Reviewerly/exHarmony huggingface dataset:
An improved Q-Learning algorithm and its application to the optimized path planning for unmanned ground robot with obstacle avoidance

In order to solve the problems in the optimum path planning of autonomous mobile robot in unknown complex environment, such as slow convergence rate and contact collision with obstacles, this paper proposes a modified Q-Learning algorithm based on flower pollination algorithm (modified Q-Learning with flower pollination algorithm, MQ-FPA). Firstly, Q-Learning is used to establish interactive relationship between autonomous mobile robot and environment; Then Flower pollination algorithm (FPA) is used to modify Q-Learning algorithm by initializing Q-table, so that the robot can explore the environment according to the transcendental knowledge gained according to FPA, which can improve the convergence rate of Q-Learning algorithm. The simulation results show that, compared with the traditional Q-Learning algorithm, the proposed MQ-FPA algorithm has the advantages of better adaptability, faster path optimization speed and better obstacle avoidance performance in different obstacle environment.
Convergence Conditions for the Quantum Relative Entropy and Other Applications of the Deneralized Quantum Dini Lemma

We describe a generalized version of the result called quantum Dini lemma that was used previously for analysis of local continuity of basic correlation and entanglement measures. The generalization consists in considering sequences of functions instead of a single function. It allows us to expand the scope of possible applications of the method. We prove two general dominated convergence theorems and the theorem about preserving local continuity under convex mixtures. By using these theorems we obtain several convergence conditions for the quantum relative entropy and for the mutual information of a quantum channel considered as a function of a pair (channel, input state). A simple convergence criterion for the von Neumann entropy is also obtained.
Workload and energy management of geo-distributed datacenters considering demand response programs

Datacenters usually consume huge energy and their workload can be adjusted dynamically, which makes them become good potential participants of demand response (DR) programs in the smart grid system. To overcome the drawbacks of centralization and enable fault-tolerance of a single datacenter, geographically distributed datacenters could be connected by a wide-area network to build up an integrated infrastructure, upon which multiple datacenter workloads could be balanced by taking advantage of space–time electricity price differences to reduce electricity costs. Particularly, when geo-distributed datacenters participate into DR programs, in order to obtain more economic benefits and to help achieve the load control objectives issued by the grid, it is necessary to consider a variety of external and internal conditions to optimize the workload and energy management strategy. Aiming at this problem, we propose an optimal power regulation scheduling method (OPRS) to simultaneously manage workload and energy consumption in geo-distributed datacenters, which comprehensively considers various factors including variation of electricity prices, operating costs, renewable energy constraints, and DR requirements. On the basis of the conventional computer room air conditioner system, additional devices are also incorporated into the architecture for datacenter cooling, including a solar-powered absorption cooling system and a chilled liquid storage system. Based on such framework, the OPRS method not only uses local renewable energy to reduce the energy consumption of the datacenter, but also can reduce the operating cost of the geo-distributed datacenters by leveraging the difference of electricity prices between time and space. Second, the method increases the frequency of uninterruptible power supply (UPS) usage and thus enhances the flexibility of datacenter power regulation. Furthermore, the OPRS method manages the workload by jointly considering task reassignment and delayed scheduling according to specific DR programs, thereby helping to maintain the stability of the regional power grid system. Experimental evaluation results show that, compared with other alternative strategies, the OPRS method can reduce the total operating cost by 19.52 %–66.96 %. Furthermore, the detailed analysis of the power regulation process illustrates that the OPRS method can also achieve better management of datacenter power consumption in the demand response stage.
Using contextual factors to predict information security overconfidence: A machine learning approach

Despite increasing investments in workforce security education and training, many organizations still face the challenge that their employees overestimate their knowledge and abilities to prevent security incidents. Since these misperceptions regarding one's competencies may entail serious consequences for business, the present paper investigates contextual influences on overconfidence susceptibility. For this purpose, we first surveyed more than 5,500 employees of an international pharmaceutical company and then applied machine learning techniques to classify them according to their likelihood of exhibiting overconfidence. Results demonstrate the significance of factors like training commitment, salary band, team size, and job experience are helping managers to adjust and improve awareness measures.
Show or tell? Exploring when (and why) teaching with language outperforms demonstration

People use a wide range of communicative acts across different modalities, from concrete demonstrations to abstract language. While these modalities are typically studied independently, we take a comparative approach and ask when and why one modality might outperform another. We present a series of real-time, multi-player experiments asking participants to teach concepts using either demonstrations or language. Our first experiment (N=416) asks when language might outperform demonstration. We manipulate the complexity of the concept being taught and find that language communicates complex concepts more effectively than demonstration. We then ask why language succeeds in this setting. We hypothesized that language allowed teachers to reference abstract object features (e.g., shapes and colors), while demonstration teachers could only provide concrete examples (specific positive or negative objects). To test this hypothesis, our second experiment (N=568) ablated object features from the teacher's interface. This manipulation severely impaired linguistic (but not demonstrative) teaching. Our findings suggest that language communicates complex concepts by directly transmitting abstract rules. In contrast, demonstrations transmit examples, requiring the learner to infer the rules.
An advanced teaching-learning-based algorithm to solve unconstrained optimization problems

The Teaching-Learning-Based Optimization (TLBO) algorithm is being extended to a broader range of applied optimization problems in the literature, mimicking the teaching-learning process. This paper proposes an Advanced Teaching-Learning-Based Optimization (Ad-TLBO) algorithm to enhance the efficiency and performance of the original version of TLBO in terms of accuracy, convergence rate, and reliability characteristics. The advancement is obtained by modifying the initialization, search approach, and structure of the two main phases of this algorithm in four steps to improve exploration and exploitation capability. Efficiency comparisons are shown in four challenges with various benchmark functions with multimodal, separable, differentiable, and continuity characteristics. The results are compared with several intelligent optimization algorithms. It is also deduced that this algorithm outperforms all investigated optimization algorithms in terms of accuracy, convergence speed, and success to reach acceptable solutions for various benchmark functions.
Automatic Cross-domain Task Plan Transfer by Caching Abstract Skills

Solving realistic robotic task planning problems is computationally demanding. To better exploit the planning effort and reduce the future planning cost, it is important to increase the reusability of successful plans. To this end, we suggest a systematic and automatable approach for plan transfer, by rethinking the plan caching procedure. Specifically, instead of caching successful plans in their original domain, we suggest transferring them upon discovery to a dynamically-defined abstract domain and cache them as “abstract skills” there. This technique allows us to maintain a unified, standardized, and compact skill database, to avoid skill redundancy, and to support lifelong operation. Cached skills can later be reconstructed into new domains on demand, and be applied to new tasks, with no human intervention. This is made possible thanks to the novel concept of “abstraction keys.” An abstraction key, when coupled with a skill, provides all the necessary information to cache it, reconstruct it, and transfer it across all domains in which it is applicable—even domains we have yet to encounter. We practically demonstrate the approach by providing two examples of such keys and explain how they can be used in a manipulation planning domain.
Exponential Convergence of Infeasibility Proofs for Kinematic Motion Planning

Proving motion planning infeasibility is an important part of a complete motion planner. Common approaches for high-dimensional motion planning are only probabilistically complete. Previously, we presented an algorithm to construct infeasibility proofs by applying machine learning to sampled configurations from a bidirectional sampling-based planner. In this work, we prove that the learned manifold converges to an infeasibility proof exponentially. Combining prior approaches for sampling-based planning and our converging infeasibility proofs, we propose the term asymptotic completeness to describe the property of returning a plan or infeasibility proof in the limit. We compare the empirical convergence of different sampling strategies to validate our analysis.
Research of possibilities of default refactoring actions in Swift language

The object of research in the paper is a built-in refactoring mechanism in the Swift programming language. Swift has gained a lot of popularity recently, which is why there are many new challenges associated with the need to support and modify the source code written in this programming language. The problem is that the more powerful refactoring mechanism that can be applied to Swift is proprietary and cannot be used by other software. Moreover, even closed-source refactoring software tools are not capable of performing more complex queries. To explore the possibilities of expanding the built-in refactoring, it is suggested to investigate the software implementation of the sourcekit component of the Swift programming language, which is responsible for working with «raw» source code, and to implement new refactoring action in practice. To implement the research plan, one refactoring activity that was not present in the refactoring utilities (adding an implementation of the Equatable protocol) was chosen. Its implementation was developed using the components and resources provided within the sourcekit component. To check the correctness and compliance with the development conditions, several tests were created and conducted. It has been discovered that both refactoring mechanisms supported by the Swift programming language have a limited context and a limited scope and application. That is why the possibility of expanding the functionality should not be based on the local level of code processing, but on the upper level, where it is possible to combine several source files, which often happens in projects. The work was directed to the development of the own refactoring action to analyze and obtain a perfect representation of the advantages and disadvantages of the existing component. As a result, a new approach to refactoring was proposed, which will allow solving the problems described above.
City-scale distance estimation via near-infrared trispectral light extinction in bad weather

