Miracle-dz/UniFAR-FTUs huggingface dataset:
The Liberalities and Tyrannies of ICTs for Vulnerable Migrants: The Status Quo, Gaps and Directions

Information and communication technologies (ICTs) have increasingly become vital for people on the move including the nearly 80 million displaced due to conflict, violence, and human right violations globally. However, existing research on ICTs and migrants, which almost entirely focused on migrants’ ICT use ‘en route’ or within developed economies principally in the perspectives of researchers from these regions, is very fragmented posing a difficulty in understanding the key objects of research. Moreover, ICTs are often celebrated as liberating and exploitable at migrants’ rational discretion even though they are ‘double-edged swords’ with significant risks, burdens, pressures and inequality challenges particularly for vulnerable migrants including those forcefully displaced and trafficked. Towards addressing these limitations and illuminating future directions, this paper, first, scrutinises the existing research vis-à-vis ICTs’ liberating and authoritarian role particularly for vulnerable migrants whereby explicating key issues in the research domain. Second, it identifies key gaps and opportunities for future research. Using a tailored methodology, broad literature relating to ICTs and migration/development published in the period 1990-2020 was surveyed resulting in 157 selected publications which were critically appraised vis-à-vis the key themes, major technologies dealt with, and methodologies and theories/concepts adopted. Furthermore, key insights, trends, gaps, and future research opportunities pertaining to both the existing and missing objects of research in ICTs and migration/development are spotlighted.
Response to Reviewers Document for “First Assessment of the Earth Heat Inventory Within CMIP5 Historical Simulations” by Francisco

The article ‘First Assessment of the Earth Heat Inventory Within CMIP5 Historical Simulations’ provides an evaluation of the Earth heat inventory from climate model simulations, and assess the dissemination of heat storage distribution in the different Earth system components. The article is well written, timely and addresses a fundamental topic, and I recommend minor revision before publication following the different aspects provided below.
Influence of the Production Process on the Binding Mechanism of Clinched Aluminum Steel Mixed Compounds

The multi-material design and the adaptability of a modern process chain require joining connections with specifically adjustable mechanical, thermal, chemical or electrical properties, whereby previous considerations have focused primarily on the mechanical properties. With clinching, the multitude of possible combinations of requirements, materials and component or joint geometry makes it impossible to determine these joint properties empirically. As a result of the established and empirically based procedure, no model exists to date that considers all questions of joinability, i.e. the materials (suitability for joining), the design (joining safety) and the production (joining possibility) and allows a calculation of the achievable properties. It is therefore necessary to describe the physical properties of the joint as a function of the three bonding mechanisms force closure, form closure and material closure in relation to the application. This approach enables the illustration of the relationships along the causal chain "joint requirement - binding mechanism - joining parameters". In this way the adaptability of the mechanical joining technology can be improved. A geometric comparison is made using metallographic cross sections, of clinched joints of the combination of aluminum and steel. The torsional testing of the rotationally symmetric clinching points for detection of the mechanical stress state are qualified as examination method and technological test. By measuring the electrical resistance in the base material, in the clinch joint and during the production cycle (after clinching, before precipitation hardening and after precipitation hardening), this change in the stress state can also be detected.
Prediction of Rebound Amount in Dry Mix Shotcrete by a Fast Adaboosting Neural Network

In this study, a new machine learning approach has been proposed to predict the rebound causing loss of material in shotcrete using the ensemble learning method. In shotcrete application, the amount of rebound material was obtained for use in a dataset. In this study, the shotcrete mixes that contain an additive of fly-ash, silica fume, and polypropylene fiber were produced besides simple shotcrete. Each mix was sprayed onto 2 wooden panels measuring 45 × 45 × 15 cm in size. The rebound material resulting from the spraying process was collected, weighed and recorded as data. The highest rebound was observed for the plain sample and the lowest for samples with substituted silica fume. Dependent and independent parameters were identified in the dataset produced as a result of experimental studies. Hyperparameters producing optimum results in the training of the model were identified for the model and boosting method. The dataset was split into training and testing sets by 80% and 20%, respectively. As a result, the model achieved a prediction performance of 84.25%. To test the performance of the proposed model, traditional machine learning algorithms were compared on the same dataset. Consequently, the proposed model was observed to have the highest accuracy.
Grandchildren and Grandparent’s Labor Force Attachment

