Reviewerly/exHarmony huggingface dataset:
Improving Path Accuracy for Autonomous Parking Systems: An Optimal Control Approach

Kinodynamic planning explores the collision-free configuration space by constructing a tree on-the-fly. The process terminates when the tree expands into a specified neighborhood of the goal configuration. Often, the resultant path does not reach the goal accurately enough, which raises the question: how does one make an accurate, kinematically feasible connection between the tree and the goal. This is the non-trivial steering problem. Aiming to balance computational efficiency and position accuracy, this work solves an approximate steering problem through applying Pontryagin's Maximum Principle (PMP). The main contributions of this work are: establishment of an exhaustive set of possible structures of optimal control solutions; and development of a custom solver based on the these structures. Simulations demonstrate the PMP-based custom solver achieves better accuracy than a PID feedback control-based approach, and is more computationally efficient than a gradient descent-based numerical optimization approach.
An Effective Constraint-Handling Improved Cuckoo Search Algorithm and Its Application in Aerodynamic Shape Optimization

This research develops an effective constraint-handling method to improve meta-heuristic algorithms' performance when solving constrained optimization problems. With the cuckoo search (CS) as the basic optimization algorithm, a constraint-handling improved cuckoo search algorithm (CICS) is presented and applied to an airfoil aerodynamic shape optimization. First, the newly developed constraint-handling (CH) method is compared to five types of traditional techniques by incorporating the cuckoo search algorithm, particle swarm optimization (PSO), and genetic algorithm (GA), respectively, on ten benchmark analytical test problems and four engineering design optimization problems. Results indicate that the present method is effective and robust, and outperforms the other constraint-handling methods, not only for cuckoo search but also for particle swarm optimization and genetic algorithm. Next, the presented constraint-handling improved cuckoo search algorithm is compared to nine types of state-of-the-art meta-heuristic algorithms. It shows better performance when solving the aforementioned constrained problems. Finally, the CICS algorithm is successfully applied to a strongly constrained aerodynamic drag minimization design problem. It also shows its feasibility to aerodynamic design optimization and superiority to the original cuckoo search algorithm.
The orbiter ecosystem for combinatorial data

We describe a very versatile, fast and useful open source software package to compute combinatorial objects up to isomorphism called Orbiter. We provide an overview of some of the design decisions made during development, and we point out similar software packages. We discuss ways in which combinatorial data can be computed, analyzed and permanently stored for later use. This paper expands on earlier work published in [8].
The Russell-Prawitz embedding and the atomization of universal instantiation

Abstract Given the recent interest in the fragment of system $\mathbf{F}$ where universal instantiation is restricted to atomic formulas, a fragment nowadays named system ${\mathbf{F}}_{\textbf{at}}$, we study directly in system $\mathbf{F}$ new conversions whose purpose is to enforce that restriction. We show some benefits of these new atomization conversions: (i) they help achieving strict simulation of proof reduction by means of the Russell–Prawitz embedding of $\textbf{IPC}$ into system $\mathbf{F}$, (ii) they are not stronger than a certain ‘dinaturality’ conversion known to generate a consistent equality of proofs, (iii) they provide the bridge between the Russell–Prawitz embedding and another translation, due to the authors, of $\textbf{IPC}$ directly into system ${\mathbf{F}}_{\textbf{at}}$ and (iv) they give means for explaining why the Russell–Prawitz translation achieves strict simulation whereas the translation into ${\mathbf{F}}_{\textbf{at}}$ does not.
Virtual classrooms to improve the teaching-learning process in Systems Engineering courses at a University from Lima North

Access to information, thanks to internet, is simple and abundant. However, in a teaching-learning process the information must be ordered and focused to achieve a good academic performance. Fortunately, there are information technologies that allow you to manage the information of a learning process on virtual platforms. The University of Sciences and Humanities has implemented a virtual platform with Moodle to improving the teaching-learning process. The teacher manages the information ordered and in sequence according to the scope of his course, considering materials of theory, practice, laboratory, readings, videos, reference links accompanied by evaluative activities such as questionnaires, forums, tasks. The student accesses this information with any device with internet access and at any time and place. This has allowed to improve the teaching-learning process with evident improvements in the general averages of the students generating satisfaction and motivation for their University training.
Effects of the use of the BAKE mobile application as an Educative instrument for teaching content for preschool education to Shipibo people in the community of Cantagallo, Lima, Peru

