Miracle-dz/UniFAR-FTUs huggingface dataset:
VideOlympics : Real-Time Evaluation of Multimedia Retrieval Systems

Alexander G. Hauptmann Carnegie Mellon University Interactive prototypes are often the best way to convince an audience of a new multimedia technology’s possible impact. Because of its dynamic audiovisual nature, a multimedia application demonstration communicates applied science more effectively than a static description in a journal publication would. Ideally, a multimedia demonstrator grasps the audience’s attention by presenting effective results, advanced multimodal interfaces, novel means of user interaction, or combinations thereof. An interactive demo offers researchers a means to engage their audience in a way that they could never achieve with a written manuscript alone. All major multimedia conferences have adopted demo sessions where researchers, equipped with their laptop-installed system, show their demo to the conference attendees on a one-on-one basis. While effective, these demo sessions lack a common denominator. Individual systems provide solutions for different tasks on different data sets, and the conference attendees have to become familiar with the peculiarities of each individual demo. Moreover, a one-on-one demo session makes it infeasible to present a system to the entire conference audience. In addition, due to their lack of focus, the impact of demo sessions on the audience is suboptimal. Establishing focus A good way to establish focus is letting several demo systems solve the same problem on a similar data set. A notable example of such a focused effort takes place at the demo session at the National Institute for Standards and Technology’s (NIST’s) Trecvid workshop (see the ‘‘Trecvid Interactive Video Retrieval Task’’ sidebar). Trecvid promotes progress in video retrieval by providing a large video collection, common retrieval tasks, uniform evaluation procedures, and a forum for researchers interested in comparing their results. In the months preceding the workshop, researchers work on a common retrieval problem. Results are submitted offline and then presented to the benchmark participants during the workshop. In the Trecvid demo session, participants showcase their video-retrieval systems. Because the audience is knowledgeable in the problem area and the task at hand, the result is a lively demo session where the audience gains deep insight in various video-retrieval systems. Unfortunately, the audience at this event is limited to researchers who participate in the evaluation campaign. Of course, the videoretrieval systems are shown at regular demo sessions, but they’re never exposed simultaneously to an audience. A common data set and a common task aid in establishing a focused demo session, but these aren’t sufficient to engage an uninformed audience.
Printed Circuit Board Defect Detection Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor real-time performance in the printed circuit board (PCB) defect detection, a PCB defect detection method based on the improved YOLOv5 is proposed, which integrates the module of multiscale detection, attention mechanism and multi-branch. A shallow detection layer is added to detect smaller defect targets and fused with features of the deep network. An optimized anchor clustering method was used to obtain a more suitable size for the dataset. The Convolutional Block Attention Module (CBAM) is introduced to reweight and assign important feature channels to learn more valuable features. The re-parameterization convolution (RepConv) module is integrated to decouple the multi-branch training model into a single-way inference model by structural re-parameterization, which improves the model’s training performance and reduces inference time. The experimental results show that the detection accuracy of the proposed algorithm reaches 98.3% on the extended dataset, which is 3.4% higher than that of the original algorithm. At the same time, a real-time detection performance of 63 FPS is achieved, which satisfies the detection requirements of the PCB.
Ziv-Zakai Bound for Compressive Time Delay Estimation From Zero-Mean Gaussian Signal

Existing stochastic Ziv-Zakai bound (ZZB) for compressive time delay estimation from compressed measurement relies on a Gaussian approximation, which makes it inaccurate in the asymptotic region when the stochastic component dominates the received signals. In this letter, we apply different random projections on zero-mean Gaussian received signal to obtain multiple compressed measurements, based on which the log-likelihood ratio test is exactly formulated as the difference of two generalized integer Gamma variables. Accordingly, we further derive the exact expression of the stochastic ZZB for compressive time delay estimation from zero-mean Gaussian signal. Simulation results show that the derived ZZB is globally tight to accurately predict the estimation performance regardless of the number of compressed measurements, and it can also accurately predict the threshold signal-to-noise ratio for the estimator when the number of compressed measurements is large.
Open-Circuit Fault Diagnosis Based on Deep Learning for Four-Level Active Neutral-Point Clamped Inverters

