Reviewerly/exHarmony huggingface dataset:
Face Recognition for Embedded System Based on Optimized Triplet Loss Neural Network

Algorithms based on Convolution neural networks are significantly better than artificially designed features. Among these methods, the network trained with the triplet loss function can usually obtain better features than the direct classification, and thus get better recognition accuracy. The input of the triplet loss function consists of an anchor picture, a positive picture and a negative picture. The distance between the anchor picture and the positive picture is reduced by training, thereby achieving the purpose of face recognition. However, the parameters of the network are usually too much that it cannot be directly applied to embedded devices. In this regard, this paper proposes a solution for compression optimization based on triplet loss network. This scheme can reduce the resource overhead of the network, improve the processing speed, and realize high-precision real-time face recognition on the embedded device. This solution greatly improves the performance of the network on embedded devices, and achieves the effect of high recognition accuracy and low resource overhead.
A Decision Procedure for String to Code Point Conversion

In text encoding standards such as Unicode, text strings are sequences of code points, each of which can be represented as a natural number. We present a decision procedure for a concatenation-free theory of strings that includes length and a conversion function from strings to integer code points. Furthermore, we show how many common string operations, such as conversions between lowercase and uppercase, can be naturally encoded using this conversion function. We describe our implementation of this approach in the SMT solver CVC4, which contains a high-performance string subsolver, and show that the use of a native procedure for code points significantly improves its performance with respect to other state-of-the-art string solvers.
Almost affinely disjoint subspaces

In this work, we introduce a natural notion concerning finite vector spaces. A family of k-dimensional subspaces of Fqn, which forms a partial spread, is called almost affinely disjoint if any (k+1)-dimensional subspace containing a subspace from the family non-trivially intersects with only a few subspaces from the family. The central question discussed in the paper is the polynomial growth (in q) of the maximal cardinality of these families given the parameters k and n. For the cases k=1 and k=2, optimal families are constructed. For other settings, we find lower and upper bounds on the polynomial growth. Additionally, some connections with problems in coding theory are shown.
Why many travelling salesman problem instances are easier than you think

While there are many inexact heuristics for generating high quality solutions to the Travelling Salesman Problem, our understanding of why these methods are effective and efficient is still limited. This paper looks at two population based heuristics: the EAX algorithm and the Mixing GA using partition crossover. We show that the local optima used to construct the initial population are also sampling edges found in the global optimum at an extremely high rate: in the majority of TSP instances, the number of global edges in the initial population is more than 73%. Next, we look at how recombination operators increase the representation of edges from the global optimum in the population, or increase the number of global edges in the best solutions in the population. We also look at TSP instances that are more difficult to solve, and again we find that edge frequency information can help to explain algorithm performance. Finally we use these result to suggest new strategies for generating high quality solutions for Travelling Salesman Problems.
Personal Identity of the Artist as a Condition and Result of Creativity in Art

The author outlines that the essence of the problem of personal identity is formulated in the form of a dilemma: the personality must be identical to itself, because it retains the inconsistency of all experiences, actions, plans throughout the life of the individual; the personality must not be identical to itself on the basis of its inclusion in the context of changing being, which inevitably implies its internal self-change. It is noted that this dilemma involves the use of the term “identity” in two contexts: in the context of comparison (the opposite meaning of “identical” is expressed in the following terms: “other”, “another”, “alien”, “unequal”, “reverse”) and in the context of development, temporality (the term “identity” becomes the opposite meaning of “changed”, “impermanent”, “developing”). Research methods: analysis of literature on the topic studied; comparison, descriptive method. The artist's creative identity as a dialectical process of changing the dominant forms, styles, and images is reviewed in the article. The artistic and ontological problem of self-identity of artistic personality is presented through the dialogue between “One” and “Other”. The artistic reality of a work of art allows the artist to know the essence of his identity in the context of intersubjectivity. It is concluded that the paradigm that allows us to detect intersubjective conditionality of identity is the relational ontology, which represents relationships as a fundamental form of being. It is emphasized that personal identity is discursively mediated by a person's self-understanding, so hermeneutics primarily becomes the methodological space in which this research is carried out. Hermeneutics proves that self-knowledge and self-understanding of a person is an interpretive process that forms an important part of the subject's ontology. According to this methodology, personal identity is mediated by its own interpretive activity as a narrative philosophy, as a person's story about himself, and as the formation of a life story. The author is impressed by the productive idea of E.G. Trubina's research on the reflection of the individual as a creative process of self-construction in relation to the modified personal identity of the artist.
Inference Tools for Markov Random Fields on Lattices: The <i>R</i> Package <b>mrf2d</b>

Markov random fields on two-dimensional lattices are behind many image analysis methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many of the popular models such as the Potts model and texture image models. The package introduces representations of dependence structures and parameters, visualization functions and efficient (C++ based) implementations of sampling algorithms, common estimation methods and other key features of the model, providing a useful framework to implement algorithms and working with the model in general. This paper presents a description and details of the package, as well as some reproducible examples of usage.
Modeling E-Textbook Tools or Encouraging Reading from Paper: What are the Effects on Medium Choice and Textbook Use?

