Reviewerly/exHarmony huggingface dataset:
Optimized bilevel classifier for brain tumor type and grade discrimination usingevolutionary fuzzy computing

In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the optimal discriminating feature set (5 features) and classification using the decision tree constructed with the reduced feature set resulted in better performance. In the second level, grade classification, a fuzzy rule-based approach was used to resolve the uncertainty indiscriminating the tumor grades II and III. Membership functions of all grades were defined for all features extracted from brain tumor grade images, to derive the fuzzy inference rules for grade discrimination. Similar to type classification with GA, better grade discrimination performance was exhibited with fuzzy inference rules derived using optimal feature set (13 features) using GA. Overall performance comparison of the proposed bilevel classifier with all features vs GA-based feature selection, shows that evolutionary computing combined with fuzzy rule-based approach is successful in reducing false positives, thereby enhancing classifierperformance.
Extracting Action Sensitive Features to Facilitate Weakly-Supervised Action Localization

Weakly-supervised temporal action localization has attracted much attention among researchers in video content analytics, thanks to its relaxed requirements of video-level annotations instead of frame-level labels. However, many current weakly-supervised action localization methods depend heavily on naive feature combination and empirical thresholds to determine temporal action boundaries, which is practically feasible but could still be sub-optimal. Inspired by the momentum term, we propose a general-purpose action recognition criterion that replaces explicit empirical thresholds. Based on such criterion, we analyze different combination of streams and propose the Action Sensitive Extractor (ASE) that produces action sensitive features. Our ASE sets temporal stream as main stream and extends with complementary spatial streams. We build our Action Sensitive Network (ASN) and evaluate on THUMOS14 and ActivityNet1.2 with different selection method. Our network yields state-of-art performance in both datasets.
Correction to: A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

The article A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming, written by Suning Liu and Haiyun Shi was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 16 December 2018 without open access.
TOOL SELECTION: LEARNING PICK-AND-PLACE OPERATIONS USING SMARTPHONE AR TECHNOLOGY

This paper presents an ongoing development process for an experimental application that aims to train an inexperienced user to operate a mobile device and receive Augmented Reality (AR) guidelines for basic pick-and-place operations.The purpose of the presented work is to offer a learning environment for placing multiple physical tools in specific places, as indicated by various guideline notes.The current phase represents the 9'th experiment from a series of others ( 24) that are conducted in order to identify the proper specifications, challenges and development estimations for a training station (room) built for manufacturing industry workers.The application is developed from a gamified perspective allowing users to explore 2 different games.According to the rules of each game, the user advances to the next level if he manages to place each object in a corresponding placeholder until all the spaces from a 2X3 grid are occupied correctly.The process is timed and the user sees a custom countdown timer alongside with guiding messages and feedback.While most of technical challenges were successfully surpassed, the interaction together with the performance measuring are currently going through an improvement process meant to identify and assess the educational advantages offered by this type of solutions.
A Hybrid Approach for the Fighting Game AI Challenge: Balancing Case Analysis and Monte Carlo Tree Search for the Ultimate Performance in Unknown Environment

The challenging nature of the Fighting Game AI Challenge originates from the short instant of response time which is a typical requirement in real-time fighting games. Handling such real-time constraint requires either tremendous computing power or a clever algorithm design. The former is uncontrollable by the participants, as for the latter, the competition has received a variety of submissions, ranging from the naivest case analysis approach to those using highly advanced computing techniques such as Genetic Algorithms (GA), Reinforcement Learning (RL) or Monte Carlo Tree Search (MCTS), but none could provide a stable solution, especially in the LUD division, where the environment setting is unknown in advance. Our study presents our submission to this challenge in which we designed a winning solution in the LUD division which, for the first time, stably outperformed all players in all competition categories. Our results demonstrate that a proper blend of case analysis and advanced algorithms could result in an ultimate performance.
Bootstrapped Policy Gradient for Difficulty Adaptation in Intelligent Tutoring Systems

