Reviewerly/exHarmony huggingface dataset:
Unit-aware multi-objective genetic programming for the prediction of the stokes flow around a sphere

In this article we apply a unit-aware Genetic Programming (GP) approach to solve a problem from the area of fluid-dynamics: The Stokes flow around a sphere. We formulate 6 test instances with different complexities and explore the capabilities of single- and multi-objective GP variants to solve this problem with physically correct units of measurement. The study is a starting point to investigate the amount of information necessary to solve fluid-dynamics-related problems, and whether the inclusion of physical dimensions is advantageous or not for such optimization tasks. From the simple flow presented in this study we aim to extend this research to more complex flows with multiple spheres and finite Reynolds numbers.
Reasoning in the Presence of Silence in Testimonies: A Logical Approach

An implicature, as opposed to a logical or formal implication, allows to make linguistic inferences during a conversation. The omission of an answer (silence) also allows to make linguistic inferences in some contexts, according to Swanson. Once previous studies of silence and the conversational implicature of Grice are reviewed, we propose definitions and semantics of three kinds of silence, when knowledge is represented in logic. As a case study for the semantics, we chose a puzzle formulated by Wylie and then apply answer set programming to generate models that reveal new information and different possibilities, including non-monotonicity in knowledge bases, that lie behind the omission in the context of testimonies. Also, a connection between false statements and silence emerged.
Thermal Imaging Face Detection Based on Transfer Learning

Thermal facial recognition is getting more and more attention now. However, due to the small number of thermal imaging facial databases, models trained with existing databases are not as robust and have low recognition rates in different environments. To solve this problem, we released a new thermal surface database and added the SE attention mechanism module and SPP module to the YOLO v3 framework to improve the detection effect of the model. Compared with the original YOLO v3, the mAP value has been improved by 22.1 % and 16.7%.
Network privacy information storage model based on holographic digital watermarking technology

Aiming at the problems of large storage error and low determination coefficient in traditional information storage model, a network privacy information storage model based on holographic digital watermarking technology is proposed. By identifying the network privacy information, the least square method is used to filter the non significant information of network privacy information, and the fuzzy adaptive equalization storage method is used to compensate the error of symbol output To the steady-state characteristic solution of network privacy information, the holographic digital watermarking technology is introduced to construct the network privacy information storage model. The experimental results show that the improved model can effectively reduce the root mean square error, improve the decision coefficient of information storage, and increase the effect of storage.
Evaluating Frequency of words and Word Cloud from Astrological sentiments using NLP

The identification of interest/disinterest over a notion is having a huge demand in the current competitive data analytical world. For example, the customer preferences in various seasons, approximate visitors to a tourist place based on scenarios like weather and special occasions in the place, and so on. While giving an opinion on any concept, natural language in form of sentences/words/symbols/ratings plays a vital role. Depends upon the context and usage of natural language, captured opinions can be interpreted as either in a positive or negative sense. The terminology used for providing the opinions is used for analysing the data in an easy way. The evaluation of the word frequencies and word cloud are identified accurately, only after a keen analysis of the collected opinions. The Term-Document Matrix is one of the techniques that identify the frequency of words in each and every document/row in the given dataset, which can be used to generate the word cloud. In this paper to identify the frequency of words from the opinions given by multi-domain personalities on Astrology, distinct Natural Language Processing (NLP) techniques are used. A word cloud can also be generated from the set of words used for the astrological dataset.
Two point-source interference: a study of the formation of concentric circular fringes

Abstract In this short paper, the hyperbola-based analysis of wave interference recently developed by Thomas is employed to study the pattern of concentric circular fringes captured on a distant screen, when its plane is oriented orthogonal to the line joining two coherent point-sources. Newton’s rings are another instance of concentric circular fringes, generated with an extended source and a plano-convex lens-glass plate combination. The underlying geometry of the latter scenario, as described in the literature, allows for the estimation of two important physical parameters viz. the wavelength of light and the refractive index of a liquid medium. It is demonstrated here that the geometrical arrangement of the former scenario can in principle, be utilized to reach the same ends as well. Additionally, the hallmark distinguishing features of both types of circular fringe patterns are qualitatively and quantitatively elucidated.
Deep Convolutional Neural Network Processing of Images for Obstacle Avoidance