In this paper, a deep learning-based fault diagnosis has been proposed for improving reliability when detect faulty switches of four-level active neutral point clamped (ANPC) inverters. The proposed method detects an open-circuit fault in a single switching device by training a 1-D Convolutional Neural Network (1-D CNN) to extract the periodic feature of pole voltage, which can be done without adding any algorithm for feature extraction. Due to the increased number of switches and conduction paths compared to a two-level inverter, the other method requires complex mathematical equations, threshold settings, and theoretical knowledge of the topology. However, the proposed data-driven deep learning-based method can be made simpler and more robust under different system conditions without using mathematical equations. Simulation results have validated the feasibility of the proposed method that show the diagnostic average accuracy rate reached 99.53 % with conditions that were untrained in the deep learning model.
Recent advances in the molecular pathology, cell biology and genetics of ciliopathies

Primary cilia have a broad tissue distribution and are present on most cell types in the human body. Until recently, they were considered to be redundant organelles, but progress over the past 5 years has led to an understanding of their role in normal mammalian development. The class of inherited disorders that involve aberrant ciliary function are known as ciliopathies, and although their range of severity can vary, they share some common and unexpected clinical phenotypes. The aim of this review is to assess recent insights into the structure, function and formation of primary cilia, and relate this to the pathology, molecular genetics and cell biology of the ciliopathies.
Accelerating the Search for Stable Full Heusler Compounds through Machine Learning

Applications for Heusler compounds are expanding in topological insulators, magnetocaloric, spintronics, and superconductivity areas. These substances are expanding the boundaries of science and offering answers to engineering problems. Our work demonstrates a discovery engine that can predict the crystal structures and chemical characteristics of 1107 Full Heusler compounds by implementing a Machine Learning approach trained with elemental descriptor data. Our approach is 50 times faster than rule-based and diffraction techniques, with a true positive rate of 0.99 for every random combination of elements on more than 1,000,000 candidates. We also compute the formation energies of these novel compounds to filter out 144 highly stable Heuslers that coincide with existing research and density functional theory trends to validate and support our findings.
Search for the Higgs Boson Produced in Association with a W Boson at CDF Run II

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions (p{bar p} {yields} W{sup {+-}}H {yields} {ell}{nu}b{bar b}) at a center of mass energy of 1.96 TeV. WH candidate events have a signature of a single lepton (E{sup {+-}}/{mu}{sup {+-}}), missing transverse energy, and two jets. The search looks for candidate events in approximately 2.7 fb{sup -1} of data recorded with the CDF II detector. The high-p{sub T} lepton (e,{mu}) in the events provides a distinct signature for triggering and most of the events in the dataset come from high-p{sub t} lepton triggers. Our analysis improves on prior searches by including events recorded on the E{sub T} + 2 Jets trigger with a lepton reconstructed as an isolated high-p{sub T} charged particle. We increase the sample purity by identifying ('tagging') long-lived b-hadrons in jets. A neural network combines distinguishing kinematic information into a function optimized for WH sensitivity. The neural network output distributions are consistent with the standard model background expectations and we set limits upper limits on the rate of Higgs production. We set 95% confidence level upper limits on the WH production cross section times branching ratio for Higgsmore » masses from 100 to 150 GeV/c{sup 2} and express our results as a ratio of the experimental limit to the theoretical Standard Model production rate. Our limits range from 3.6 (4.3 expected) to 61.1 (43.2 expected) for Higgs masses from 100 to 150 GeV/c{sup 2}, respectively. « less
Design Methodology for Single-Channel CNN-Based FER Systems

