Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Research Publication and Reviews

Taking responsibility of a child is a disputing task for active persons. In this article, we present an imaginative child listening whole that admits persons to confirm their child by chance and in actual time for action or event. The projected whole is established the “Raspberry PI 3B” label, a Pi camcorder, a sound and hotness sensors. To be more adept, this structure uses a convolutional interconnected system to label and define the child rank in welcome cradle. The exercise and the exploratory results of the projected arrangement explain allure adeptness and veracity and by virtue of what it can considerably help persons to protect their child.
UDC 577.15 PROTEOLYTIC ENZYMES IN CHEESE MAKING

There has been an increasing consumer demand for cheese along with a search for products with new organoleptic parameters, which has resulted in extensive research on alternative milk coagulants. The ratio of proteolytic activity to milk-clotting activity determines the requirements for proteases used in the cheese making process. To date, plant enzymes have largely been used for this purpose, along with traditional enzymes of animal origin, chymosin and pepsin. The most popular types of proteases used in the food industry, especially in cheese making, are plant proteases belonging to the cysteine (papain, bromelain, ficin), aspartate (cinarase, cardosin), and serine (kukumizin, leucine) group of proteases. The aspartate proteases of microbial origin mucorpepsin and endotyapepsin have found wide application in cheese production due to low production costs and high organoleptic characteristics of the final product. The use of plant and microbial milk-clotting enzymes as an alternative to animal-derived enzymes allows not only to diversify the assortment of cheeses on the market but also to solve ethical and economic issues. In addition, vegetable and microbial preparations meet the requirements of vegetarianism, halal, and kosher food, thus further opening the market.
Reasoning with Knowledge Graph Visualizations - A Mental Models Perspective

How do people reason with knowledge graphs and how can we support these reasoning activities? When working on questions like these, we propose to take into account a central theory of cognitive science, which states that knowledge is represented in the human mind as “mental models”, i.e. as structural and procedural internal representations of external phenomena (Johnson-Laird, 1983). For the construction of such mental models, multiple pieces of information are relationally interwoven by different kinds of inferential reasoning (Goodwin and Johnson-Laird, 2005). In this cognitive theory we see a clear similarity to the conception of knowledge graphs, which also model information structurally and relationally. While knowledge graph technologies primarily aim to make large data collections (up to the global web) more “readable”, understandable, and computable for machines regarding the semantic structuring of largely language-based data (Berners-Lee et al., 2001) they do create new challenges regarding human readability. In this paper we take a closer look at knowledge graph visualizations and how they can support reasoning on knowledge graphs from a mental models perspective. We illustrate this question by means of a current H2020 research and innovation project in the cultural heritage information domain, in which we construct a knowledge graph and aim to make it visually accessible for cultural heritage experts and laypeople.
How T.V. Advertising Works: A Meta-Analysis of 389 Real World Split Cable T.V. Advertising Experiments

The authors analyze results of 389 BehaviorScan® matched household, consumer panel, split cable, real world T.V. advertising weight, and copy tests. Additionally, study sponsors—packaged goods advertisers, T.V. networks, and advertising agencies—filled out questionnaires on 140 of the tests, which could test common beliefs about how T.V. advertising works, to evaluate strategic, media, and copy variables unavailable from the BehaviorScan® results. Although some of the variables did indeed identify T.V. advertising that positively affected sales, many of the variables did not differentiate among the sales effects of different advertising treatments. For example, increasing advertising budgets in relation to competitors does not increase sales in general. However, changing brand, copy, and media strategy in categories with many purchase occasions in which in-store merchandising is low increases the likelihood of T.V. advertising positively affecting sales. The authors’ data do not show a strong relationship between standard recall and persuasion copy test measures and sales effectiveness. The data also suggest different variable formulations for choice and market response models that include advertising.
EVALUATION OF FOUR ADVANCED PLASTICITY AND HYPOPLASTICITY MODELS IN SIMULATING CYCLIC RESPONSE OF SANDS

Numerous constitutive models have been developed for the simulation of the response of granular soils under cyclic loading. While these models have succeeded in capturing certain aspects of the stress-strain response under a number of idealized loading paths, certain common limitations are encountered in simulating these and other paths, and also certain complex aspects of response. Examples of these include cyclic oedometeric stiﬀness, shear strain accumulation in cyclic mobility, cyclic liquefaction strength curves, among others. These limitations are rather crucial for the end-users. Discussing these limitations and providing the mechanisms to avoid them if possible, therefore, would be of great value for both applications and further developments. Relying on cyclic loading experimental test data of Karlsruhe ﬁne sand, the present study conducts direct comparison between the experiments and the corresponding simulation results using four advanced constitutive models: two bounding surface elastoplasticity [1, 2] and two hypoplasticity models [3, 4] – with the models in each category following a hierarchical order of complex-ity. The presented results elaborate on the speciﬁc capabilities and limitations of these advanced models in simulating several essential aspects of cyclic loading of sands.
Deep Reinforcement Learning for Rapid Communication Network Recovery with Multiple Repair Crews

Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network recovery efforts have been proven to be too slow in the past. Rapidly deployable networks (RDN) are communication networks that can be configured as a wireless mesh network and can be integrated into an existing communication network. As the name suggests, RDNs have a quick setup time and are highly transportable. The technologies behind RDNs for communication networks have received considerable advancements in recent times. Nonetheless, the deployment strategy of such a network remains open. The existing solutions on rapid post-disaster network recovery are built in an inflexible way. First, each of them is designed around a specific problem. Making slight modifications to the problem greatly increases the complexity of the algorithm and can require major design changes to the system. Second, the proposed solutions are unable to adapt to unexpected circumstances, such as repair times taking longer than anticipated. We propose an online network recovery approach to solve these flexibility issues. With the optimization objective of maximizing a network’s weighted connectivity while minimizing the overall recovery process duration, we design a Deep Reinforcement Learning (DRL) system to produce optimal RDN deployment decisions. Experiments show our Deep Q-network (DQN) algorithm outperform greedy and naive approaches on any disaster scenario.
I vy C ross: A Privacy-Preserving and Concurrency Control Framework for Blockchain Interoperability

—Interoperability is a fundamental challenge for long-envisioned blockchain applications. A mainstream approach is to use Trusted Execution Environment (TEEs) to support interoperable off-chain execution. However, this incurs multiple TEEs conﬁgured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEEs far from practical. The purpose of this paper is to ﬁll this gap and design a practical interoperability mechanism with simpliﬁed TEEs as the underlying architecture. Speciﬁcally, we present IvyCross, a TEE-based framework that achieves low-cost, privacy-preserving, and race-free blockchain interoperability. Speciﬁcally, IvyCross allows running arbitrary smart contracts across heterogenous blockchains atop only two distributed TEE-powered hosts. We design an incentive scheme based on smart contracts to stimulate the honest behavior of two hosts, bypassing the requirement of the number of TEEs and large memory storage. We examine the conditions to guarantee the uniqueness of the Nash Equilibrium via Sequential Game Theory. Furthermore, a novel extended optimistic concurrency control protocol is designed to guarantee the correctness of concurrent execution of off-chain contracts. We formally prove the security of IvyCross in the Universal Composability framework and implement a proof-of-concept prototype atop Bitcoin, Ethereum, and FISCO BOCS. The experiments indicate that (i) IvyCross is able to support privacy-preserving and multiple-round smart contracts for cross-chain communication; (ii) IvyCross successfully decreases the off-chain costs on storage and communication of a TEE without using complex cryptographic primitives; and (iii) the total on-chain transaction fees in cross-chain communication are relatively low, within ranges of 0.2 USD ∼ 1 USD.
National Wage Setting

How do firms set wages across space? Using vacancy data with detailed job-level information supplemented with a survey of HR managers and self-reported data on workers’ wages, we show that, within the firm, 30-40% of posted wages for a given job are identical across space. Compared to differences between firms, nominal posted wages within the firm vary relatively little with local prices, a pattern that is present for realized wages as well. Using the pass-through of local shocks to wages in other locations of the firm, we argue that the limited variation of wages within firms is due to national wage setting, meaning that firms choose rigid pay structures in which they set very similar nominal wages for the same job in different regions. Our survey suggests that one reason firms set wages nationally is that nominal, rather than real, wage comparisons matter to workers. JEL Codes: J24, J45, J33, H56 *We thank Lila Englander, Michael Galperin, Nidhaan Jain, and Isaac Liu for excellent research assistance; Anna Stansbury for sharing code; and Otis Reid for helpful discussions that shaped the development of the project. We also thank Adrien Bilal, Ellora Derenoncourt, Jonathan Dingel, Alex Frankel, Erik Hurst, Anders Humlum and Simon Jager for helpful discussions that shaped this draft. First version: September 2021.
Numerical simulation of paraffin melting in circular tube using LBM

