Reviewerly/exHarmony huggingface dataset:
P-Wave Identification with Deep Neural Network

Seismic phase recognition is the foundation of seismic localization and the essential part of automatic seismic monitoring and processing. How to identify P-wave from large amount of noise data is the key problem here. Accurate automatic processing can manage large amount of data, so as to free up manpower. We trained a deep neural network (DNN) to identify P-waves from noises. In order to reduce the training dataset amount and improve the performance of the network, we introduce short-time Fourier transform (STFT) at front-end of the network, which not only retains the data information, but also makes the characteristics more obvious which reduces the burden of the network. The results show that for dataset of regional and distant earthquakes, recall ratio for p-wave is 90% on the average, and precision ratio is around 70%, indicating that DNN along with appropriate signal processing such as STFT perform well in P-wave identification.
Hide text depending on the three channels of pixels in color images using the modified LSB algorithm

At the moment, with the great development of information and communications technology, the transfer of confidential and sensitive data through public communications such as the Internet is very difficult to keep them from hackers and attackers. Therefore, it is necessary to work on the development of new and innovative ways to transfer such information and protect it to ensure that it reaches the desired goal. The goal of a new technique to hide information design not only hides the secret message behind the center cover, but it also provides increased security. The most common way to transfer important and confidential data is through embedding it into cover medium files in a way that does not affect the accuracy of the carrier file, which is known as hiding. In this paper, encryption and concealment techniques were used to protect data transferred from attackers. The proposed method relied on encryption of confidential information using the encryption key and the Xnor gate, after which the encrypted information was hidden in a color image using the LSB algorithm. The method of concealment depends on the extraction of chromatic channels of three RGB for each pixel and specifying the channel in which the bit of the encryption message will be hidden. Some metrics have been adopted to measure the quality of the resulting picture after hiding as PSNR and MSE, and achieve good results.
Jeff Da at COIN - Shared Task

We introduce a simple yet effective method of integrating contextual embeddings with commonsense graph embeddings, dubbed BERT Infused Graphs: Matching Over Other em-beDdings.First, we introduce a preprocessing method to improve the speed of querying knowledge bases.Then, we develop a method of creating knowledge embeddings from each knowledge base.We introduce a method of aligning tokens between two misaligned tokenization methods.Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings.We also show BERTs tendency to correct lower accuracy question types.Our model achieves a higher accuracy than BERT, and we score fifth on the official leaderboard of the shared task and score the highest without any additional language model pretraining.
Efficient Gradient Descent via Value Staleness Analysis for Heterogeneous Deep Learning Systems

Recently, parameter servers have shown great performance advantages in training large-scale deep learning (DL) systems. However, most of existing systems implicitly assume that the environment is homogeneous and then adopts stale synchronous parallel (SSP) mechanism in computation. Such assumption does not hold in many real-world cases. Although continuous efforts are paid to accommodate heterogeneity, they merely focus on adjusting learning rate according to easily-accessible iteration staleness. Unfortunately, since the parameters dramatically vary among both servers and workers, learning rate determined by iteration staleness-based analysis will slow down the computation speed and waste the effectiveness of iterations. We reveal that using value staleness, a novel abstraction proposed by us, to analyze the difference of parameters can better reflect the effectiveness of each iteration. The inspiration of value staleness comes from our comparison among workers and servers, while it is often neglected by previous work. These observations motivate us to propose a value-staleness-aware stochastic gradient descent (VSGD) scheme, which adopts the value staleness to apply dynamic learning rate assignments for each iteration in parameter servers. The evaluation under various benchmarks demonstrate that VSGD significantly outperforms the previous schemes, for instance, the computation speed is accelerated to 1.26x and the error rate is minimized to 75% compared with SSP.
Investigating the Validity of Different Peer Groupings in the Assessment of English Writings

