Reviewerly/exHarmony huggingface dataset:
Off-Policy Evaluation of the Performance of a Robot Swarm: Importance Sampling to Assess Potential Modifications to the Finite-State Machine That Controls the Robots

Due to the decentralized, loosely coupled nature of a swarm and to the lack of a general design methodology, the development of control software for robot swarms is typically an iterative process. Control software is generally modified and refined repeatedly, either manually or automatically, until satisfactory results are obtained. In this paper, we propose a technique based on off-policy evaluation to estimate how the performance of an instance of control software—implemented as a probabilistic finite-state machine—would be impacted by modifying the structure and the value of the parameters. The proposed technique is particularly appealing when coupled with automatic design methods belonging to the AutoMoDe family, as it can exploit the data generated during the design process. The technique can be used either to reduce the complexity of the control software generated, improving therefore its readability, or to evaluate perturbations of the parameters, which could help in prioritizing the exploration of the neighborhood of the current solution within an iterative improvement algorithm. To evaluate the technique, we apply it to control software generated with an AutoMoDe method, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="minf1"><mml:mrow><mml:mi mathvariant="normal">Chocolate</mml:mi><mml:mo>−</mml:mo><mml:mn>6</mml:mn><mml:mi mathvariant="normal">S</mml:mi><mml:mtext> </mml:mtext></mml:mrow></mml:math> . In a first experiment, we use the proposed technique to estimate the impact of removing a state from a probabilistic finite-state machine. In a second experiment, we use it to predict the impact of changing the value of the parameters. The results show that the technique is promising and significantly better than a naive estimation. We discuss the limitations of the current implementation of the technique, and we sketch possible improvements, extensions, and generalizations.
A Novel Approach for Image Encryption Using Zaslavskii Map and Arnold’s Cat Map

Recently, transformation of information is an emerging area of interest because of efficient Internet technology and faster communication medium. Nowadays, huge numbers of data transferred over many transferring mediums, out of which image is one of them. To maintain confidentiality and provide ownership of the data, we need to do any cryptographic technique like encryption. In this paper, we proposed a new cryptography technique for image encryption which is a combination of two different techniques for image encryption to increase the security of the data. The two different techniques are Zaslavskii map and Arnold’s cat map. Both combinedly used for encryption as well as decryption. In this work, we first encrypt the image using Zaslavskii method, and then again, that encrypted image re-encrypted using Arnold’s cat map to re-encrypt that, to get the required image using two times encryption. Then, in the reverse process, the final encrypted image undergoes first decryption using Arnold’s cat map, and that decrypted image further undergoes Zaslavskii decryption to get back the final derived image which should be like the original image. This proposed technique gives users more key values for better security and unpredictability, which shows good entropy value and good correlation value. The efficiency of image encryption techniques is verified by number of pixels change rate (NPCR) and unified average chaning intensity (UACI) which is used for any small change in pixels, along with entropy and correlation coefficient values.
Viability of Mobile phone cameras in professional broadcasting: A case study of camera Efficiency of Apple iPhone 11

In recent years, technological advances and mass production of smartphones have made them both applicable and accessible for a range of photography and videography. Many electronic media organizations are broadcasting mobile footages as part of their citizen journalism using their smartphones. The video and audio quality of smartphones are weak in terms of brightness, contrast and colour balance compared to traditional television cameras, also known as electronic news gathering (ENG) cameras. Cell phones currently come installed with high-resolution video recording facilities, yet major television news networks despite everything aren't utilizing it to record news stories. Superior video quality depends on several factors like image sensor, optical Zoom, Physical IRIS and shutter, which is available in all ENG cameras. The audio quality of ENG cameras is also relatively crisper and more precise due to the availability of many manual settings and external microphone attachment options. The latest smartphones can record a video of decent quality subject to good lighting conditions. This paper reviews the art of learning visual communication through lenses, sensors, and other physical attributes of video cameras while drawing a comparison between the ENG and Smartphone cameras (iPhone 11). Experimental design was used to test the Depth of field, brightness, contrast, and colour fidelity. The results of the study's experiments indicate that ENG camera's image quality is superior compared to iphone 11 under low lighting conditions.
A Performance Analysis of Virtual Mail Server on Type-2 Hypervisors

