Reviewerly/exHarmony huggingface dataset:
Automatic Segmentation of Liver from CT Scans with CCP–TSPM Algorithm

With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver’s anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex–concave point for tree structured part model (CCP–TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP–TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method’s effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method.
Using mobile applications to teach vocabulary: Saudi EFL teachers’ perceptions

This paper aimed at investigating the potential effectiveness of using mobile applications to teach vocabulary in public schools in Saudi Arabia. It was carried out in Sabia, Jazan, Saudi Arabia in the academic year 2018. It is a descriptive survey paper done to survey EFL teachers' perspectives about using vocabulary m-applications. The sample consisted of 113 teachers. This paper depended on one instrument to collect data which is a 25-item, 5-category and closed-answer questionnaire. It addressed five questions concerning usability, motivation, collaboration, vocabulary learning and mobilebased vocabulary instruction (MBVI) versus traditional vocabulary instruction (TVI). The findings revealed that vocabulary m-applications is of great usefulness to vocabulary instruction and learning in general. The teachers considered m-applications simple and usable and preferred MBVI to TVI because of its capabilities and acceptance among students. To effectively use vocabulary m-applications, students should be well trained and classrooms should be more mobile-oriented. Also, teachers must be technically and pedagogically competent. Keywords: Mobile applications, MALL, vocabulary learning, motivation, collaboration, mobile-based vocabulary instruction
Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion

With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.
Characteristics of Students’ Cognitive Conflict in Solving a Problem Based on Information Processing Theory

The purpose of this study was to describe the characteristics of studentsâ€™ cognitive conflict in solving problems based on information processing theory. This research was descriptive qualitative, and data were collected through direct observation, semi-structured in-depth interviews, and tests. Tests were in the form of story questions with distance material that had been previously studied by students. Participants in this study were grade 6 elementary school students. Out of 32 sixth-grade students, two were selected as participants in the study. The results of the study show that there are two characteristics of studentsâ€™ cognitive conflict in solving problems: errorâ€”cognitive conflictâ€”equilibriumâ€”solving the problem and errorâ€”cognitive conflictâ€”equilibriumâ€”canâ€™t solve the problem. Participants experienced difficulties in accessing information contained in long-term memory, so participants were constrained in connecting the newly received information to their short-term memories. The difficulty in accessing this information resulted in participantsâ€™ experiencing cognitive conflict. Information was not directly connected between what had been given and what was already available in the cognitive structure. These results indicate that cognitive conflict occurs during information processing if the information received by sensory memory and transferred to short-term memory cannot be directly linked to information in long-term memory. https://doi.org/10.26803/ijlter.18.2.6
DETECTION AND HANDLING OF DIFFERENT TYPES OF CONCEPT DRIFT IN NEWS RECOMMENDATION SYSTEMS

To address the increase in volume of data streams online users interact with, there are a growing number of tools and models to summarize and extract information.These tools use prediction models to personalize and extract useful information.However, data streams are highly prone to the phenomena of concept drift, in which the data distribution changes over time.To maintain the performance level of these models, models should adapt to handle the existence of adrift.In this work, we present the Incremental Knowledge Concept Drift (IKCD) algorithm, an adaptive unsupervised learning algorithm for recommendation systems in news data stream.Data modelling in IKCD uses k-means clustering to determine the occurrence of a drift while avoiding the dependency on the availability of data labels.Once a drift is detected, new retraining data is composed from the old and new concept.IKCD is tested using synthetic and real benchmark datasets from various domains, which demonstrate the different drift types and with different rate of change.Experimental results illustrate an enhanced performance with respect to (a) reducing model sensitivity to noise, (b) reducing model rebuilding frequency up to 50% in case of re-occurring drift and (c) increasing accuracy of the model by about 10% with respect the accuracy of confidence distribution batch detection algorithm.
Research on the Visualization of Spatio-Temporal Data

Spatio-temporal data visualization technology, with the help of data analysis methods and interactive technologies, help people to understand the information, knowledge, and wisdom behind the data more intuitively and efficiently. It has become a powerful tool for people to analyze and control data in the era of information explosion. This paper summarize visualization methods for spatio-temporal data and provides a classification of existing analytical methods, visualization techniques, tools and field of application, and briefly analyze the challenges and development trends of spatio-temporal data visualization.
AUGMENTED REALITY (AR) AND VIRTUAL REALITY (VR): THE FUTURE OF INTERACTIVE VOCATIONAL EDUCATION AND TRAINING FOR PEOPLE WITH HANDICAP