Peer assessment is an indispensable part in classroom assessment and it serves as a very useful way of promoting learning. However, different ways of peer grouping may influence the validity of peer assessment. This study analyzes the quality and quantity of feedback, adoption rate of feedback as well as scores of students’ original drafts and the revised versions. It finds that all ways of grouping can promote learning but the degree of validity varies among groups. Besides, accuracy and adoption rate are high in students’ feedback, which means peer feedback is effective to a great extent. Among all the ways of grouping, homogeneous grouping i.e. pairing students with the same or similar language proficiency level can archive more significant promotion in learning. In general, students hold a positive view towards the validity of peer assessment.
Hybrid recommendation algorithm based on logistic regression refinement sorting model

The mainstream recommendation systems mainly use content-based method or collaborative filtering method. However, in specific recommendation scenarios, hybrid algorithm often performs better than single algorithm. We introduce a new recommendation method based on hybrid algorithm, which combined with logistic regression refinement sorting model. Our method can achieve higher accuracy rate and recall rate when we need to consider item and user features comprehensively. We recall and sort items by the hybrid algorithm based on content-based method and collaborative filtering method. After recalling process, we obtain preliminary rough sorting recommendation lists. Then we use logistic regression refinement sorting model to train the rough sorting results. The recommendation results can be more accurate after refinement sorting. We used the song data of a music website as experimental data and set three comparative experiments under different feature weight values. The experimental results show that when we consider the item and user features comprehensively, our method is better than other mainstream methods in accuracy rate and recall rate.
Exploiting Frame Semantics and Frame-Semantic Parsing for Automatic Extraction of Typological Information from Descriptive Grammars of Natural Languages

We describe a novel system for automatic extraction of typological linguistic information from descriptive grammars of natural languages, applying the theory of frame semantics in the form of framesemantic parsing.The current proof-ofconcept system covers a few selected linguistic features, but the methodology is general and can be extended not only to other typological features but also to descriptive grammars written in languages other than English.Such a system is expected to be a useful assistance for automatic curation of typological databases which otherwise are built manually, a very labor and time consuming as well as cognitively taxing enterprise.
Objects detection and tracking using fast principle component purist and kalman filter

The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Region-convolution neural networks (Fast-RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms.
Analysis of Documents Published in Scopus Database on Special Education Learning Through Mobile Learning: A Content Analysis

Special education is one of the educational fields in which technology plays a significant role. Mobile technologies change our lifestyles as well as the way we learn. Mobile learning is the use of mobile technologies alone or in combination with other information and communication technologies to enable learning any-where, anytime. The purpose of this study is to determine the recent trends in special education through mobile learning. Technology integration studies in the literature generally focus on visual dictionaries, sign language, vocabulary exer-cises or story books. Besides, it is seen that the studies report the results, not the process. However, there are few studies describing the whole technology integra-tion process with a holistic approach. The study is conducted employing docu-ment analysis and related content analysis among the qualitative research method-ology. Through the search conducted on Scopus database with the keywords "mobile learning and special education" the study is based on the analysis of the documents published. All articles published in scopus were examined. Their dis-tribution by the years, subject areas, document types, country/regions and the af-filiated universities of the authors themes have been examined. The varied find-ings reveal that the analysis of the studies published on Scopus database is im-portant in terms of content for the significance of special education through mo-bile learning. Mobile learning method used in special education is among the ef-fective results. However, only a few researches have been conducted..
Position‐pose measurement of crane sway based on monocular vision

The method of spatial positioning and pose measurement using computer vision have been widely used in satellite docking, motion control systems, navigation and guidance areas. A monocular vision measurement method of the telescope's spatial pose based on infrared target was proposed for the crane sway during industrial driving. An experimental system for monocular vision measurement of the crane spreader was established. Through the corresponding relationship between the geometric position of the infrared light spots and the imaging projection, the non-linear optimisation iterative method was used to solve the spatial pose parameters that were converted into four spreader model parameters. The system was measured under static conditions and dynamic pendulum for accuracy and real-time analysis. The experimental results show that the system can meet the real time and accuracy requirements of the spatial pose measurement of the crane sway.
Feasibility study of catheter segmentation in 3D Frustum ultrasounds by DCNN

