Reviewerly/exHarmony huggingface dataset:
An Active Set Method for Solving Certain Support Vector Machine Problems

We propose an active set method to solve the dual of the convex quadratic programming problem which is the core of the support vector machine (SVM) training. The method stems from the more general method developed by the first author. By using the special handling of certain quadratic programming problems where the Hessian matrix in the objective function is given as a product of a matrix and its transpose, and by simplifying the solution of the linear system arising at each iteration of the method, we were able to produce an implementation for certain SVMs. The results of an experiment using MATLAB are reported.
Image Annotation with Decision Tree

This chapter introduces one of the most intuitive and traditional learning machines which is DTDecision Tree (DT) . The chapter begins with the introduction of DTDecision Tree (DT) concept and the formulation of DT using a standard ID3ID3 algorithm with focus on splitting criterionSplitting criterion . It is followed by describing another commonly used DTDecision Tree (DT) algorithm: C4.5C4.5 . After the introduction of both ID3ID3 and C4.5, the two subbranches of CARTClassification And Regression Tree (CART) are described: classification treeClassification tree and regression treeRegression tree . An application of regression treeRegression tree is shown on a real dataset to demonstrate how the CARTClassification And Regression Tree (CART) actually works. The chapter is highlighted and concluded with an application of DTDecision Tree (DT) on image classificationImage classification using a working example or case study. It demonstrates how a DTDecision Tree (DT) is built step by step and how the DT is used for prediction.
Ant Colony Optimization method analyzing for Sequential Pattern Mining (Case Study: iGracias Telkom University)

With the development of internet technology, information becomes very easy to obtain. Especially the use of web-based information systems are very widely used to disseminate information to the world. Similarly, in the Eld of education that makes the website as a source of information for all students, lecturers, to employees. In general, each user access to the website can form the access pattern. Then, the accessing pattern can be used to do the development on the website to facilitate the user get the desired menu. By using Ant Colony Optimization (ACO), the system can output the pattern of access based on the interests of all users. The selection of the Ant Colony Optimization (ACO) method due to the ow when the user accesses the website has in common with the ants while building the road to the food. In this experiment, pheromone will be modification with interest time and interest visit user to release the result that can approach the pattern of user access when accessing the website. Also, after testing the threshold was obtained 0.02 for the student group, 0.05 for the lecturer group, and 0.025 for the employee group which can produce a pattern that represents the user's habit patterns.
Learning optimal actions with imperfect images

Deep reinforcement learning has been successful in training an agent to play at human-level in Atari games. Here, outputs of the game images were fed into a deep neural network to compute optimal actions. Conceptually, reinforcement learning can be viewed as the intersection of planning, uncertainty, and learning. In this paper, deep reinforcement learning method is applied to solve a problem formulated as a partially observable Markov decision process (POMDP). Specifically, the input images are perturbed to introduce imperfect knowledge. POMDP formulations assume uncertainties in the true state space and thus a more accurate representation of the real-world scenarios. The deep Q-network is adopted to see if an optimal sequence of actions can be learned when the inputs are not fully observable. Experimental results indicated that optimal strategies were discovered by deep reinforcement learning in majority of test cases, albeit slower to converge to the optimal solution.
Dynamic Modeling of the Relationship Between Risks of Activities and Reduction of Activities Time in the Post-Implementation Phase of the Enterprise Resource Planning System Using Fuzzy Cognitive Maps

In recent years Many Iranian organizations have used ERP systems to improve the performance and effectiveness of their organizational activities, while very few of them have gained sustainable advantages of this system. One of those benefits is reducing of cycle time of production and one of the factors that reduces the cycle time of production is to reduce the time of activities. The successful implementation of an ERP system is just the first step in achieving a successful system and its sustainable advantages depend on continuous support, maintenance and upgrade of the system in the exploitation or post implementation phase. The post implementation phase is exactly where real challenges begin and many dangerous risks are likely to occur. In this study, the relationship between the risks of activities and the time of activities in post-implementation phase is dynamically modelled using Fuzzy Cognitive Maps. The main advantage of fuzzy cognitive map is its ability to model complex phenomena based on expert perceptions. This tool modeling related and uncertain events by imitation of human reasoning. For this purpose, 36 risks were identified and collected and categorized into 5 groups of user, technical, managerial, operational and external risks, and their relationships between them and reduction of time of activities were modelled. According to the findings of this study, managerial risks had the greatest impact on other system risks and output.
Superconducting single-photon detectors as smart sensors