One challenge for an intelligent interactive tutoring agent is to autonomously determine the difficulty levels of the questions presented to the users. This difficulty adaptation problem can be formulated as a sequential decision making problem which can be solved by Reinforcement learning (RL) methods. However, the cost of taking an action is an important consideration when applying RL in real-time responsive application involving human in the loop. Sample efficient algorithms are therefore critical for such applications, especially when the action space is large. This paper proposes a bootstrapped policy gradient (BPG) framework, which can incorporate prior knowledge into policy gradient to enhance sample efficiency. The core idea is to update the summed probability of a set of related actions rather than a single action at gradient estimation sample. A sufficient condition for unbiased convergence is provided and proved. We apply the BPG to solve the difficulty adaptation problem in a challenging environment with large action space and short horizon, and it achieves fast and unbiased convergence both in theory and in practice. We also generalize BPG to multi-dimensional continuous action domain in general actor-critic reinforcement learning algorithms with no prior knowledge required.
Assessing the Participatory Design of a Project-based Course on Computer Network Applications

New teaching methodologies which foster student involvement, such as project-based learning, are nowadays part of the study curriculum of many engineering schools.Project-based learning courses, however, often build upon other previously taught technical courses, where the technical content for the project to be developed is studied.That type of course design focuses on building the transversal capabilities of students, and the technical challenges of the project are the mean to acquire these non-technical skills.In this paper, we present and assess a project-based course on computer network applications of a computer science school, which has been designed to improve within the same course both the transversal and technical skills of the students.The proposition of interest is that the course not only aims to train the students' transversal skills by a group work project, but also to practise new technical topics and technologies.We argue that the key element of the proposed course design is that each student project group defines with the instructor the project they would like to develop in the course.We present first the design of the course and then an assessment with questionnaires, which were conducted over two semesters with the students enrolled in the course.The obtained results indicate that the students achieved both technical and transversal skills, while the instructors need to be flexible to adapt to diverse technical topics of the proposed projects.
Post-Editing in Practice: Process, Product and Networks

The potential benefits of integrating machine translation into human translation workflows are now widely recognised. In many sectors of the translation industry, translators’ throughput is improved with the use of machine translation as a tool in the translating process. Post-editing of machine translation is also a service in its own right, with specific guidelines and, more recently, an international standard. We introduce this special issue by providing a brief overview of post-editing as a practice, service and research topic. Contributions to the issue are then presented. The issue moves from a magnified perspective of translators’ work to important aspects of translation products, translators’ attitudes and translator training. It has four themes: the post-editing process; reception of post-edited products; attitudes and perceptions; and competence, training and education.
The Use of Mobile Application as a Media in Physics Learning

This article aimed to investigate the use of mobile application among physics teachers in terms of its use as a tool for physics experiment and the obstacles faced by teachers. The data were collected using a questionnaire consisted of 15 questions. The participants were physics teachers in SMP/MTs and SMA/MA (junior high schools and high schools) in West Java, Indonesia. The questionnaire was delivered through social media Whatsapp in a form of Google Form. The results showed that mobile application had not been widely used by the teachers as a tool for physics experiment. This condition occured because of their limited knowledge in using mobile application as a tool for physics experiment. It is expected that the use of mobile application training will be a solution in updating ICT skills of physics teachers.
SentiNeural: A Depression Clustering Technique for Egyptian Women Sentiments

Online Sentiments Analysis is a trending research domain of study which is based on natural language processing, artificial intelligence, and computational linguistics.Negation sentiments usually are not included in sentiment's analysis process.The depression analysis can be improved by negative sentiments processing.The negation sentiments may contribute to classify the depression problems and its causes.The proposed clustering technique can detect female sentiments from the sentiment's text through cause's classification, and the written sentiment style.The combination of sentiment analysis and neural network is a promising solution for creating a new clustering algorithm.According to Egypt Independent Journal in 2018, 7% of Egyptians suffer from mental illness reported by the Public Health Ministry in Egypt.But the real statistics is more than the mentioned percentage which causes major social problems such as divorce, avoiding responsibilities, or nonmarriage.This paper will address the real statistics and cluster the depression causes and social status for each sentiment.Online women sentiments are the essential focus of this research.The proposed technique consists of two algorithms clustering for user's sex and classification algorithm for causes and responsibilities of women.The proposed clustering algorithm can recognize automatically for the sentiments user sex (females or males) and the level of depression automatically.The neural network clustering approach will produce accurate analysis results.The hardness of depression analysis implicitly and explicitly demonstrated in the different classifications for sentiments.This paper introduces a new technique for clustering sentiments and evaluating Egyptian women depression based on social sentiments.
Effects of quantum noises on χ state-based quantum steganography protocol