Virtualization is now the most efficient and successful procedure to use server resources. Virtualization is a technology that will efficiently allow the instructions or programs based on the available hardware. With the help of virtualization, we can achieve benefits like reduced capital and operating costs, minimized downtime, increased productivity, and good disaster recovery, etc. This work compares the virtual mail server performance on 3 different Hypervisor software. The 3 different type-2 hypervisor software we chose to compare were Kernel-based Virtual Machine (KVM), Oracle VirtualBox, and VMware. A computer system that sends and also receives an email over a network either on LAN or the internet is called a Mail Server. Although large ISPs and public email services have their dedicated hardware, in most cases, the web servers and mail servers are hosted on a single machine by virtualization. When a Mail server is implemented on the hypervisor, it is then considered as a virtual mail server. A virtual mail server is good at load balancing and has many more benefits. This paper presents how to implement a virtual mail server and shows the comparative results of the virtual mail server on different type-2 hypervisor software. HIGHLIGHTS Virtualization will efficiently run the instructions or programs based on the available hardware The virtual mail server has more significant advantages than the standard mail server. It helps in achieving faster deployment of mail servers with fewer hardware requirements Comparison of the performance of the virtual mail server on three different Type-2 Hypervisor software: Kernel-based Virtual Machine (KVM), Oracle VirtualBox, and VMware The web servers and mail servers can be hosted on a single machine by virtualization A virtual mail server is best for load balancing and disaster recovery as well
Convergence Properties of Stochastic Hypergradients.

Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning. A key step to tackle these problems in the design of optimization algorithms for bilevel optimization is the efficient computation of the gradient of the upper-level objective (hypergradient). In this work, we study stochastic approximation schemes for the hypergradient, which are important when the lower-level problem is empirical risk minimization on a large dataset. We provide iteration complexity bounds for the mean square error of the hypergradient approximation, under the assumption that the lower-level problem is accessible only through a stochastic mapping which is a contraction in expectation. Preliminary numerical experiments support our theoretical analysis.
Section 230 and global content moderation: Uncertainty in the time of COVID-19

Content moderation was already a highly polarized policy topic before the COVID-19 pandemic. The subsequent onslaught of misinformation and disinformation on COVID-19 has also led to multiple platform policy responses by private US-based companies and beyond. Discussion of these policies has also contributed to a sense that this level of media attention to a formerly somewhat obscure US law – Section 230 of the Communications Decency Act – could lead to a change in that law. This commentary provides a look into this small piece of legislation that has historically had a big impact on western internet governance. This is framed within a more global view of internet governance, and the impact this may have on the internet more broadly.
Use of Data Mining Technologies in an English Online Test Results Management System

The systematic management of English online test results can ensure the fairness of the test, guarantee the accuracy and safety of the test results, and reduce the consumption of manpower and materials. Unfortunately, the existing data mining and management strategy for learner scores cannot track the learning process or score change of learners. This paper innovatively applies the trajectory data mining technology to the design of an English online process test results management system. After analyzing the functional requirements of the system, four basic information lists were constructed in SQL Server 2005. Then, an improved k-means clustering algorithm and the trajectory frequent pattern mining algorithm were combined to cluster the test results and analyze the learning trajectory deviation of the learners. Next, the four system functions were detailed, including login, entry of test results, trajectory setting, and deviation analysis. The effectiveness of our algorithm, and the performance of our system were fully verified through experiments.
Fixation guided network for salient object detection

Convolutional neural network (CNN) based salient object detection (SOD) has achieved great development in recent years. However, in some challenging cases, i.e. small-scale salient object, low contrast salient object and cluttered background, existing salient object detect methods are still not satisfying. In order to accurately detect salient objects, SOD networks need to fix the position of most salient part. Fixation prediction (FP) focuses on the most visual attractive regions, so we think it could assist in locating salient objects. As far as we know, there are few methods jointly consider SOD and FP tasks. In this paper, we propose a fixation guided salient object detection network (FGNet) to leverage the correlation between SOD and FP. FGNet consists of two branches to deal with fixation prediction and salient object detection respectively. Further, an effective feature cooperation module (FCM) is proposed to fuse complementary information between the two branches. Extensive experiments on four popular datasets and comparisons with twelve state-of-the-art methods show that the proposed FGNet well captures the main context of images and locates salient objects more accurately.
Distance learning in the time of pandemic as a challenge facing the equal status of the public school

A public school is an institution for all pupils in a given administrative region. In its actions, it must be guided by the principles of tolerance and social egalitarianism. Social egalitarianism is framed in terms of equal educational opportunities and social justice. The COVID-19 pandemic has forced a significant change in the school practice. Distance learning has become the main form. In this article, we discuss the issue of public school social egalitarianism in the context of the results of the students’ opinion poll on the transformation of traditional forms of education into distance learning.
Simulation and construction of physical reservoir based on gas molecules/carbon nanotubes/polyoxometalate composite structure