The aim of this paper is to present our didactic-methodical concept for a virtual interactive vocational education and training using the VR and AR technologies. Since we are a dual VET Institutions for handicapped, we primarily focus on young people with special needs, autism, and significant social interaction difficulties. Our concept can also be applied to other target groups. The virtual environment offers the trainees the ability to interact and experiment with items and constructs in a similar way they would do in real world. The AR-Glasses promise through the free visualization meaningful application scenarios for the teaching and learning concept; the wearer is supported during his activity by additional context-based virtual information and objects. Our method is based on the educational goals, the cognitive and psychophysiological aspects of the trainees, the technological aspects and the pedagogical strategies of goal-oriented teaching. In this paper we present some application examples of these technologies in different areas of our VET such as automotive mechatronics engineering, electrotechnology and Automation, technical product design, information technology and In-house virtual vocational School.
Knowledge based Recommendation System in Semantic Web - A Survey

Knowledge based recommendation systems use knowledge about users and products to make recommendations.Knowledge-based recommendations are not dependent on the rating, nor do they have to gather information about a particular user to give recommendations.Knowledge acquisition is the most important task for constructing knowledge-based recommendation system.Acquired knowledge must be represented in some structured machinereadable form, e.g., as ontology to support reasoning about what products meets the user's requirements.In Semantic Web, knowledge is represented in the form of ontology.Representation of knowledge in structured form of ontology in Semantic Web makes the application of knowledge based recommendations system on Semantic Web very easy, as there is no need to construct knowledge base from scratch.Performance of knowledge based recommendations systems can be enhanced by exploiting ontology reasoning characteristics.This paper explores different techniques used to generate knowledge-based recommendations highlighting the advantages of knowledge based recommendation system over other recommendation techniques.
Hippocampal Segmentation From Longitudinal Infant Brain MR Images via Classification-Guided Boundary Regression

Hippocampal segmentation from infant brain MR images is indispensable for studying early brain development. However, most of the hippocampal segmentation methods were developed for population-based adult brain images, which are not suitable for longitudinal infant brain images acquired in the first year of life due to the low image contrast and variable development patterns of the hippocampal structure. To address these challenges, we propose a classification-guided boundary regression method to first detect hippocampal boundaries in the longitudinal infant brain images and then use those detected boundaries to guide the deformable model for final segmentation. Specifically, we first employ a classification-guided regression forest to predict the 3D displacements from individual image voxels to the potential hippocampal boundaries. These predicted displacements then determine the boundary maps by a voting strategy. Second, we iteratively enhance the voted hippocampal boundary map by incorporating the spatial context information given the tentative boundary estimation of the current time point. Besides, the longitudinal context information from all time points of the temporal sequence of the same subject (i.e., given their tentative segmentation results) is also utilized to facilitate accurate segmentation. Finally, a deformable model is applied to the enhanced voted boundary maps for achieving the longitudinal hippocampal segmentation. The experiments on infant brain MR images acquired from 2-week-old to 1-year-old show promising hippocampal segmentation results, indicating the applicability of our method in early brain development study.
An Efficient Steganography Technique for Images using Chaotic Bitstream

Steganography is a science of security technique through invisible communication; hiding secret message into cover objects without any suspicion.Protection of the hidden information from an adversary is the main purpose of any steganography system even if the embedding method is obvious.In this paper, an effective image steganography system based on a least significant bit (LSB) algorithm and chaos is proposed.The proposed method contains a spatial domain technique in which the Logistic map is used for generating chaotic bitstream and bitwise XOR operation which is used to create control bit.Control bit determines whether the LSB of a specific pixel is changed or not according to the secret message.This provides direct manipulation over the pixels of the image with a very low precision hence enhances the system security.In this study, gray image is used as secret message and a larger scale image as cover image.Experimental results demonstrate that the proposed method is very efficient to detect LSB replacement in the algorithm.Moreover, the proposed algorithm is highly sensitive to the stego key parameters due to complex structure of chaos which provides high level of security in the whole system and effectively hides and detects the image information.
Virtual Machine Failure Prediction Method Based on AdaBoost-Hidden Markov Model

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this Study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.
Creating a Multimodal Translation Tool and Testing Machine Translation Integration Using Touch and Voice

Commercial software tools for translation have, until now, been based on the traditional input modes of keyboard and mouse, latterly with a small amount of speech recognition input becoming popular. In order to test whether a greater variety of input modes might aid translation from scratch, translation using translation memories, or machine translation postediting, we developed a web-based translation editing interface that permits multimodal input via touch-enabled screens and speech recognition in addition to keyboard and mouse. The tool also conforms to web accessibility standards. This article describes the tool and its development process over several iterations. Between these iterations we carried out two usability studies, also reported here. Findings were promising, albeit somewhat inconclusive. Participants liked the tool and the speech recognition functionality. Reports of the touchscreen were mixed, and we consider that it may require further research to incorporate touch into a translation interface in a usable way.
Paraxial lens design of anamorphic lenses with a fixed anamorphic ratio

