Reviewerly/exHarmony huggingface dataset:
Research on Image Cognitive Learning Application

Symbolic learning theory is a very important topic in the current information explosion era. The 21st century is an era of "image civilization", which is characterized by the popularity of images and penetrate deeply all aspects of life. A large number of images appear in human life through the digital network, and unconsciously humans receive many image symbols. However, symbols recognition in images is an important key. The paper through the method of image reorganization, Exploring the influence of image symbols on human cognition by using the cultural products born from traditional Japanese aesthetics. Using Saussure's (1857-1913) Semiology to illustrate image cognition, and through the questionnaire survey method, for people's past experience in aesthetics and the relationship between cultural history and image cognition, and this paper look forward to providing a reference method of learning and teaching of symbols for future.
Distributed Human 3D Pose Estimation and Action Recognition

In this paper, we propose a distributed solution for 3D human pose estimation using a RGBD camera network. The key feature of our method is a dynamic hybrid consensus filter (DHCF) is introduced to fuse the multiple view information of cameras. In contrast to the centralized fusion solution, the DHCF algorithm can be used in a distributed network, which requires no central information fusion center. Therefore, the DHCF based fusion algorithm can benefit from many advantages of distributed network. We also show that the proposed fusion algorithm can handle the occlusion problems effectively, and achieve higher action recognition rate compared to the ones using only single view information.
Classification of Lung Nodules into Benign or Malignant and Development of a CBIR System for Lung CT Scans

Computer Aided Diagnosis (CAD) systems provide a great assistance to the radiologists in screening the CT scans of lung cancer patients in identifying and classifying the lung nodules. The decision of the CAD systems can be further improved with the help of Content Based Image Retrieval (CBIR) systems which provide better visualization assistance by displaying most similar images for a given query image thus helping the radiologists with better visualization of the instances. This paper has two proposals: (i) Deep learning approach-autoencoder, to classify lung nodules in to benign or malignant (ii) CBIR system that accepts an input query image of a nodule and retrieves most similar images. The proposed classification approach is evaluated with sensitivity, specificity and accuracy and obtained values (%) are 89.4, 91.4, and 87.4 respectively. Similarly, the CBIR system’s performance is examined with respect to precision, recall and F-score whose obtained values (%) are 92%, 81% and 85% respectively. These results are compared with existing works and found to be performing better.
TIP

While digital tools are more and more used in classrooms, teachers' common practice remains to use photocopied paper documents to share and collect learning exercises from their students. With the Tangible e-Ink Paper (TIP) system, we aim to explore the use of tangible manipulatives interacting with paper sheets as a bridge between digital and paper traces of learning. Featuring an e-Ink display, a paper-based localisation system and a wireless connection, TIPs are envisioned to be used as a versatile tool across various curriculum activities. In this paper, we present the design principles of TIPs and a first functional prototype. We conclude by presenting future works in the evaluation of TIPs as a distributed sensor for teachers in their classroom, including learning scenario examples to illustrate our statements.
Guiding word alignment with prior knowledge to improve English-Arabic Machine Translation

Word alignment is a crucial step during the development process of machine translation system. However, a major challenge for researchers still remains the lack of an effective English/Arabic alignment system. In this article, we propose a new alignment process, which is based on morphological preprocessing and incorporation of a bilingual dictionary as an additional source to support some alignment choices. Test results show that the use of the dictionary has improved the quality of alignment and therefore increase the BLEU score by 5%.
Continuous integration for Laravel applications with GitLab

Modern software development industries are willing to use Continuous Integration (CI) instead of traditional integration for their changing business requirements. Developers are facing challenges in manual processes like code review, code merging, and code testing when they integrate their code frequently. CI is one of the most popular and commonly aspired to agile practices. CI checks in a change, checks out all the sources, builds everything, runs all the unit tests and reports back with immediate feedback. GitLab is a powerful version controlling system and GitLab offers the built-in continuous integration and delivery tool called GitLab CI. This paper proposes the implementation of GitLab CI to support CI pipelines in all Laravel applications with real-world scenarios. It describes how development processes are carried out using CI and how GitLab CI reduces the integration risks by waiting to merge the work of all developers during the development cycle. It provides a generalized GitLab CI process to use readily in all Laravel applications.
A Real-Time Dynamic Trajectory Planning for Autonomous Driving Vehicles