This document includes the experience in the design, development and validation of a mobile application conceptualized as an instrument for basic teaching and self-learning of the Shipibo - Konibo language. It was created to be used in Bilingual Educative Institutions of preschool level, located in the Peruvian native communities.This App is intended to support the different education gaps in Shipibo-Konibo communities. The App name is BAKE that means child in Shipibo-Konibo language. It works offline and it has been developed for mobile devices with android operative system, considering they would be used in the remote Shipibo-Konibo communities.As part of the validation processes, a comparison test between the use of the application and the use of traditional education in Cantagallo community in Lima (the only Shipibo-Konibo community in Lima) has been developed. The results have been favorable and capabilities have even been identified in the reduction of gaps and overcoming existing limitations in traditional education in Bilingual Educative Institutions.
Effects of Time in Virtual Reality Learning Environments Linked with Materials Science and Engineering

The increasing presence of virtual reality learning environments (VRLEs) in university classrooms makes it necessary to study what factors influence the effectiveness of this type of teaching tool. In particular, when planning to use a VRLE in class to support the classes, a careful design of the application to achieve a high level of efficiency at the formative level must be carried out. This article discusses key aspects that need to be taken into account during the design of a VRLE that have been determined to be increasingly important for students to achieve a higher level of meaningful learning (and, thanks to it, the knowledge acquired through the use of the VRLE will last in their memory for a longer time) and also feel a greater motivation to use it to: (i) adapt both the level of interactivity as well as the way the VRLE conducts the student through the virtual experiment; and (ii) maintain a look and a handling mode of the VRLE similar to other virtual environments that exist at the present time (e.g. video games). The study carried out and described in this article highlights the effectiveness of using in certain cases a step-by-step guidance protocol to improve long-term learning of concepts under study. In addition, the importance of using modern development tools to achieve a high level of motivation among students is emphasized.
MURAVE: A New Rank Code-Based Signature with MUltiple RAnk VErification

We propose a new rank metric code-based signature scheme constructed via the Schnorr approach. Our scheme is designed in a way to avoid leakage of the information on the support for the secret key used in the signature generation. We define some new problems in rank metric code-based cryptography: the Rank Support Basis Decomposition problem and the Advanced Rank Support Basis Decomposition problem. We also discuss their hardness and solving complexity. Furthermore, we give a proof in the $$\mathsf{EUF}\text {-}\mathsf{CMA}$$ security model, by reducing the security of our scheme to the Rank Syndrome Decoding problem, the Ideal LRPC Codes Indistinguishability problem and the Decisional Rank Support Basis Decomposition problem. We analyze the practical security for our scheme against the known attacks on rank metric signature schemes. Our scheme is efficient in terms of key size (5.33 KB) and of signature sizes (9.69 KB) at 128-bit classical security level.
Asymptotic analysis of locally weighted jackknife prediction

Locally weighted jackknife prediction(LW-JP) is a variant of conformal prediction, which can output interval prediction in regression problems built on traditional learning algorithms for point prediction named as underlying algorithms. Although empirical validity and efficiency of LW-JP have been reported in some works, there lacks theoretical understanding of it. This paper gives some theoretical analysis of LW-JP in the asymptotic setting, where the number of the training samples approaches infinity. Under some regularity assumptions and conditions, the asymptotic validity of LW-JP is proved in the nonlinear regression case with heteroscedastic errors. The proof is an extension of the asymptotic analysis of leave-one-out prediction intervals in linear regression with homoscedastic errors. Based on our analysis, two conformal regressors built on LW-JP are proposed and the experimental results showed that the algorithms are not only valid interval predictors, but also achieve the state-of-the-art performance of conformal regressors.
Heterogeneous Large-Scale Distributed Systems on Machine Learning

Tensor flow is an interface for communicating AI calculations and a use for performing calculations like this. A calculation communicated using tensor flow can be done with virtually zero changes in a wide range of heterogeneous frameworks, ranging from cell phones, for example, telephones and tablets to massive scale-appropriate structures of many computers and a large number of computational gadgets, for example, GPU cards. The framework is adaptable and can be used to communicate a wide range of calculations, including the preparation and derivation of calculations for deep neural network models, and has been used to guide the analysis and send AI frameworks to more than twelve software engineering zones and different fields, including discourse recognition, sight of PCs, electronic technology, data recovery, everyday language handling, retrieval of spatial data, and discovery of device medication. This chapter demonstrates the tensor flow interface and the interface we worked with at Google.
Camera motion estimation algorithm for IoT devices based on optimized feature tracking method

This paper presents an optimized feature tracking mechanism. The proposed mechanism outperforms our previously proposed algorithms which are on the basis of feature matching. Specifically, for the work process of the proposal, extracting the FAST feature from input images at first. Then, by applying optical flow algorithm, the feature points appeared in different images will be tracked and marked out in the images. Afterwards, we apply the optimized PnP algorithm to estimate the camera motion. The results of the evaluation experiments which are conducted on raspberry pi indicate that our proposed mechanism provides a 16.7% improvement in time-efficiency compared with our previous work. Furthermore, the proposed mechanism still keeps the estimation error at a lowest level, as our previous work did. In summary, the proposed mechanism achieves higher time-efficiency and good estimation accuracy on IoT devices, which only have constrained sources.
Blockchain applied to academic environments as a way to ensure educational process quality control

Blockchain introduces a new on-demand consensus paradigm where a set of P2P network nodes agrees on the order in which blocks of transactions are aggregated into a chain of blocks to provide a reliable, safe, scalable, immutable and distributed environment targeting the implementation and storage of transactions on the Internet. In such a way, the requirement of a credible party is eliminated, and a decentralized trust entity is created digitally. Through the use of electronic contracts, the relationships previously specified in documents and moderated by human beings can now be transformed into code and inserted into the blockchain, from the use of only driven-computer environments. This article presents a blockchain platform for innovation in the academic management, implementing a model composed of smart contracts to replace basic activities such as enrollment, school history and medical certifications verification, from a control of quality and safety previously unimaginable.
Knowledge Integration by Probabilistic Argumentation

While existing inference engines solved real world problems using probabilistic knowledge representation, one challenging task is to efficiently utilize the representation under a situation of uncertainty during conflict resolution. This paper presents a new approach to straightforwardly combine a rule-based system (RB) with a probabilistic graphical inference framework, i.e., naïve Bayesian network (BN), towards probabilistic argumentation via a so-called probabilistic assumption-based argumentation (PABA) framework. A rule-based system (RB) formalizes its rules into defeasible logic under the assumption-based argumentation (ABA) framework while the Bayesian network (BN) provides probabilistic reasoning. By knowledge integration, while the former provides a solid testbed for inference, the latter helps the former to solve persistent conflicts by setting an acceptance threshold. By experiments, effectiveness of this approach on conflict resolution is shown via an example of liver disorder diagnosis.
Recommending peer reviewers in modern code review

Modern code review is a common practice used by software developers to ensure high software quality in open source and industrial projects. During code review, developers submit their code changes which should be reviewed, via tool-based code review platforms, before being integrated into the codebase. Then, reviewers provide their feedback to developers, and may request further modifications before finally accepting or rejecting the submitted code changes. However, the identification of appropriate reviewers is still a tedious task as the number of code reviews to be performed is inflated with the increasing number of code changes and the increasing size of software development teams in today's large and active software projects. To help developers with the review process, we introduce a multi-objective search-based approach to find the appropriate set of reviewers. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize two conflicting objectives (i) maximize reviewers expertise with the changed files, and (ii) minimize reviewers workload in terms of their current open code reviews. We conduct a preliminary evaluation on two open source projects to evaluate our approach. Results indicate that our approach is efficient as compared to state-of-the-art approaches.
Double-Authentication-Preventing Signatures in the Standard Model

A double-authentication preventing signature (DAPS) scheme is a digital signature scheme equipped with a self-enforcement mechanism. Messages consist of an address and a payload component, and a signer is penalized if she signs two messages with the same addresses but different payloads. The penalty is the disclosure of the signer’s signing key. Most of the existing DAPS schemes are proved secure in the random oracle model (ROM), while the efficient ones in the standard model only support address spaces of polynomial size. We present DAPS schemes that are efficient, secure in the standard model under standard assumptions and support large address spaces. Our main construction builds on vector commitments (VC) and double-trapdoor chameleon hash functions (DCH). We also provide a DAPS realization from Groth-Sahai (GS) proofs that builds on a generic construction by Derler et al., which they instantiate in the ROM. The GS-based construction, while less efficient than our main one, shows that a general yet efficient instantiation of DAPS in the standard model is possible. An interesting feature of our main construction is that it can be easily modified to guarantee security even in the most challenging setting where no trusted setup is provided. It seems to be the first construction achieving this in the standard model.
An Enhancement of Underwater Images Based on Contrast Restricted Adaptive Histogram Equalization for Image Enhancement