If the stop lies on the right of the anamorphic lenses, the entrance pupils in the tangential plane and the sagittal plane might not coincide with each other. In this condition, the anamorphic ratio will change according to the position of the object, which is not preferred in filming and machine vision. In view of this, a systematic approach for the paraxial design of anamorphic lenses with common entrance pupils is provided. Two basic types of anamorphic attachment are presented: basic anamorphic attachment and basic anamorphic zoom attachment. The anamorphic ratio can be changed by changing the interval distances between lens components if anamorphic zoom attachments are adopted. Formulas defining the interval distances between lens elements, the object position, the stop position, and the anamorphic ratio are derived. Illustrating figures and examples are also provided for each lens type.
ANALISIS FAKTOR-FAKTOR KESULITAN MEMBACA MENULIS DAN BERHITUNG SISWA KELAS 1 SD

The research was aimed at knowing what factors affected the difficulty in reading, writing, and computing in first-grade elementary school. This used descriptive research. The subject of this research were 39 students of grade 1 at SD Negeri 1 Pancurendang. The data were collected through observation, interview, and documentation. Based on the result of the research, most of the students were able to read, write and compute well, but only two were not. The difficulties in reading, writing, and computing were affected by several factors such as the family environment factor, the parent’s attention, guidance and supervision which were very, important and influencing the student’s interest and motivation as well as the student’s environment in the learning process. To overcome the difficulties in reading, writing, and computing, the teacher and parents gave attention, training, and guidance so that the learning process could run well.
Using Complex Numbers in Website Ranking Calculations: A Non-ad hoc Alternative to Google’s PageRank

This paper presents an alternative to Google's PageRank, i.e., it presents an algorithm used to calculate the score for a webpage using complex numbers that overcomes the problems inherent in Google's method.This algorithm was inspired by eigenvector centrality in social network analyses and is designed to reproduce the ranking results of Google's PageRank and to satisfy the condition of soundness.This algorithm can be developed further to achieve more desirable outcomes.
Highly Efficient English MOOC Teaching Model Based on Frontline Education Analysis

The MOOC teaching model is a teaching method emerging with the rapid devel-opment of the Internet, characterized by openness, large scale, interactivity and feedback. However, at present, English is mostly taught in the traditional cram-ming model, which has such problems as fossilized teaching content, small cor-pus, single teaching method and lack of a feedback mechanism. Under this mod-el, students lack the enthusiasm and initiative in English learning. By introducing MOOC, this paper innovates a new teaching model that combines the traditional English teaching model with the MOOC teaching resources. This model proves to be able to increase the classroom efficiency and students’ enthusiasm and awareness of independent learning and also help significantly improve their abili-ties of reading, deep analysis, expression and cooperation. The introduction of the MOOC teaching model is an attempt in English teaching, which is in line with the contemporary education philosophy in this new era.
Code Complexity in Introductory Programming Courses

Instructors of introductory programming courses would benefit from having a metric for evaluating the sophistication of student code.Since introductory programming courses pack a wide spectrum of topics in a short timeframe, student code changes quickly, raising questions of whether existing software complexity metrics effectively reflect student growth as reflected in their code.We investigate code produced by over 800 students in two different Python-based CS1 courses to determine if frequently used code quality and complexity metrics (e.g., cyclomatic and Halstead complexities) or metrics based on length and syntactic complexity are more effective as a heuristic for gauging students' progress through a course.We conclude that the traditional metrics do not correlate well with time passed in the course.In contrast, metrics based on syntactic complexity and solution size correlate strongly with time in the course, suggesting that they may be more appropriate for evaluating how student code evolves in a course context.
On Annotation Methodologies for Image Search Evaluation

Image search engines differ significantly from general web search engines in the way of presenting search results. The difference leads to different interaction and examination behavior patterns, and therefore requires changes in evaluation methodologies. However, evaluation of image search still utilizes the methods for general web search. In particular, offline metrics are calculated based on coarse-fine topical relevance judgments with the assumption that users examine results in a sequential manner. In this article, we investigate annotation methods via crowdsourcing for image search evaluation based on a lab-based user study. Using user satisfaction as the golden standard, we make several interesting findings. First, instead of item-based annotation, annotating relevance in a row-based way is more efficient without hurting performance. Second, besides topical relevance, image quality plays a crucial role when evaluating the image search results, and the importance of image quality changes with search intent. Third, compared to traditional four-level scales, the fine-grain annotation method outperforms significantly. To our best knowledge, our work is the first to systematically study how diverse factors in data annotation impact image search evaluation. Our results suggest different strategies for exploiting the crowdsourcing to get data annotated under different conditions.
The Actor-Dueling-Critic Method for Reinforcement Learning

Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments.