Reviewerly/exHarmony huggingface dataset:
Improved calibration method for displacement transformation coefficient in optical and visual measurements

Optical and visual measurement technology is used widely in fields that involve geometric measurements, and among such technology are laser and vision-based displacement measuring modules (LVDMMs). The displacement transformation coefficient (DTC) of an LVDMM changes with the coordinates in the camera image coordinate system during the displacement measuring process, and these changes affect the displacement measurement accuracy of LVDMMs in the full field of view (FFOV). To give LVDMMs higher accuracy in the FFOV and make them adaptable to widely varying measurement demands, a new calibration method is proposed to improve the displacement measurement accuracy of LVDMMs in the FFOV. First, an image coordinate system, a pixel measurement coordinate system, and a displacement measurement coordinate system are established on the laser receiving screen of the LVDMM. In addition, marker spots in the FFOV are selected, and the DTCs at the marker spots are obtained from calibration experiments. Also, a fitting method based on locally weighted scatterplot smoothing (LOWESS) is selected, and with this fitting method the distribution functions of the DTCs in the FFOV are obtained based on the DTCs at the marker spots. Finally, the calibrated distribution functions of the DTCs are applied to the LVDMM, and experiments conducted to verify the displacement measurement accuracies are reported. The results show that the FFOV measurement accuracies for horizontal and vertical displacements are better than ±15 µm and ±19 µm, respectively, and that for oblique displacement is better than ±24 µm. Compared with the traditional calibration method, the displacement measurement error in the FFOV is now 90% smaller. This research on an improved calibration method has certain significance for improving the measurement accuracy of LVDMMs in the FFOV, and it provides a new method and idea for other vision-based fields in which camera parameters must be calibrated.
Proposal for the distribution of multiphoton entanglement with optimal rate-distance scaling

We propose a protocol to perform long-range distribution of near-maximally entangled multiphoton states, allowing versatile applications such as quantum key distribution (QKD) and quantum metrology which can provide alternatives to state-of-the-art protocols. Our scheme uses resources available within the current integrated quantum photonic technology: squeezed vacuum states and photon-number-resolving detectors. The distributed entanglement can be certified by Bell tests which have the potential to be loophole free, and may be directly used in well established QKD protocols. Generally, this provides measurement-device-independent (MDI) levels of security, which may be upgraded to fully device-independent (DI) security if the Bell test is loophole-free. In both cases, the protocol is robust to extremely high transmission losses, matching the optimal $O(\sqrt{\eta})$ scaling of key rate with channel transmittance.
Relationship Between Classroom Management Strategy Focused on Student External Behaviour and Student Achievement in Low SES Schools: What Can Teachers Do?

Various international studies record a significant difference between the achievements of students from different socioeconomic status (SES), so it is necessary to look for measures that help create favorable conditions for higher achievements of all students, regardless of their socioeconomic background.The purpose of this study is to clarify the relationship between classroom management strategies focused on student external behaviour and student achievement in low SES schools, and what the teacher's work guidelines could be.The analysis of the learning data of four Lithuanian schools with an unfavourable SES context shows that the progress of students in subjects of mathematics and biology is not related to the teacher's praise, although such an intervention tool can be used if it is characterized by individual attention and care.It was also found that as the number of remarks sent by the mathematics teacher to the student and/or parents increases, the probability of getting a lower math grade increases, meaning that remarks are not effective for higher student achievement.We dare say that in order to create a favourable learning environment for students from low SES, teachers must first try to avoid classroom management problems (that is, student misbehaviour) by building good relationships with students, choosing an appropriate teaching strategy, adopting a growth mindset, and reducing working memory demands for the students who have these difficulties.Also, teachers must react appropriately when there are problems with students' behaviour.Interpersonal communication with students, individual attention to their needs are extremely important ways for teachers who work in schools with unfavourable SES contexts.
The Other Side of the Mobile World: Mobile Mobbing