Scattering of light and absorption color affects the images of underwater. Due to this visibility and contrast on underwater, images are reduced. Dark channel prior is used typically for restoration. Poor resolution and contrast are exhibited by the images of underwater due to scattering of light and absorption of it in the environment of underwater. Color is caused by this situation. Due to this, it is difficult to analyze the image of underwater in an efficient manner for the object identification. In this paper, adaptive histogram equalization (AHE)-based new underwater image enhancement technique is proposed to get enhanced results. In the formula of gray-level mapping, parameter β is introduced by AHE algorithm. In new histogram, the spacing between two adjacent gray levels is adjusted adaptively to take target function as information entropy. In image, excessive local area and gray pixel merger are avoided by this. Settings of camera will not affect the performance of AHE as shown by validation and various image processing application’s accuracy are enhanced. The results of image enhancement methods are measured using the metrics like underwater image quality measure (UIQM), underwater color image quality evaluation (UCIQE), and patch-based contrast quality index (PCQI).
A Versatile Hypergraph Model for Document Collections

Efficiently and effectively representing large collections of text is of central importance to information retrieval tasks such as summarization and search. Since models for these tasks frequently rely on an implicit graph structure of the documents or their contents, graph-based document representations are naturally appealing. For tasks that consider the joint occurrence of words or entities, however, existing document representations often fall short in capturing cooccurrences of higher order, higher multiplicity, or at varying proximity levels. Furthermore, while numerous applications benefit from structured knowledge sources, external data sources are rarely considered as integral parts of existing document models.
Elman and Jordan Recurrence in Convolutional Neural Networks Using Attention Window

Retina of the human eyes constantly foveate on the target objects in a large visual field to recognize the object, and that foveated region is called central fovea. At each instance of the eye movements, human visual system integrates the significant information observed at the central fovea of the visual field inside a spatiotemporal memory to perform an object’s pattern recognition task. This integration property is widely useful in an artificial domain when the image size is too large. Therefore, presenting the full image to the visual pattern recognition systems in deep learning research is computationally expensive and biologically implausible. Being inspired from this biological hypothesis, we proposed five variations of Elman and Jordan recurrence in convolutional neural networks (EJRCNNs). Each of the five networks takes input of a series of small attention windows, which is cropped out from different locations in the image. Here, the attention window contributes as central fovea of the human visual system. The proposed networks integrate the information presented in all of the attention windows inside the context layers using recurrence connections in convolutional and fully connected layers. After processing all of the attention windows, networks perform the classification task. Elman and Jordan recurrences only in fully connected layers were partially explored in some previous studies using the full image. Each of the networks is trained on the MNIST [1] handwritten digit database. From our extensive experiments, the networks provide a better correlation of the spatiotemporal information by outperforming the RNN.
High Speed Front-Vehicle Detection Based on Video Multi-feature Fusion

Aiming at the scene where the speed of the vehicle on the highway is fast, this paper proposes a method to detect the front-vehicle in the video. Firstly, the method detects the lanes based on Canny edge detection and Hough transform to determine the vehicle’s driving area. Secondly, we use the multi-feature obtained by the combination of the histogram of oriented gradient (HOG) feature, the color feature and the Harr feature of the vehicle to train support vector machine (SVM) classifier, and then the classifier detects the vehicles in the driving area. The experimental results show that the SVM classifier trained by the multi-feature fusion method has a better detection result than a single feature, and compared with the detection of vehicles in the entire image, the detection time can be greatly shortened in the driving area.
Real-time Inferencing and Training of Artificial Neural Network for Adaptive Latency Negation in Distributed Virtual Environments

With recent trends to move more computation and applications to the cloud, latency is one of the major factors that can degrade the experience. This is even more true when it comes to distributed virtual environments (DVE) where users are interacting in real-time with a server-side environment. Past works have attempted to use user motion prediction to negate the effects of latency in VR systems but often struggle with long prediction lengths or fast user motion speed. In our previous work, we found that using an artificial neural network (ANN) for user motion prediction was feasible; however, our test showed relatively simple ANNs struggled when the latency varied, or user behavior changed drastically. To combat these issues, we are proposing the use of real-time inferencing and training (RTIT) to give our stacked LSTM the capability to adapt. By utilizing RTIT, our model is able to maintain a low prediction error when the system experiences both various amounts of latency and different interaction patterns. In addition, as the latency and user motion speed rise, our method remains robust longer than polynomial regression-based predictors.