Nowadays, 3D ultrasound (US) has been employed rapidly in medical intervention therapies, such as cardiac catheterization. To efficiently interpret 3D US images and localize the catheter during the surgery, an experienced sonographer is required. As a consequence, image-based catheter detection can be a benefit to sonographer to localize the instrument in the 3D US images timely. Conventionally, the 3D imaging methods are based on the Cartesian domain, which is limited by bandwidth and information lose when it is converted from the original acquisition space-Frustum domain. The exploration of catheter segmentation in Frustum space helps to reduce the computational cost and improve efficiency. In this paper, we present a catheter segmentation method in 3D Frustum image via a deep convolutional network (DCNN). To better describe 3D information and reduce the complexity of DCNN, cross-planes with spatial gaps are extracted for each voxel. Then, the cross-planes of the voxel are processed by the DCNN to distinguish it, whether it is a catheter voxel or not. To accelerate the prediction efficiency on whole US Frustum volume, a filter-based pre-selection is applied to reduce the computational cost of the DCNN. Based on experiments on the ex-vivo dataset, our proposed method can segment the catheter in Frustum images with 0.67 Dice score within 3 seconds, which indicates the possibility of real-time application.
The PRISMA: A Visual Feedback Display for Learning Scenarios

Ambient displays can play an important role in the teaching process guiding students to perform learning activities in public spaces. In the last years, automated feedback is becoming popular due to the proliferation of sensors, actuators, mobile devices, and networks. The contribution of this paper is twofold: (1) first, we present the implementation of an ambient display designed to provide feedback in learning scenarios using different actuators; (2) second, we present the results of a survey to investigate how adequate might be Bluetooth technology to sense and attract students to perform a learning activity.
A Role-Based Capability Modeling Approach for Adaptive Information Systems

Most modeling approaches lack in their ability to cover a full-fledged view of a software system’s business requirements, goals, and capabilities and to specify aspects of flexibility and variability. The modeling language Capability Driven Development (CDD) allows modeling capabilities and their relation to the execution context. However, its context-dependency lacks the possibility to define dynamic structural information that may be part of the context: persons, their roles, and the impact of objects that are involved in a particular execution occurrence. To solve this issue, we extended the CDD method with the BROS modeling approach, a role-based structural modeling language that allows the definition of context-dependent and dynamic structure of an information system. In this paper, we propose the integrated combination of the two modeling approaches by extending the CDD meta-model with necessary concepts from BROS. This combination allows for technical development of the information system (BROS) by starting with capability modeling using CDD. We demonstrate the combined meta-model in an example based on a real-world use case. With it, we show the benefits of modeling detailed business requirements regarding context comprising environment- and object-related information.
Education in the Disruption Era

The disruption era is the occurrence of fundamental changes in all aspects of life. The new system comes to replace the old and obsolete system that is not in accordance with the requirements of the times. In terms of education, disruption will change conventional teaching patterns to digital. The emergence of innovative applications in the world of education can make it easier for everyone to seek knowledge wherever and whenever. The use of social media or online tutoring applications now is greatly benefits for the reach of a wider and more equal audience. This study will discuss the need for our understanding of education in the era of disruption, and how educators deal with this phenomenon with more emphasis on the need for education that can better adapt to current conditions.
Video-based violence detection by human action analysis with neural network

In recent years, human action analysis is a focal point in video processing, especially on action recognition and safety surveillance. It always performs as an auxiliary tool to minimize the manpower-resource on special tasks. This paper explores the human action analysis in a specified situation, based on the human posture extraction by pose-estimation algorithm. Deep neural network (DNN) methods was used, composed of residual learning blocks for feature extraction and recurrent neural network for time-series data learning. All these modules can be applied on real-time videos, classifying different security levels of actions between two people, with 91.8% accuracy on test set. Meanwhile, some other classical network structures were compared as baselines. After forward inference process of the neural network model, a logic enhancement algorithm was raised and applied in this paper, due to the prediction error between two classes. Experiments were conducted on real-time videos, achieving satisfying performance.
Enhancement of mammogram images by using entropy improvement approach

Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10–30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering non-local filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.
Hiding Decision Tree Rules in Medical Data: A Case Study.