E-textbooks have become more popular with college students, but there are concerns that reading is not as effective from screens as paper. In addition, students may not take advantage of tools afforded by e-textbooks. The purpose of this study was to determine if encouraging students to read from paper or modeling e-textbook tools would be better for students in terms of reading and using their textbooks. Two instructors randomly assigned students (N = 144) to view a video and answer an essay question about either the benefits of reading from paper, how to use e-textbook tools, or general information about open educational resources (control). Findings indicated that students told about the benefits of reading from paper were not more likely to read the textbook from paper. Students across conditions generally reported using the textbooks in a similar manner, except students in the e-textbook tools condition reported more notetaking while reading than students in the paper condition. Finally, student medium preference for studying did not change based on condition. Findings from this study provide guidance for how instructors should advise students on reading their course textbooks.
Succinct contrast sets via false positive controlling with an application in clinical process redesign

Many applications of intelligent systems involve understanding a group of contrastively different outcome (e.g., all survivors of a deadly cancer, a top performing team in a large corporation). The intelligent system needs to identify attributes (features) which best describe or explain the group versus its alternatives. In data mining, this problem is studied under the framework of contrast set mining (CSM). Although CSM is not new, the era of big data has produced new computational and statistical challenges. In particular, existing algorithms fail (1) to perform efficiently in terms of runtime on large-scale datasets and (2) to accommodate simultaneous inference on an overwhelming array of features which are often repetitive and collinear. In this paper, we develop a CSM algorithm which addresses both challenges. The computational challenge is addressed with a tree structure and two theorems while the statistical challenge is addressed with the application of false discovery rate for multiple testing. The computational and statistical advantages of the proposed algorithm over three state-of-the-art algorithms are demonstrated with comprehensive experiments. In addition, we also show the effectiveness of our proposed method in an intelligence-system application involving hospital process redesign. The proposed method not only improves the performance of machine learning systems, but also generates succinct and insightful patterns directly relevant to clinical decision-making.
LoCAR – Low-Cost Autonomous Robot for Object Detection with Voice Command and MobileNets

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Detection of circlelike overlapping objects in thermal spray images

Abstract In this paper, we present a new algorithm for the detection of distorted and overlapping circlelike objects in noisy grayscale images. Its main step is an edge detection using rotated difference kernel estimators. To the resulting estimated edge points, circles are fitted in an iterative manner using a circular clustering algorithm. A new measure of similarity can assess the performance of algorithms for the detection of circlelike objects, even if the number of detected circles does not coincide with the number of true circles. We apply the algorithm to scanning electron microscope images of a high‐velocity oxygen fuel (HVOF) spray process, which is a popular coating technique. There, a metal powder is fed into a jet, gets accelerated and heated up by means of a mixture of oxygen and fuel, and finally deposits as coating upon a substrate. If the process is stopped before a continuous layer is formed, the molten metal powder solidifies in form of small, almost circular so‐called splats, which vary with regard to their shape, size, and structure and can overlap each other. As these properties are challenging for existing image processing algorithms, engineers analyze splat images manually up to now. We further compare our new algorithm with a baseline approach that uses the Laplacian of Gaussian blob detection. It turns out that our algorithm performs better on a set of test images of round, spattered, and overlapping circles.
Improved genetic algorithm for intrinsic parameters estimation of on-orbit space cameras

Computer vision plays a key role to measure the relative posture and position between the spacecrafts, especially in various important space tasks. As one of the essential steps for computer vision, camera calibration is important for obtaining precise three-dimensional contours of the space target. However, it is impossible to use the traditional calibration targets to calibrate the space camera in orbit. To solve this problem, in this paper, we attack the on-orbit space camera calibration problem by using two steps. First, we only use two images of the solar panel, which is a commonly used element of majority human-made spacecraft, to generate an approximate initial estimation of the camera intrinsic parameters. In order to improve the robustness and accuracy of our method, the second step optimizes the initial solution by using an improved genetic algorithm (IGA). Simulated and real experiments prove that the proposed method is accurate and flexible, and shows good robust performance. Therefore, our method has realistic significance for various space tasks.
Statistical Learning in Wasserstein Space