Facial Expression Recognition (FER) systems classify emotions by using geometrical approaches or Machine Learning (ML) algorithms such as Convolutional Neural Networks (CNNs). Due to their complexity, these FER systems need to be implemented on high-performance hardware, which makes them unsuitable for embedded devices. To address this challenge, we propose a methodology for the design of low-complexity, CNN-based FER systems. Our methodology includes data preprocessing, Local Binary Pattern (LBP) implementation, Data Augmentation (DA), and CNN design. Here, we also introduce the Model M6, a single-channel CNN that reaches an accuracy of 94% in less than 30 epochs. M6 has 306,182 parameters that correspond to 1.17 MB of memory. Therefore, our methodology and M6 model are feasible for implementation onto embedded systems capable of computing floating point operations. We validated our methodology and M6 model using 66 tests with 6 CNN models and 4 training parameters (batch size, learning rate, number of epochs, optimizer). This validation was performed using the Japanese Female Facial Expression (JAFFE) dataset and TensorFlow. In each test, the relationship between parameters, layers, overfitting, and underfitting was studied. Moreover, we present a step-by-step guideline on how to design the single-channel CNN and provide open-source code for readers interested in reproducing our work.
Web Injection and Banking Trojan Malware -A Systematic Literature Review

Banking trojan malware focuses on stealing credentials for financial services. A common technique used to facilitate this theft is web injection. Web injects work by modifying web page code or intercepting user input within a browser. Multiple papers in the literature discuss web injects and banking trojan malware; however, this paper extends the existing literature by answering key questions related to web inject usage in banking trojan malware.Specifically, this paper systematically analyzes the available literature to describe which threat actors use banking trojans, identify the malware families employing web injects, enumerate web injection techniques, and define the victims of banking trojans. To answer these questions, a 3-phase systematic literature review was conducted. In total 258 articles were reviewed and analyzed using a custom classification schema. The analysis revealed that web injects in banking malware trojans are a large threat in the cyber landscape.
Optical Sensors for Planetary Radiant Energy (OSPREy): Calibration and Validation of Current and Next-Generation NASA Missions

AbstractThe primary objective of the OSPREy activity is to develop and deploy a new class of commercial radiometersto support existing and next-generation NASA ocean color satellites. An EPIC sensor is a hybridspectral devicecombining measurements of spectral irradiance and radiance from recently developed microradiometers with acommercially available hyperspectral spectrograph plus a pointing system. Over common parts of the spectrum,ﬁxed-wavelength microradiometers and the spectrograph can be continuously intracompared to maintain sta-bility and accuracy of the system. Radiance sensors have a nine-position ﬁlter-wheel assembly in line with thespectrograph ﬁber optics to permit hyperspectral polarimetric measurements (three polarized ﬁlters), direct-Sunviewing (neutral density ﬁlters), stray-light correction (395nm cut-on ﬁlter), and dark current measurements(opaquedisk). RadiancesensorsalsohaveanintegratedcameraforlocatingtheSunandMoon(inlieuofaquad-rant detector), as well as verifying the condition of all targets (cloud-free solar and lunar disks, cloud presencein sky data, and sea surface debris or foam detection). All sensors include thermal stability control to increaseﬁlter and detector stability and reduce drift. The ﬁeld sensor suite includes shadowband accessories to the solarirradiance sensors, which are combined with radiance observations of the Sun, Moon, sea, and sky across a widespectral range—the UV, through the visible (VIS) and into the short-wave infrared (SWIR); 305–1,640nm is thefull spectral range for radiance, as well as irradiance because of a new cosine collector design. The diversity oftargets and measurement types provides an unprecedented number of near-simultaneous atmospheric and oce-anic data products. This new sampling capability is expected to improve a) the atmospheric correction of oceancolor data; b) the accuracy in satellite data products, particularly in optically complex (coastal) waters; and c)the understanding of the interaction between the ocean and atmosphere. The combination of wide spectral anddynamic ranges with accurate pointing are also anticipated to have application to terrestrial observations.
Runtime Composition of Iterations for Fusing Loop-carried Sparse Dependence

