Reviewerly/exHarmony huggingface dataset:
Neighborhood Based Optimization Algorithm

Evolutionary Computation (EC) algorithms are proposed as stochastic methods to solve complex optimization problems. Recently, the design of EC methods incorporates the design of complex operators to simulate the used metaphor. On the other hand, in a multi-agent system, the complex interactions among agents are coordinated, by the synergy of simple local behaviors. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO combines the simplicity of local consensus formulations, and reactive responses, based on neighborhood movement decisions to conduct the search strategy. NCCO considers a double pair of evolutionary operators for exploration-exploitation stages. The first pair; separation-alignment, conducts the search into wider zones, while the second pair; cohesion-seek, locally explores and exploits search spaces concentrating all agents into groups. These features guarantee leaderless movement decisions, since traditional EC approaches follow false-positive solutions, increasing the possibility of being trapped in local minima. Under such assumptions, the proposed algorithm exhibits a higher performance, against several state-of-art EC approaches evaluating common benchmark functions, and engineering design problems.
Agent-Based Education Environment in Museums

This paper presents an agent-based education system featuring context-awareness. It detects the locations of visitors and deploys agents at computers near their current locations. When visitors move between exhibits at a museum, their agents follow them to annotate the exhibits in personalized form and navigate them to the next exhibits along their routes. The system enables visitors to interact with agents by their movements rather than by using portable devices, as visitor movement is one of the most basic and natural behaviors in museums. This paper also describes insights gained from our past experiments on agent-based education services.
Multiple Characters Matching in Document Image Using Handwriting Recoganization

Objectives: To propose suitable system to filter the particular keyword in the document image.Methods/Statistical Analysis: Input image is pre-processed by using Gabor filtering.The preprocessed image is segmented by using segmentation free method.Then the feature extraction is done by using block based method.The Connectionist Temporal Classification (CTC) token passing calculation restores an arrangement of words from the lexicon.Then the word matching is performed by using normalized cross correlation.Findings: The performance of the proposed and existing methods are analysed with the help of word similarity, recognition accuracy, Recognition rate, computational time.Template matching method is utilized to discover and perceive the format picture which is found in the given info picture.Standardized cross connection techniques give precise outcome for the archive pictures.Application/Improvements: There are many applications such as data entry from printed paper data records, whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation.
A Model-Based Theorem Prover for Epistemic Graphs for Argumentation

Epistemic graphs are a recent proposal for probabilistic argumentation that allows for modelling an agent’s degree of belief in an argument and how belief in one argument may influence the belief in other arguments. These beliefs are represented by probability distributions and how they affect each other is represented by logical constraints on these distributions. Within the full language of epistemic constraints, we distinguish a restricted class which offers computational benefits while still being powerful enough to allow for handling of many other argumentation formalisms and that can be used in applications that, for instance, rely on Likert scales. In this paper, we propose a model-based theorem prover for reasoning with the restricted epistemic language.
Analysis and Design Information System Development of IT Care Application in Astra Credit Company

Abstract Business competition that keep intensify in automotive financing services provider field encourages all companies involved to continue improving themselves in order to competing with other companies, one of the ways is to utilize Information Technology (IT). Astra Credit Company (ACC) is one of automotive financing service provider which has already applied IT. As a big company, the large scale of the utilization of IT in ACC also raised the need for managing the IT Helpdesk. Currently ACC already uses an application to support IT Helpdesk business process called IT Care. But as the development of business process and the changing needs of the company, the system is no longer able to support business process needs. Based on those needs this research was conducted to develop system design that can meet current business needs. Design and analysis process done through System Development Life Cycle methodology with Object Oriented Analysis and Design approach using Unified Model Language diagram. System analysist and design activities developed several processes i.e. Chart Of Account management based with time estimation, ticket submission, ticket assignment with consideration of staff balancing work load, completion ticket based on priority, also reporting of IT staff performance and ticket completion. Conclusions from the results of the analysis and design of IT Care systems are a proposed IT Care system design that is expected to overcome the problems in the existing IT Care system.
Flicker reduction method for 120 fps shooting under 100 Hz light fluctuation by using a double rolling shutter