We seek a generalization of regression and principle component analysis (PCA) in a metric space where data points are distributions metrized by the Wasserstein metric. We recast these analyses as multimarginal optimal transport problems. The particular formulation allows efficient computation, ensures existence of optimal solutions, and admits a probabilistic interpretation over the space of paths (line segments). Application of the theory to the interpolation of empirical distributions, images, power spectra, as well as assessing uncertainty in experimental designs, is envisioned.
A weighted population update rule for PACO applied to the single machine total weighted tardiness problem

In this paper a new population update rule for population based ant colony optimization (PACO) is proposed. PACO is a well known alternative to the standard ant colony optimization algorithm. The new update rule allows to weight different parts of the solutions. PACO with the new update rule is evaluated for the example of the single machine total weighted tardiness problem (SMTWTP). This is an `NP-hard optimization problem where the aim is to schedule jobs on a single machine such that their total weighted tardiness is minimized. PACO with the new population update rule is evaluated with several benchmark instances from the OR-Library. Moreover, the impact of the weights of the jobs on the solutions in the population and on the convergence of the algorithm are analyzed experimentally. The results show that PACO with the new update rule has on average better solution quality than PACO with the standard update rule.
Text Classification on Islamic Jurisprudence using Machine Learning Techniques

Abstract Indonesia, which is the world’s most populous Muslim-majority nation, Islamic education is not an alternative to secular education but compulsory to any Muslims learn the way of life. The two primary sources of the religion of Islam is the Quran and Hadith. These two are where the majority of the teachings come from. However, when looking for guidance, a Muslim often refers to Islamic scholars to interpret the verses and hadith for them and educate themselves on a topic. However, due to many circumstances, people could not meet and ask a scholar personally regarding Islamic jurisprudence. Chatbot is one of the solutions. Chatbot offers users new opportunities to improve the learning and engagement process, reducing the typical cost of face-to-face consultation. However, to train a chatbot on Islamic Jurisprudence, a text classifier is needed to build a strong knowledge base for the chatbot. This study adopted a standard methodology for building a text classification model. This study used 600 Islamic jurisprudence text data obtained from the books and the web written by an influential Islamic scholar named Ustadz Abdul Somad (UAS). Machine learning algorithms such as Bayesian Network and Naïve Bayes, were employed to classify the text data. Based on the experimental testing results, the Naïve Bayes algorithm is more accurate in all evaluation models, 84.25% using training set, and 76.54% using 10-Fold cross-validation. Meanwhile, the Bayesian Network algorithm is faster in terms of time taken on all evaluation models. Thus, it can be concluded that the text classification model using Naïve Bayes and String to Word Vector filter have the potential to be used effectively but still has plenty of room for improvement.
PowerCoord: Power capping coordination for multi-CPU/GPU servers using reinforcement learning

Modern supercomputers and cloud providers rely on server nodes that are equipped with multiple CPU sockets and general purpose GPUs (GPGPUs) to handle the high demand for intensive computations. These nodes consume much higher power than commodity servers, and integrating them with power capping systems used in modern clusters presents new challenges. In this paper, we propose a new power capping controller, PowerCoord, that is specifically designed for servers with multiple CPU and GPU sockets that are running multiple jobs at a time. PowerCoord coordinates among the various power domains (e.g., CPU sockets and GPUs) inside a node server to meet target power caps, while seeking to maximize throughput. Our approach also takes into consideration job deadlines and priorities. Because performance modeling for co-located jobs is error-prone, PowerCoord uses a learning method. PowerCoord has a number of heuristic policies to allocate power among the various CPUs and GPUs, and it uses reinforcement learning for policy selection during runtime. Based on the observed state of the system, PowerCoord shifts the distribution of selected policies. We implement our power cap controller on a real multi-CPU/GPU server with low overhead, and we demonstrate that it is able to meet target power caps while maximizing the throughput, and balancing other demands such as priorities and deadlines. Our results show PowerCoord improves the server throughput on average by 18% compared with the case when power is not coordinated among CPU/GPU domains. Also, PowerCoord improves the server throughput on average by 11% compared with prior work that uses a heuristic approach to coordinate the power among domains.
Simulation Analysis for Trajectory Tracking Control of 5-DOFs Robotic Arm using ANFIS Approach