As workforces age and life expectancy grows, understanding what motivates workers to strengthen or weaken their labor force attachment is a matter of growing policy concern. This paper asks how grandparents change their labor force attachment when grandchildren arrive by first using a multigenerational sample from the Panel Study of Income Dynamics (PSID) to study individual-level responses, and then use Current Population Survey (CPS) data to study how grandparenthood trends change labor force participation rate of older male workers. Grandchildren’s impact on age of retirement, hours worked, whether the grandfather is in the labor force, or the grandmother reports non-zero annual hours worked are estimated. Endogeneity between fertility timing and grandparent characteristics is instrumented for by exploiting exogenous state-by-year variation in access to reproductive technologies. I find that grandfathers work 339 fewer hours and become 19.5% more likely to retire, while grandmothers respond to the marginal grandchild by becoming 10% more likely to retire and working 132 fewer hours a year if non-retired. This paper shows evidence that the arrival of grandchildren does change grandparents’ labor supply, but that trends in grandparenthood have only had a muted impact on trends in older men’s labor force participation (LFP) rate. In a predictive exercise simulating labor force participation rates, the response to grandchildren is specification-sensitive, but interactions between grandchildren measures and Social Security benefits indicating that a 1 point increase in the fraction grandparent decreases the LFP rate by 0.18 points, and by 4.1 points with a 1 child rise in the average number of grandchildren. Collectively, across alternative fertility and grandchildren histories, trends in the simulated LFP rates do not meaningfully change from trends in the observed LFP rate, although the levels of participation would have been between 3-5 points higher between 1962-1994 if the Baby Boom had not occurred. JEL Codes: J13, J14, J22, J26
METHOD VALIDATION FOR DETERMINATION OF 23 ORGANOPHOSPHORUS PESTICIDE RESIDUES IN CHILLI BY GAS CHROMATOGRAPHY

Laboratory method was validated for the determination of 23 organophosphorus pesticides in chilli. QuEChERS extraction method using ethylacetate as extracting solvent followed by use of sodium sulphate and magnesium sulphate for moisture removal. Cleanup of samples was done using PSA, C18 and Activated charcoal. LOD (Limit of Detection) and LOQ (Limit of Quantification) of the pesticide for chilli was calculated. LOD value is lowest for phorate (0.005 mg/kg) and highest for parathion methyl (0.1 mg/kg) whereas LOQ value is lowest for phorate (0.015 mg/kg) and highest for parathion methyl (0.3 mg/kg). MRL (Maximum Residue Limit) values of phorate is lowest (0.05mg/kg) and highest for malathion (3.0 mg/kg) whereas ADI (Acceptable Daily Intake) values for diazenon, malathion, phasalone are highest (0.02 mg/kg) and lowest for fenthion, monocrotophos (0.0005mg/kg). Recovery studies at 0.05 ppm level of standard mixture of 23 pesticides was carried out and was found that recovery lies within the range of 82.67% to 93.33%. It is observed that mean recovered amount at 0.05 mg/kg spike concentration of 23 pesticides in chilli matrix are in the range of 0.0413 mg/kg to 0.0467 mg/kg and highest recovery percent for chlorpyrifos (93.33%) and lowest recovery percent for ethion (82.67%), rest pesticides recovery percent in between the range whereas relative standard deviation are in the range of 2.2 to 8.571, these values lies below codex permissible limit of 15. Linearity was checked by calibration curve, plotted between an area World Journal of Pharmaceutical Research SJIF Impact Factor 7.523 Volume 6, Issue 6, 1183-1197. Research Article ISSN 2277– 7105 *Corresponding Author Dr. Neelam Richhariya Govt. Indra College Satna Madhya Pradesh-485001, India. Article Received on 07 April 2017, Revised on 27 April 2017, Accepted on 17 May 2017 DOI: 10.20959/wjpr20176-8586
Untangle : Critiquing Disentangled Recommendations

The core principle behind most collaborative ﬁltering methods is to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus diﬃcult for users to control their recommendations based on particular aspects (critiquing). In this work, we propose Untangle: a recommendation model that gives users control over the recommendation list with respect to speciﬁc item attributes, (e
Building a QA System (IID SQuAD Track)

The goal of the project is to build a deep-learning based question-answering (QA) system that performs well on the SQUAD dataset. For this, I implement three different models : (i) Enhance the provided baseline BiDAF [1] model with character embeddings, (ii) QANet [2] model and (iii) a 4-layer transformer encoder model. On the SQUAD dev set, adding character embeddings to basline BiDAF increases the EM and F1 scores to 59.6 and 63.12 respectively. The QANet model achieves EM score of 57.28 and F1 score of 60.59 while the transformer encoder model achieves EM score of 52.19 and F1 score of 52.19 on the dev set.
Gated Self-Attention for SQuAD Question Answering