Physical reservoir computing (PRC) is a recently introduced framework using the complex dynamics of physical systems for information processing. In this paper, a physical reservoir based on a molecular network of gas molecules/and single-walled carbon nanotubes (SWCNTs)/polyoxometalate (POM) is proposed. The simulation of electronic properties and preliminary experiments on the electrical behavior of the POM -decorated SWCNT composite structure modified by redox gas molecules present its high potential as a configurable neuromorphic unit to construct the physical reservoir.
An authenticated key scheme over elliptic curves for topological networks

Nodes of sensor networks may be resource-constrained devices, often having a limited lifetime, making sensor networks remarkably dynamic environments. Managing a cryptographic protocol on such setups may require a disproportionate effort when it comes to update the secret parameters of new nodes that enter the network in place of dismantled sensors. For this reason, the designers of schemes for sensor network are always concerned with the need of scalable and adaptable solutions. In this work, we present a novel elliptic-curve based solution, derived from the previously released cryptographic protocol TAKS, which addresses this issue. We give a formal description of the scheme, built on a two-dimensional vector space over a prime field and over elliptic curves, where node topology is more relevant than node identity, allowing a dynamic handling of the network and reducing the cost of network updates. We compare our solution with classical Diffie-Hellman-like protocols and we also study some security concerns and their relation to the related discrete logarithm problem over elliptic curves.
Generalized Eigenvalue Proximal Support Vector Machine for Functional Data Classification

Functional data analysis has become a research hotspot in the field of data mining. Traditional data mining methods regard functional data as a discrete and limited observation sequence, ignoring the continuity. In this paper, the functional data classification is addressed, proposing a functional generalized eigenvalue proximal support vector machine (FGEPSVM). Specifically, we find two nonparallel hyperplanes in function space, a positive functional hyperplane, and a functional negative hyperplane. The former is closest to the positive functional data and furthest from the negative functional data, while the latter has the opposite properties. By introducing the orthonormal basis, the problem in function space is transformed into the ones in vector space. It should be pointed out that the higher-order derivative information is applied from two aspects. We apply the derivatives alone or the weighted linear combination of the original function and the derivatives. It can be expected that to improve the classification accuracy by using more data information. Experiments on artificial datasets and benchmark datasets show the effectiveness of our FGEPSVM for functional data classification.
Full-dimensional Schrödinger wavefunction calculations using tensors and quantum computers: the Cartesian component-separated approach

Traditional methods in quantum chemistry rely on Hartree-Fock-based Slater-determinant (SD) representations, whose underlying zeroth-order picture assumes separability by particle. Here, we explore a radically different approach, based on separability by Cartesian component, rather than by particle [J. Jerke and B. Poirier, J. Chem. Phys., 2018, 148, 104101]. The approach appears to be very well suited for 3D grid-based methods in quantum chemistry, and thereby also for so-called "first-quantized" quantum computing. We first present an overview of the approach as implemented on classical computers, including numerical results that justify performance claims. In particular, we perform numerical calculations with four explicit electrons that are equivalent to full-CI matrix diagonalization with nearly 1015 SDs. We then present an implementation for quantum computers for which the number of quantum gates (and to a lesser extent, the number of qubits) can be dramatically reduced, in comparison with other quantum circuitry that has been envisioned for implementing first-quantized "quantum computational chemistry" (QCC).
An Improved Chan-Vese Model

Chan-Vese model is a typical geometric active contour model which is able to effectively detect weak edge and discontinuous edge by virtue of global information of image, with favorable noise resistance; however, periodical reinitialization of level set function is required during the process of its curve evolution, which will inevitably increase the time for detection of targets. In this paper, a penalty term is added into Chan-Vese model, and a new geometric active contour model is proposed to overcome the disadvantage of Chan-Vese model for re-initialization of level set function. The experiment shows that the new model will promote the speed of curve evolution, and the initial curve can be defined as any closed shape.
Unsupervised Relation Extraction Using Sentence Encoding

Relation extraction between two named entities from unstructured text is an important natural language processing task. In the absence of labelled data, semi-supervised and unsupervised approaches are used to extract relations. We present a novel approach that uses sentence encoding for unsupervised relation extraction. We use a pre-trained, SBERT based model for sentence encoding. Our approach classifies identical sentences using a clustering algorithm. These sentences are used to extract relations between two named entities in a given text. The system calculates a confidence value above a certain threshold to avoid semantic drift. The experimental results show that without any explicit feature selection and independent of the size of the corpus, our proposed approach achieves a better F-score than state-of-the-art unsupervised models.
Agile Tasking of Robotic Systems with Explicit Autonomy

Task agility is an increasingly desirable feature for robots in application domains such as manufacturing. The Canonical Robot Command Language (CRCL) is a lightweight information model built for agile tasking of robotic systems. CRCL replaces the underlying complex proprietary robot programming interface with a standard interface. In this paper, we exchange the automated planning component that CRCL used in the past for a rational agent in the Gwendolen agent programming language, thus providing greater possibilities for formal verification and explicit autonomy. We evaluate our approach by performing agile tasking in a kitting case study.
A Systematic Review on Data Mining Rules Generation Optimizing via Genetic Algorithm

In this techno savvy eon, companies have wide range of data and without converting it into appropriate information it is of no use. KDD ( Knowledge Discovery Database), analysis process of data mining that helps the corporate to mine or extract useful information for “Decision Making process”. To mine the correct information there are plethora of techniques for mining the data or information for illustration Apriori, Pattern finding, Clustering, Classification, Prediction, Time series and so. Moreover, the emerging optimization technique is Genetic algorithm. This paper summarizes data mining algorithms with multiple datasets.
Internal proof calculi for modal logics with separating conjunction

Abstract Modal separation logics are formalisms that combine modal operators to reason locally, with separating connectives that allow to perform global updates on the models. In this work, we design Hilbert-style proof systems for the modal separation logics $\text {MSL}(\ast ,\langle \neq \rangle )$ and $\text {MSL}(\ast ,\Diamond )$, where $\ast $ is the separating conjunction, $\Diamond $ is the standard modal operator and $\langle \neq \rangle $ is the difference modality. The calculi only use the logical languages at hand (no external features such as labels) and can be divided in two main parts. First, normal forms for formulae are designed and the calculi allow to transform every formula into a formula in normal form. Second, another part of the calculi is dedicated to the axiomatization for formulae in normal form, which may still require non-trivial developments but is more manageable.
Using the iSpring Suite computer platform in distance learning

The article discusses the experience of using the iSpring Suite 9 computer platform as an information tool for conducting distance classes with second-year students in the training for “Business Informatics”. Self-isolation requirements in the face of the COVID-19 pandemic have required the development and use of innovative remote learning methods. The need to conduct practical classes on solving technical problems with mandatory time control and prompt receipt of the results of the solution by the teacher determined the requirements for the choice of a computer program as a tool for providing distance work in the field of education. As a result, the choice was made in favor of the corporate training platform iSpring. According to the results of the spring semester, the experience of working with iSpring Suite 9 in creating electronic tests and conducting practical exercises in distance mode was recognized as successful and will be used in the future to conduct classes with students of the distance department of Novosibirsk State Technical University.
Research on Design Automatic Illustration Recommendation Method Based on Artificial Intelligence

With the continuous innovation and development of artificial intelligence, great changes have taken place in various industries relying on high-tech. As an art based on technological invention, illustration is also facing new challenges and opportunities. In the new era of technological innovation and technology application, the future illustration technology will continue to promote intelligent, which indicates that the industry will usher in a new period of transformation and upgrading. At the same time, the quality of illustrations in China and even the world will develop in the direction of high quality and high efficiency. This paper takes the development of artificial intelligence technology in the new era as the research starting point, maps the image features with the subjective style discrimination ability features, and then according to the demand, the illustration recommendation method based on the optimization processing model is used to carry out the illustration recommendation, and the pre recommendation results are obtained. Finally, the final illustration recommendation is obtained by optimizing the pre recommendation results through the user feedback algorithm oriented to the illustration recommendation result. We compare the performance of the proposed method with the two linear models based on SVM, and find that the proposed model can better recommend illustrations for users.
Global-Localized Agent Graph Convolution for Multi-Agent Reinforcement Learning

A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It is a challenging problem to enable agent to learn global and localized cooperate information simultaneously in multi-agent systems. In this paper, we model the global and localized cooperation among agents by global and localized agent graphs and propose a novel graph convolutional reinforcement learning mechanism based on these two graphs which allows each agent to communicate with neighbors and all a-gents to cooperate at the high level. Experiments on the large-scale multi-agent scenarios in StarCraft II show that our pro-posed method gets better performance compared with state-of-the-art algorithms and allows agents learning to cooperate efficiently.