Reviewerly/exHarmony huggingface dataset:
GenerativeMap: Visualization and Exploration of Dynamic Density Maps via Generative Learning Model

The density map is widely used for data sampling, time-varying detection, ensemble representation, etc. The visualization of dynamic evolution is a challenging task when exploring spatiotemporal data. Many approaches have been provided to explore the variation of data patterns over time, which commonly need multiple parameters and preprocessing works. Image generation is a well-known topic in deep learning, and a variety of generating models have been promoted in recent years. In this paper, we introduce a general pipeline called GenerativeMap to extract dynamics of density maps by generating interpolation information. First, a trained generative model comprises an important part of our approach, which can generate nonlinear and natural results by implementing a few parameters. Second, a visual presentation is proposed to show the density change, which is combined with the level of detail and blue noise sampling for a better visual effect. Third, for dynamic visualization of large-scale density maps, we extend this approach to show the evolution in regions of interest, which costs less to overcome the drawback of the learning-based generative model. We demonstrate our method on different types of cases, and we evaluate and compare the approach from multiple aspects. The results help identify the effectiveness of our approach and confirm its applicability in different scenarios.
Implementation of Re-used Shipping Containers for Green Architecture (Case Study: ITSB Creative Hub-Cikarang)

The largest of CO2 emissions on earth derives from construction activities. It is necessary to solve the problem to reduce the impact of CO2 emissions. One of the solution to reduce the impact of CO2 emission because of construction activity is using re-used material for building construction, such as re-used shipping container because the re-used material has low embodied energy. This paper has three purposes, and there are: explaining the application of re-used containers as building materials in the context of green architecture, explaining the application of building design using re-used containers as material, and explaining the advantages and disadvantages of used containers as building materials. Creative Hub ITSB as a case study owned by the campus of ITSB. The building construction consists of 20 units of a re-used container (20 feet size). The prefabrication construction uses for each steel material. Each component of the building assembled in the workshop, then it delivered to site by truck. The main issues that need to be solved are a matter of the delivery system, the structure, and joints, and the component assembly. Cross-ventilation system and insulating material also crucial because it can reduce building temperature.
SECURE DATA TRANSMISSION IN MP3 FILE USING LSB AND ECHO HIDING

Steganography is the hiding secret message in the various media and very popular in today network transmission and information security. Although many of the secure transmission have been done in digital image, a little work has been done in audio mp3 file. Hiding data in mp3 file is the most researchable area and preferable way to carry secret data. Therefore this paper proposes a way of hiding secret message into mp3 file by using LSB, Least Significant Bit technique and Echo Hiding. LSB is the most popular steganographic technique but there is a weak point in including noise, compression technique and extraction process. To compensate that weakness, the combination of Echo Hiding in time spectrum is proposed. The integration is used in serial. To test the effectives of the methods, various types of genres is prepared in different compression rates. The experimental results are carried out for different size of the secret message files.
Geochemistry and tectonic setting of the supracrustal rocks from the central part of the Bundelkhand craton, India

Supracrustal rocks (mafics and ultramafics) occurs along with banded iron formation, and felsic volcanics around Babina, Dhaura, and Mauranipur linear east-west trends in central part of the Bundelkhand craton represent Archean crust. The mafic and ultramafic rocks geochemically classified into Komatiite and Basaltic Komatiite and have high-Fe Tholeiitic in composition which may relate with the primitive mantle. The major and trace element geochemistry of mafic and ultramafic rocks correspond to hydrated mantle with wedge tectonic sources and ocean ridge geological characteristics.
Social Media Sentiment Analysis using Machine Learning and Optimization Techniques

Recently, there are emergence and advent of data Interpersonal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g.tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociology, and etc., which are resulted from offering immense opportunities for studying and analyzing human views and sentiment.The objective of sentiment analysis is to classify a speaker's or a writer's attitude towards various events or topics and arranging data into positive, negative or neutral categories.Sentiment analysis means determining the views of a user from the textual content regarding that topic i.e. how one feels about it.It might be used to classify the text content.Various researchers have used a widespread sort of methods to teach the classifiers for the Twitter dataset with various results.The research uses a hybrid method of using Swarm Intelligence optimization algorithms with classifiers.For each tweet, preprocessing will be done by performing various processes i.e.Tokenization; removal of stop-words and emoticons; stemming.Then their feature vectors are being made by the calculation of TF-IDF and optimized with (PSO) and (ACO) before performing the binary text categorization.Naïve Bayes and Support Vector Machine may be those machine learning technicalities used for the binary classification of tweets.
Shape Based Image Retrieval Using Fused Features

For content-based image retrieval, the shape is one of the most important discriminatory elements.The form captures most of the perceptual information of the observed objects on images in many applications, while colour and texture can often be omitted without affecting the performance of the retrieval.Unfortunately, there may be significant changes in shape, such as deformation, scaling, changes in orientation noise, and partial concealment.Accurate shape description remains, therefore, a challenging technical issue.The study performs experimental analysis to identify the problem.The adoption of the MPEG-7 and KIMIA-99 standard has significant importance to simplify the image retrieval process.The Fourier Descriptors, Moment-Based Features, Hierarchical Centroids and Histogram of Oriented Gradients have been applied for extraction of images from datasets.The fusion of features has been done by Discriminant Correlation Analysis and Direct Concatenation of features it has been evident that by fusion of features we obtained approximately 90% accurate and better results.
Strict Subspace and Label-Space Structure for Domain Adaptation

