing waste concrete as recycled coarse aggregates (RCA) could reduce the negative environmental impact associated with new concrete production. However, limited studies have investigated the performance of structural elements made of recycled aggregate concrete (RAC). This paper discusses the seismic performance of RCA bridge piers. The conducted test compares the performance of 100% RCA bridge pier with conventional bridge pier containing natural coarse aggregates (NCA). The test simulated a real bridge pier located in the Vancouver area scaled down to 1/3 scale. The specimen has a diameter of 300 mm and 1730 mm height. The samples were tested under quasi-static cyclic loading simulating real earthquake motion in the Vancouver area. Each specimen was subjected to a combination of constant axial loading equal to 10% of its axial capacity and cyclic lateral loading developed by hydraulic jacking system. This paper assesses the seismic performance using hysteresis curves, energy dissipation, residual deformation and ductility measures for each specimen and provides insights on the feasibility of replacing NCA by RCA in structural elements.
Alleviation of technological stresses by a feed supplement

Technological stresses affect the productivity of broiler-type chicken and are related directly to poultry farming intensification. Heat stress occurring in conditions of high density keeping, especially at high summer ambient temperatures, is very important. Stress factors influence the metabolic processes in poultry, while reducing the production parameters of operation and, by extension, the efficiency of broiler farming. The objective of this research was to identify the efficiency of a feed supplement in alleviating technological stresses in poultry industry. Use of supplement (the preparation contains succinic acid, L-carnitine, betaine, inorganic salts of zinc, manganese, copper and lithium) preserved the production performance of broiler-type chickens in the pre-slaughter period, by reducing the technological load on the poultry body, as it was indicated by an increase in the efficiency of fattening by 16.2 conditional units, meat yield by 1.4%, gross income by 49.2% and a decline in mortality rate by 1.5%. The data on production efficiency indicators of broiler feeding were obtained on a large sample 6136 heads.
The Multiverse: Logical Modularity for Proof Assistants

Proof assistants play a dual role as programming languages and logical systems. As programming languages, proof assistants offer standard modularity mechanisms such as first-class functions, type polymorphism and modules. As logical systems, however, modularity is lacking, and understandably so: incompatible reasoning principles -- such as univalence and uniqueness of identity proofs -- can indirectly lead to logical inconsistency when used in a given development, even when they appear to be confined to different modules. The lack of logical modularity in proof assistants also hinders the adoption of richer programming constructs, such as effects. We propose the multiverse, a general type-theoretic approach to endow proof assistants with logical modularity. The multiverse consists of multiple universe hierarchies that statically describe the reasoning principles and effects available to define a term at a given type. We identify sufficient conditions for this structuring to modularly ensure that incompatible principles do not interfere, and to locally restrict the power of dependent elimination when necessary. This extensible approach generalizes the ad-hoc treatment of the sort of propositions in the Coq proof assistant. We illustrate the power of the multiverse by describing the inclusion of Coq-style propositions, the strict propositions of Gilbert et al., the exceptional type theory of P\'edrot and Tabareau, and general axiomatic extensions of the logic.
Modelling Tuberculosis with Conditional Latent Period

In this research work, we modeled Tuberculosis (TB) with conditional latent period using Delay Differential Equation. The model variables were divided into six compartments – susceptible, vaccinated latent (low and high), infected and recovered humans. We investigate the Disease Free Equilibrium (DFE) of the model and find the conditions that guarantee the asymptotic stability of corresponding steady states using stability theory of differential equations. The numerical simulations were carried out to test the effect of influence of key parameters on the spread of the TB epidemic, in particular the treatment and vaccination parameters to support the analytical solution and the stability theorem. It was shown that the introduced vaccination, high latent period and treatment parameter helps in controlling and eradication of TB epidemic in the population with respect to time. It is good to conclude that high effective treatment should be administered as well as high vaccination as this helps a long way in controlling the TB epidemic.
Unsupervised Semantic Face Synthesis

We propose a novel application of unsupervised image-to-image translation networks in the domain of semantic image synthesis. Given images sampled from marginal distribution p(x2) and semantic masks sampled from conditional distribution p(x1|x2), our goal is to estimate the two conditionals p(x2|x1) which is a many-to-one mapping and p(x1|x2) which is a one-to-many mapping. We achieve this by training MUNIT [1] on an unpaired semantic mask to image dataset.
Using cash transfers to improve child health in low- and middle-income countries

Evidence from 13 low- and middle-income countries shows that cash transfer programs conditional on the use of health products and services generally increase uptake and improve child health outcomes in the short term and can improve cognition and educational outcomes in the longer term. Cash transfers without these conditions are spent according to household priorities, which may not necessarily include health. While such transfers can have important non-health benefits, their effect on child health depends on the context and is typically smaller than that of conditional transfers
Deep Kurve!

In this work we explore reinforcement-learning algorithms for training an agent to play the cult-classic computer game Achtung Die Kurve! In this effort we modify an existing PyGame environment and experiment training an agent using open-source stochastic policy gradient, proximal policy optimization, advantage actor critic, Q-learning, and MuZero implementations. Ultimately, our experiments are unsuccessful in training an agent to have consistent and signiﬁcantly improvemed performance compared to a random policy. We conclude with discussion and recommendations for achieving improved performance in future work.
Super operator Technique in Investigation of the Dynamics of a Two Non-Interacting Qubit System Coupled to a Thermal Reservoir

In this paper, we clarify the applicability of the super operator technique for describing the dissipative quantum dynamics of a system consists of two qubits coupled with a thermal bath at finite temperature. By using super operator technique, we solve the master equation and find the matrix elements of the density operator. Considering the qubits to be initially prepared in a general mixed state, we explore the influence of dissipation on dynamical behavior of system. As expected, we obtain analytical evidence that the thermal photons and spontaneous emission of atoms are responsible for dissipation of entanglement of system.
Let’s SOUP up XR: Collected thoughts from an IEEE VR workshop on privacy in mixed reality

This paper presents insights from the PrXR workshop conducted at IEEE VR 2021. We identiﬁed several topic areas re-lated to privacy and security risks for virtual, augmented, and mixed-reality (XR) applications. Risks are presented from the perspective of the XR community. We attempt to thematically group the workshop ﬁndings and highlight the challenges brought up by the participants. The identiﬁed research topics serve as a roadmap to push forward privacy and security research in the context of XR.
Secure and secret cooperation in robot swarms

Robot swarms exploiting Merkle trees achieve secret and secure cooperation in sequential missions. The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method, operators can provide the “blueprint” of the swarm’s mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate toward mission completion, have to “prove” their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission’s high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role.