Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking of Computer Vision Algorithms for Driver Monitoring on Automotive-grade Devices

The continuing evolution of technologies in the automotive industry has led to the development of the so-called Advanced Driver Assistance Systems (ADAS). ADAS is the term used to describe vehicle-based intelligent safety systems designed to support the driver, with the aim to significantly improve his safety, and the driving safety in general. In terms of development, current ADAS technologies are based on control functions about the vehicle movements with respect to the objects and entities detected in the same environment (e.g., other vehicles, pedestrian, roads, etc.). However, there is an ever growing interest on the use of internal cameras to infer additional information regarding the driver status (e.g., weakness, level of attention). The purpose of such technologies is to provide accurate details about the environment in order to increase safety and smart driving. In the last few years, Computer Vision technology has achieved impressive results on several tasks related to recognition and detection of customized objects/entities on images and videos. However, automotive-grade devices’ hardware resources are limited, with respect to the once usually required for the implementation of modern Computer Vision algorithms. In this work, we present a benchmarking evaluation of a standard Computer Vision algorithm for the driver behaviour monitoring through face detection and analysis, comparing the performances obtained on a common laptop with the same experiments on an existing commercial automotive-grade device based on the Accordo5 processor by STMicroelectronics.
S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a $Q$ -learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter’s risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.
Automatic and objective extraction of channel networks from topography in systems

34 Automatic and objective extraction of channel networks from topography in systems 35 with multiple interconnected channels, like braided rivers and estuaries, remains a ma36 jor challenge in hydrology and geomorphology. Representing channelized systems as 37 networks provides a mathematical framework for analyzing transport and geomorphol38 ogy. In this paper, we introduce a mathematically rigorous methodology and software 39 for extracting channel network topology and geometry from digital elevation models 40 (DEMs) and analyze such channel networks in estuaries and braided rivers. Channels 41 are represented as network links, while channel confluences and bifurcations are rep42 resented as network nodes. We analyze and compare DEMs from the field and those 43 generated by numerical modeling. We introduce a metric called the sand function 44 that characterizes the volume of deposited material separating channels to quantify 45 the spatial scale attributed to each link. Scale asymmetry is observed in the majority 46 of links downstream of bifurcations, indicating geometric asymmetry and bifurcation 47 stability. The length of links relative to system size scales with sand function scale to 48 the power of 0.24-0.35, while the number of nodes decreases against system scale and 49 does not exhibit power-law behavior. Link depth distributions indicate that the estu50 aries studied tend to organize around a deep main channel that exists at the largest 51 scale while braided rivers have channel depths that are more evenly distributed across 52 scales. The methods and results presented establish a benchmark for quantifying the 53 topology and geometry of multi-channel networks from DEMs with an automatic and 54 objective tool. 55 Plain Language Summary 56 Channels are features of the Earth’s surface that carry water, sediment, nutrients, 57 and organisms across the continents towards the coasts. Scientists have long recognized 58 that knowing the shapes, sizes and connections of channels in rivers, estuaries and 59 deltas is vital for predicting future change. A useful way to represent channels is with 60 a network. However, automatically identifying channel networks from surface elevation 61 has been a major challenge because channels display a wide range of different shapes, 62 sizes, and patterns, and often have many intersections with other channels. We have 63 developed a method for identifying channel networks from height maps. We first find 64 the “lowest path” in a channel network, meaning the channel that is at generally lower 65 elevations than all other channels. We subsequently find the next lowest paths, where 66 the measure for channel separation is the volume of sand that needs to be displaced to 67 join neighboring channels. This method allows us to identify the channel network. We 68 show previously unknown similarities and differences between the channel networks 69 of estuaries and wide rivers with sand bars. Our work helps researchers more fully 70 understand and predict how channel networks develop and evolve. 71
Did pastoralism delay the collapse of the green Sahara 6000 years ago?