We present a new type of a quantum detector, namely, the superconducting single-photon detector (SSPD), and demonstrate that it can be implemented in various single-photon counting applications, ranging from the visible light to near-infrared telecommunication wavelengths. We describe the physics of the photoresponse of a superconducting nanostripe to a flux of single optical photons and present the operation principle of SSPDs, stressing that, currently, they significantly outperform any competing, research or commercial devices in terms of their quantum efficiency, counting rate, jitter, and unwanted dark counts. SSPDs integrated with a cryogenic HEMT read-out circuit can provide some level of both the energy and number resolution of an unknown incident photon flux, making them uniquely suitable for various photon sensing applications.
Locally Differentially Private Minimum Finding

We investigate a problem of finding the minimum, in which each user has a real value, and we want to estimate the minimum of these values under the local differential privacy constraint. We reveal that this problem is fundamentally difficult, and we cannot construct a consistent mechanism in the worst case. Instead of considering the worst case, we aim to construct a private mechanism whose error rate is adaptive to the easiness of estimation of the minimum. As a measure of easiness, we introduce a parameter α that characterizes the fatness of the minimum-side tail of the user data distribution. As a result, we reveal that the mechanism can achieve O((ln6N/ε2N)1/2α) error without knowledge of α and the error rate is near-optimal in the sense that any mechanism incurs Ω((1/ε2N)1/2α) error. Furthermore, we demonstrate that our mechanism outperforms a naive mechanism by empirical evaluations on synthetic datasets. Also, we conducted experiments on the MovieLens dataset and a purchase history dataset and demonstrate that our algorithm achieves Õ((1/N)1/2α) error adaptively to α.
Improvements of bat algorithm using crossover technique and hybridization with Nelder-Mead simplex method

Purpose This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering. Design/methodology/approach For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm. Findings First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB. Originality/value The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.
Navigation and collision avoidance with human augmented supervisory training and fine tuning via reinforcement learning

Robust navigation and orientation under complex conditions is a must for autonomous drones operating in new and varied environments. Creating drones with adequate behaviors can be a challenge from both a training standpoint and a generalization standpoint. Using human expertise data is an option to help bootstrap the learning process; however, using the human data can lead to side consequences that are not immediately intuitive. This study focuses on applying varying levels of human input to an agent to determine how this input affects the agent's performance. The Unreal Engine and the Airsim plugin are used to train a quadcopter agent in an abstract "blocks world" type environment. Six agents in total are trained, with the first five having increasing amounts of human input and the sixth agent having no human input. A variety of metrics are looked at, including total goals achieved and time to achieve some number of goals.
Sequent calculus for 3-valued paraconsistent logic QMPT0

Abstract We present a sequent calculus of a paraconsistent logic QMPT0, which has the paraconsistent-type excluded middle law (PEML) as an initial sequent. Our system shows that the presence of PEML is essentially important for QMPT0. It also has special rules when the set of constant symbols is finite. We also discuss the cut-elimination property of our system.
Exploration and Mining of Multipurpose Cooperative Business Data

In the Philippines, cooperative transition to becoming technology-driven actors in the development sector by initiating the development of ICT solutions specifically catered to cooperatives and their operations. Without data mining behind crucial decision making in a cooperative management, the fate of these cooperatives may be likened to banks on the verge of bankruptcy. Therefore, cooperative basic business data mining to discover patterns and trends helpful in decision making is being introduced in this study. All districts than can be equated to municipalities are well represented by members of the multipurpose cooperative. There is an equally efficient collection measures of the cooperative management on equity and Mutual Aid System (MAS).
Case-study analysis of apparent camouflage-pattern color using segment-weighted spectra

