Miracle-dz/UniFAR-FTUs huggingface dataset:
No Fragile Family Left Behind - Targeted Indicators of Academic Performance

Academic performance is a key component in the development and subsequent empowerment of youth. It is affected by a large number of different factors, such as inherent ability and socioeconomic circumstance, which can vary widely amongst different individuals. In particular, children from disadvantaged families face unique challenges that do not occur in normal families. We analyze the Fragile Families Challenge (FFC) dataset using data science algorithms, and study the relationship between the features reported and GPA scores. We grouped GPA scores into three groups (top, middle and low) and used a random forest classifier to predict the GPA class for each subject. Then, we used a recently developed algorithm---Local Interpretable Model-Agnostic Explanations (LIME)---to cluster subjects into subgroups based on the factors affecting each individual. We further analyzed the clusters to elucidate the differences occurring within different subgroups in the community of disadvantaged individuals and figure out which set of features matter for each subgroup. Conventional studies seek factors which apply to all members of a population, and often overlook the unique needs of individuals with different backgrounds and characteristics or divide individuals into predetermined subgroups and study which factors affect each subgroup. The approach used here can find correlations which are specific to individual subjects, and can inform the formulation of targeted and effective intervention strategies. Our study contributes to the field of social science by highlighting the differences of indicators of academic performance in different subgroups. Also, our novel data science pipeline contributes to the fields of data science and computational social science.
Capturing real-world gaze behaviour: live and unplugged

Understanding human gaze behaviour has benefits from scientific understanding to many application domains. Current practices constrain possible use cases, requiring experimentation restricted to a lab setting or controlled environment. In this paper, we demonstrate a flexible unconstrained end-to-end solution that allows for collection and analysis of gaze data in real-world settings. To achieve these objectives, rich 3D models of the real world are derived along with strategies for associating experimental eye-tracking data with these models. In particular, we demonstrate the strength of photogrammetry in allowing these capabilities to be realized, and demonstrate the first complete solution for 3D gaze analysis in large-scale outdoor environments using standard camera technology without fiducial markers. The paper also presents techniques for quantitative analysis and visualization of 3D gaze data. As a whole, the body of techniques presented provides a foundation for future research, with new opportunities for experimental studies and computational modeling efforts.
Detection and Recognition of U.S. Warning Signs on Curves

Traffic sign detection and recognition has been studied in multiple areas including civil and transportation engineering, automated driving, and computer vision. However, previous work has devoted relatively less attention to U.S. signs. Among all types of U.S. signs, warning signs are the most crucial to road safety. In this work, we propose a customized detection and recognition method for U.S. warning signs that does not require training. For detection, preprocessing with a two-layer HSV-B filter and style transfer reduces color bias and a substantial number of false positives. We formulate the recognition process as a template-matching problem in which pre-trained deep networks serve as feature extractors and we use the cosine distance as the distance metric. Best results on selected images from Georgia State Route 2 achieve above 90% precision and recall in detection and 92.6% accuracy in recognition. Further tests on large datasets demonstrate that the proposed method is promising, and it can support transportation agencies in the management of their warning sign assets.
Density-Based Multiscale Analysis for Clustering in Strong Noise Settings With Varying Densities

Finding meaningful clustering patterns in data can be very challenging when the clusters are of arbitrary shapes, different sizes, or densities, and especially when the data set contains high percentage (e.g., 80%) of noise. Unfortunately, most existing clustering techniques cannot properly handle this tough situation and often result in dramatically deteriorating performance. In this paper, a purposefully designed clustering algorithm called Density-Based Multiscale Analysis for Clustering (DBMAC)-II is proposed, which is an improved version of the latest strong-noise clustering algorithm DBMAC. DBMAC is proposed under the assumption that all clusters are homogeneous and cannot work well when the data set contains clusters of varying densities. DBMAC-II overcomes the limitation of DBMAC by executing the multiscale analysis iteratively and can conduct strong noise-robust clustering without any strict assumption on the shapes and densities of clusters. In DBMAC-II, each data point or object is mapped into a feature space using its <inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula>-neighborhood statistics with different <inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> (radius) values, which is similar to DBMAC. In general, the higher the value of <inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula>-neighborhood statistics, the more likely the object is considered as a “clustered” object. Instead of trying to find a single optimal <inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> value, a set of radius values appropriate for separating “clustered” objects and “noisy” objects is identified, using a formal statistical method for multimodality test, referred to as multiscale analysis. For clusters with varying densities, multiscale analysis is applied to extract the clusters with the highest density from the current data set iteratively. Moreover, a statistical uniformity test for measuring clustering tendency is used as the self-adaptive stopping criterion of the iteration. Comprehensive experimental studies on a series of challenging benchmark data sets demonstrate that DBMAC-II is not only superior to classical density-based clustering approaches, including DBSCAN, OPTICS, and HDBSCAN, but also can consistently outperform the latest strong-noise robust clustering techniques, such as Skinny-dip.
Voltage Reduction Technique for Use With Electrochemical Impedance Spectroscopy in High-Voltage Fuel Cell and Battery Systems