During the early Holocene, northern Africa was relatively humid and supported a large human population. Around 5,500 years ago (5.5 ka) the regional climate deteriorated and the population declined substantially. Evidence suggests these changes were coeval, but it is unclear whether or not humans influenced this ecological collapse. Using a climate-vegetation model driven by global forcings, we forecast the length of the Holocene African Humid Period (AHP). The model indicates that the system was most likely to collapse between 7-6 ka, which is at least 500 years before the observed collapse. Archaeological and ethnographic evidence from northern Africa, suggests that the shift from hunter-gatherer to pastoralist societies around 7 ka was an effective adaptation to the orbitally-driven regional environmental changes. The pastoralist strategy may even have slowed the deterioration caused by orbital driven climate change. This supports the view that modern pastoralists are beneficial for the management of the world’s dryland environments.
A Theoretical Model for Information Security Policy Compliance Culture

This paper provides a different perspective on information security management by investigating information security policy compliance culture. The results in this paper are drawn from the thesis in which the researchers sought to address the gap by employing a mixed method in developing a theoretical model. The resulting theoretical model was then subjected to a validation process through Confirmatory Factor Analysis using JASP-analytical software. Hypotheses were derived from the emergent model that formed the basis of developing the questionnaire instrument. This paper, therefore, presents the results of the validation process and synthesizes the final theoretical model constructs that explain information security policy compliance culture. The results validated the theoretical model with factor loading all above (0.5) thresholds and significance of (p < 0.001). The resulting model showed that information security managers should consider organizational, behavioral, and external factors while developing information security policy compliance culture strategies.
Gas Entrainment at Free Surface of Liquid, (I)

The air entrainment mechanisms and rate at the free surface of water were investigated experimentally for a drawing flow into a vertical suction pipe in a cylindrical test vessel. It was found that the flow mechanisms of air entrainment were grouped into three typical types; (1) the vortex-induced entrainment, (2) the conically depressed surface with entrainment of relatively large bubbles, and (3) the falling flow along the side wall of the suction pipe with entrainment of fine bubbles. In the absence of a forced circulation, the air flow rate of the vortex-induced entrainment was well correlated by the Froude number. The proposed analytical model for the air entrainment rate of the vortex-in-duced entrainment agreed well with the experimental results in the presence of a forced circulation. The transition mechanisms between three types of entrainment were investigated, and it was found that the entrainment rate has the minimum value in the transition between the vortex-induced entrainment and the fallin.
Organic depth profiling of C60 and C60/phthalocyanine layers using argon clusters

Molecular semiconductor devices, such as light‐emitting diodes and photovoltaic cells, have recently received considerable attention because of their compatibility with flexible substrates and large‐area applications. Because of the importance of the interfacial properties for the performance of the devices, these organic (multi)layers constitute an important field of application for molecular depth profiling by SIMS. In this contribution, we investigate the use of C60n+ and Ar1000–2000+ cluster projectiles at different energies (ranging from 2.5 to 20 keV) as sputter ions for the organic depth profiling of fullerene‐based films and heterojunctions. The bilayers consist of C60 fullerenes on tin phthalocyanine (SnPc), deposited on silicon substrates. Our preliminary results showed that C60 films could not be successfully profiled using C60n+ ions in regular analysis conditions (room temperature). In contrast, with Ar clusters, the depth profiling is successful (except for 20 keV Ar1000) and the sputtered volume shows a linear relationship with the Ar cluster energy. Surprisingly, for a given total energy of the projectiles, Ar2000 sputters approximately two times more than Ar1000. The observations are tentatively explained as being the result of a balance between the sputtering and the cross‐linking efficiency for the different bombardment conditions, larger clusters being expected to naturally induce less cross‐linking than smaller clusters with the same total energy. Copyright © 2012 John Wiley & Sons, Ltd.
Revisiting graph neural networks and distance encoding from a practical view

