Reviewerly/exHarmony huggingface dataset:
Multi-label classification of line chart images using convolutional neural networks

In this paper, we propose a new convolutional neural network (CNN) architecture to build a multi-label classifier that categorizes line chart images according to their characteristics. The class labels are organized in the form of trend property (increasing or decreasing) and functional property (linear or exponential). In the proposed method, the Canny edge detection technique is applied as a data preprocessing step to increase both the classification accuracy and training speed. In addition, two different multi-label solution approaches are compared: label powerset (LP) and binary relevance (BR) methods. The experimental studies show that the proposed LP-CNN model achieves 93.75% accuracy, while the BR-CNN model reaches 92.97% accuracy on the test set, which contains real-world line chart images. The aim of this study is to build an efficient classifier that can be used for many purposes, such as automatically captioning the chart images, providing recommendations, redesigning charts, organizing a collection of chart images and developing better search engines.
ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario

Abstract The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges.
Cooperative Path Planning for Aerial Recovery of a UAV Swarm Using Genetic Algorithm and Homotopic Approach

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of “launch-recovery-relaunch” will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem.
New Formulation of Mixed-Integer Conic Programming for Globally Optimal Grasp Planning

We present a two-level branch-and-bound (BB) algorithm to compute the optimal gripper pose that maximizes a grasp metric in a restricted search space. Our method can take the gripper's kinematics feasibility into consideration to ensure that a given gripper can reach the set of grasp points without collisions or predict infeasibility with finite-time termination when no pose exists for a given set of grasp points. Our main technical contribution is a novel mixed-integer conic programming (MICP) formulation for the inverse kinematics of the gripper that uses a small number of binary variables and tightened constraints, which can be efficiently solved via a low-level BB algorithm. Our experiments show that optimal gripper poses for various target objects can be computed taking 20-180 minutes of computation on a desktop machine and the computed grasp quality, in terms of the Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> metric, is better than those generated using sampling-based planners.
PENINGKATAN MINAT DAN HASIL BELAJAR KETERAMPILAN BERBICARA MENGGUNAKAN METODE MIND MAP PADA MAHASISWA KELAS IA PBSI IKIP PGRI BOJONEGORO TAHUN AKADEMIK 2019/2020

This research aims to increase the interest and learning outcomes of speaking skills in IA class students of PBSI IKIP IKRI PGRI Bojonegoro Academic Year 2019/2020. This class action research uses 2 cycles, each cycle consisting of planning, implementation, observation, and reflection. This research was conducted in the IA class of PBSI IKIP IKIP PGRI Bojonegoro Academic Year 2019/2020 odd semester with a total of 30 students with details of 8 male and 22 female, aged between 17-24 years. The data collection techniques of this study used questionnaires, observations, interviews and documentation. The results of the questionnaire in cycle 1 amounted to 60%, an increase in interest in learning to speak increased in cycle 2, namely 96.7%. Student learning outcomes average pre-cycle knowledge value of 65.4, cycle 1 an average value of 72, 7, and cycle 2 77.0. While the average value of the skills value is 69.0 in the pre-cycle stage, 76.2 in cycle 1 and 78.0 in cycle 2. The conclusion of this study mind map method applied can increase the interest and learning outcomes of speaking skills in IA class students PBSI IKIP PGRI Bojonegoro Study Program Academic Year 2019/2020.
Distance Learning of Students: Problems and Prospects of Its Development After the Coronavirus Pandemic

The article is devoted to generalization of the first experience of mass distance learning received in the conditions of the coronavirus pandemic. The author of the article points out that a lot of debates in domestic universities about organizing distance learning for students, when diametrically opposed points of view were expressed about its effectiveness and inefficiency, necessity and uselessness, etc. could be observed. It is noted that in the context of the coronavirus pandemic, domestic universities had to switch to using distance learning methods when teaching students. Research methods: reviews and articles of teachers and university specialists on the first results of the organization of distance learning for students in domestic universities in the conditions of the coronavirus pandemic as well as the method of participatory observation and conversations with university lecturers. Results of research. The majority of Russian universities were not ready for such a simultaneous and complete transition to distance learning. This is due to the fact that neither teachers nor students were prepared for such a critical situation, which had never been before and was not supposed to be. Nevertheless, teachers and students since the beginning of the crisis have established communications by their usual means using social networks and e-mail. The transition to information platforms, which were created at the university to control the learning process, led to new problems, as it was necessary to restructure the work of both teachers and students, increasing the tension of their work, often without the possibility of obtaining technical support for the transition to new platforms.
A new approach for web usage mining using case based reasoning

