Miracle-dz/UniFAR-FTUs huggingface dataset:
Twice as Hard to Get Half as Far? Differences in Sheepskin Effects Between Afro-Colombian and Non-Afro-Colombian Women

ABSTRACT This article explores Belman and Heywood’s sheepskin effect hypothesis using a modified Mincerian wage equation to test the sheepskin effect of returns on education in Colombia. This analysis is based on the 2014 Living Standards Survey from the National Department of Statistics. It includes variables that capture the possession of different levels of qualifications. A test for whether Afro-Colombian women obtain larger returns for signals of high productivity (e.g., a university degree) and lower returns for signals of low productivity (e.g., a secondary school diploma), compared to the non-Afro-Colombian women in the sample, found differential sheepskin effects. Afro-Colombian women in this sample received lower returns for low productivity signals (high school diplomas) than their counterparts do. In contrast, returns on higher levels of education (graduate studies) are higher for Afro-Colombian women than for the other analyzed group. Hence, the results suggest that Afro-Colombian women, as the saying goes, need to work twice as hard to get half as far. They need to rush to achieve higher levels of education, amidst severe hardship, to overcome the existing differences in the returns on education.
A Multidisciplinary Diabetes Clinic Improves Clinical and Behavioral Outcomes in a Primary Care Setting

Background: The percentage of adults achieving hemoglobin A1c goals less than 7% remains a challenge. The study objective was to evaluate effects of a multidisciplinary approach on behavioral outcomes and mean change in A1c in immediate start (intervention) versus 6-month delay (control) groups at 6 months. Methods: The study assessed 111 patients recruited from a safety-net primary care clinic with a pharmacist-led multidisciplinary team and found that the intervention improved mean A1c outcomes for patients with type 2 diabetes. A1c values were measured every 3 months, and a self-efficacy scale to measure behaviors was evaluated at baseline and 6 months. Results: After 6 months from baseline, the intervention group showed an A1c decrease of 2.4 compared with the control group’s 1.1 decrease. Mean increase in self-efficacy score in the intervention group at baseline versus after 6 months showed a statistically significant change (P = .01) compared with the control group (P = .26). Results revealed a post hoc association between A1c and PHQ-9 such that patients with higher baseline PHQ-9 scores experienced greater mean decrease in A1c. In the immediate start arm, mean A1c values decreased from 10.6 at baseline to 7.7 at month 12. For the delayed intervention group, mean A1c values decreased from 10.2 at baseline to 9.0 after 6 months. Conclusions: Use of a multidisciplinary clinic team in a safety-net primary care practice improved mean A1c control and behavioral outcomes for patients with type 2 diabetes as compared to control group.
Theoretical Investigations for the Defect Structure and the Spin Hamiltonian Parameters of Divalent Cobalt in LiMgPO4

The spin Hamiltonian parameters (SHPs, g factors and hyperfine structure constants) defect structure for LiMgPO4 doped with 0.1% Co2+ at 4.2 K are theoretically investigated from the perturbation formulas of the SHPs for a rhombically distorted octahedral 3d7 cluster. The impurity Co2+ on host Mg2+ site is found to suffer the larger axial relative compression ratio ρ (≈ 0.76%) and the planar angular variation Δφ (≈ 6.64°) related to the host oxygen octahedron due to size mismatch. The calculated SHPs based on the above defect structure show good agreement with the measured results. Present study can be helpful to the preparation and characterization for the local structures for transition-metal impurities in lithium-magnesium phosphate, which may is helpful to search for the phosphor materials with better dosimetric characteristics.
Structural-Mechanical Properties of Polyurethane Surface after Carbon Ion Subplantation