Graph neural networks (GNNs) are widely used in the applications based on graph structured data, such as node classification and link prediction. However, GNNs are often used as a black-box tool and rarely get in-depth investigated regarding whether they fit certain applications that may have various properties. A recently proposed technique distance encoding (DE) (Li et al. 2020) magically makes GNNs work well in many applications, including node classification and link prediction. The theory provided in (Li et al. 2020) supports DE by proving that DE improves the representation power of GNNs. However, it is not obvious how the theory assists the applications accordingly. Here, we revisit GNNs and DE from a more practical point of view. We want to explain how DE makes GNNs fit for node classification and link prediction. Specifically, for link prediction, DE can be viewed as a way to establish correlations between a pair of node representations. For node classification, the problem becomes more complicated as different classification tasks may hold node labels that indicate different physical meanings. We focus on the most widely-considered node classification scenarios and categorize the node labels into two types, community type and structure type, and then analyze different mechanisms that GNNs adopt to predict these two types of labels. We also run extensive experiments to compare eight different configurations of GNNs paired with DE to predict node labels over eight real-world graphs. The results demonstrate the uniform effectiveness of DE to predict structure-type labels. Lastly, we reach three pieces of conclusions on how to use GNNs and DE properly in tasks of node classification.
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the proposal of fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates. However, there is little to no work that explores whether fair ranking algorithms actually improve real world outcomes (e.g., hiring decisions) for underrepresented groups. Furthermore, there is no clear understanding as to how other factors (e.g., job context, inherent biases of the employers) may impact the efficacy of fair ranking in practice. In this work, we analyze various sources of gender biases in online hiring platforms, including the job context and inherent biases of employers and establish how these factors interact with ranking algorithms to affect hiring decisions. To the best of our knowledge, this work makes the first attempt at studying the interplay between the aforementioned factors in the context of online hiring. We carry out a large-scale user study simulating online hiring scenarios with data from TaskRabbit, a popular online freelancing site. Our results demonstrate that while fair ranking algorithms generally improve the selection rates of underrepresented minorities, their effectiveness relies heavily on the job contexts and candidate profiles.
Farthest sampling segmentation of triangulated surfaces

In this paper we introduce Farthest Sampling Segmentation (FSS), a new method for segmentation of triangulated surfaces, which consists of two fundamental steps: the computation of a submatrix $W^k$ of the affinity matrix $W$ and the application of the k-means clustering algorithm to the rows of $W^k$. The submatrix $W^k$ is obtained computing the affinity between all triangles and only a few special triangles: those which are farthest in the defined metric. This is equivalent to select a sample of columns of $W$ without constructing it completely. The proposed method is computationally cheaper than other segmentation algorithms, since it only calculates few columns of $W$ and it does not require the eigendecomposition of $W$ or of any submatrix of $W$. We prove that the orthogonal projection of $W$ on the space generated by the columns of $W^k$ coincides with the orthogonal projection of $W$ on the space generated by the $k$ eigenvectors computed by Nystr\"om's method using the columns of $W^k$ as a sample of $W$. Further, it is shown that for increasing size $k$, the proximity relationship among the rows of $W^k$ tends to faithfully reflect the proximity among the corresponding rows of $W$. The FSS method does not depend on parameters that must be tuned by hand and it is very flexible, since it can handle any metric to define the distance between triangles. Numerical experiments with several metrics and a large variety of 3D triangular meshes show that the segmentations obtained computing less than the 10% of columns $W$ are as good as those obtained from clustering the rows of the full matrix $W$.
The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods.

The use of observational time series data to assess the impact of multi-time point interventions is becoming increasingly common as more health and activity data are collected and digitized via wearables, social media, and electronic health records. Such time series may involve hundreds or thousands of irregularly sampled observations. One common analysis approach is to simplify such time series by first discretizing them into sequences before applying a discrete-time estimation method that adjusts for time-dependent confounding. In certain settings, this discretization results in sequences with many time points; however, the empirical properties of longitudinal causal estimators have not been systematically compared on long sequences. We compare three representative longitudinal causal estimation methods on simulated and real clinical data. Our simulations and analyses assume a Markov structure and that longitudinal treatments/exposures are binary-valued and have at most a single jump point. We identify sources of bias that arise from temporally discretizing the data and provide practical guidance for discretizing data and choosing between methods when working with long sequences. Additionally, we compare these estimators on real electronic health record data, evaluating the impact of early treatment for patients with a life-threatening complication of infection called sepsis.
Hypergeometric viable models in f(R) gravity