Paraffin melting is widely used in the fields of phase change materials (PCMs) energy storage, gathering and transportation pipeline paraffin removal, etc. Analysis of the phase change mechanism and influencing factors of paraffin melting in the circular tube deeply has important guiding significance for improving the heat storage capacity by changing the structure of phase change material storage device and ensuring the safe transportation of crude oil in the pipeline. A double distribution lattice Boltzmann model based on enthalpy method is established to simulate the temperature field and the flow field of paraffin melting in a circular tube in this paper. The influence of different Rayleigh (Ra) number and Prandtl (Pr) number on the paraffin melting process in a circular tube is analyzed. The results show that the natural convection process is strengthened with the increase of the Ra number, and the decrease of the average Nusselt (Nu) number on the wall is smooth in the transition stage of wax melting due to the existence of fuzzy zone. The melting rate of paraffin can be accelerated or reduced by controlling the number Pr, so as to meet the relevant requirements of engineering.
Digital Twin for Secure Semiconductor Lifecycle Management: Prospects and Applications

—The expansive globalization of the semiconductor supply chain has introduced numerous untrusted entities into different stages of a device’s lifecycle, enabling them to compromise its security. To make matters worse, the increasing complexity in the design as well as aggressive time-to-market requirements of the newer generation of integrated circuits can lead either designers to unintentionally introduce security vulnerabilities or veriﬁcation engineers to fail in detecting them earlier in design lifecycle, often due to the limitation of traditional veriﬁcation and testing methodologies. These overlooked or undetected vulnerabilities can be exploited by malicious entities in subsequent stages of the lifecycle through an ever-widening variety of hardware attacks. The ability to ascertain the provenance of these vulnerabilities, after they have been unearthed at a later stage, becomes a pressing issue when the security assurance across the whole lifecycle is required to be ensured and generationally improved to thwart emerging attacks. We posit that if there is a malicious or unintentional breach of security policies of a device, it will be reﬂected in the form of anomalies in the data collected through traditional design, veriﬁcation, validation, and testing activities throughout the lifecycle. With that, a digital simulacrum of a device’s lifecycle, called a digital twin (DT), can be formed by the data gathered from different stages to secure the lifecycle of the device. The DT can analyze the collected data through its constituent AI and data analytics algorithms to trace the origin of a detected hardware attack or vulnerability to the associated stage of the lifecycle. We refer to this functionality of the DT as Backward Trust Analysis . We also introduce the notion of Forward Trust Analysis which refers to the scalability and adaptability of the DT to unforeseen threats as they emerge. In this paper, we put forward a realization of intertwined relationships of security vulnerabilities with data available from the silicon lifecycle and formulate different components of an AI driven DT framework. The proposed DT framework leverages these relationships to achieve aforementioned security objectives through causality analysis, and thus accomplish end-to-end security-aware management of the entire semiconductor lifecycle. We put a perspective on how the limitations of existing ad-hoc-style security solutions can be overcome by the data oriented anal- ysis that underpins our approach. With several threat and attack scenarios, we demonstrate how advanced modeling techniques can perform relational learning to identify such attacks. Finally, we provide potential future research avenues and challenges for realization of the digital twin framework to enable secure semiconductor lifecycle management. Developed a DT to address operational issues in linear construction sector.
Properties of a Possible Unification Algebra

An algebra providing a possible basis for the standard model is presented. The algebra is generated by combining the trigintaduonion Cayley-Dickson algebra with the complexified space-time Clifford algebra. Subalgebras are assigned to represent multivectors for transverse coordinates. When a requirement for isotropy with respect to spatial coordinates is applied to those subalgebras, the structure generated forms a pattern matching that of the fermions and bosons of the standard model.
Microsoft Word-PSCC 2020 20200519-副本

Since wind turbines or photovoltaic (PV) panels are generally connected to the power grid by power electronic inverters, the power system inertia is gradually decreasing along with the growing share of renewable energy. This jeopardizes the system frequency response dynamics so that the corresponding frequency security issue is becoming the bottle-neck factor that restricts the development of high renewable energy penetration. Consequently, power system scheduling models need to incorporate frequency dynamics. The difficulty lies in how to formulate the frequency security constraints from the perspective of hourly load-generation balance since the frequency dynamics have a shorter time scale (5~30s). Several modeling methods have been proposed based on different assumptions and simplifications. However, their accuracy is not clear. We first propose a novel method to formulate linear frequency security constraints, which considers more details of frequency response dynamics. Then, an evaluation methodology is designed to quantify the accuracy of those frequency constraints. Using this evaluation method, we compare two typical methods in recent literature with the proposed method. The results show the effectiveness and superiority of our proposed method.
Sequential epiretinal stimulation improves discrimination in simple shape discrimination tasks only