This study proposes a flicker reduction method for 120 fps shooting under 100 Hz light fluctuation. In 120 fps videos, a 100 Hz light fluctuation causes a 20 Hz flicker, which is an aliasing artifact induced by its sampling frequency. In this method, the frame period of 1/120 s is divided into 1/150 s and 1/600 s exposure by using a double rolling shutter. Each pixel alternatively outputs 1/150 s and 1/600 s exposure signals which are readout by a readout circuit operated at double the rate of a normal 120 fps operation. A 120 fps signal with an exposure time of 1/100 s is obtained by summing three consecutive signals with exposures of 1/600, 1/150, and 1/600 s. This method is effective for flicker reduction even in the presence of several light sources with different amplitudes and phases. We implemented this method to an 8K camera and examined the flicker reduction effect for an 8K 120 fps video. As a result, the 20 Hz flicker was suppressed to less than one-tenth.
Shape object selection using the chi-square method

Abstract This paper is focused on identifying the shape the shape of the object. There are some basic shape objects such as rectangle, circle, triangle, square, hexagonal, an ellipse. The method that used in this paper was chi-square feature selection to select a relevant feature toward those basic shape objects. Then, the proximity calculation between the training image and real image that was already available in the database. The chi-square feature selection process eliminates the irrelevant features in each figure that was in the image database. As final results, the system succeeds to detect an appropriate shape of an object by determining the distance of Euclidean between training image and the real image.
On bipartite operators defined by sets of completely different permutations

We introduce a class of bipartite operators acting on H⊗H (H being an n-dimensional Hilbert space) defined by a set of n Completely Different Permutations (CDPs). Bipartite operators are of particular importance in quantum information theory to represent states and observables of composite quantum systems. It turns out that any set of CDPs gives rise to a certain direct sum decomposition of the total Hilbert space which enables one simple construction of the corresponding bipartite operator. Interestingly, if set of CDPs defines an abelian group, then the corresponding bipartite operator displays an additional property – the partially transposed operator again corresponds to (in general different) set of CDPs. Therefore, our technique may be used to construct new classes of the so-called PPT (positive under partial transposition) states which are of great importance for quantum information. Using well-known relation between bipartite operators and linear maps, one can use also construct linear maps related to CDPs.
The Nonlinear Filtering Feature Identification Methods for the Duffing Chaotic Fractal State

Abstract With the wide applications of weak signal detection techniques for Duffing system, it is important to study the fast and accurate identification methods for fractal state, such as in the field of all kinds of signals detections, by fractal state discriminant of Duffing, the target detections can be achieved under the conditions of extremely low signal-to-noise ratios. Aiming at the existing problems of the algorithm, a new idea is put forward, the Duffing oscillator is converted to the state equation of nonlinear filters, respectively by CKF(Cubature Kalman Filter,) PF(Particle Filter) and EKF(Extended Kalman Filter) three nonlinear filtering algorithm for recursive estimations, the inherent rules whose filtering parameters change with the system state changing will be researched, and a discrimination method of oscillator Fractal state is proposed; the experimental results show that the three kinds of filtering methods can all achieve the Duffing oscillator state tracking, and with the help of the filtering characteristics, the identification of the oscillator state can be distinguished and feature extraction can both realize the qualitative and quantitative detections.
Rendering-dependent compression and quality evaluation for light field contents

Light field rendering promises to overcome the limitations of stereoscopic representation by allowing for a more seamless transition between multiple point of views, thus giving a more faithful representation of 3D scenes. However, it is indisputable that there is a need for light field displays on which the data can be natively visualised, fuelled by the recent innovations in the realm of acquisition and compression of light field contents. Assessing the visual quality of light field contents on native light field display is of extreme importance in future development of both new rendering methods, as well as new compression solutions. However, the limited availability of light field displays restrict the possibility of using them to carry out subjective tests. Moreover, hardware limitations in prototype models may lessen considerably the perceptual quality of experience in consuming light field contents. In this paper, we compare three different compression approaches for multi-layer displays, through both objective quality metrics and subjective quality assessment. Furthermore, we analyze the results obtained through subjective tests conducted using a prototype multi-layer display, and a recently-proposed framework to conduct quality assessment of light field contents rendered through a tensor display simulator in 2D screens. Using statistical tools, we assess the correlation among the two settings and we draw useful conclusions for future design of compression solutions and subjective tests for light field contents with muti-layer rendering.
AV1 In-loop Super-resolution Framework