This paper proposes a novel physics-based model for city-scale relative distance estimation using atmospheric optics. The suspended particles in the atmosphere can attenuate nearly every wavelength of the visible spectrum, particularly when the particle concentration is high. In this regard a trispectral distance estimation model is derived by utilizing the extinction coefficient differences between three selected near-infrared wavelengths. It may be noted that the proposed approach eliminates the influence of the surface materials (of the buildings) by linearly interpolating the reflectance spectra. As the atmospheric extinction coefficient is related to the wavelength and meteorological conditions, the visibility information is also utilized to make the proposed model simple and effective. A trispectral imaging system, constituting of a monochrome sensor, three narrow-band filters and an automatic filter rotation device, is utilized to capture the data across three near-infrared spectral bands. Experiments conducted in this study validate the superior performance of the proposed model for city-scale distance estimation.
Inclusive Education and Health Performance in Sub Saharan Africa

The study assesses the effect of inclusive education on health performance in 48 Sub Saharan African countries from 2000 to 2020. The study adopted the Driscoll/Kraay technique to address cross-sectional dependence and the GMM strategy to address potential endogeneity. The study employed three indicators of health performance which are the total life expectancy, the female life expectancy and the male life expectancy. Three gender parity index of educational enrolments are employed: primary education, secondary and the tertiary education as indicators of inclusive education. The findings of the study reveal that inclusive education enhances the health situation of individuals in Sub Saharan Africa. The findings further show that the health situation of both the male and the female are improved by inclusive education. The study recommends policymakers in this region to invest more in the education and the health sector so as to enhance the health performance of the citizens.
Performance Comparison and Analysis of Paxos, Raft and PBFT Using NS3

In today’s technological developments, the need for a system to guarantee security, transparency, and transaction speed is the underlying point in creating new technologies. Blockchain is a digital data recording and storage technology that is interconnected between one device and another and cannot be changed by anyone due to the implementation of cryptography. The consensus algorithm is a mechanism used by computers and blockchain systems in approving the addition of new data. In Blockchain, no one authority oversees the activities where the entire system is made in a decentralized manner so that decision making, verification, and authentication are on the blockchain must involve all users in it. Therefore, the consensus is needed by the blockchain in forming an efficient, fair, reliable, and secure mechanism so that all parties involved in it can have a vote. In this paper, we evaluate the performance of several consensus algorithms, such as Paxos, Raft, and PBFT, by simulating the time to reach consensus using the NS3 network simulator. We chose Paxos because this algorithm is the forerunner of the consensus algorithm, while Raft and PBFT are algorithms that have evolved from Paxos, which are still widely implemented in blockchain technology until now. Finally, based on the evaluation results, it was found that the PBFT algorithm has a speed five times faster than Raft and six times faster than Paxos to reach consensus. So we consider the PBFT algorithm to have the best speed and scalability. We hope that this research can be used as a reference for implementing the consensus algorithm in the development of blockchain technology.
Architecture of the Graphics System for Embedded Real-Time Operating Systems

This paper presents a new architecture of a graphics system for microkernel operating systems, including real-time operating systems. The following major parts of the architecture are presented: a user-level subsystem responsible for interaction with user applications; a bottom-level subsystem providing the functionality for graphics drivers, such as managing graphics output, video memory management, etc.; a kernel-level subsystem providing interaction with the kernel, and performing low-level operations, such as working with physical memory, processes, etc. The mechanisms of interaction of user applications with the user level of the graphics system, as well as interaction of the different levels of the graphics subsystem are presented. The paper pays much attention to various approaches to testing: the use of unit testing, testing using hardware and software emulators. Another important characteristic of a graphics system is its performance, in particular the performance of low-level operations such as memory allocation: the developed architecture suggests using a separate memory allocator which is faster than standard memory allocation functions. Comparison of the performances of graphics system implementation for microkernel real-time operating system and graphics server Xorg is presented, showing significant superiority of the proposed architecture in a number of work scenarios.
Cooperative attention generative adversarial network for unsupervised domain adaptation