The innovations brought by the digital age leave deep traces in the lives of individuals of all ages. With technological tools becoming an integral part of life, we are observing changes in the behaviors and reactions of individuals. Mobile technology pushes individuals to ease and drags them to loneliness in society. Individuals who withdraw into their shells are socializing on mobile platforms. The progress of science and technology from past to present has brought changes in society. Rapidly advancing technology shapes the lives of individuals in many areas. Digitalization has been efficient in the exposure of individuals to an insidious application of mobbing. In this context, the mobile world has come to the point that it threatens the living space of the individual with the content that is a design product it offers. In particular, the role of digital media in a way that interferes with every aspect of life has turned into digital despotism. In this study, which is considered a compilation, we used document analysis as a data collection tool. We obtained data by examining the documents related to the subject and handled it through descriptive analysis. In this context, while trying to examine the concept of "mobile mobbing", the other side of the digital world; we discussed digitalization, mobbing, digital violence, cyberbullying, digitalization, and its aspects on human life. As a result, we have reached a high relationship between mobile mobbing and its effect on individuals. Among these results, there are changes in their behaviors and habits, the digital world has turned into a covert mobbing tool, social media contents influence the attitudes of users through hidden messages, exerts pressure on their behavior by directing them, disturbing insults and threats, etc. are shared in communication styles, we have concluded that mobbing is experienced in a similar digital environment. In this context, we have listed suggestions and measures against mobile mobbing, with digital media users being more careful and conscious of techniques, respecting safe sharing with courtesy rules in their discourse and actions.
Visual Superordinate Abstraction for Robust Concept Learning

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,⋯} ∈ “color” subspace yet cube ∈ “shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces (i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.
Two Classes of Optimal Few-Weight Codes Over $${\mathbb F}_{q}+u{\mathbb F}_{q}$$

In this paper, we construct two families of linear codes over the ring $${\mathbb F}_{q}+u{\mathbb F}_{q}$$ by the defining set approach, where q is a prime power and $$u^2=0$$ . We completely determine their Lee weight distributions, which shows that these codes have few Lee weights. Via the Gray map, we obtain a family of near Griesmer codes over $${\mathbb F}_{q}$$ , which is also distance-optimal, and a family of linear codes over $${\mathbb F}_{q}$$ , whose optimality is characterized with an explicit computable criterion using the Griesmer bound.
On the 2-Adic Complexity of Cyclotomic Binary Sequences with Period $$p^2$$ and $$2p^2$$

Let p be a prime. In this paper, we obtain the 2-adic complexity of all almost balanced cyclotomic binary sequence of order two with period $$p^2$$ ; and also show the 2-adic complexity of several non-trivial balanced cyclotomic binary sequences of order two with period $$2p^2$$ .
Two Classes of Constacyclic Codes with Variable Parameters

Constacyclic codes over finite fields are a family of linear codes and contain cyclic codes as a subclass. Constacyclic codes are closely related to many areas of mathematics and outperform cyclic codes in several aspects. Hence, constacyclic codes are of theoretical importance. On the other hand, constacyclic codes are important in practice, as they have rich algebraic structures and may have efficient decoding algorithms. In this extended abstract, two classes of constacyclic codes are constructed using a general construction of constacyclic codes with cyclic codes. The first class of constacyclic codes is motivated by the punctured Dilix cyclic codes, and the second class is motivated by the punctured generalised Reed-Muller codes. The two classes of constacyclic codes contain optimal linear codes. The parameters of the two classes of constacyclic codes are analysed, and some open problems are presented in this extended abstract.
Polynomial Constructions of Chudnovsky-type Algorithms for Multiplication in Finite Fields with Linear Bilinear Complexity