Ion-plasma modification of polymers has many potential applications, in particular, in the development of biomedical products. Treatment of soft polymers can easily damage the surface; low-energy plasma and subsequent investigation of the structural and mechanical properties of the surface are required. Polyurethane is a widely used block copolymer. Subplantation of carbon ions heterogeneously changes the structural and mechanical properties of the surface (relief, stiffness, thickness of the modified coating), forming a graphene-like nanolayer. Uniaxial deformation of the treated materials in some cases leads to the damage of the surface (local nanocracks, folds). Materials have increased hydrophobicity, good deformability (valid for certain treatment regimes) and can find application in design of products with improved biomedical properties.
Testing of Carbon Nanotubes and their Influence on the Physical and Mechanical Characteristics of Polyester Resin

Carbon nanotubes have been studied by scanning electron microscopy. Measurement modes - accelerating voltage 10 kV, current in microscopy mode 10-9 A. The sample was applied to a metal mesh. Spectroscopic data indicate that this sample consists of carbon (peak at 280 eV). There are no other intense peaks in the spectrum; no impurities were detected by this method. Determination of physical and mechanical characteristics was carried out on a testing machine model N5K-S.
Synthesis of Carbon Nitride Intercalation Compound Composite and Study of Visible Light-Induced Photocatalytic Performance

Novel visible light-induced carbon nitride intercalation compound (CNIC) with iron oxide composite photocatalyst with different content of iron oxide were synthesized through simple mixing and heat treatment. The composite photocatalyst was characterized by Fourier transform infrared (FT-IR) spectroscopy and BET surface area measurements. The photocatalytic oxidation ability of the novel composite photocatalyst was evaluated using methyl range (MO) as a target pollutant. The experimental results showed the composites with a content of iron oxide exhibited the higher photocatalytic activity than either single-phase iron oxide or CNIC under visible-light irradiation. The as-prepared composite photocatalyst exhibits an improved photocatalytic activity due to enhancement of photo-generated electron–hole separations at the interface.
Labeling enhancement of hydrophilic and hydrophobic molecules in tissue sections

In this study, the efficiency of uni- and bi-directional molecular diffusion in porcine liver tissue sections was studied. Tissue sections on the order of a few hundred microns in thickness were used. We found that bi-directional diffusion is more efficient in delivering probe molecules. Therefore, bi-directional labeling is useful in visualizing tissue architecture in three-dimensions.
Playbook: An Animation-Based Game for Improving Children's Creativity and Problem Solving Skills

In this paper, we describe the design, development and preliminary evaluation of a novel animation tool for smartphones and tablets, with the goal of improving creativity and problem-solving ability among novice users, mainly young children. The user first draws the objects with markers and crayons on paper and takes a photo with their mobile or tablet to import their drawings into the app as game sprites. They then design their animation scene and point to a part of the character and selecting its animation type from existing buttons. Hence the selected part is animated in a vertex shader while ensuring a smooth transition with the non-pointed part. The results of a preliminary evaluation showed that young children enjoyed interacting with our tool and would recommend it to their friends. Keywords and Phrases: Creativity; video games; visual animation; ease of use.
Hybrid solution of challenges future problems in the new generation of the artificial intelligence industry used operations research industrial processes

Key technologies such as a new generation of industrial systems highly depends on artificial intelligence, and electronic physical systems that can digitize the entire supply chain together with data mining, machine learning, and more. At present, uses of artificial intelligence-based solutions are very important to improve the accuracy and efficiency of production processes. Artificial intelligence (AI) is playing a key role in the fourth industrial revolution, and we see significant improvements in different methods of machine learning. Artificial intelligence is widely used by practitioner engineers to solve various problems. This journal provides an international forum for quick articles that describes the practical application of artificial intelligence in all areas of mechanical engineering. Many researchers cited the development of technology in industrial fields to reduce problems in industry. Both the Operations Research (OR) community and Artificial Intelligence (AI) show that these problems are still interesting. While AI focuses linearly on increasing production and mitigating industry difficulties that may be seen as a revolution in the future. AI techniques offer a richer and more flexible presentation of real problems. The article presents the architecture of the industrial laboratory and the challenges associated with the use of artificial intelligence in industrial processes.
A Survey of Similarity Measures for Time stamped Temporal Datasets