Deep Convolutional Neural Networks have been found to be quite successful at processing images and determining their classifications, such as distinguishing dogs from cats in a set of images. With this in mind, we implemented a deep network system for obstacle avoidance of an autonomous robot driving in a real-world lab environment. The network was first trained on the CIFAR10 dataset using a replication of Alex Krizhevsky’s network architecture. The network was then altered by replacing the final fully connected layer with a new fully connected layer with three outputs and random starting weights. The entire network was then fine-tuned using images from the actual environment that were taken by the robot’s camera as it was remotely driven in the lab by a human operator. The network learned the correct responses of left, right, or straight for each of the images with a very low error rate when checked on test images. In addition, ten tests on the actual robot showed that it could successfully and consistently drive through the lab while avoiding obstacles.
Automatic detection and tracking the moving objects observed by an unmanned aerial vehicles video camera

An algorithm of automatic detection and tracking the moving objects for the use in equipment on board of unmanned aerial vehicles is considered. The developed algorithm is based on a tracking specially selected points for a certain period. Tracked points are selected from the areas on the current frame, where the pixel intensity differs from the intensities of the same pixels in previous frames, aligned with the current frame using projective transformation. If the displacement of the tracked points is not fixed on several adjacent frames, they are being deleted, and new points from the areas presumably belonging to moving objects in the current frame are added instead. On each frame the points similar by the location and shape of trajectories of movement are combined into groups that presumably correspond to moving objects. Objects are tracked by comparing the groups of moving points with the points of neighboring frames. Groups of moving points from neighboring frames are matched if they contain a large number of common tracked points. The algorithm allows simultaneous tracking of more than 20 objects in real time. The indication of objects as moving occurs only if during the time of its tracking it has shifted a considerable distance. The algorithm has a low percentage of false detections of moving objects, it detects well small objects and is capable reliably to accompany moving objects.
Beyond Probability Ranking Principle: Modeling the Dependencies among Documents

Probability Ranking Principle (PRP)[31], which assumes that each document has a unique and independent probability to satisfy a particular information need, is one of the fundamental principles for ranking. Traditionally, heuristic ranking features and well-known learning-to-rank approaches have been designed by following the PRP principle. Recently, neural IR models, which adopt deep learning to enhance the ranking performances, also obey the PRP principle. Though it has been widely used for nearly five decades, in-depth analysis shows that PRP is not an optimal principle for ranking, due to its independent assumption that each document should be independent of the rest candidates. Counter examples include pseudo relevance feedback[24], interactive information retrieval[46], search result diversification[10] etc. To solve the problem, researchers recently proposed to model the dependencies among the documents during the designing of ranking models. A number of ranking models have been proposed and state-of-the-art ranking performances have been achieved. This tutorial aims to give a comprehensive survey on these recently developed ranking models that go beyond the PRP principle. The tutorial tries to categorize these models based on their intrinsic assumptions: assuming that the documents are independent, sequentially dependent, or globally dependent. In this way, we expect the researchers focusing on ranking in search and recommendation can have a novel angle of view on the designing of ranking models, and therefore can stimulate new ideas on developing novel ranking models.
Incentivizing WiFi-Based Multilateration Location Verification

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (also known as verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV, which motivates the participation of both provers and verifiers. More importantly, we consider practical situations, where the provers have various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness, individual rationality, computational efficiency, budget balance,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nonnegative social welfare.</i> The desired properties are validated through both theoretical analysis and extensive simulations.
A Novel Metaheuristic Approach for Image Contrast Enhancement Based on Gray-Scale Mapping

Image Contrast Enhancement (ICE) is a crucial step in several image processing and artificial vision applications. The principal purpose of ICE is to enhance the quality of the visual information in the processed images. Histogram Equalization (HE) is one of the most common methods for solving the Image Contrast Enhancement problem. Nonetheless, the HE method may produce unnatural-looking images that are not desirable in diverse applications where brightness preservation is necessary. Metaheuristic algorithms are intelligent and stochastic approaches used to solve optimization problems in diverse image processing applications. This article presents a method for image contrast enhancement based on the Moth Swarm Algorithm (MSA). The Moth Swarm Algorithm is a robust metaheuristic algorithm (MA) that efficiently resolves general problems due to its balance between exploration and exploitation. The proposed method consists in a cost function which use the resolution and the edge information of the image additionally the symmetric Kullback-Leibler entropy. The proposed method is tested on a set of general-purpose images and compared with the results obtained by several well-known metaheuristic algorithms, Artificial Bee Colony (ABC) algorithm, Differential Evolution (DE), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). From the experimental results presents, it is observed that the proposed method highlights the important details of the image, also enhancing its human visual representation. In this paper, a global technique for contrast enhancement is presented, the results are presented employing three of the most used quality indexes in the literature.
Fabrication and characterization of superconducting multiqubit device with niobium base layer*

Superconducting transmon qubits are the leading platform in solid-state quantum computing and quantum simulation applications. In this work, we develop a fabrication process for the transmon multiqubit device with niobium base layers, shadow evaporated Josephson junctions, and airbridges across the qubit control lines to suppress crosstalk. Our results show that the multiqubit devices have well-characterized readout resonators, and the energy relaxation and Ramsey (spin-echo) dephasing times up to ~40 μs and 14 (47) μs, respectively. We perform single-qubit gate operations which demonstrate a maximum gate fidelity of 99.97%. In addition, two-qubit vacuum Rabi oscillations are measured to evaluate the coupling strength between qubits, and the crosstalk among qubits is found to be less than 1% with the fabricated airbridges. Further improvements of the qubit coherence performance using the present fabrication process will be discussed.
Collaborative-Based Movie Recommender System—A Proposed Model

Recommendation systems are intelligent search systems that predict the preferred information and provide suggestions to users. Major platforms such as Amazon, Netflix, Youtube improves users’ experience. Recommender engines operate on background, reading users’ behavior, and suggesting items that are most likely to engage with. Approaches like content filtering lead to ambiguous content or item cold-start issues. To tackle the demerits of sparsity of data and cold-start, the paper focuses on a system with a memory-based collaborative approach, employed with a similarity analysis of users’ perspectives using Pearson correlation. In addition to this, a systematic study and a proposed model are presented along with the memory-based algorithm. The study shows that the Pearson correlation approach can achieve better research objectives. The user preferences have been collected from various microblogging platforms to analyze the present trends and responses of users. Promising results are produced by the experiments done over the public database.
GraphicalAI: A User-Centric Approach to Develop Artificial Intelligence and Machine Learning Applications using a Visual and Graphical Language

With the increasing popularity of Artificial Intelligence (AI) and Machine Learning (ML), developing AI-based applications is in high demand in various industries. However, the AI development is still based on traditional programming frameworks and languages, which prevents domain experts from contributing to it without collaborating with developers. This research is to show how graphical software allows users from many domain (e.g., Doctors, Accountants, Advertisers) to build AI applications, train AI models without any prior knowledge of programming, and many of its unnecessary concepts. Using nodes and connectors as the primary graphical components, the application, GraphicalAI, is to show how graphics can be designed in a way to easily prototype any kinds of AI models. To enable domain experts to design AI models using the power of graphics and our human vision.
Ensuring Traceability on Management of IT Infrastructures : Orchestrator based on a Distributed Ledger

To support new and more complex software systems, tools, and applications, the IT Infrastructures have evolved to be more flexible and grow both in size and complexity. To manage those Infrastructures, several tools are used. For large datacenters and public cloud infrastructures, complex managements systems have been developed in-house, according to specific implementations and philosophies. However, for the small, business owned, infrastructures, such tools are usually used independently. In this paper, we present the architecture of a new Orchestrator tool that aims to provide a single point of management, while also maintaining a resource inventory, by means of a Distributed Ledger, in order to maintain records of all the changes in the infrastructure, ensuring traceability over past changes, and even allowing for rollback of certain actions.
Assumptions and Guarantees for Composable Models in Papyrus for Robotics