Dependence between iterations in sparse computations causes inefficient use of memory and computation resources. This paper proposes sparse fusion, a technique that generates efficient parallel code for the combination of two sparse matrix kernels, where at least one of the kernels has loop-carried dependencies. Existing implementations optimize individual sparse kernels separately. However, this approach leads to synchronization overheads and load imbalance due to the irregular dependence patterns of sparse kernels, as well as inefficient cache usage due to their irregular memory access patterns. Sparse fusion uses a novel inspection strategy and code transformation to generate parallel fused code optimized for data locality and load balance. Sparse fusion outperforms the best of unfused implementations using ParSy and MKL by an average of 4.2× and is faster than the best of fused implementations using existing scheduling algorithms, such as LBC, DAGP, and wavefront by an average of 4× for various kernel combinations.
Financial regulation : changing the rules of the game

During the 19802, dramatic changes took place worldwide in the area of financial regulation. Governments in both developed and developing countries implemented extensive regulatory reform, relaxing or removing those economic and structural regulations that inhibit innovation and stifle competition. In their place, they introduced or strengthened prudential, organizational, and protective controls intended to promote efficiency while also safeguarding stability and fairness in the financial system. This book analyzes the theoretical and practical arguments for and against different types of regulations, and reviews postwar experience with regulatory reform. Detailed case studies discuss the experiences of Japan, Indonesia, Chile, Malaysia, Norway, the US, Germany, the UK, Canada and Italy. The book also discusses extensively a number of important policy issues: the pace and sequence of financial liberalization; the handling of banking crises; the modes of bank restructuring; the scope of prudential regulation and supervision; the role of deposit insurance; the case for and against universal banking and financial conglomerates; and the use of holding company structures. This book is intended for policymakers, advisers, and researchers who seek to understand the advantages and disadvantages of different types of financial regulations and to determine the appropriate dimensions, pace, and sequence of regulatory reform in the financial sector.
Scalable Blockchain Architecture: Leveraging Hybrid Shard Generation and Data Partitioning

—Blockchain technology has gained widespread recognition and adoption in various domains, but its implementation beyond crypto currencies faces a significant challenge - poor scalability. The serial execution of transactions in existing blockchain systems hampers transaction throughput and increases network latency, limiting overall system performance. In response to this limitation, this paper proposes a static analysis-driven data partitioning approach to enhance blockchain system scalability. By enabling parallel and distributed transaction execution through a simultaneous block-level transaction approach, the proposed technique substantially improves transaction throughput and reduces network latency. The study employs a hybrid shard generation algorithm within the Geth node of the blockchain network to create multiple shards or partitions. Experimental results indicate promising outcomes, with miners experiencing a remarkable speedup of 1.91x and validators achieving 1.90x, along with a substantial 35.34% reduction in network latency. These findings provide valuable insights and offer scalable solutions, empowering researchers and practitioners to address scalability concerns and promoting broader adoption of blockchain technology across various industries.
Internet Addiction Mediates the Relationships Among Internet Skill and Gratification of Using Internet with Social Isolation Among Malaysian Youths: The Moderating Roles of Gender and Race

Excessive use of the Internet in recent years has caused different social problems. One of the most important consequences of excessive Internet usage is Internet addiction among students which directly affects their academic performance personal relationships with others, leading to social isolation. This study attempts to identify the relationships among gratification of using the Internet, Internet skill, Internet addiction, and social isolation among university students. In addition, the mediating role of Internet addiction between gratification of using the Internet and Internet skill with social isolation, and the role of race and gender were examined. A total of 440 university students in the age range of 18 to 40 years from the University of Putra Malaysia (UPM) participated in the study. Students completed a self-rank questionnaire measuring their Internet dependency. Structural Equation Modeling estimated that Internet addiction had moderating effects on gratification of using the Internet and social isolation. The moderation of race and gender on the model was not supported. In addition, females were more addicted and consequently more isolated from the society in comparison to males, though there were differences for Malay and non-Malay students.
A review of fire and oak regeneration and overstory recruitment