This paper presents a real-time dynamic trajectory planning algorithm that provides an optimal trajectory along with appropriate acceleration and speed for autonomous driving vehicles. To this end, a center line is first constructed based on a group of predesigned center waypoints that are generally used as a reference line. Then, a limited number of path candidates are generated by the arc length and offset to the center line in the Frenet coordinate system, and the speed space is discretized using polynomial functions. The optimal path trajectory is determined considering the objectives of safety, smoothness, comfortability and consistency, together with the associated acceleration and speed identified. The simulation results verifies the effectiveness of the proposed approach.
A New Adaptive Differential Evolution Algorithms

Abstract In this paper, we describe a New Adaptive Differential Evolution algorithm (NADE) based on adaptive mutation operator, crossover operator and new mutation strategy. It is mainly aimed at the existence of individual aggregation and the reduction of population diversity in the calculation process of Differential Evolution algorithm (DE), which makes the algorithm easy to get early. The problems of ripeness, slow convergence speed and low convergence accuracy are improved. The improved differential evolution algorithm is tested by five commonly used test functions, and the test results are compared with the other three algorithms. The results show that the proposed algorithm performs better in convergence speed, convergence precision and global convergence ability.
An Adaptive Learning System based on a matching Jobs and Resumes Engine

In this paper we will present a model that match the Jobs to the resume of a candidate to help him define the needs in terms of learning to follow in order to get the job targeted. For this we review existing literature on job offers representation, candidate's resumes and adaptive learning systems, then we propose a model using a matching approach for the learner's profile and the Job characteristics.
Detecting possible persons of interest in a physical activity program using step entries: Including a web‐based application for outlier detection and decision‐making

Abstract According to recent statistics from the World Health Organization, 23% of people aged 18 years and over are not sufficiently physically active. Strangely, this is at a time when, due to the improvement in sensor technology, physical activity programs that track physical activity have become popular. However, some participants who enroll in these programs cheat by manipulating the data they enter. This can be discouraging for other participants, also invalidating the overall accuracy of program outcomes. Therefore, detecting these participants and discarding their manipulated entries is important in order to maintain the quality of the program. Currently, most of these physical activity programs use manual processes to detect and reject fraudulent step entries by reviewing the participant's demographic profiles along with their longitudinal step count performance data. In this study, a process, including two parallel models for detecting person of interest characteristics and abnormal step count entries, is developed. The first model uses the penalized logistic regression with Synthetic Minority Over‐sampling Technique subsampling to address the imbalance in the proportion of genuine and persons of interest. Having a highly imbalanced distribution between genuine and person of interest profiles makes this task more challenging. The second model uses a variety of outlier detection methods to detect and reject abnormal step entries based on previously entered data. This process will be more efficient and productive compared to the current manual system and will support better decision‐making in the future. The proposed system can be applied for other fraud detection applications after suitable adjustments.
Image Super Resolution Reconstruction Algorithm Based on Deep Learning

Super Resolution Convolutional Neural Network (SRCNN) solves the problems of poor robustness and complex calculation of traditional image super-resolution reconstruction algorithm, but its training data set and the number of layers of neural network is relatively small, and the edge and texture detail information are not handled well. For the above problems, the Maxout activation function is adopted in this paper to avoid the problems encountered by traditional activation functions such as gradient disappearance or overflow. Then the combination of Maxout and Dropout can train large data set and deepen neural network. Experimental results show that, compared with the classical algorithm, the algorithm proposed in this paper can train a large amount of data, improve the quality of reconstructed images and the generalization ability of the network model, and can enhance the robustness of the model.
Pedestrian Detection with Central-Line Heatmap Regression

The bounding box of a pedestrian is supposed to be in a rectangular shape with a normalized aspect ratio as it is used in annotation for pedestrian detection problem. However, detectors based on bounding-box regression suffer from hard negatives which are not accurate but contain parts of the human body. The main reason of this defect is that the detector is not highly aware of the central of a pedestrian. Motivated by this, we adopt a unique form of annotation which explicitly marks out the central of a pedestrian with a 1-pixel vertical line. We propose a novel one-stage detector which regresses a heatmap of lines using this annotation. The network of the detector constructs a hierarchy of feature maps and a built-in spatial module utilizes the spatial relations between points for enhancing the performance. Furthermore, a fast and effective CDF line-detector is presented for capturing salient responses in the heatmap to yield detections. Promising result is achieved on Caltech pedestrian dataset.
Taxonomical hierarchy of canonicalized relations from multiple Knowledge Bases