Since the good application of quantum mechanism in the field of communication, quantum secure communication has become a research hotspot. The existing quantum secure communication protocols usually assume that the quantum channel is noise-free. But the inevitable quantum noise in quantum channel will greatly interferes the transmission of quantum bits or quantum states, seriously damaging the security and reliability of the quantum system. This paper analyzes and discusses the performance of a $\chi$ state based steganography protocol under four main quantum noises, i.e., Amplitude Damping (AD), Phase damping (Phs), Bit Flip (BF) and Depolarizing (D). The results show that the protocol is least affected by amplitude damping noise when only the sender's first transmission in quantum channel is affected by quantum noise. Then, we analyze the performance of the protocol when both the sender's two transmissions are affected by quantum noise, and find that the specific combination of different noises will increase the performance of the protocol in quantum noisy channel. This means that an extra quantum noise can be intentionally added to quantum channel according to the noise intensity, so that the protocol can improve performance under the influence of quantum noises. Finally, the detailed mathematical analysis proves the conclusions.
Location Mapping And Tsunami Disaster Evacuation Pathway Using Dijkstra Algorithm In Kota Sigli District, Pidie District

Sigli City Sub District is one of the Sub Districts affected by the 2004 tsunami. At that time, the community did not understand the danger of the tsunami and did not understand how to save themselves. The purpose of this study was to identify and to map out effective tsunami evacuation locations and routes in Sigli City Sub District using Dijkstra algorithm. Primary data (tsunami inundation) for this study were obtained from interviews with community representatives involving 32 people in 16 villages. Administrative map, topographic map, population density map, contour map and land use map (secondary data) were obtained from relevant institutions. The results of the study indicate that horizontal evacuation can be done through available paved road living the coastal area towads 4 recommended locations based on the physical feasibility of the land. For vertical evacuation, 24 buildings available in 7 villages can be recommended, on the condition that their structural feasibility and access standard are met.
Digital Library Resources for the Users: An Overview

The Information Technology is changing the way faculty teachers and students learn. As the technology is changing rapidly, libraries are constantly required to evaluate new technologies and apply them to identify users to provide the best services. The paper provides a brief introduction to digital library resources. Kinds of Digital library resources, merits and issues are discussed. Keywords: ICT, Digital libraries, E-resources, Trends Cite this Article M.B. Kotur, M.N. Mulimani. Digital Library Resources for the Users: An Over View. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 111s–114s.
Text complexity as an indicator of translational style

This paper is to explore the applicability of the concept of text complexity within systemic functional linguistics to the analysis of translational stylistics. Text complexity is measured in terms of grammatical intricacy and lexical density. The case study concerns four English translations of the Platform Sutra, which is itself a composite text consisting of the Chan master Huineng's personal narrative, public sermons, ritual instruction and conversations with students. Text complexity of the four translations is analysed both intertextually and intratextually. Intertextually, degrees of text complexity of Huineng's words in different translations are compared; intratextually, the variation of text complexity of Huineng's words in different registers within one translation is investigated. Differences among translations are interpreted in terms of the translating purposes and strategies, as well as the translator's attention to the inherent heterogeneity of the source text.
Estimating Supervisor Set Using Machine Learning and Optimal Control

The paper deals with the problem of finding an estimation of supervisor set and content of machine learning block. We propose a construction of a probability distribution of the learning set using the empirical risk functional defined by Vapnik and applying a new dual dynamic programming ideas to formulate a new optimization problem. As a consequence we state and prove a verification theorem for an approximate probability distribution defining the approximation of the supervisor set.
3D Point Clouds and Eye Tracking for Investigating the Perception and Acceptance of Power Lines in Different Landscapes

