Reviewerly/exHarmony huggingface dataset:
Adaptive Hybrid Representation for Graph-Based Semi-supervised Classification

Building an informative graph over a collection of images or signals is one of the most important tasks in semi-supervised learning (SSL). Local Hybrid Coding (LHC) was recently proposed as an alternative to the sparse coding scheme that is used in Sparse Representation Classifier (SRC). The LHC blends sparsity and bases-locality criteria in a unified optimization problem. This paper introduces a data-driven graph construction method that exploits and extends the LHC scheme. We propose a new coding scheme coined Adaptive Local Hybrid Coding (ALHC). The main contributions are as follows. First, the proposed coding scheme automatically selects the local and non-local bases of LHC using data similarities calculated by Locality-constrained Linear code. Second, the estimated similarities are used in the regularization of the final solution. Third, the proposed ALHC scheme is used in order to construct graphs over image datasets. For SSL tasks adopting label propagation, we show that the proposed graph outperforms many state-of-the art graphs on three public face datasets.
Switched-system-based four-scroll chaotic attractor generation and its application in color image encryption

In this paper, a four-scroll chaotic attractor is generated based on a switched system, and moreover, it is applied to color image encryption. First, the canonical Chen system and a modified chaotic system are used as two subsystems to construct the switched system. Meanwhile, a state-dependent switching law with a threshold is designed to regulate the switching between the two subsystems. Then, a four-scroll chaotic attractor is generated by tuning the threshold value. Last, the chaotic sequence extracted from the switched chaotic system is applied to image encryption. The experimental results show that the encryption performance of this switched chaotic system is better than that of a single Chen system.
Fuzzy Twin Support Vector Machine Based on Intra-class Hyperplane

Abstract Aiming at the sensitivity of twin support vector machines to noise and outliers, an improved fuzzy twin support vector machine is proposed. Aiming at the shortcomings of fuzzy membership based on the distance between a sample and its class center, this algorithm proposes a new fuzzy membership function that can effectively reflect sample uncertainty— based on intra-class hyperplane and sample affinity. When determining the membership function, the intra-class hyperplane replaces the class center, reducing the dependence on the sample set geometry and improving the model generalization ability. At the same time, considering the relation between each sample and the confusion degree of sample points, the sample affinity is applied to the design of membership function, so as to effectively distinguish the effective sample from noise and outliers and improve the classification accuracy. The experimental results show that compared with the twin support vector machine and the classic fuzzy support vector machine, the improved fuzzy twin support vector machine has better classification performance.
Artificial Neural Networks in Forecasting Cancer Therapy Methods and Costs of Cancer Patient Treatment. Case Study for Breast Cancer

Forecasting of cancer therapy and its cost is a key factor in the process of managing the oncological patient’s treatment. This research is based on two predictive models of artificial neural networks (ANNs). The paper scope also includes a statistical analysis of correlation of the ANN variables. Research shows that ANNs are a viable alternative to traditional method of estimating the cost of a cancer therapy, especially in situations of poor recognition of the nature of medical relations between costs and their cost drivers, or in situations of non-linear, multidimensional relations between variables. Studies have also shown that ANN is an adequate method for predicting cancer therapy. The first contribution of this study is application of an innovative, complex approach based on two ANN models in the management of oncological patient treatment. The second contribution is the use of medical specifications required by the oncological patient directly to estimate the costs of cancer therapy.
Design of fuzzy logic controller for temperature control of small-scale food storage

A Fuzzy Logic Controller (FLC) is developed to control the temperature of small-scale food storage. The FLC has two components such as the algorithm part and the hardware part. For the algorithm part, The FLC takes two input parameter such as the temperature and the humidity of the storage and takes one output parameter such as the speed of the fan. The membership function is designed as following hot, fair, cold, and too cold as for the membership functions of the temperature parameter. For the humidity membership functions are dry, dry enough, normal, less moist, and moist. As for the fan speed, the membership functions are fast, less fast, fair, slow, and too slow. On the other hand, for the hardware, the DHT11 senses the temperature and the humidity. For the cooling process, thermoelectric is used to decrease the temperature. The results show that the FLC can be used to control the temperature of the storage to achieve as the minimum temperature as 12.5°C.
Gabriel Graph Transductive Approach to Dataset Shift

It is not uncommon for data obtained from systems to change after the model is learned. These occurrences are named dataset shifts and to deal with them models with the ability to adapt to data changes must be used. A strategy that can be easily integrated to other classifiers is proposed. It creates a geometrical representation of data that extracts information from both labelled and unlabelled data. Then data entropy and Jensen-Shannon dissimilarity tests are used during the model selection to handle cases where data shift. Results have shown that the proposed method is promising because of its simple integration with state of art classifiers and its performance in enhancing said classifiers accuracy in the studied cases.
Structural Fusion/Aggregation of Bayesian Networks via Greedy Equivalence Search Learning Algorithm