The separation of concerns helps to manage the intrinsic complexity of defining robotics components, systems and missions. This separation of concerns is supported by the Rob-MoSys modelling approach addressing both the modelling needs of the robotics domain and identifying the involved stakeholders and required expertise. In this multi-stakeholder context, there are pressing concerns about non-functional characteristics including safety aspects (e.g., collaborative robots, increasing risks to humans and the environment where robotic systems operate). It is of special interest to explicitly establish the non-functional assumptions and guarantees. This assures that the their validity can be automatically evaluated, in particular during the definition of a system as a composition of several component definitions. We present how we extended one of the RobMoSys implementations, Papyrus for Robotics, for contracts modelling and assertions validation. Notably this includes the meta-modelling decisions to allow extensibility for assertion languages.
A Framework for Cloud-based Testing of Multi-variant Cyber-physical Systems

Cyber-physical systems and IoT applications such as robotic production cells usually offer manifold configuration possibilities, e.g., with respect to the number and position of robotic arms, grippers, and movements and handling actions. This makes it hard to test such systems, since there is a huge number of possible test cases, and the execution of each test case involves complex simulations of physical interactions. In this article, we describe a method and tool chain which allow to configure and execute test suites for multi-variant cyber-physical systems in the cloud. We use feature modelling for describing the variability, a keyword-based approach for the specification of tests, and lightweight containers for the deployment of testing tasks onto computational resources. Our results show both the feasibility of the approach, as well as pointing out directions for future improvements.
Model Optimization Techniques for Embedded Artificial Intelligence

Deep learning has made remarkable progress in computer vision and neural language processing tasks. CNNs has become the most popular structure due to its high accuracy and end-to-end training ability. However, the complex CNNs models bring high computing and storage cost which make it difficult to be deployed on various hardware platforms. Tremendous computations also become a barrier in achieving real-time performance. In order to solve these problems, recent studies have proposed significant techniques to compress the model to minimize the computation and storage intensity. Popular model optimization techniques include network pruning, low precision quantization and dynamic inference. In this paper, we compare and discuss state-of-the-art methods within the range of these three methods as a way to guide software and hardware developers to select the best method for their objective.
New Results on Self-Dual Generalized Reed-Solomon Codes

This paper focuses on constructions of MDS self-dual codes from (extended) generalized Reed-Solomon (GRS) codes. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q = r^{2}$ </tex-math></inline-formula> be an odd prime power. We show that, there exists a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -ary self-dual (extended) GRS code for each even length in the range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[{2r,3r-3}]$ </tex-math></inline-formula> , and for each singly even length in the range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[3r-1,4r]$ </tex-math></inline-formula> . This extends the only known consecutive range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[2,2r]$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[{2,3r-3}]$ </tex-math></inline-formula> for this case. Furthermore, our general constructions provide many MDS self-dual codes with new parameters which, to the best of our knowledge, were not reported before.
Behavior Self-Organization Supports Task Inference for Continual Robot Learning

Recent advances in robot learning have enabled robots to become increasingly better at mastering a predefined set of tasks. On the other hand, as humans, we have the ability to learn a growing set of tasks over our lifetime. Continual robot learning is an emerging research direction with the goal of endowing robots with this ability. In order to learn new tasks over time, the robot first needs to infer the task at hand. Task inference, however, has received little attention in the multi-task learning literature. In this paper, we propose a novel approach to continual learning of robotic control tasks. Our approach performs unsupervised learning of behavior embeddings by incrementally self-organizing demonstrated behaviors. Task inference is made by finding the nearest behavior embedding to a demonstrated behavior, which is used together with the environment state as input to a multi-task policy trained with reinforcement learning to optimize performance over tasks. Unlike previous approaches, our approach makes no assumptions about task distribution and requires no task exploration to infer tasks. We evaluate our approach in experiments with concurrently and sequentially presented tasks and show that it outperforms other multi-task learning approaches in terms of generalization performance and convergence speed, particularly in the continual learning setting.