Temporal transactional databases are transactional databases which store data in a temporal aspect. Usage of similarity of measures in temporal data mining tasks have gained significant importance to retrieve information and interesting patterns in data. It is always crucial to understand and decide what similarity measure we should use while performing a data mining task and this is always driven by the actual data and nature of the temporal data sets. The main objective of this research is to perform a detailed survey of the various similarity measures used in the temporal data mining in recent research contributions. This paper also provides insights on how these similarity measures are used in the Temporal association rule mining algorithms based on the works carried out in the literature.
BADUSB-C: Revisiting BadUSB with Type-C

The security of the Universal Serial Bus (USB) protocol has been paid extensive attention to because of its wide usage. Due to the trust-by-default characteristics, USB security has caused severe problems. For example, a well-known firmware attack, BadUSB, performs malicious operations on the victim hosts through disguising ordinary USB devices as human interface devices like keyboards and mice. However, BadUSB suffers from several limitations. Attackers cannot obtain the status of User Interface (UI) to conduct precise attacks and get the visual feedback of their attacks. In this work, we extended BadUSB to support the new USB Type-C features and proposed a multi-mode attack model, BADUSB-C. This obtains UI status to make attacks more precise and effective. To the best of our knowledge, BADUSB-C is the first attack model utilizing USB Type-C. To validate the usability and effectiveness, we conducted extensive experiments to simulate daily usage and summarized the private information collected. We also discussed the recommended countermeasures for our attack model, including isolated UI rendering, which may be inspiring for future research on defense methods.
SECA: A Feedback Rules Model in a Ubiquitous Microlearning Context

Understanding learner behavior is the key to the success of any learning process. The more we know about learners, the more likely we are to personalize learning experiences and provide successful feedback. This paper presents a feedback rules model called SECA: (i) Scenario, that defines the context behavior in a microlearning environment, (ii) Event, provided by a predictive model, (iii) Condition, that evaluates the events, and (iv) Action, that provides the learner’s feedback. The proposal is achieved through a controlled experiment in which a microlearning environment is available to collect data from a ubiquitous context, and predictive analytics are applied to guide the definition of a set of feedback rules intended to support the learner’s learning process. In the end, we presented an exemplified set of feedback rules, which could be used to provide automatic recommendations and improve the learner experience. Thus, the experiment allows us to analyze the learner behavior in a ubiquitous microlearning context from a feedback perspective.
Risk Assessment in Smartphones: Comprehensive Analysis

Smartphones are multi-purpose and widely used devices in various fields, in terms of personal use, and companies and governments. Increasing dependence on them opened the way for increased violations on its users, as it became a fat substance for criminals, for their illegal practices, so it encountered serious and targeted security threats. This paper presents a set of rules and controls that will reduce the risks that threaten smartphone users. Furthermore, this work provides the methods of risk assessment that are related to smartphones. It identifies the assets and lists several different threats that are relevant. Each risk has its impact on the assets, this is described by mapping it with the likelihood of each threat.
EchoVib: Exploring Voice Authentication via Unique Non-Linear Vibrations of Short Replayed Speech