Data sharing among health organizations has become an increasingly common process, but any organization will most likely try to hide some sensitive patterns before it shares its data with others. This article focuses on the protection of sensitive patterns when we assume that decision trees will be the models to be induced. We apply a heuristic approach to hideany arbitrary rule from the derivation of a binary decision tree. The proposed hiding method is preferred over other heuristic solutions such as output disturbance or encryption methods that limit data usability, as the raw data itself can then more easily be offered for access by any third parties.
Approaches to visualizing big text data at the stage of collection and pre-processing

This paper describes the text data analysis in the course of management decision making.We examine in detail the process of collection of text data for further analysis and the use of imaging to increase the efficiency of human resources during collection and data preprocessing.A modification of the algorithm for creating an "n-gram cloud" visualization is proposed, which makes visualization accessible to people with visual impairments.Also, a method of visualization of n-gram vector representation models (word embedding) is proposed.On the basis of the conducted research, a part of a software package was implemented, which is responsible for creating interactive visualizations in a browser and interoperating with them.
A Spiral Software Engineering Model to Inspire Innovation and Creativity of University Students

Youth, especially students in universities, have tremendous energy that could be utilized to support their universities’ visions and their societies. However, students face several problems -for reasons related to their personalities, monetary support, and mentoring. During incubation, an idea goes several steps to crystallize, develop, and then be evaluated to gain success. However, the innovation process is agile, unstable, and subject to the risk of failure. Therefore, this paper proposes a spiral software model, called iSpiral, starting from student ideas and ending with creative operational projects. The proposed model was compared with selected models over a set of innovative criteria. The proposed iSpiral model was found more general and actionable, especially over agility and control criteria. The proposed iSpiral model was applied to a series of interrelated student ideas. The proposed model switched unmanaged and risky initiatives into active-inspired projects.
AKUNTABILITAS PUBLIK DALAM MANAJEMEN BENCANA TSUNAMI SELAT SUNDA

The application of accountability in disaster management is a very important factor and is one part of the principles of good governance.This study aims to analyze how public accountability in the management of the Sunda Strait tsunami disaster.The research method used is a qualitative approach through literature surveys.Data collection techniques through the search for literary sources derived from government documents, media, books, and relevant journals.The results of this study indicate that public accountability in disaster management that occurs in the Sunda Strait, especially those that affect the coast of Banten, shows less well.This has become a challenge for serious evaluation and improvement so that it is expected that the impact of the disaster will not take many victims and the evacuation and recovery process of victims both physical and non-physical will run optimally.
The Potential of Open Data to Automatically Create Learning Resources for Smart Learning Environments

Smart Education requires bridging formal and informal learning experience. However, how to create contextualized learning resources that support this bridging remains a problem. In this paper, we propose to exploit the open data available in the Web to automatically create contextualized learning resources. Our preliminary results are promising, as our system creates thousands of learning resources related to formal education concepts and physical locations in the student’s local municipality. As part of our future work, we will explore how to integrate these resources into a Smart Learning Environment.
Building English Learning Application in University Based on Web and Mobile

Abstract This study aims to determine the effectiveness of English in universities by using web-based and mobile applications. Method used was descriptive method and case study at one private university in Bandung with data collection techniques carried out by random sampling and questionnaire, carried out by taking observation data, interviewing students, literature studies that support problem solving theory. Results showed that, web and mobile-based English learning is expected to help students to be able to receive studies outside the hours set by the campus, as well as related to academics such as searching for literature and writing scientific papers. This is due to the application can cut time in learning and the use of learning applications is very helpful for students.