An attractive application of electrochemical impedance spectroscopy (EIS) is for diagnostic of a fuel cell (FC) or a battery system during operation. The use of EIS, however, is mostly limited to low-voltage (LV) FC systems and laboratory environments. Hence, the application of EIS in advanced diagnostics of a high-power system certainly lacks due to the voltage limitation and/or cost of the equipment. In this paper, a precision, low-cost electronic interface is proposed which enables the use of existing LV ac diagnostic tools with a production-size FC or battery stacks without the need for postprocessing of data. The interface, a dc level reducer (DLR), reduces only the dc component of the stack voltage to a safe voltage of <60 without altering the ac diagnostic components. This paper explains in detail the development of the DLR circuitry. The scalability and real-world capability of the interface are demonstrated by developing it for two voltage ratings. A set of circuits rated for 30 V is tested with a nine-cell proton exchange membrane FC (PEMFC) stack, and circuits rated for 200 V are tested on 90- and 110-cell commercial PEMFC stacks. The stack voltage is reduced by 60% on the nine-cell stack, and 60%–90% on the 90- and 110-cell stacks. The accuracy is measured using EIS data for 74 frequency points in the range of 0.1–20 kHz, with and without the DLR. The maximum relative error for point-versus-point comparison of the impedance is measured at 0.8% and 1.4% for 30- and 200-V rated circuits, respectively. These errors are well within the error of the industrial measurement equipment used, proving the fidelity of the ac signal output from the DLR.
Presenting BLUE: A robot for localization in unstructured environments

Mobile robotics researchers need real experimental platforms, real environments and real-time feedback. Simulators can boost development processes, but there is no substitute for the real world. In this work we present BLUE, a car-like robot designed to support research in autonomous navigation. We explain the process of turning a commercial electric cart into a fully operative robot that is easy to use and debug. Finally, we describe a number of outdoor experiments and provide a GitHub repository containing the ROS-based open-source software and the electronic designs.
Breast Cancer Risk : A Meta-analysis Breast Density and Parenchymal Patterns as Markers of Updated

Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for >14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.482.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and z75% relative to <5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range. (Cancer Epidemiol Biomarkers Prev 2006;15(6):1159–69)
IRT Modeling in the Presence of Zero-Inflation With Application to Psychiatric Disorder Severity

Item response theory (IRT) has been increasingly utilized in psychiatry for the purpose of describing the relationship among items in psychiatric disorder symptom batteries hypothesized to be indicators of an underlying latent continuous trait representing the severity of the psychiatric disorder. It is common to find zero-inflated (ZI) data such that a large proportion of the sample has none of the symptoms. It has been argued that standard IRT models of psychiatric disorder symptoms may be problematic due to the unipolar nature of many clinical traits. In the current article, the authors propose to address this by using a mixture model to approximate the unknown latent trait distribution in the IRT model while allowing for the presence of a non-pathological subgroup. The basic idea is that instead of assuming normality for the underlying trait, the latent trait will be allowed to follow a mixture of normals including a degenerate component that is fixed to represent a non-pathological group for whom the psychiatric symptoms simply are not relevant and hence are all expected to be zero. The authors demonstrate how the ZI mixture IRT method can be implemented in Mplus and present a simulation study comparing its performance with a standard IRT model assuming normality under different scenarios representative of psychiatric disorder symptom batteries. The model incorrectly assuming normality is shown to have biased discrimination and severity estimates. An application further illustrates the method using data from an alcohol use disorder criteria battery.
Design Space Exploration of Magnetic Tunnel Junction based Stochastic Computing in Deep Learning