Fire has played a prominent role in the history of oak in eastern North America, and it is useful today for promoting oak regeneration where competition with other woody vegetation is a problem and for managing savannas and woodlands. We spent the last century extinguishing wildfire from forests for good reason, but now we must spend some time relearning how to use fire as a tool for sustaining oak-dominated ecosystems. The use of fire to favor oak in forested settings where timber production is a goal is very different from its role in managing savannas and woodlands. Fire as a tool originated in wildlife management and then found application in savanna and woodland restoration. In the past 30 years, there has been increased emphasis on fire in silviculture research and forest management. This paper discusses the use of fire in the context of oak ecology and silviculture with emphasis on promoting oak regeneration and dominance. Highlights include: (1) fire interactions with acorns and oak seedlings, and advance reproduction; (2) fire-induced changes in stand structure and understory and implications for oak regeneration; (3) fire as part of the regeneration prescription; and (4) the role of fire in the process of oak recruitment into the overstory.
Potential Effects on Tobacco Tax Revenues of a Ban on the Sale of Flavored Tobacco Products

In recent years, state and local governments in the U.S. have implemented policies banning the sale of flavored tobacco products and/or flavored liquids used in vaping. Some of the policies are relatively comprehensive, with the most comprehensive banning the sale of all flavored products, including menthol cigarettes and mint/menthol flavored eliquids. At the same time, policies banning the sale of flavored tobacco products have been implemented at the national level in a few countries, including Canada (following the implementation of provinciallevel flavor bans in several provinces), Ethiopia, Senegal, and Uganda, while a growing number of others have adopted but not yet implemented similar policies. Efforts to evaluate the effects of these policies on prevalence, consumption, and sale of tobacco and vaping products are ongoing and new evidence is rapidly emerging.
Basalts and gabbros from Mare Crisium - Evidence for extreme fractional crystallization

Petrographic and electron microprobe techniques were used to examine igneous fragments in Luna 24 samples 24088 and 24105. It is the complex chemistry of the pyroxenes that distinguishes the different rock types. Basaltic pyroxenes exhibit an Fe-enrichment trend; the evolutionary trends are more complex in the gabbros, with enrichments in both Fe and Ti and a depletion in Cr. These chemical evolutionary trends are displayed by a progressive variation in rock types from Mg-rich olivine-gabbro to olivine-gabbro, and to ferrogabbro and ferrotroctolite. The low TiO2 content of the primary melt, possibly represented by the least-evolved Mg-rich olivine-gabbro, retarded the formation of early ilmenite and spinel, such that 'Fenner Trend' Fe enrichment occurred. The ferrotroctolite is probably the end product of chemical evolution by extreme fractional crystallization, controlled primarily by olivine and pyroxene crystallization.
Screencast Feedback: Can I Use It?

This study examined student perceptions of screencast feedback and their learning behaviors following screencast feedback in an online graduate course. While there is widespread research on instructor feedback, there is far less literature focusing on video-based feedback and self-regulatory behavior within a Caribbean online learning environment. This case study addressed this gap by examining twelve management students’ perceptions of screencast feedback on their online learning experience. Data were collected using visual documentation, student interviews and focus groups. The results suggest that students have positive perceptions of video-based feedback in adding value to the online learning experience. Emergent themes placed most value on the potential improved intimacy, communication, and timeliness of screencast feedback. The findings also corroborate preliminary research about the role video-based feedback plays in fostering self-regulated learning (SRL). This has implications for the design and development of instructor feedback to include video-based cues and feedback messages that promote SRL.