The AV1 codec added a new in-loop super-resolution mode that allows a frame to be encoded at a lower resolution, and then super-resolved normatively to the full resolution, before updating the reference buffers. While encoding at lower resolution and super-resolving to a higher resolution is not a new concept, this is the first time such a mode has been normatively incorporated in a standardized video codec. To this end, AV1 has not only added support for across-scale motion prediction to allow predicting a lower resolution version of a frame from higher resolution reference buffers, but also made various simplifications to the super-resolving process itself after reconstruction to make it both software and hardware-friendly in implementation. Specifically, the super-resolving process in AV1 comprises of normative linear upscaling, followed by a restoration operation to recover the high frequencies using another AV1 tool called loop-restoration that includes a Wiener or Self-guided filter selected in a block switchable manner. Further, in order to enable a cost-effective hardware solution with limited line-buffers, this mode only allows the upscaling/downscaling operation to be horizontal. In this paper, we provide the details of the super-resolution mode in AV1 and some results showcasing the benefits of the same.
Inhomogeneous Diophantine approximation over fields of formal power series

We prove a sharp analogue of Minkowski's inhomogeneous approximation theorem over fields of power series $\mathbb{F}_q((T^{-1}))$. Furthermore, we study the approximation to a given point $\underline{y}$ in $\mathbb{F}_q((T^{-1}))^2$ by the $SL_2(\mathbb{F}_q[T])$-orbit of a given point $\underline{x}$ in $\mathbb{F}_q((T^{-1}))^2$.
Progress Report

In on-going work, we are exploring reference capabilities for arrays, with the intention of carrying over previous results on statically guaranteed data-race freedom to parallel array algorithms. Reference capabilities typically restrict incoming pointers to an object to one (uniqueness), or restrict operations via multiple pointer to a single object (e.g., to only read). Extending such a design to arrays involve operations such as logically partitioning an array so that even though there are multiple pointers to a single array, these pointers cannot access the same elements.
Goal-based progression synthesis in a korean learning game

Designing educational games is difficult, and ideally designers should be able to rely on tools that take some of the burden off them by generating content automatically. Previous work in automatic level generation and sequencing for educational games has primarily focused on achieving a gradual increase of difficulty. However, engagement often comes from a sense of accomplishment after completing hard tasks [27]. For this reason, many games feature "boss levels" that are more intense. In this paper, we propose that a good progression should be goal-based, meaning that it should build up towards some hard tasks (goals) as soon as possible to create a sense of satisfaction. To achieve this, we propose a graph-based algorithm for automatically synthesizing goal-based learning progressions of user-specific length. We also introduce Katchi, a Korean language learning puzzle game that is designed to be highly parameterizable, to evaluate our synthesized progression. In an evaluation of a Korean learning game with 248 participants, our synthesized progression performed similarly to an expert-designed progression in terms of both our engagement and learning metrics, demonstrating that our algorithm is capable of automatically synthesizing goal-based progressions that are comparable to the manually created progressions.
Spectral Expansion Method for Cloud Reliability Analysis

Cloud computing is a computing hypothesis, where a huge group of systems is linked together in private, public, or hybrid network, to offer dynamically amendable infrastructure for data storage, file storage, and application. With this emerging technology, application hosting, delivery, content storage, and reduced computation cost are achieved, and it acts as an essential module for the backbone of the Internet of Things (IoT). The efficiency of cloud service providers (CSP) could be improved by considering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these factors leads to efficiency in designing a scheduler for CSP. These metrics also improved the quality of service (QoS) in the cloud. Many existing models and approaches evaluate these metrics. But these existing approaches do not offer efficient outcome. In this paper, a prominent performance model named the “spectral expansion method (SPM)” evaluates cloud reliability. The spectral expansion method (SPM) is a huge technique useful in reliability and performance modelling of the computing system. This approach solves the Markov model of cloud service providers (CSP) to predict the reliability. The SPM is better compared to matrix-geometric methods.
The r-Hamming gap and distance-gap-preserving mappings from binary vectors to permutations

The well known Hamming distance between a pair of permutations (or strings) of the same length is simply the number of pairs of different digits in these permutations. It has been an interesting topic of research to find mappings from the binary vectors to permutations of the same length such that the Hamming distance is preserved or increased. As a natural variation we introduce the Hamming gap of radius r between two permutations, which is, in a way, equivalent to the number of digits where the corresponding pair of entries differ by at least r. This is called the r-Hamming gap. We first discuss the properties of this new concept. We then show mappings from binary vectors to permutations such that the images of a pair of binary vectors (at Hamming distance d) have r-Hamming gap at least d. We also show the generalization of our findings to permutations on Zn (where n ≡ 0) instead of [n].
Optimal state discrimination over quantum channels