Machine comprehension and question answering are central questions in natural language processing, as they require modeling interactions between the passage and the question. In this paper, we build on the multi-stage hierarchical process BiDAF described in Seo et al. (2017)’s Bi-Directional Attention Flow for Machine Comprehension. We utilize tools from the R-Net model described in R-Net: Machine Reading Comprehension with Self-Matching Networks, testing different combinations of model components. We experiment with different types of encoding, such as using a Gated Recurrent Unit (GRU) or a Convolutional Neural Network (CNN), and attention mechanisms, such as comparing context-query attention layers and contemplating the usage of gates. We ultimately introduce a modified form of BiDAF which utilizes both an LSTM and a CNN in its encoding layer, as well as BiDAF’s context-query attention layer followed by R-Net’s self-attention layer. We conduct various experiments on the SQuAD datasets, yielding competitive results on the CS224N SQuAD Leaderboard.
ASYNC-RED: A PROVABLY CONVERGENT ASYN-

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing on multicore systems. We address this issue by proposing a new asynchronous RED (ASYNC-RED) algorithm that enables asynchronous parallel processing of data, making it significantly faster than its serial counterparts for large-scale inverse problems. The computational complexity of ASYNC-RED is further reduced by using a random subset of measurements at every iteration. We present complete theoretical analysis of the algorithm by establishing its convergence under explicit assumptions on the data-fidelity and the denoiser. We validate ASYNC-RED on image recovery using pre-trained deep denoisers as priors.
Business contributions to the sustainable development goals through community sustainability partnerships

Purpose This study aims to understand how businesses can contribute to the achievement of the UN sustainable development goals (SDGs) by implementing Local Agenda 21 (or equivalent) plans in partnership with other organizations situated in their city. To this end, the present study examines drivers and outcomes from the perspective of business partners, as well as their relationships to the SDGs. Design/methodology/approach Through a mixed-methods approach this research studies 71 businesses from four large cross-sector partnerships formed to achieve local sustainability goals. Data were collected through a survey to determine why firms partner and what outcomes they obtain from partnering. Qualitative content analyses are used to determine the relationships between business drivers and outcomes from partnering for local sustainability and the SDGs. Findings From a resource-based view (RBV) perspective, findings show the value of local sustainability partnerships in relation to the SDGs. Many SDG targets are aligned with the top reasons why businesses join large community sustainability partnerships. Also, through the outcomes achieved by participating in the partnership businesses can further the SDGs. Research limitations/implications This research contributes to the literature and to practice through the understanding of businesses partnering for local sustainability, and its relationships to global sustainability. Firstly, the connections of business partners to local and global sustainability are better understood. Of note is the contribution made to the literature on sustainability-related drivers and outcomes expanding and refining RBV literature. Secondly, a positive connection has been established between businesses and the SDGs, proposing a virtuous model of relationship that summarizes the findings from this research. And thirdly, large cross-sector social partnerships are better understood. Practical implications Small- and medium-sized enterprises and large corporations with local offices can further both local and global sustainable development by engaging in local cross-sector sustainability partnerships. Social implications These research findings are crucial for those leading sustainability initiatives, so they can engage businesses actively in light of the important role they play in society improving their contributions and the chances for sustainability partnerships to achieve their goals. Originality/value This research contributes to the scale conversation by exploring community sustainability partnerships as a means to understand how business engagement in sustainability at the local level can contribute to the achievement of the SDGs and, ultimately, to global sustainability.
Phase-Field Investigation on the Peritectic Transition in Fe-C System

Abstract We adopt a thermodynamically consistent multi-phase, multi-component phase-field model to investigate the morphological evolution of peritectic transition in carbon steel though 2-D and 3-D simulations. By using phase-field method, we rationalize the peritectic solidification in both 2-D and 3-D simulations under different liquid supersaturations as well as on the δ particle with distinct microstructures. Through the comparison between 2-D and 3-D simulation results, we clarify the reason for the different growth rate of γ phase in two and three dimensions. In 3-D simulation, we observe the unequal growth rate of γ phase in radial and axis directions. In addition, a novel measurement method is proposed to determine the dynamic contact angle. We anticipate that the simulation results can be applied to interpret the isothermal peritectic transition with a liquid supersaturation in alloys.
Examining Fine Art as an Alternative Investment