Aggregating a set of Bayesian Networks (BNs), also known as BN fusion, has been studied in the literature, providing a precise theoretical framework for the structural phase. This phase depends on a total ordering of the variables, but both the problem of searching for the optimal consensus structure (according to standard problem definition), as well as the one of looking for the optimal ordering are NP-hard. In this paper we start from this theoretical framework and extend it from a practical point of view. We propose a heuristic method to identify a suitable order of the variables, which allows us to obtain consensus BNs having (by far) less edges than those obtained by using random orderings. Furthermore, we apply an optimization method based on the GES algorithm to remove the extra edges. As GES is a data-driven method and we have not data but a set of incoming networks, we propose to use the independences codified in the incoming networks to determine a score in order to evaluate the goodness of removing a given edge. From the experiments carried out, we observe that our heuristic is very competitive, driving the fusion process to solutions close to the optimal one.
Determining the Common Set of Weight in Data Envelopment Analysis with Linear Programs

Abstract Data Envelopmemt Analysis (DEA) serves to assess efficiency in the use of resources (input) to achieve results (output) whose purpose is to maximize efficiency. DEA was first introduced by Charnes, Cooper dan Rhodes in 1978. DEA is a nonparametric approach which is basically the development of Linear Programming (LP). The characteristic of DEA CCR model is that it allows the DMU to measure the maximum efficiency value with the most favorable weight. The efficiency of the DMU obtained differs from the different sets of weight may not be comparable and rank on the same basis. In DEA model optional sets of input weight and output weight are usually assumed to represent the DMU that is considered the best to compare with all DMUs. This set of weights is usually different for each DMU. In this journal to determine the set of weights in the DEA is performed with a linear programs. After the linear program is determined, the value of the output weight and input weight is solved using the software POM-QM for Windows.
Top Management Support during Large-scale Software Systems Implementations: Important Issues in Management Process

Background/Objectives: Top management support factor is of enormous importance for Large-scale Software Systems (LSS) literature.Method/ Findings: It has been recognized as one of the most commonly observed Critical Success Factor (CSF) element in the successful implementation in the context of Enterprise Resource Planning (ERP) systems.The study is to develop CSF-Live!which is a method for measuring, monitoring, and controlling critical success factors of large-scale software systems?To achieve such goal, we apply CSF-Live for the top management support CSF.The CSF-Live uses the Goal/Question/Metric paradigm (GQM) to yield a flexible framework containing several metrics that we used to develop a formulation, which enables the measurement of the top management support CSF. Application/Improvements: The formulation that we developed for the top management support CSF is crucial to maintain the top management support during LSS.
An Application for the Geometrical Based Optimization for a Common Tool Design in Cold Forging Process

Nowadays, the production time is a key to minimize the process sequence which leads to lower the whole product cost. One of the leadtimes is the installation of the forming dies to the press. There are many methodologies having been developed to solve this problem, such as a Single Minute Exchange Die System (SMED) which could minimize the setting up die to almost 25-30 percent. However, this system would not consider the minimization of the forming process or sequence. Most of the automotive industries would lighten this problem by centralizing common parts which could be utilized with the same component dimension. This part could be used in different car models. However, this technique could only reduce the time in development of the forming processes/dies, but the number of the forming dies is still remain unchanged. Therefore, in this research the geometrical based methodology integrated with Finite Element Modeling would be proposed to develop generalized common dies which could be used with the same part configurations/shapes but difference in dimensions at some areas. This technique is applied and validated with different pulley models. The results show that almost 60 percent of the forming dies would be reduced by this technique.
Precise positioning in the in-situ deflectometric measurement of optical surfaces

Phase measuring deflectometry is a powerful in-situ measuring technique for complex specular surfaces. Its measuring accuracy depends on the quality of geometric calibration. An in-situ deflectometric measuring system is integrated into a single point diamond turning machine. An accurate self-calibration method is proposed to refine the positions of the camera and the screen. A world coordinate system is established by introducing a flat mirror without markers. The geometric positions are solved by minimizing the deviations of the traced screen pixels. The tracing deviations caused by the form errors behave differently with those caused by the position errors. Precise localization of the measured surface can be realized by error separation, so that detecting of feature points can be avoided. Experimental results demonstrate that the measurement error is below 300 nm.
Image style transfer learning for style‐strength control