Magnetic tunnel junction (MTJ) is considered as a promising memory candidate in the more than Moore era because of high power efficiency, fast access speed, nearly infinite endurance and easy 3D integration. The nondeterministic switching behavior has been profited to exploit new directions for computing methods, such as stochastic computing. In this paper, the application of stochastic switching behavior in stochastic computing is explored for deep neural network (DNN). Stochastic computing method features low logic complexity, low energy consumption and fine-grained parallelism, boosting the performance of DNN system by combining MTJ. As a key block of stochastic computing, MTJ based true random number generator design is presented in details. The functionality has been validated by combining the hardware design and post-processing in software. Simulation results are demonstrated visibly by handwritten digits recognition test to show the accuracy. Furthermore, the performance is investigated in terms of accuracy, energy consumption and memory occupation to find more efficient techniques.
In silico Phylogenetic Analysis of hAT Transposable Elements in Plants

Transposable elements of the hAT family exhibit a cross-kingdom distribution. The plant hAT transposons are proposed to play a critical role in plant adaptive evolution and DNA damage repair. The sequencing of an increasing number of plant genomes has facilitated the discovery of a plethora of hAT elements. This enabled us to perform an in-depth phylogenetic analysis of consensus hAT sequences in the fully-sequenced genomes of 11 plant species that represent diverse taxonomic divisions. Four putative nucleotide sequences were detected in cottonwood that were similar to the corresponding animal hAT elements, which are possibly sequence artifacts. Phylogenetic trees were constructed based both on the known and putative hAT sequences, by employing two different methods of phylogenetic inference. On the basis of the reconstructed phylogeny, plant hAT elements have rather evolved through kingdom-specific vertical gene transfer and gene amplifications within eudicotyledons, monocotyledons, and chlorophytes. Furthermore, the plant hAT sequences were searched for conserved DNA and amino acid sequence features. In this way, diagnostic sequence patterns were detected which allowed us to assign functional annotations to the plant hAT sequences.
SoCaST*: Personalized Event Recommendations for Event-Based Social Networks: A Multi-Criteria Decision Making Approach

In event-based social networks, user preferences mined from the influences of geographical locations, event categories, and social and temporal preferences have been exploited for event recommendations by assuming that each of these influences has the same weight for all users. However, in the reality, a user would have different degrees of importance for these influences on deciding whether to participate in an event. In this paper, we propose a personalized event recommendation framework called SoCaST*, which employs the multi-criteria decision making approach to rank events. In SoCaST*, preference models are built to compute geographical, categorical, social, and temporal influences, and a personalized weight is estimated for each criterion (i.e., each influence). By utilizing the personalized criterion’s weight, dominance intensity measures (i.e., dominating and dominated measures) are computed for alternatives (i.e., candidate events) of each criterion, and the set of alternatives is ranked based on the estimated dominance intensity measures to recommend $k$ top-ranked events. Extensive experiments are conducted based on two large real-world data sets collected from https://www.Meetup. com to evaluate the performance of SoCaST*. Experimental results show that SoCaST* performs better than the state-of-the-art techniques designed for event recommendations.
Exploiting side-information for resilient GNSS positioning in mobile phones

Today's smartphones widely exploit Global Navigation Satellite System (GNSS) as a positioning source, but the impact of spoofing attacks on these devices has never been extensively investigated. Resource constrained mobile phones are designed to provide position, velocity and time to the end user even in the most challenging environments, where the GNSS signal is hardly available in the first place. This approach pursues usability and energy efficiency rather than security, thus leaving the smartphones exposed to potential vulnerabilities. Spoofing attacks are becoming a serious threat to applications that rely on GNSS: building a GNSS spoofer barely requires some basic knowledge and a few hundred euros. Differently from autonomous GNSS receivers, mobile devices have access to redundant positioning information (Assisted GNSS (A-GNSS), sensors, etc.) that can be useful for security purposes. The aim of this paper is to point out some simple expedients and cross checks that do not require modifications to the devices hardware, allowing to enhance security at a minimum cost, increasing their resilience to spoofing attacks. To this purpose, we first assess whether currently available smartphones already employ these checks for security purposes. We exploit our GNSS signal generator to carry out a systematic test campaign on several devices from different brands. In the test results the inconsistency between the GNSS signal and other information sources was never reported to the user. Indeed, the result of a spoofing attack was often the computation of fake PVT coordinates, or in the best case, a denial of service with unknown cause.
Identifying Discriminative Attributes to Gain Insights Regarding Child Obesity in Hispanic Preschoolers Using Machine Learning Techniques