Chudnovsky-type algorithms for the multiplication in finite extensions of finite fields are well-known for having a good bilinear complexity, both asymptotically and at finite distance. More precisely, for every degree n of the extension, the existence of a family of algorithms with linear bilinear complexity in n has been proved using the original method applied to an explicit recursive tower of function fields. However, there is currently no method to build these algorithms in polynomial time. Nevertheless, one can construct in polynomial time a Chudnovsky-type algorithm over the projective line for the multiplication in any extension degree, with a quasi-linear bilinear complexity. In this paper, we prove that we can obtain algorithms both constructible in polynomial time and having a linear bilinear complexity by mixing up these two strategies.
Reduction-Free Multiplication for Finite Fields and Polynomial Rings

The complexity of the multiplication operation over polynomial rings and finite fields drastically changes with the selection of the defining polynomial of the respective mathematical structure. Trinomials and pentanomials are the most natural choices for the best arithmetic. In this paper, we first present a study in which a special type of trinomial does not require any reduction steps. We then introduce two new algorithms, FIKO and RF-FIKO, fully interleaved bit-parallel Karatsuba-Ofman multipliers where the latter is only concerned with the three Karatsuba-Ofman terms and is free from the bipartite reduction circuits. All algorithms are implemented in FPGA and ASIC, and detailed implementation results are presented, showing significant improvements to existing methods.
On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts

In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i.e. the policy’s value is estimated with a high-probability confidence interval. In particular, we begin with a multi-agent MDP controlled by a known policy but with unknown transition dynamics. One agent is then removed from the system - i.e. the system experiences node dropout - forming a new MDP of the remaining agents, with a new state space, action space, and new transition dynamics. We assume that the effect of removing an agent corresponds to the marginalization of its factor in the transition dynamics. The reward function may likewise be marginalized, or it may be entirely redefined for the new system. Robust policy importance sampling is then used to evaluate candidate policies for the new system, and estimated values are presented with probabilistic confidence bounds. This computation is completed with no observations of the new system, meaning that a safe policy may be found before dropout occurs. The utility of this approach is demonstrated in simulation and compared to Monte Carlo simulation of the new system.
Information Transfer in Spintronics Networks under Worst Case Uncertain Parameter Errors

A novel quantum landscape optimization with respect to bias field control inputs is developed with the goal of achieving optimal transfer fidelity subject to robustness against bias field, spin couplings and other uncertainties. This objective is achieved by minimization of a convex combination of fidelity error and worst-case perturbation of fidelity error under directional perturbation of uncertain parameters. The novelty is that the end-point perturbations of the parameters are points of a random uniform sampling of the sphere centered at the nominal values of the parameters. This reveals that the previously developed perfect state transfer with zero sensitivity solution keeps high fidelity and robustness under large rather than differential perturbations.
A Pursuit Evasion Approach for Avoiding an Inattentive Human in the Presence of a Static Obstacle

We consider a mobile robot, modelled as a Dubins’ car, following a path. In the vicinity of the path is a human, modelled as an agile point mass, and a static obstacle. The robot’s objective is to follow the path unless it is absolutely necessary to deviate so as to avoid collision with the static obstacle or human. We seek to guarantee robot’s safety, even if the human is distracted or inattentive, and thus we consider worst-case motions for the human. The resulting problem takes the form of a reversed homicidal chauffeur game, but with the addition of a static obstacle. The static obstacle can interfere with the escape route of the robot, and thus fundamentally changes the form of the game. We propose a navigation algorithm for the robot that provably guarantees safety and that attempts to delay its reaction for as long as possible. We validate the proposed approach in simulation and provide a comparison to existing collision avoidance methods.
Power-Optimized Processor Slowdown Control

For user-serving computational workloads in data centers (like ML/AI inference tasks with e-commerce applications), managing both the latency experienced by the user and the energy consumption of such workloads is essential. The slowdown quality-of-service metric is increasingly used in real-world systems, as it captures the perceived user delay proportional to the expected job service time. In this work, we introduce a framework to control job slowdown in a data center with multi-class workloads, by adjusting the processor rate. Increasing the processor rate decreases slowdown at the cost of additional power; the goal is to select the processor rate to optimize this tradeoff. We present optimal rate control characterizations for both 1) when the sizes of all jobs in the queue are observed and 2) when only the head-of-line job size and the total queue length are observed. We compare the controls analytically and numerically, and demonstrate how this framework can be employed to achieve desired slowdown targets with minimal power.
Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks

Implicit neural networks (INNs) are a class of learning models that use implicit algebraic equations as layers and have been shown to exhibit several notable benefits over traditional feedforward neural networks (FFNNs). In this paper, we use interval reachability analysis to study robustness of INNs and compare them with FFNNs. We first introduce the notion of tight inclusion function and use it to provide the tightest rectangular over-approximation of the neural network’s input-output map. We also show that tight inclusion functions lead to sharper robustness guarantees than the well-studied robustness measures of Lipschitz constants. Like exact Lipschitz constants, tight inclusions functions are computationally challenging to obtain, and thus we develop a framework based upon mixed monotonicity and contraction theory to estimate the tight inclusion functions for INNs. We show that our approach performs at least as well as, and generally better than, state-ofthe-art interval-bound propagation methods for INNs. Finally, we design a novel optimization problem for training robust INNs and we provide empirical evidence that suitably-trained INNs can be more robust than comparably-trained FFNNs.
Variational Kalman Filtering with H<sub>∞</sub>-Based Correction for Robust Bayesian Learning in High Dimensions

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> -norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large-scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a frame-work that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞-</inf> norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF-H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> recursion that employs consecutive variational inference and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.
dSLAP: Distributed Safe Learning and Planning for Multi-robot Systems

This paper considers the problem where a group of mobile robots subject to unknown external disturbances aim to safely reach goal regions. We develop a distributed safe learning and planning algorithm that allows the robots to learn about the external unknown disturbances and safely navigate through the environment via their single trajectories. We use Gaussian process regression for online learning where variance is adopted to quantify the learning uncertainty. By leveraging set-valued analysis, the developed algorithm enables fast adaptation to newly learned models while avoiding collision against the learning uncertainty. Active learning is then applied to return a control policy such that the robots are able to actively explore the unknown disturbances and reach their goal regions in time. Sufficient conditions are established to guarantee the safety of the robots. A set of simulations are conducted for evaluation.
Policy gradient primal-dual mirror descent for constrained MDPs with large state spaces

We study constrained sequential decision-making problems modeled by constrained Markov decision processes with potentially infinite state spaces. We propose a Bregman distance-based direct policy search method – policy gradient primal-dual mirror descent – which includes the natural policy primal-dual method and the projected policy primal-dual method as two special cases. When the exact gradient is known, we prove dimension-free global convergence with a sublinear rate in both optimality gap and constraint violation. When the exact gradient is not available, we instantiate our algorithm in the linear function approximation setting and establish sample complexity guarantees. The introduction of the Bregman-distance regularizers enjoys the dimension-free property with applicability to large-scale spaces, the first of its kind in the constrained RL literature.
Optimally regularized local basis function approach to identification of time-varying systems

Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state of the art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that when the internal correlation structure of estimated parameters is known, the tracking performance of the local basis function estimation algorithms can be further improved by means of regularization. The optimal form of the regularization matrix is derived analytically and it is shown that the best settings of the regularized algorithm can be determined in the computationally efficient way using cross-validation.
Expert System for Early Detection of Child Intelligence Development Using Website-Based Multiple Intelligence

Every child when born into the world, he brings various types of innate intelligence unique to one another. Children's intelligence is one aspect of the child's initial ability to understand every pattern and understanding in thinking. Multiple intelligences are one of the intelligences that can help find characteristics in early childhood. With this expert system, it is hoped that it can become a medium for parents to find the dominance of intelligence in early childhood. The method of identifying children's intelligence used in this system uses forward chaining and certain factors which retrieve intelligence data from several sources and published journals. The results that this researcher wants is to find dominance intelligence in early childhood as well as an assessment of user satisfaction with the application.