Image style transferring is a process of generating an output image in a target style from a given pair of content and target style images. Recently, a simple linear interpolation technique in encoded feature space has been employed in this process to generate output images of intermediate style because controlling the strength of style transferring effect is a key function of an image editing filter. However, this simple technique is effective in generating images of around full style but around zero style hence it cannot smoothly control the style transferring effect from an original content image to a fully stylised image. In this Letter, the authors tackle the missing work on style-strength control from content reconstruction to full style generation. To deal with this problem, they propose to use additional unbiased training data and losses for a style transfer network to learn an unbiased regression between output style strength and style control parameter. Experimental results verified that the proposed method achieved a full range of style-strength control from zero style to full style with no additive complexity in image generating process.
Toward emotional recognition during HCI using marker-based automated video tracking

Postural movement of a seated person, as determined by lateral aspect video analysis, can be used to estimate learning-relevant emotions. In this article the motion of a person interacting with a computer is automatically extracted from a video by detecting the position of motion-tracking markers on the person’s body. The detection is done by detecting candidate areas for marker with a Convolutional Neural Network and the correct candidate areas are found by template matching. Several markers are detected in more than 99 % of the video frames while one is detected in only ≈ 80,2 % of the frames. The template matching can also detect the correct template in ≈ 80 of the frames. This means that almost always when the correct candidates are extracted, the template matching is successful. Suggestions for how the performance can be improved are given along with possible use of the marker positions for estimating sagittal plane motion.
Evolving unsupervised neural networks for Slither.io

Slither.io is a massively multiplayer online game in which up to 500 players control worm-like avatars and consume food to grow with the goal of becoming the largest player while avoiding running into one another. The platform serves as a good testbed for developing AI controlled agents due to its accessibility, mechanical simplicity, and unpredictability. In this paper, we develop a Slither.io bot using neuroevolution of augmenting topologies (NEAT) and compare its performance to that of the best open source bot available online (a high-performing expert system bot). With a fitness function based on the final size of the agent, our results show steady improvement in average score. We discuss the unique emergent behaviors observed by our top performing agents.
Fast and accurate 3D reconstruction based on grating projection

Abstract The principle model and calibration method of measurement are very important to the measurement system. In the vision measurement system, the auxiliary light source, such as laser, interference light, sine grating and so on, is usually needed in order to achieve high precision measurement results. In the existing 3D measurement methods for the surface topography of complex objects, in order to complete the measurement of absolute phase, it is usually necessary to process at least 6 fringe images, which limits the measurement speed. A method for obtaining the 3D shape of an object by using four sinusoidal fringes is presented. The truncated phase of the sinusoidal fringe is obtained by using the four-step phase shift. When the fringe order is obtained, the time of data processing can be reduced and the measurement speed can be further improved. The reconstruction time is less than two minutes and the maximum absolute error and maximum RMS error are 0.033mm and 0.029mm respectively when the height of the face model is 45mm. The validity and practicability of the method are verified, and it has a wide application prospect in high-speed and real-time 3D measurement of complex topography.
Discovering Analytical Preferences for Personalizing What-If Scenarios

In this paper, we expose a hybridization methodology for helping to overcome the pitfalls of conventional What-If analysis process design and development by discovering the best recommendations for What-If analysis scenarios’ parameters using OLAP preferences. The hybridization process aims at assisting users during the decision-making processes by suggesting the most adequate scenario parameters according to their needs, making What-If scenarios more valuable, helping them during decision-making processes. The hybridization process provides several advantages to companies by making possible to study the behavior of a system without building it or creating the circumstances to make it happen in a business real-world system. Thus, knowing existing approaches for extracting preferences when dealing with OLAP application environments has clear business advantages. This work is about this, with a particular focus on discovering analytical preferences for personalizing What-If application scenarios.
Online Environment for Prototyping and Testing Graph and Network Algorithms

In this paper there is presented an online, high-available software environment for prototyping and testing graph & network algorithms. The environment is divided into two components: an algorithms’ code editor and a graph & network visual editor. The aim of the tool was to provide an easily accessible, extendable environment for prototyping and testing algorithms with real-time interactive visualization. Developed environment can be also successfully used for educational purposes in graph and network related university courses.
Evaluating Electronic Ink Display Technology for Use in Drawing and Note Taking

In recent years, electronic paper – a display technology known for its low power consumption and paper-like appearance has started emerging in several consumer products, replacing reading and writing on regular paper to some extent. In this paper, the capabilities of electronic ink technology is evaluated in the context of drawing and note taking. Our tests were done by letting test participants use two different tablet devices as replacements for daily activities that usually require pen and paper; the reMarkable, using an electronic ink display, and the Apple iPad, using an LCD screen. The results from the tests showed that for writing, the reMarkable was superior to the iPad, although insufficient when it comes to software and user experience. This shows that the nature of tasks performed throughout a day benefits from a less niched, more do-all device such as an iPad. In summary, the results from this evaluation shows that while the technology has come a long way mimicking the look and feel of paper, integrating electronic paper technology into a tablet in this case demands a little too much of the technology itself. Ultimately, electronic paper still has some way to go before being the go to paper replacement that has been long sought after.
Texture Retrieval in the Wild Through Detection-Based Attributes

Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the textile of interest). In this paper we show that a texel-based representation fits well with this task. In particular, we refer to Texel-Att, a recent texel-based descriptor which has shown to capture fine grained variations of a texture, for retrieval purposes. After a brief explanation of Texel-Att, we will show in our experiments that this descriptor is robust to distortions resulting from acquisitions in the wild by setting up an experiment in which textures from the ElBa (an Element-Based texture dataset) are artificially distorted and then used to retrieve the original image. We compare our approach with existing descriptors using a simple ranking framework based on distance functions. Results show that even under extreme conditions (such as a down-sampling with a factor of 10), we perform better than alternative approaches.
Neural Network Approach for Multiple-Class Classification

Abstract Classification is a very important problem-solving technique. Given a set of attribute values about an object, a classifier determines which category the object belongs to. A classification task can be a binary or multiple classification; it can also be a single-label or multiple-label classification. Neural networks are an effective technique in the field of artificial intelligence. In this paper, we develop different schemes of neural networks for solving binary, multiple, single-label, and multiple-label classification problems. The ideas behind them are described in detail.
Ordering Argumentation Frameworks

This paper introduces two orderings over abstract argumentation frameworks to compare justification status under argumentation semantics. Given two argumentation frameworks $$AF_1$$ and $$AF_2$$ and an argumentation semantics $$\sigma $$ , $$AF_2$$ is more $$\sharp $$ -general than (or equal to) $$AF_1$$ (written $$AF_1\sqsubseteq _{\sigma }^{\sharp } AF_2$$ ) if for any $$\sigma $$ -extension F of $$AF_2$$ there is a $$\sigma $$ -extension E of $$AF_1$$ such that $$E\subseteq F$$ . In contrast, $$AF_2$$ is more $$\flat $$ -general than (or equal to) $$AF_1$$ (written $$AF_1\sqsubseteq _{\sigma }^{\flat } AF_2$$ ) if for any $$\sigma $$ -extension E of $$AF_1$$ there is a $$\sigma $$ -extension F of $$AF_2$$ such that $$E\subseteq F$$ . We show that if $$AF_1\sqsubseteq _{\sigma }^{\sharp } AF_2$$ then $$AF_2$$ skeptically accepts arguments more than $$AF_1$$ (under the $$\sigma $$ -semantics) while if $$AF_1\sqsubseteq _{\sigma }^{\flat } AF_2$$ then $$AF_2$$ credulously accepts arguments more than $$AF_1$$ . Mathematically, these orders constitute pre-order sets over the set of all argumentation frameworks. Next we consider comparing two AFs under dynamic environments by observing the effect of incorporating new information into given AFs. We introduce two orderings in such dynamic environments and show its connection to strong equivalence between argumentation frameworks.
Predict Foreign Exchange Revenue from CPO Commodity Exports Using Backpropagation Neural Network

Abstract Export of CPO and its derivative products is one of the biggest revenues for Indonesian foreign exchange. Through this research, predictions of foreign exchange earnings from CPO exports are made using backpropagation artificial neural networks. The data used in the form of export data for the period September 2013 to December 2016. The final results of this research using epoch 10000, learning rate 0.2, and hidden layer 10.
Exploring technology design among mobile entrepreneurs in Kampala: An open space workshop

Abstract Information and communication technologies (ICTs) are located at the center of international development and considered vital for the progress of the current sustainable development goals. ICTs are embedded in cultural structures and social norms, which provide both possibilities and challenges to the people working with technologies such as mobile phones. Drawing upon theories and methodologies of feminist and postcolonial technosciences, my research objective is an exploration of how young entrepreneurs in Uganda affect and are affected by technology design. This article is based on conversations from an Open space workshop that took place at a tech hub in Kampala in 2012. The theme of the workshop was “The mobile futures of Uganda: sharing visions and challenges for today and tomorrow.” The workshop discussions showcase how norms and values are embedded in technology design and how colonial relationships still linger and affect contemporary design and technology practices. Together with my empirical material, I perform technoscientific stories that discuss challenges in entrepreneurship, working in software development and user design practices. I argue that studying the social and political dimensions of technology design is crucial for forming more inclusive and heterogeneous ICT practices.