A cosmologically viable hypergeometric model within the framework of the modified gravity theory f(R) has been derived based on the requirements of asymptotic behavior towards ΛCDM, the presence of an inflection point in the f(R) curve, and the viability conditions dictated by the phase space curves (m, r), where m and r denote characteristic functions of the model. To examine the constraints associated with these viability criteria, the models were expressed in terms of a dimensionless variable, namely R → x and f(R) → y(x) = x + h(x) + λ, where h(x) represents the deviation of the model from General Relativity. By employing the geometric properties imposed by the inflection point, differential equations were formulated to establish the relationship between h′(x) and h″(x). The resulting solutions yielded models of the Starobinsky (2007) and Hu-Sawicki types. However, it was subsequently discovered that these differential equations correspond to specific cases of a hypergeometric differential equation, indicating that these models can be derived from a more general hypergeometric model. The parameter domains of this model were thoroughly analyzed to ensure its viability.
The Unintended Consequences of Stay-at-Home Policies on Work Outcomes: The Impacts of Lockdown Orders on Content Creation

The COVID-19 pandemic has posed an unprecedented challenge to individuals around the globe. To mitigate the spread of the virus, many states in the U.S. issued lockdown orders to urge their residents to stay at their homes, avoid get-togethers, and minimize physical interactions. While many offline workers are experiencing significant challenges performing their duties, digital technologies have provided ample tools for individuals to continue working and to maintain their productivity. Although using digital platforms to build resilience in remote work is effective, other aspects of remote work (beyond the continuation of work) should also be considered in gauging true resilience. In this study, we focus on content creators, and investigate how restrictions in individual’s physical environment impact their online content creation behavior. Exploiting a natural experimental setting wherein four states issued state-wide lockdown orders on the same day whereas five states never issued a lockdown order, and using a unique dataset collected from a short video-sharing social media platform, we study the impact of lockdown orders on content creators’ behaviors in terms of content volume, content novelty, and content optimism. We combined econometric methods (difference-in-differences estimations of a matched sample) with machine learning-based natural language processing to show that on average, compared to the users residing in non-lockdown states, the users residing in lockdown states create more content after the lockdown order enforcement. However, we find a decrease in the novelty level and optimism of the content generated by the latter group. Our findings have important contributions to the digital resilience literature and shed light on managers’ decision-making process related to the adjustment of employees’ work mode in the long run. Our findings also have important implications for designing digital platforms that are used for building resilience in an ecosystem.
Diversifying Relevant Phrases

Diverse keyword suggestions for a given landing page or matching queries to diverse documents is an active research area in online advertising. Modern search engines provide advertisers with products like Dynamic Search Ads and Smart Campaigns where they extract meaningful keywords/phrases from the advertiser's product inventory. These keywords/phrases are representative of a diverse spectrum of advertiser's interests. In this paper, we address the problem of obtaining relevant yet diverse keywords/phrases for any given document. We formulate this as an optimization problem, maximizing the parameterized trade-off between diversity and relevance constrained over number of possible keywords/phrases. We show that this is a combinatorial NP-hard optimization problem. We propose two approaches based on convex relaxations varying in complexity and performance. In the first approach, we show that the optimization problem reduces to an eigen value problem. In the second approach, we show that the optimization problem reduces to minimizing a quadratic form over an l1-ball. Subsequently, we show that this is equivalent to a semi-definite optimization problem. To prove the efficacy of our proposed formulation, we evaluate it on various real-world datasets and compare it to the state-of-the-art heuristic approaches.
ON Effect of Right-Half-Plane Zero Present in Buck Converters With Input Current Source in Wireless Power Receiver Systems

In wireless power receiver systems, the buck converter is widely used to step down the higher rectified voltage derived from the wireless receiver coil, to a lower output voltage for the immediate battery charging process. In this article, the presence and effect of the right-half-plane (RHP) zeros found in the small-signal inductor-current-to-duty-ratio and output-voltage-to-duty-ratio transfer functions of the buck converter in the wireless power receiver system on the control performance, are investigated. It is found and mathematically proved that the RHP zeros are introduced by the current source nature of the system attributed to the series-series compensation and finite dc-link capacitance. The RHP zero not only results in nonmonotonic open-loop dynamic response but also complicates the design of feedback control and causes potential closed-loop instability. Theoretical and experimental results are provided to validate the presence of the RHP zeros and their effect on open-loop and closed-loop dynamic responses.