Advanced camouflage patterns, consisting of highly detailed camouflage patterning, require additional methodologies for color evaluation, which is with respect to realistic field conditions. A quantitative metric for evaluation of comouflage patterns, as viewed under realistic field conditions, is “apparent color,” which is the combination of all visible wavelengths (380-700 nm) of light reflected from large camouflage-pattern samples (&ge;1m<sup>2</sup> ) for a given standoff distance (25-100 ft). Camouflage patterns lose resolution with increasing standoff distance, and eventually all colors within the pattern combine and thus appear monotone (the “apparent color” of the camouflage pattern). This paper presents a case-study analysis of apparent camouflage-pattern color using segment-weighted reflectance spectra for the purpose of evaluating apparent color of advanced camouflage patterns with respect to realistic field conditions. Simulation of apparent camouflage-pattern color using this methodology is based on decomposition of camouflage-pattern reflectance with respect to component segments of camouflage patterns.
On the Need for Intellectual Property Protection in Model-Driven Co-Engineering Processes

We live in an increasingly complex world where all systems tend to include heterogeneous and interconnected components. To cope with these systems, industry is shifting towards co-engineering development processes where partners with very different roles and access needs must collaborate together. Therefore, protecting the intellectual property (IP) of the shared assets is a must. Model-Driven Engineering (MDE) may play a key role in the successful enactment of industrial co-engineering processes but only if it succeeds at integrating at its core the concern for IP protection, that has been up to the date largely ignored. In order to advance in this direction, we provide in this paper an initial roadmap towards the holistic protection of IP in collaborative modeling scenarios and we discuss how existing technologies such as Cryptography, Access-control (AC) or Digital Rights Management (DRM) are adapted and integrated in a framework for IP protection in the MDE.
Evaluation of Basic Modules for Isolated Spelling Error Correction in Polish Texts

Spelling error correction is an important problem in natural language processing, as a prerequisite for good performance in downstream tasks as well as an important feature in user-facing applications. For texts in Polish language, there exist works on specific error correction solutions, often developed for dealing with specialized corpora, but not evaluations of many different approaches on big resources of errors. We begin to address this problem by testing some basic and promising methods on PlEWi, a corpus of annotated spelling extracted from Polish Wikipedia. We focus on isolated correction (without context) of non-word errors (ones producing forms that are out-of-vocabulary). The modules may be further combined with appropriate solutions for error detection and context awareness. Following our results, combining edit distance with cosine distance of semantic vectors may be suggested for interpretable systems, while an LSTM network, particularly enhanced by contextualized character embeddings such as ELMo, seems to offer the best raw performance.
Merging Worlds – Aligning Safety and Security

Safety and security are important domains in current industrial control systems. While safety protects the human and the system itself from failures, security protects the system from malicious entities and their activities. One is static, admitted, and is not allowed to change over years, the other has to adopt dynamically to changes in the threat landscape. Due to digitalization and the need for more performance and a better maintainability, isolated systems, responsible for controlling signals in the railway domain or a power plant in the energy sector, are connected to large networks, using standard protocols and commercial-off-the-shelf components. Because of this change suddenly IT-Security becomes an important topic and it must be integrated in safety components, which due to the contradicting requirements of the domains can be challenging. In a first approach one could think of designing the safety system and applying security to it as a shell afterwards. Although this approach may be applicable and lead to a useable result, better methods may exist, which result in a more efficient design process and a more secure solution. In our integrated approach security and safety lifecycles are merged and several activities like estimating the impact of an error only must be performed once for both domains. Especially in risk analysis, the derivation of security requirements and the maintenance phase, several processes can be combined and will be shown in this work.
Solving Influence Diagrams with Simple Propagation

Recently, Simple Propagation was introduced as an algorithm for belief update in Bayesian networks using message passing in a junction tree. The algorithm differs from other message passing algorithms such as Lazy Propagation in the message construction process. The message construction process in Simple Propagation identifies relevant potentials and variables to eliminate using the one-in, one-out-principle. This paper introduces Simple Propagation as a solution algorithm for influence diagrams with discrete variables. The one-in, one-out-principle is not directly applicable to influence diagrams. Hence, the principle is extended to cope with decision variables, utility functions, and precedence constraints to solve influence diagrams. Simple Propagation is demonstrated on an extensive example and a number of useful and interesting properties of the algorithm are described.
Assemble: The Artifact as a Collaborative Tool in Knit Design Research