This work addresses two important questions pertinent to Relation Extraction (RE). First, what are all possible relations that could exist between any two given entity types? Second, how do we define an unambiguous taxonomical (is-a) hierarchy among the identified relations? To address the first question, we use three resources Wikipedia Infobox, Wikidata, and DBpedia. This study focuses on relations between person, organization and location entity types. We exploit Wikidata and DBpedia in a data-driven manner, and Wikipedia Infobox templates manually to generate lists of relations. Further, to address the second question, we canonicalize, filter, and combine the identified relations from the three resources to construct a taxonomical hierarchy. This hierarchy contains 623 canonical relations with the highest contribution from Wikipedia Infobox followed by DBpedia and Wikidata. The generated relation list subsumes an average of 85% of relations from RE datasets when entity types are restricted 1.
Identify OS from encrypted traffic with TCP/IP stack fingerprinting

More and more security vulnerabilities are closely related to operating system (OS) information, but how to accurately identify OS versions on a real-world dynamic network in encrypted traffic is still a challenge. In this paper, we propose a comprehensive passive OS identification method based on encrypted traffic. It takes advantage of several features in TLS headers and TCP/IP headers. Moreover, we also consider flow statistic features for each session. We collect a large dataset of more than 2 million samples to evaluate the performance of our approach. According to the experimental results, the performance of the proposed method is preferable to the traditional method.
A Hybrid Statistical Approach for Named Entity Recognition for Amazighe Language

Recognition of named entities (NEs) from computer readable natural language text is significant task of information extraction (IE) and natural language processing (NLP). Named entity (NE) extraction is important step for processing unstructured content. Unstructured data is computationally opaque. Computers require computationally transparent data for processing. There are various different approaches that are applied for extraction of entities from text. Named entity recognition (NER)is probably the first step towards information extraction that seeks to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. NER is used in many fields in NLP, and it can help resolving many real NLP tasks.This paper elaborates need of NE recognition and discusses issues and challenges involved in NE recognition tasks for Amazighe language. It also explores various methods and techniques that are useful for creation of learning resources and lexicons that are important for extraction of NEs from natural language unstructured text.
Implementation and Initial Evaluation of Virtually Transformable Plushies

In this paper, we propose virtual-reality-based transformable plushies. This system replaces the appearance of a plushie into another appearance in the virtual world. The user could feel like as if he/she is virtually playing with a plushie that has different size and appearance though the actual plushie never changes. We developed a prototype system "VRushie" and conducted some evaluation experiments. From the results, we obtained two findings: (1)The subjects were tolerant to the increasing scale transformation of virtual plushies. (2)The subjects were not so tolerant to the whole appearance transformation unless the virtual and real plushies have common body parts.
Between the Abstract and the Concrete: A Constraint-Based Approach to Navigating Instrumental Space

Abstract This article deals with a way that algorithmic composition systems can be informed by material realities of musical performance. After a general discussion of the relation of abstract algorithms to concrete materiality, the article focuses on the idea of an instrument's space of possibilities. It briefly discusses a number of compositional approaches that seek to derive musical structure from bodily movements and from the physical properties of instruments. The last part describes a new open-source JavaScript library called OboeJS and a Web application based on this library. The system is an experimental exploration of the idea of instrumental space and an attempt to bring together abstract algorithmic processing and the concrete possibilities of a musical instrument. The system implements a flexible constraint-based search algorithm for the generation of oboe fingering sequences. This tool is presented as part of a wider approach to algorithmic composition that aims not to map data output of generative procedures to “sound generators” (e.g., performers, instruments, sound synthesis processes). Instead, I propose to derive structure from the space of possibilities of the instrument itself, which in this case is the oboe.
The Effect of Willingness to Participate in Family Value to Intergenerational Learning Program among College Students

The Purpose of the study aimed to know college students' willingness to participate in family value to intergenerational.To investigate college students' willingness, the researcher designed the questionnaire after reviewed related literature.The questionnaire's reliability and validity were established through item analysis and factor analysis of the pilot study.A private university students were invited to complete the questionnaire.The researcher purposive sampled the college students to complete the questionnaire.Six hundred and sixty-eight questionnaires were collected.The researcher used descriptive statistics, standard mean deviation, on-way ANOVA, contingency coefficient, and multiple regression analysis to analysis the data.Five research findings of the current study were 1) College student's family value were tended to be traditional especially in the domain of family value but tin the domain of gender role was more modern.2) In family value, significant differences were found in different gender and school years.3) College students were lacked of knowledge about intergenerational learning program.4) In the willingness of taking intergenerational learning program, significant differences were found in different genders, school years, and family types.5) College students' family value and their willingness of participation intergenerational learning program were positively correlated.
Transition Control on Hybrid Unmanned Aerial Vehicles (UAV) using Altitude Change