Childhood obesity is a significant problem in the United States, which affects millions of children and adolescents. Children who are obese have been found to be at greater risk for developing obesity-related health problems, such as cardiovascular disease, type 2 diabetes, and cancer later in life. Particularly, Hispanic preschoolers aged 2 to 5 years old have the highest overweight or obesity prevalence among all reported races and ethnic groups. Unfortunately, few research studies are available to identify the root cause of such a high obesity prevalence in this ethnic group. To address this issue, we recruited 238 Hispanic mothers of preschoolers to diagnose the social and epidemiological family conditions associated with barriers that challenge healthy eating. Both qualitative (focus groups, interviews) and quantitative (surveys) methods were used to assess participants behaviors. Based on the collected data, which is a large set of environmental, dietary, and feeding practices data, we utilized a well-known machine learning technique, C4.5 decision tree, to determine which variables might be important to gain insights about childhood obesity in Hispanic preschoolers. Machine learning techniques are particularly amenable to this study because they can reveal the relationship between variables as well as how each variable is related to child obesity.
New mechanism for repeated posted price auction with a strategic buyer without discounting

On ad exchange platforms the place for advertisement is sold through different kinds of auctions. However, it is not uncommon the situation where the seller repeatedly encounters only one buyer, thus the posted price auction degenerates into a monopoly-monopsony game with asymmetric information and nearly an infinite number of rounds; on each round the seller proposes the price and the buyer accepts or rejects it. I learned this problem from a discussion with members of Yandex research team and my main motivation was to find an incentive-compatible seller's strategy. In this short paper such a strategy is proposed and a corresponding distortion at the top type lower bound (Spence-Mirrlees property, actually) for the surplus of the buyer is established; this shows that the proposed strategy is the best possible. The key ingredients are the following. The main leash that the buyer has is the frequency of accepted deals. Once this frequency (as a function on the buyer's type) is fixed, the strategy randomly chooses between the {\it rewarding} price which incentivises the buyer to reveal his type (the higher the type, the more average surplus the buyer has), the {\it adaptation} price which allows the buyer to communicate that his type is higher then the current guess of the cook, and the {\it type confirmation} price which disincentivises the buyer to pretend that his type is higher than it is.
Contracts for Systems Design: Theory

Aircrafts, trains, cars, plants, distributed telecommunication military or health care systems, and more, involve systems design as a critical step. Complexity has caused system design times and costs to go severely over budget so as to threaten the health of entire industrial sectors. Heuristic methods and standard practices do not seem to scale with complexity so that novel design methods and tools based on a strong theoretical foundation are sorely needed. Model-based design as well as other methodologies such as layered and compositional design have been used recently but a unified intellectual framework with a complete design flow supported by formal tools is still lacking. Recently an “orthogonal” approach has been proposed that can be applied to all methodologies introduced thus far to provide a rigorous scaffolding for verification, analysis and abstraction/refinement: contractbased design. Several results have been obtained in this domain but a unified treatment of the topic that can help in putting contract-based design in perspective is missing. This paper intends to provide such treatment where contracts are precisely defined and characterized so that they can be used in design methodologies such as the ones mentioned above with no ambiguity. In addition, the paper provides an important link between interface and contract theories to show similarities and correspondences. This paper is complemented by a companion paper where contract based design is illustrated through use cases.
Saving Energy for Cloud Applications in Mobile Devices Using Nearby Resources

In this paper, we present a middleware to save energy in mobile computing devices that offload tasks to a remote server in the cloud. Saving energy in these devices is very important to prolong the battery life and avoid overheating. The middleware uses an available nearby device called auxiliary server either as a surrogate for the remote one, or as a proxy to pass the data between the mobile device and the remote server. The main idea is to reduce the energy consumption of the communication with the remote server by using a high-speed or a low-power local connection with the auxiliary server instead. The paper also analyzes when it is beneficial to use the auxiliary server based on the response time from the remote server and the bandwidth of the remote connection. The proposed middleware is evaluated using different benchmarks, including commonly used applications in mobile devices, and simulations. Furthermore, it is compared to state-of-the art approaches in this area. The experiments show that The middleware is energy-efficient especially when the bandwidth of the remote communication is relatively low or the server is overloaded.