This paper presents a new perspective on the role of the artifact in knit design research. The artifact is presented as a stimulus for interdisciplinary research where practice based enquiry provides only part of the required methodological approach. Reporting on a major collaboration between the curator of the Oriental Collection at The Royal Armories, Leeds, and researchers from the School of Design at The University of Leeds, this paper pursues two key trajectories. Firstly, the paper outlines how the artifact can be used as a design stimulus for interdisciplinary collaboration. This is assessed across multiple stages of design and through the production of several perspectives on key thematic ideas. Secondly, the paper reports on how collaboration, specifically in the development of these alternative perspectives, can lead to materials innovation. These two points of discussion are contextualized with reference to interior architectural installation, Inflection, and supported by analysis of The Knitting Machine, Cocoon and knitted exterior installation Configure. Findings highlight how the use of artifacts enables practitioners to adapt thinking using material practice in order to generate new knowledge.
Business Intelligence: Determination of Customers Satisfaction with the Detection of Facial Expression

Nowadays, effective use of technology in the competitive world is the most important factor in business success. Since the stakeholders and especially customers are the most important factor in the profitability of organizations, the use of new methods in determining their satisfaction is of particular importance. In this paper, business intelligence for determining customer image satisfaction using image mining technology was performed. First, 400 images of customers’ faces were selected from the database. Investigating the facial expressions and the use of experts suggests that 4 states of 7 faces are sufficient to determine satisfaction. In the next step, the results of the study were extracted with using three algorithms and were analyzed in the following with three Classification Regression Tree (CRT) algorithms, Neural Network and Decision Tree. In the end between image mining algorithms the Bayesian Method and among data mining algorithms the CRT algorithm were detected with the least error for satisfaction detection.
Trajectory-Based Off-Policy Deep Reinforcement Learning

Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining recent improvements in the reuse of off-policy data and exploration in parameter space with deterministic behavioral policies. The resulting objective is amenable to standard neural network optimization strategies like stochastic gradient descent or stochastic gradient Hamiltonian Monte Carlo. Incorporation of previous rollouts via importance sampling greatly improves data-efficiency, whilst stochastic optimization schemes facilitate the escape from local optima. We evaluate the proposed approach on a series of continuous control benchmark tasks. The results show that the proposed algorithm is able to successfully and reliably learn solutions using fewer system interactions than standard policy gradient methods.
Clustered Exact Daum-Huang Particle Flow Filter

Unlike the conventional particle filters, particle flow filters do not rely on proposal density and importance sampling; they employ flow of the particles through a methodology derived from the log-homotopy scheme and ensure successful migration of the particles. Amongst the efficient implementations of particle filters, Exact Daum-Huang (EDH) filter pursues the calculation of migration parameters all together. An improved version of it, Localized Exact Daum-Huang (LEDH) filter, calculates the migration parameters separately. In this study, the main objective is to reduce the cost of calculation in LEDH filters which is due to exhaustive calculation of each migration parameter. We proposed the Clustered Exact Daum-Huang (CEDH) filter. The main impact of CEDH is the clustering of the particles considering the ones producing similar errors and then calculating the same migration parameters for the particles within each cluster. Through clustering and handling the particles with high errors, their engagement and influence can be balanced, and the system can greatly reduce the negative effects of such particles on the overall system. We implement the filter successfully for the scenario of high dimensional target tracking. The results are compared to those obtained with EDH and LEDH filters to validate its efficiency.
Dynamic Cell Structure via Recursive-Recurrent Neural Networks

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neural networks, our algorithm is able to construct customized cell structures for each data sample and time step, leading to an efficient sample-level architecture search model. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better performance compared to the GRU structure for language modelling with the same network capacity.
Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet

Deep learning (DL) has gained a lot of popularity in the science and business community. It has been successful in a range of applications, especially in computer vision. This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet. This is a valuable contribution because in general convolutional networks are not scale invariant. Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images.
Study on the Service Personnel and Service Place in Children's Language Training Service Regarding the E-business

Human resources and place resources are the most important service resources in the service industry. Children's language training services under the era of e-commerce are intangible, simultaneous production and consumption, and strong interaction. Service personnel and service venues combine the characteristics of educational activities and learning activities with more complex quality characteristics, which are important factors influencing the quality of children's language training services.