The design of a transition state arrangement is needed to make a hybrid UAV that has two engines, tricopter and fixed wing. Those can track vertical take off and landing (VTOL) and cruising with minimum errors. The setting of the transition state is completed by adjusting the height of the UAV. Firmly, it can be achieved by giving a weight values to the each UAV engine. In this study, the transition state is sought by calculating the change of altitude UAV. When the UAV has reached the expected height, the active machine will change. During the take off, the active engine becomes a tricopter and after the UAV reaches the expected height, the active machine will become a fixed wing. Meanwhile on landing, the active engine is a fixed wing which later when the speed of the propulsion engine has dropped, the active engine becomes a tricopter until the UAV reaches the landing pad. Tracking settings are carried out using the PID MRAC. From the simulation, it can be known that UAVs can track in accordance with the expected trajectory, starting from take off, then doing cruishing and then landing with RMSE value 1.82. On the Y axis tracking, there is a deviation of 0.3 m at 5 m. The UAV takes 9 seconds so that the UAV can reach the expected height.
Handling Incomplete and Delayed Information Using Optimal Scheduling of Big Data Stream

Consistently, massive volumes of sensory, transactional, and web information are persistently produced as streams, which need to be investigated online as they arrive. The arrival rate of the big data stream may vary after some time. Scheduling plays a key part in big data streaming applications in a big data stream computing environment. In this paper, optimal scheduling is proposed on big data streams to handle the incomplete and delayed information. In this process, big data stream is taken as an input. The input big data stream consists of number of data streams and each data stream consists of number of tasks. Initially, the input big data stream is analyzed and the task is selected by calculating the features such as volatility, Hurst exponent, and distance. Enthalpy value is then computed based on the extracted feature for each data streams and the computed enthalpy value is taken as a feedback ID. Finally, krill herd optimization algorithm is used for the optimal scheduling of tasks based on the generated feedback ID. The results have shown that our proposed model outperformed popular scheduling algorithms in terms of computational time, schedule time, and throughput.
A Natural Language and Interactive End-to-End Querying and Reporting System

Natural language query understanding for unstructured textual sources has seen significant progress over the last couple of decades. For structured data, while the ecosystem has evolved with regard to data storage and retrieval mechanisms, the query language has remained predominantly SQL (or SQL-like). Towards making the latter more natural there has been recent research emphasis on Natural Language Interface to DataBases (NLIDB) systems. Piggybacking on the rise of 'deep learning' systems, the state-of-the-art NLIDB solutions over large parallel and standard benchmarks (viz, WikiSQL and Spider) primarily rely on attention based sequence-to-sequence models.
Implementation of 3-D Multiple Linear Regression in Hardware using the Xilinx Spartan-3AN FPGA

In this paper, a linear regression algorithm implementation in hardware using the Field Programmable Gate Array (FPGA) is presented. A two-dimensional (2-D) simple linear regression aims to develop a linear equation of two variables based on observed data values in two dimensions. A more complex problem that this considered in this paper is the three-dimensional (3-D) multiple linear regression that approximates a linear equation of three variables based on a set of observed data points in three dimensions. The algorithm was initially modelled and verified using Python, NumPy and Matplotlib. The linear regression equation was then translated to hardware using a VHDL description of the algorithm targeting the Xilinx Spartan-3AN FPGA. In this paper, the design and simulation of the algorithm based on using the available hardware resources within the FPGA are introduced and discussed.
Beyond Reality

In virtual reality (VR), a new language of sound design is emerging. As directors grapple to find solutions to some of the inherent problems of telling a story in VR—for instance, the audience's ability to control the field of view—sound designers are playing a new role in subconsciously guiding the audience's attention and consequently, are framing the narrative. However, developing a new language of sound design requires time for creative experimentation, and in direct opposition to this, a typical VR workflow often features compressed project timelines, software difficulties, and budgetary constraints. Turning to VR sound research offers little guidance to sound designers, where decades of research has focused on high fidelity and realistic sound representation in the name of presence and uninterrupted immersion [McRoberts, 2018], largely ignoring the potential contribution of cinematic sound design practices that use creative sound to guide an audience's emotion. Angela McArthur, Rebecca Stewart, and Mark Sandler go as far as to argue that unrealistic and creative sound design may be crucial for an audience's emotional engagement in virtual reality [McArthur et al., 2017].