In this study, we present a new approach for Web Usage Mining using Case Based Reasoning. Case-Based Reasoning techniques are a knowledge-based problem-solving approach which is based on the reuse of previous work experience. Thus, the past experience can be deemed as an efficient guide for solving new problems. Web personalization systems which have the capability to adapt the next set of visited pages to individual users according to their interests and navigational behaviors have been proposed. The proposed architecture consists of a number of components, namely, basic log preprocessing, pattern discovery methods (By Case Based Reasoning and peer to peer similarity—Clustering—association rules mining methods), and recommendations. One of the issues considered in this study is that there are no recommendations to those who are different from the existing users in the log file. Also, it is one of the challenges facing the recommendations systems. To deal with this problem, Apriori algorithm was designed individually in order to be utilized in presenting recommendations; in other words, in cases where recommendations may be inadequate, using association rules can enhance the overall system performance recommendations. A new method used in this study is clustering algorithms for Nominal web data. Our evaluations show that the proposed method along with Standard case-classified Log provides more effective recommendations for the users than the Logs with no case classification.
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow

To reduce the training time of large-scale Deep Neural Networks (DNNs), Deep Learning (DL) scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model’s accuracy. To address these problems, we create HyPar-Flow—a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF’s Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6 $$\times $$ speedup over Horovod-based data-parallel training, 2) 110 $$\times $$ speedup over single-node on 128 Stampede2 nodes, and 3) 481 $$\times $$ speedup over single-node on 512 Frontera nodes.
An image-based geometric model for numerical simulation of blood perfusion within the liver lobules

An image-based numerical algorithm is presented for simulating blood flow through the liver tissue. First, a geometric model is constructed by applying image processing techniques on a real microscopic image of a liver tissue. Then, incompressible blood flow through liver lobules is simulated. Effects of tissue heterogeneity and deformity, presence/absence of the second central vein in a particular lobule, and apparent sinusoids density in the liver cross section on the blood flow are investigated. Numerical results indicate that the existence of thick low permeability vascular septum, high permeability sinusoids, and lobule tissue heterogeneity can considerably affect interlobular and intralobular blood flow.
Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Abstract Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap.
A Tight Runtime Analysis for the $${(\mu + \lambda )}$$ EA

Despite significant progress in the theory of evolutionary algorithms, the theoretical understanding of evolutionary algorithms which use non-trivial populations remains challenging and only few rigorous results exist. Already for the most basic problem, the determination of the asymptotic runtime of the $(\mu+\lambda)$ evolutionary algorithm on the simple OneMax benchmark function, only the special cases $\mu=1$ and $\lambda=1$ have been solved. In this work, we analyze this long-standing problem and show the asymptotically tight result that the runtime $T$, the number of iterations until the optimum is found, satisfies \[E[T] = \Theta\bigg(\frac{n\log n}{\lambda}+\frac{n}{\lambda / \mu} + \frac{n\log^+\log^+ \lambda/ \mu}{\log^+ \lambda / \mu}\bigg),\] where $\log^+ x := \max\{1, \log x\}$ for all $x > 0$. The same methods allow to improve the previous-best $O(\frac{n \log n}{\lambda} + n \log \lambda)$ runtime guarantee for the $(\lambda+\lambda)$~EA with fair parent selection to a tight $\Theta(\frac{n \log n}{\lambda} + n)$ runtime result.
Simulation of Border Control in an Ongoing Web-based Experiment for Estimating Morphing Detection Performance of Humans

A morphed face image injected into an identity document destroys the unique link between a person and a document meaning that such a multi-identity document may be successfully used by several persons for face-recognition-based identity verification. A morphed face in an electronic machine readable travel document may allow a wanted criminal to illicitly cross a border. This paper describes an improvement of our ongoing web-based experiment for a border control simulation in which human examiners should first detect high-resolution morphed face images and second match potentially morphed document images against "live" faces of travelers. The error rates of humans in both parts of the experiment are compared with those of automated morphing detectors and face recognition systems. This experiment improves understanding the capabilities and limits of humans in withstanding the face morphing attack as well as the factors influencing their performance.
Subsumption Demodulation in First-Order Theorem Proving

Motivated by applications of first-order theorem proving to software analysis, we introduce a new inference rule, called subsumption demodulation, to improve support for reasoning with conditional equalities in superposition-based theorem proving. We show that subsumption demodulation is a simplification rule that does not require radical changes to the underlying superposition calculus. We implemented subsumption demodulation in the theorem prover Vampire, by extending Vampire with a new clause index and adapting its multi-literal matching component. Our experiments, using the TPTP and SMT-LIB repositories, show that subsumption demodulation in Vampire can solve many new problems that could so far not be solved by state-of-the-art reasoners.
Remember the Facts? Investigating Answer-Aware Neural Question Generation for Text Comprehension