The perception of the visual landscape impact is a significant factor explaining the public’s acceptance of energy infrastructure developments. Yet, there is lack of knowledge how people perceive and accept power lines in certain landscape types and in combination with wind turbines, a required setting to achieve goals of the energy turnaround. The goal of this work was to demonstrate how 3D point cloud visualizations could be used for an eye tracking study to systematically investigate the perception of landscape scenarios with power lines. 3D visualizations of near-natural and urban landscapes were prepared based on data from airborne and terrestrial laser scanning. These scenes were altered with varying amounts of the respective infrastructure, and they provided the stimuli in a laboratory experiment with 49 participants. Eye tracking and questionnaires served for measuring the participants’ responses. The results show that the point cloud-based simulations offered suitable stimuli for the eye tracking study. Particularly for the analysis of guided perceptions, the approach fostered an understanding of disturbing landscape elements. A comparative in situ eye tracking study is recommended to further evaluate the quality of the point cloud simulations, whether they produce similar responses as in the real world.
Towards Verified Blockchain Architectures: A Case Study on Interactive Architecture Verification

With the emergence of cryptocurrencies, Blockchain architectures have become more and more important. In such architectures, components maintain and exchange a list of records in a way which makes the entries persistent, i.e., resistant to modifications. Thereby, the architecture is dynamic in the sense that components may join or leave the network and connections between them may change over time. The dynamic nature of Blockchain architectures makes their verification a challenge, since it involves reasoning about potentially unbounded number of components. To this end, we developed FACTum, an approach for the specification and interactive verification of dynamic architectures based on the interactive theorem prover Isabelle. In this paper we report on the outcome of applying the approach to formally specify a version of Blockchain architectures and verify that the list entries of such architectures are indeed persistent.
Full-field, high-frequency, heterodyne interferometry for dynamic metrology based on phase detection using a modified time-of-flight camera

We describe a full field heterodyne interferometry system where the object beam is shifted by a high frequency with respect to the reference beam. The optical path difference between the object and reference beam is encoded in the phase of the envelope of the interference signal which is at the difference frequency. Conventionally, high frequency heterodyne interferometry is restricted to measurement of a single (or a few) point(s) as is the case in displacement measuring interferometers for precision machine tool axis feedback. This is because of the problem of demodulating the signal phase simultaneously for many pixels comprising a full-field image. This problem is overcome here by exploiting the capability of the special pixel structure employed in a Time of Flight (ToF) camera. This structure enables the measurement of the envelope phase of an optical signal at every pixel with respect to an electronic reference at the same frequency. ToF cameras are designed to measure the distance to an object in its field of view by detecting the phase delay, due to time of flight, of reflected light from a modulated source synchronized to the camera. In the described experimental interferometer, a Twyman-Green architecture is used with an acousto-optic modulator to produce interfering beams with a difference frequency of 20MHz. The image detector is a modified ToF camera based on a Texas Instruments OPT8241 sensor. The interferometer directly outputs a wrapped optical phase map with 12-bit resolution (equivalent OPD resolution 0.15 nm) at greater than 50 frames per second with no post-processing. The phase reconstruction is highly insensitive to the reference/object beam intensity ratio or to environmental noise.
The Multiplication Method with Scaling the Result for High-Precision Residue Positional Interval Logarithmic Computations

Introduction. The solution of the simulation problems critical to rounding errors, including the problems of computational mathematics, mathematical physics, optimal control, biochemistry, quantum mechanics, mathematical programming and cryptography, requires the accuracy from 100 to 1 000 decimal digits and more. The main lack of high-precision software libraries is a significant decrease of the speed-in-action, unacceptable for practical problems, in particular, when performing multiplication. A way to increase computation performance over very long numbers is using the residue number system. In this work, we discuss a new fast multiplication method with scaling the result using original hybrid residue positional interval logarithmic floating-point number representation. Materials and Methods. The new way of the organizing numerical information is a residue positional interval logarithmic number representation in which the mantissa is presented in the residue number system, and information on an absolute value (the characteristic) in the interval logarithmic number system that makes it possible to accelerate performance of comparison and scaling is developed to increase the speed of calculations; to compare modular numbers, the provisions of interval analysis are used; to scale modular numbers, the properties of the logarithmic number system and approximate interval calculations using the Chinese reminder theorem are used. Results. A new fast multiplication method of floating-point residue-represented numbers is developed and patented; the authors evaluated the developed method speed-in action, compared the developed method with classical and pipelined multiplication methods of long numbers. Discussion and Conclusion. The developed method is 2.4–4.0 times faster than the pipelined multiplication method, and is 6.4–12.9 times faster than classical multiplication methods.