In this paper, the Scorbot-ER 5 Plus robotic arm is considered to track two different trajectories i.e., straight line and semi-circular one. For the same, the forward kinematic analysis is performed using DH convention. However, due to the unavailability of the closed form, solving the inverse kinematic problem is a quite challenging task. Therefore, in this work, adaptive neuro fuzzy inference system (ANFIS) technique is utilized to find out the inverse kinematic solutions. The recursive Newton-Euler method is employed to carry out the dynamic analysis for the Scorbot-ER 5 Plus. The Proportional-Integral-Derivative (PID) control strategy is implemented in Simulink/MATLAB to ensure the precise tracking of desired trajectories. In the proposed control strategy, the ANFIS based inverse kinematic solutions are exploited as input joint variables for each trajectory. It has been observed from the simulation analysis that ANFIS based PID control shows promising results for required trajectories.
The application of Usability Testing Method for Evaluating the New Student Acceptance (NSA) System

Abstract The new student acceptance (NSA) system is a design system to automate the selection of new student admissions, starting from the registration process, and the selection process to announce the results of online elections. It executes usability evaluation to determine the level of effectiveness, efficiency, and user satisfaction. This study focuses on testing the usability of the NSA system using usability testing methods on aspects of effectiveness, efficiency, and user satisfaction at SMAN 1 Pringgarata . This study is quantitative with a descriptive research method approach. The population in this study was 40 people and the number of samples in this study was eight people. Performance measurements, retrospective think-aloud, and questionnaire techniques are techniques used for data collection. The task scenario and System Usability Scale (SUS) questionnaire with a Likert scale is the instrument used. Data analysis used descriptive statistics, user success rates and Mann Whitney U-tests. The results showed that, (1) the NSA system is effective with value 98.5%, (2) it system is efficient with the value 6 over 0, (3) after implementing the NSA system, it satisfies users (80 &gt;68). The recommendation given is the addition of images and icons on the main page of verification so that the pages on the system are more interesting and varied.
Mineral sources of aqua regia extractable base cations in Scottish soils interpreted from Cubist models trained with quantitative mineralogy data

The long-term potential capacity for soils to deliver essential elements to plants and the soil solution is often assessed using aqua regia (AR) extractions, and understanding specific mineral contributions to extracted element concentrations is central to the accurate interpretation of such data. The objective of this study was to assess whether the supplying quantitative soil mineralogy data to the Cubist machine learning algorithm could result in accurate prediction of AR extractable base cation concentrations (Naaqr, Mgaqr, Kaqr and Caaqr), and consequently facilitate the interpretation of meaningful mineral sources of these elements. Mineral concentrations were quantified in 191 soil samples collected by horizon from 96 sites across Scotland. These mineral concentrations together with pH and soil organic matter data were used to train Cubist models for the prediction of log(Naaqr), log(Mgaqr), log(Kaqr), and log(Caaqr), yielding concordance correlation coefficients of 0.77, 0.87, 0.78 and 0.71, respectively when assessed by cross validation. The dominant variables selected by Cubist for the conditions of the models contrasted substantially between the four elements, aligning with the independent nature of each base cation interpreted from compositional biplot analysis. Namely, plagioclase concentrations were predominantly selected by Cubist for log(Naaqr) prediction, chlorite concentrations for log(Mgaqr) prediction, dioctahedral mica concentrations for log(Kaqr) prediction, and pH for log(Caaqr) prediction. The selection of these variables is found to be geochemically appropriate for each element, demonstrating how data-driven methods can yield meaningful conclusions with respect to mineral sources of AR extractable elements. In validating the utility of combining quantitative soil mineralogy data with the Cubist machine learning algorithm on the relatively well characterised base cations, future work can progress towards using similar approaches to define mineral sources of more challenging trace elements in new detail.
Towards zero shot learning of geometry of motion streams and its application to anomaly recognition

Visual anomaly recognition (VAR) is the core part of many intelligent systems. However, vagueness in definitions and lack of a priori knowledge about the distribution of anomalies make VAR a challenging problem. Supervised solutions often fail to work in such scenarios due to a lack of ability to adapt with concept drifts. To this end, we have studied the effect of temporal derivatives over differential manifolds for designing a zero-shot (label agnostic) VAR solution. The rationale behind this work is leveraging the genericity and discriminative representation available in the geometric-structure of motion-tensors. Our approach proceeds by drawing segments of temporal-derivatives from raw image-sequences and projecting them over Grassmann product space before clustering. Suitability of the proposed approach is corroborated with extensive experiments and comparisons with other arts.