Reading is a crucial skill in the 21st century. Thus, scaffolding text comprehension by automatically generated questions may greatly profit learners. Yet, the state-of-the-art methods for automatic question generation, answer-aware neural question generators (NQGs), are rarely seen in the educational domain. Hence, we investigate the quality of questions generated by a novel approach comprising an answer-aware NQG and two novel answer candidate selection strategies based on semantic graph matching. In median, the approach generates clear, answerable and useful factual questions outperforming an answer-unaware NQG on educational datasets as shown by automatic and human evaluation. Furthermore, we analyze the types of questions generated, showing that the question types differ across answer selection strategies yet remain factual.
Virtual Embodiment Using 180° Stereoscopic Video

One of the most exciting possibilities of virtual reality is inducing in participants the illusion of owning a virtual body. This has become an established methodological paradigm allowing the study of the psychological and neural correlates of various scenarios that are impossible in the real world, such as gender or age switching. Thus far, full-body ownership illusions have been implemented by using real-time body tracking and avatars based on computer-generated imagery (CGI). We propose an alternative technique to induce perceived ownership over a (photorealistic) virtual body using 180° stereoscopic video, synchronous touch, and narration. We describe the technical components of our novel technique and an example implementation as part of a science-art project that enables participants to experience virtual bodies of different ages, and present the results of an experimental evaluation study based on this experience. Consistent with previous virtual embodiment studies using CGI-based techniques, we found that participants accept a photorealistic virtual body as their own irrespective of its appearance as indicated by similar ratings of the strength of body ownership over a virtual body of a child versus an adult. We further show that our novel technique can alter participants' cognition in accordance with the characteristics of their virtual body. Specifically, young adult participants who were embodied in the virtual body of a child significantly overestimated the duration of the virtual reality experience compared to a control group who was embodied in a virtual body of their own age. This finding corresponds to chronological age differences in time estimations and extends previous research on virtual child embodiment. Overall, these findings provide initial evidence for the potential of our novel technique to create photorealistic embodiment experiences with comparable psychological effects as have been found using CGI-based techniques while reducing the costs and technical complexity in the production and application of virtual body ownership illusions.
Open-Set Events Identification Based on Deep Metric-Learning for DMZI Perimeter System

Intrusion monitoring based on optical fiber distributed sensing is a pattern recognition problem of target events. Due to the addition of uncontrollable unknown events in practical applications, the open-set problem brought about greatly reduces the accuracy of traditional events recognition methods. In order to solve this problem, this paper design a deep metric-learning network, combined with Recurrent Plot (RP) coding to improve the accuracy of target events recognition in an open environment. In this paper, the RP algorithm is used to encode the intrusion signals into images that reflect the signal's motion. The deep metric-learning network is used to project the image into the feature space. And feature centers of each class can be calculated rely on the training samples. So, the network identifies the intrusion event according to the distance between the test sample and the feature center. By setting the appropriate threshold, the samples whose distance exceeds the threshold are identified as unknown class to solve the open-set problem. In the experiments, the dual Mach-Zehnder interferometry (DMZI) distributed optical fiber perimeter system is built to collect event signals. For 7 known event classes the highest identification accuracy can reach 99.7%. After adding 3 unknown event classes, the accuracy rate can exceed 93.3% with appropriate threshold. And the total response time is only 0.4 s. This novel method guarantees the accuracy of target identification under the introduction of unknown events and improves the robustness of the distributed optical fiber perimeter security system.
Teaching STEM Competencies Through an Educational Mobile Robot

The STEM (Science-Technology-Engineering-Mathematics) competences have taken the classroom of new generations, due to the need to instill interest in technical sciences and promote the careers of the future. In underdeveloped countries, free access to technology is limited by the scarcity of economic resources, for this it is required low-cost tools that facilitate better multidisciplinary learning. This work presents the implementation of an educational mobile robot to teach STEM competencies; the physical structure has been designed using 3D modeling and printing in PLA. The electronic system presented in this work is based on the Arduino embedded card that connects distance, weight, temperature and color sensors, two DC motors, and an LCD screen. In addition, the mobile robot has Bluetooth communication to connect it to external devices. The interaction with the user (student) is done through a mobile application and an HMI that is displayed on a personal computer. The robot’s features allow the measurement of physical variables, conversion of magnitudes, analysis of the operation of sensors and actuators, and the use of control interfaces. Experimental performance tests are carried out by individuals with an average age of 12 years (K-12), who are subjected to a learning test before and after applying this proposal. Finally, usability tests are carried out on teachers to validate the system developed.
Compositionality Decomposed: How do Neural Networks Generalise? (Extended Abstract)

Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally. As a response to this controversy, we present a set of tests that provide a bridge between, on the one hand, the vast amount of linguistic and philosophical theory about compositionality of language and, on the other, the successful neural models of language. We collect different interpretations of compositionality and translate them into five theoretically grounded tests for models that are formulated on a task-independent level. To demonstrate the usefulness of this evaluation paradigm, we instantiate these five tests on a highly compositional data set which we dub PCFG SET, apply the resulting tests to three popular sequence-to-sequence models and provide an in-depth analysis of the results.