Recent advances in speaker verification and speech processing technology have seen voice authentication being adopted on a wide scale in commercial applications like online banking and customer care support and on devices such as smartphones and IoT voice assistant systems. However, it has been shown that the current voice authentication systems can be ineffective against voice synthesis attacks that mimic a user's voice to high precision. In this work, we suggest a paradigm shift from the traditional voice authentication systems operating in the audio domain but susceptible to speech synthesis attacks (in the same audio domain). We leverage a motion sensor's capability to pick up phonatory vibrations, that can help to uniquely identify a user via voice signatures in the vibration domain. The user's speech is played/echoed back by a device's speaker for a short duration (hence our method is termed EchoVib) and the resulting non-linear phonatory vibrations are picked up by the motion sensor for speaker recognition. The uniqueness of the device's speaker and its accelerometer results in a device-specific fingerprint in response to the echoed speech. The use of the vibration domain and its non-linear relationship with audio allows EchoVib to resist the state-of-the-art voice synthesis attacks, shown to be successful in the audio domain. We develop an instance of EchoVib using the onboard loudspeaker and the accelerometer embedded in smartphones, as the authenticator, based on machine learning techniques. Our evaluation shows that even with the low-quality loudspeaker and the low-sampling rate of accelerometer recordings, EchoVib can identify users with an accuracy of over 90%. We also analyze our system against state-of-art-voice synthesis attacks and show that it can distinguish between the morphed and the original speaker's voice samples, correctly rejecting the morphed samples with a success rate of 85% for voice conversion and voice modeling attacks. We believe that using the vibration domain to detect synthesized speech attacks is effective due to the hardness of preserving the unique phonatory vibration signatures and is difficult to mimic due to the non-linear mapping of the unique speaker and accelerometer response in the vibration domain to the voice in the audio domain.
Detecting Overlapping Communities in Social Networks Using A Modified Segmentation by Weighted Aggregation Approach

Detecting communities of common behaviors, interests, and interactions in social networks is essential to model a network's structure. Overlapping community detection is an NP-Hard problem. Several solutions have been proposed; however, most of these techniques are computationally expensive. We have developed a fast-hierarchical algorithm using the notion of segmentation by weighted aggregation. Experimental results on synthetic and real benchmark networks show that the proposed algorithm effectively finds communities (Clusters) with varied overlap and non-exhaustiveness structures. Our method outperforms the state-of-the-art hierarchical clustering algorithms measured by the F-measure and the computational time.
Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review.
A Deep Multi-Task Learning Framework for Brain Tumor Segmentation

Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.
Interactive Photo Editing on Smartphones via Intrinsic Decomposition

Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel‐value, is highly under‐constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth‐based priors, which nowadays is easy to acquire with high‐end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB‐D images on smartphones and the algorithmic as well as design choices therein. Unlike state‐of‐the‐art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi‐scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non‐convex optimization (iPiano) to ensure albedo sparsity. Our GPU‐based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high‐quality outputs. Furthermore, our proposed approach for specularity removal outperforms state‐of‐the‐art approaches for real‐world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.
State of the Art on Computational Design of Assemblies with Rigid Parts

An assembly refers to a collection of parts joined together to achieve a specific form and/or functionality. Designing assemblies is a non‐trivial task as a slight local modification on a part's geometry or its joining method could have a global impact on the structural and/or functional performance of the whole assembly. Assemblies can be classified as structures that transmit force to carry loads and mechanisms that transfer motion and force to perform mechanical work. In this state‐of‐the‐art report, we focus on computational design of structures with rigid parts, which generally can be formulated as a geometric modeling and optimization problem. We broadly classify existing computational design approaches, mainly from the computer graphics community, according to high‐level design objectives, including fabricability, structural stability, reconfigurability, and tileability. Computational analysis of various aspects of assemblies is an integral component in these design approaches. We review different classes of computational analysis and design methods, discuss their strengths and limitations, make connections among them, and propose possible directions for future research.
Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

Combining diverse sampling techniques via multiple importance sampling (MIS) is key to achieving robustness in modern Monte Carlo light transport simulation. Many such methods additionally employ correlated path sampling to boost efficiency. Photon mapping, bidirectional path tracing, and path‐reuse algorithms construct sets of paths that share a common prefix. This correlation is ignored by classical MIS heuristics, which can result in poor technique combination and noisy images. We propose a practical and robust solution to that problem. Our idea is to incorporate correlation knowledge into the balance heuristic, based on known path densities that are already required for MIS. This correlation‐aware heuristic can achieve considerably lower error than the balance heuristic, while avoiding computational and memory overhead.