The performance of deep learning models suffers the domain shift between the training dataset and test dataset frequently. Domain adaptation is a popular machine learning technique to tackle it. Generally, existing domain adaptation methods learn domain-invariant features and seldom consider class-level matching. To address it, we propose a Cooperative Attention Generative Adversarial Network (CAGAN) by generating verisimilar target samples with given class labels and implementing class-level transfer. Specifically, we integrate Coupled Generative Adversarial Networks (CoGAN) into a classification network. The shared generator fed with class semantic codes steers downstream generators to produce source and target samples with the same high-level semantics. However, the single weight-sharing mechanism cannot guarantee the semantic consistency of generated sample pairs in an enormous domain gap, so we propose a semantic-consistent loss to reduce the domain shift in the shared generative space. In addition, attention layers with adaptive factors are proposed to embed into the shared generator, contributing to capturing more suitable representations of both domains. Extensive experiments demonstrate that our proposed model can achieve the best or comparable results on several standard domain adaptation benchmarks.
A kernel-free Laplacian quadratic surface optimal margin distribution machine with application to credit risk assessment

This paper proposes a novel binary classification approach named Laplacian quadratic surface optimal margin distribution machine (LapQSODM), for semisupervised learning. This new model exploits the geometric information embedded in unlabeled samples through a manifold regularizer to overcome the problem of insufficient labeled samples. Different from the traditional support vector machines (SVMs) based on the largest minimum margin idea and using the kernel technique to address the nonlinearity in the datasets, our proposed model optimizes the margin distribution and directly generates a quadratic surface to perform the classification. This new model not only improves the generalization performance but also avoids the difficulty of searching for an appropriate kernel function and tuning its parameters. For the regular-scale datasets, we extend the classical conjugate gradient algorithm for the proposed method and design an easy iterative method to calculate its exact step size; for the large-scale datasets, we develop an unbiased estimation for the LapQSODM gradient by utilizing one or several samples and design a stochastic gradient descent with variance reduction (SVRG) algorithm that is efficient and effective. Then, we conduct a comprehensive numerical experiment on both artificial and public benchmark datasets. The experimental results show LapQSODM has a better generalization performance than most of the well-known benchmark methods and is more robust due to the optimization of the margin distribution. Finally, we apply LapQSODM to three realistic credit risk assessment problems. The promising numerical results demonstrate the great potential and ability of our model in the real practice.
Teacher–Student Mutual Learning for efficient source-free unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to alleviate domain shifts by transferring relevant domain information from a fully labeled source domain to an unknown target domain. Although significant progress has been made recently, the use of UDA in real-world applications is still limited owing to low-resource computers and privacy issues. To further extend the flexibility of the target model, this study attempts to address a new setting of UDA, where only a source model is provided, and target models with various network architectures can be learned according to the deployment environment. Therefore, we propose a novel two-stage Cross-domain Knowledge Distillation method via Teacher–Student Mutual Learning, termed CdKD-TSML, which enables the peer networks to employ pseudo labels assigned by one another as supplemental supervision. In the first stage, to depress the inevitable noise in hard pseudo labels generated by the self-supervised clustering procedure, we further propose to softly refine the pseudo labels with mutual learning, where networks and label predictions are online optimized cooperatively by distilling knowledge from each other. In the second stage, we jointly fine-tune the distilled models via a cooperative consistency learning strategy, which selects pseudo-labeled samples from the two peers to update the networks, thereby enhancing the generalization of the models. We theoretically demonstrate that the target error of CdKD-TSML is minimized by simultaneously decreasing the pseudo-label noise and alleviating the sample selection bias. Experiments on three challenging UDA datasets demonstrate that CdKD-TSML yields superior results compared to other state-of-the-art methods, proving its effectiveness in this novel setting.