Objective. Electrical stimulation of the retina can elicit flashes of light called phosphenes, which can be used to restore rudimentary vision for people with blindness. Functional sight requires stimulation of multiple electrodes to create patterned vision, but phosphenes tend to merge together in an uninterpretable way. Sequentially stimulating electrodes in human visual cortex has recently demonstrated that shapes could be ‘drawn’ with better perceptual resolution relative to simultaneous stimulation. The goal of this study was to evaluate if sequential stimulation would also form clearer shapes when the retina is the neural target. Approach. Two human participants with retinitis pigmentosa who had Argus® II epiretinal prostheses participated in this study. We evaluated different temporal parameters for sequential stimulation and performed phosphene shape mapping and forced choice discrimination tasks. For the discrimination tasks, performance was compared between stimulating electrodes simultaneously versus sequentially. Main results. Phosphenes elicited by different electrodes were reported as vastly different shapes. For sequential stimulation, the optimal pulse train duration was 200 ms when stimulating at 20 Hz and the optimal gap interval was tied between 0 and 50 ms. Sequential electrode stimulation outperformed simultaneous stimulation in simple discrimination tasks, in which shapes were created by stimulating 3–4 electrodes, but not in more complex discrimination tasks involving ≥5 electrodes. The efficacy of sequential stimulation depended strongly on selecting electrodes that elicited phosphenes with similar shapes and sizes. Significance. An epiretinal prosthesis can produce coherent simple shapes with a sequential stimulation paradigm, which can be used as rudimentary visual feedback. However, success in creating more complex shapes, such as letters of the alphabet, is still limited. Sequential stimulation may be most beneficial for epiretinal prostheses in simple tasks, such as basic navigation, rather than complex tasks such as novel object identification.
Panoramic Human Activity Recognition

To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneous achieve the individual action, social group activity, and global activity recognition. This is a challenging yet practical problem in real-world applications. For this problem, we develop a novel hierarchical graph neural network to progressively represent and model the multi-granularity human activities and mutual social relations for a crowd of people. We further build a benchmark to evaluate the proposed method and other existing related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We will release the source code and benchmark to the public for promoting the study on this problem.
Feedforward PID Control of Full-Car with Parallel Active Link Suspension for Improved Chassis Attitude Stabilization

PID control is commonly utilized in an active suspension system to achieve desirable chassis attitude, where, due to delays, feedback information has much difficulty regu-lating the roll and pitch behavior, and stabilizing the chassis attitude, which may result in roll over when the vehicle steers at a large longitudinal velocity. To address the problem of the feedback delays in chassis attitude stabilization, in this paper, a feedforward control strategy is proposed to combine with a previously developed PID control scheme in the recently introduced Parallel Active Link Suspension (PALS). Numerical simulations with a nonlinear multi-body vehicle model are performed, where a set of ISO driving maneuvers are tested. Results demonstrate the feedforward-based control scheme has improved suspension performance as compared to the conventional PID control, with faster speed of response in brake in a turn and step steer maneuvers, and surviving the fishhook maneuver (although displaying two-wheel lift-off) with 50 mph maneuver entrance speed at which conventional PID control rolls over.
Featured Trajectory Generation for TrackPuzzle

Indoor route graph learning is critically important for autonomous indoor navigation. A key problem for crowd-sourcing indoor route graph learning is featured trajectory generation. In this paper, a system is provided to generate featured trajectories by crowd-sourcing smartphone data. Firstly, we propose a more accurate PDR algorithm for the generation of trajectory motion data. This algorithm uses ADAPTIV as the step counting method and uses the step estimation algorithm o make the trajectory more accurate in length. Next, the barometer is used to segment the tracks of different floors, and the track floors are obtained by WiFi feature clustering. Finally, by finding the turning point as the feature point of the trajectory, the vertices and edges of the trajectory are extracted to reduce the noise of the long straight trajectory.
Asymptotic Normality in Linear Regression with Approximately Sparse Structure

In this paper, we study the asymptotic normality in high-dimensional linear regression. We focus on the case where the covariance matrix of the regression variables has a KMS structure, in asymptotic settings where the number of predictors, p, is proportional to the number of observations, n. The main result of the paper is the derivation of the exact asymptotic distribution for the suitably centered and normalized squared norm of the product between predictor matrix, X, and outcome variable, Y, i.e., the statistic ∥X′Y∥22, under rather unrestrictive assumptions for the model parameters βj. We employ variance-gamma distribution in order to derive the results, which, along with the asymptotic results, allows us to easily define the exact distribution of the statistic. Additionally, we consider a specific case of approximate sparsity of the model parameter vector β and perform a Monte Carlo simulation study. The simulation results suggest that the statistic approaches the limiting distribution fairly quickly even under high variable multi-correlation and relatively small number of observations, suggesting possible applications to the construction of statistical testing procedures for the real-world data and related problems.