Quantum state discrimination is a fundamental task in quantum information theory and also an important building block in practical quantum information applications. We here consider optimal state discrimination in a noisy environment in which quantum states may be corrupted due to the intervention of an environment. We show that even if there exists an unwanted interaction with an environment and the channel describing the interaction is not yet identified, a measurement prepared in the beginning for optimal state discrimination can be preserved as an optimal measurement ever after a channel use. This means that in a practical realization of quantum communication, verification of a channel can be circumvented and the cost of quantum tomography is saved. We also show a protocol of preserving an optimal measurement for state discrimination.
Image disambiguation with deep neural networks

In many signal recovery applications, measurement data is comprised of multiple signals observed concurrently. For instance, in multiplexed imaging, several scene subimages are sensed simultaneously using a single detector. This technique allows for a wider field-of-view without requiring a larger focal plane array. However, the resulting measurement is a superposition of multiple images that must be separated into distinct components. In this paper, we explore deep neural network architectures for this image disambiguation process. In particular, we investigate how existing training data can be leveraged and improve performance. We demonstrate the effectiveness of our proposed methods on numerical experiments using the MNIST dataset.
3D shop floor characterization of radii and chamfers

The sharp edges of most precision machined surface must be ‘broken’, which involves rounding them or adding a flat chamfer. Part longevity, human safety during handling, and proper performance of the component is the intended application of this edge breaking. Traditional visual inspection or single-trace metrology is insufficient to assure parts are within specifications. This paper will present a handheld, 3D-measurement device using polarized structured light which allows for rapid, simple, shop-floor measurement of edge break, chamfer angles, and other precision part geometries. A variety of component measurements will be presented along with studies on accuracy, repeatability, and reproducibility in real-world environments.
An Improved Agent Strategy Training Method Based on DIAL

Abstract The traditional multi-agent method has a slow convergence speed and needs to train many parameter groups. If no algorithm optimization is performed, it takes a lot of time to reach a large number in order to achieve optimal results in the whole process. We optimize the parameter settings in the traditional algorithm training process, accelerate the convergence speed of the intelligent body in learning, and achieve the convergence effect faster.
BREAK, MAKE and TAKE: an information retrieval approach

Ranking functions used in information retrieval are primarily used in the search engines and they are often adopted for various language processing applications. This paper introduces some novel heuristics combined with probabilistic retrieval functions and are employed in the domain of approximate string similarity problem. Various algorithms have been proposed in the literature to solve approximate string similarity problems; however, none of them makes use of probabilistic retrieval functions. We are the first to explore the intersection between these two areas, that is between string similarity and information retrieval, and propose heuristic designs to resolve this problem. First, we propose chunking heuristic function, called BREAK. We show the variants BREAK-1, -2, -OFF, which split up the terms with the sequential notion. Then we propose BREAK-n, which generalizes these variants and scales to larger datasets. In order to relate these split-ups, we propose a graphical error modelling heuristics MAKE over the BREAK variants. Finally, we propose TAKE curve, a novel feature engineering probabilistic distribution, which replaces the prevalent normalization heuristics. Taking the advantage of flexibility over the choice of heuristics, we assess the variants on the cognate detection, mutant identification and problems based on isolated spelling correction. In the extensive evaluation methods, we found that our designs perform better than prevalent heuristics and are robust against database characteristics.
Creating and Optimizing Learning Maps by Negative and Positive Convolutional Neural Networks

Abstract In this paper, a model of creating and optimizing learning maps by negative and positive convolutional neural networks is established. Firstly, the ontology knowledge base, learning maps and convolutional neural network are introduced. Then after three steps of reverse link, reverse pooling, reverse convolution of negative convolutional neural networks (RCNN), three kinds of learning maps: linear learning map, tree learning map, and graphic learning map are created. Learners learn knowledge by the direction of learning maps, different learning matrices are obtained in the process of learning, and notes and ideas are recorded in electric note books. At last, positive convolutional neural networks (CNN) are used to optimize leaning maps. After three operations of convolution learning map matrix, pooling learning nodes, link knowledge domains, the results of weakening what learners are not interested in, weakening the task that learners think is difficult, reducing the repetition of public knowledge and randomly selecting according to the complexity of the learning map can reach. Improving learners’ confidence and Enriching the content of the learning maps have been done also.