The 2008 economic crisis has greatly affected how people are choosing to invest their money. Many investors have been more cautious of investment opportunities and the risks they impose, which has led to an increased interest in alternative investments. People are starting to look beyond the typical alternative investments such as hedge funds, commodities, and private equities. In order to examine the possible gain from investing in art, I studied the returns in the art market compared with returns of other types of financial investments, in particular equities. In this paper, I have used an art index that was developed by Dr. Roman Kraeussl, which was created by employing a two-step hedonic regression methodology on art prices, devised by Kraeussl and van Esland (2008). When computing annualized returns I found that art yields the highest return at 12% followed by returns from the S&P 500 at 3.6%. Government bonds and treasury bills had negative annual returns of -8% and -17.5% respectively. Looking at the Sharpe Ratio, the results suggest that art is twice as risky as equities. A CAPM regression showed that the Top 500 Art Index moves in the same direction as the S&P 500 and had a correlation of 13.9%. Based on these results, I concluded that fine art is not a useful asset to hedge returns of the S&P 500. Though it has monetary value and possibilities of appreciation and high returns, there is too much risk involved for any investor to be convinced that art would be a reliable investment opportunity. There are many costs, such as transaction costs, commission rates, auction premiums, etc. that greatly influence the purchase of art that are different than for most financial assets. There are also social and emotional factors that affect the decision to buy art since most people who purchase artwork have some sort of connection to the piece. A great deal of uncertainty exists if you do not choose to buy art for its fiscal benefits, which increases its risk. Purchasing art for its financial incentive could be successful if the buyer has the money, is knowledgeable about the market, and has some attachment to the work of art.
POLITECNICO DI TORINO Master degree in Physics of Complex Systems Lossy compression and other hard optimization problems : a statistical physics approach

The task of data compression originally arises in the field of communications, where messages from a sourcemust be delivered to a receiver in themost compact form, possibly with loss of information. Besides the countless practical applications in engineering, data compression is interesting enough to be studied on its own, its foundations being rooted in probability theory, optimization and other related disciplines which are in turn deeply linked to statistical physics. The class of problems analyzed here can be described as follows: given a random bit string of fixed length, design a compressing algorithm that casts it into a shorter string and a de-compressing algorithm that is able to reconstruct the original message up to some error. A cunning approach from Information Theory addresses the challenge by setting up a constrained optimization problem. It just so happens that such problem is completely equivalent to that of finding the ground state of an Ising model defined on a graph that encodes the system of constraints. This opens up theway for a variety ofwell-known tools from statistical physics to be used in order to find an approximate solution that works in polynomial time, in our case the Belief Propagation algorithm. An efficient computer implementation is presented alongwith the results and a proof of exactness of the algorithm under some conditions is given. From the practical point of view, it was found that better performances are achieved by working with strings of numbers taking values in the so-called Galois Field GF(q), which can be thought of as a generalization of bits, taking values in 0, . , q − 1. From the theoretical point of view, the most relevant observation is that a small modification of the graph’s structure can lead to a remarkable increase in performance. There is good reason to think that this fact, together with the rest of our method, may be applied to other hard problems in the constrained optimization family.
Improve DistilIBERT-based Question Answering model performance on out-of-domain datasets by Mixing Right Experts

While large language representation model like BERT [1] shows its great potential to improve the performance of Question Answering (QA) system, such QA systems fail to extend its performance beyond in-domain datasets. This project aims to improve the performance of DistiIBERT-based QA model trained on in-domain datasets in out-of-domain datasets by only using provided datasets. We did extensive analysis cross in-domain and out-of-domain datasets to build essential insights, e.g. domain-finetuning may cause model performance to degenerate. By using such insights to mix expert models that are carefully chosen, we achieved F1 score of 61.7 (ranked 6th out of 74 in test leaderboard) and EM score of 44.4 (ranked 2nd out of 74 in test leaderboard) in out-of-domain test datasets as of March 19, 2021.
T HE B ENEFIT OF D ISTRACTION : D ENOISING R EMOTE V ITALS M EASUREMENTS USING I NVERSE A TTENTION

Attention is a powerful concept in computer vision. End-to-end networks that learn to focus selectively on regions of an image or video often perform strongly. However, other image regions, while not necessarily containing the signal of interest, may contain useful context. We present an approach that exploits the idea that statistics of noise may be shared between the regions that contain the signal of interest and those that do not. Our technique uses the inverse of an attention mask to generate a noise estimate that is then used to denoise temporal observations. We apply this to the task of camera-based physiological measurement. A convolutional attention network is used to learn which regions of a video contain the physiological signal and generate a preliminary estimate. A noise estimate is obtained by using the pixel intensities in the inverse regions of the learned attention mask, this in turn is used to reﬁne the estimate of the physiological signal. We perform experiments on two large benchmark datasets and show that this approach produces state-of-the-art results, increasing the signal-to-noise ratio by up to 5.8 dB, reducing heart rate and breathing rate estimation error by as much as 30%, recovering subtle pulse waveform dynamics, and generalizing from RGB to NIR videos without retraining.