One of the most important issues of transfer learning is domain adaptation which aims at adapting a classifier or model trained in the source domain for use in the target domain, while two domains may be different but related. Intuitively, a good feature representation across domain is crucial. In this paper, we put forward a novel feature representation approach for unsupervised domain adaptation, namely Strict Subspace and Label-space Structure for Domain Adaptation (SSLS). SSLS learns two feature representations that project the source domain and target domain into two different subspaces where marginal and conditional distribution shift can be reduced effectively. Specially, we make the distances of corresponding points in the projection subspaces as well as the label space close by Laplacian graph, which will guarantee the strictness of subspace structure and the quality of the pseudo labels. Extensive experiments verify that our method is superior to several state-of-the-art methods on three real world cross-domain visual recognition tasks Office+Caltech, USPS+MNIST, and PIE.
SATIN: A Secure and Trustworthy Asynchronous Introspection on Multi-Core ARM Processors

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.
BIPA LEARNING MEDIA: MANAGEMENT AND PROBLEMS

Learning media has an understanding of tools in the learning process both inside and outside the classroom. Media is a teacher teaching aid (teaching aids), can be a visual aid or other tool that can provide concrete experiences to students. Now media is a tool that is integrated in the teaching and learning process, because of its function as an intermediary for messages. The message conveyed by the message giver in this case is the teacher and recipient of the message or the student. The media in its implementation experienced several problems. This condition is also influenced by the class being taught. Regular classes and BIPA classes certainly have different treatments. The problem of media in the BIPA class is a serious matter to be considered by the instructor.
TransI: Translating Infinite Dimensional Embeddings Based on Trend Smooth Distance

Knowledge representation learning aims to transform entities and relationships in a knowledge base into computable forms, so that an efficient calculation can be realized. It is of great significance to the construction, reasoning and application of knowledge base. The traditional translation-based models mainly obtain the finite dimension vector representation of entities or relationships by projecting to finite dimensional Euclidean space. These simple and effective methods greatly improve the efficiency and accuracy of knowledge representation. However, they ignore a fact that the semantic space develops and grows forever with the passage of time. Finite dimensional Euclidean space is not enough in capacity for vectorizing infinitely growing semantic space. Time is moving forward forever, so knowledge base would expand infinitely with time. This determines that the vector representation of entities and relationships should support infinite capacity. We fill the gap by putting forward TransI (Translating Infinite Dimensional Embeddings) model, which extends knowledge representation learning from finite dimensions to infinite dimensions. It is trained by Trend Smooth Distance based on the idea of continuous infinite dimension vector representation. The Training Efficiency of TransI model is obviously better than TransE under the same setting, and its effect of Dimension Reduction Clustering is more obvious.
One Step Quantum Key Distribution Protocol Based on the Hyperentangled Bell State

Considering the security and efficiency of quantum communication system, one step quantum key distribution protocol based on hyperentangled Bell state is proposed. Every 6 classical bits are divided into a group, which are encoded by single particle and hyperentangled Bell state, the sender Alice sends them to receiver Bob, Bob randomly selects the position of hyperentangled Bell state to measure and detects the existence of eavesdropper Eve by comparing measurement results in his hand and transmitted by Alice. Quantum key distribution protocol based on grouping is proposed to solve the storage problem of quantum information and improve the maneuverability, a security analysis is calculated under the intercept-measure-resend attack which introduce at least an error rate of 46.875%, if Eve wants to get all the information, the probability of detection eavesdropping under the entanglement-measure attack is 93.75%, so it proves that proposed protocol does not need to store the quantum state and asymptotically secure under intercept-measure-resend attack and entanglement-measure attack.
INTERACTIVE TOOL FOR VISUALIZATION OF TOPIC MODELS

Digital data are all around us and occurs in various forms as videos, pictures or texts.Digital documents represent the vast majority of such data.It can be e-news, social media contributions and so on.They can contain useful information, but due to their amount, it is time-consuming to find relevant information for the concrete company or persons.For that reason, there is a need for their automatic analysis.One of the areas which dealt with textual data analysis is topic modeling.It showed us a new way of how to automatically browse, search and summarize data in the organization.Topic modeling can be useful for time-based analysis of crises, elections, news feeds, launching of new products on the market, and other tasks which led to decision support tasks.In this paper, we aim to survey and compare topic modeling methods and propose web application to visualize extracted topics using topic modeling method called Latent Dirichlet Allocation (LDA).The comparison of selected standard topic modeling methods was experimentally tested on two selected textual datasets (20Newsgroup and Reuters) using standard evaluation metric.The proposed web application was implemented to use LDA and can extract topic models from textual documents datasets, visualize them and show their evolution over time.
Information technology in holography

Creating a holographic image is a complex process that requires knowledge in the field of informatics, physics, geometry, and design. To achieve results, a large number of specific equipment and information technology is used. That is why holographic projectors are rather a luxury item than a productive component of advertising or education. The use of 3D technology will greatly enhance the capabilities of the hologram and reduce the cost of devices. The information technology software used in the construction and reproduction of holograms is based on web design, video editing, and 3D modeling.
Auxiliary Topology Based Global Quantum Key Distribution for Secure Multicast Service

A new node structure is designed to distribute global quantum keys for secure multicast services and a novel auxiliary topology based routing and keys assignment algorithm is proposed and evaluated in terms of security probability.
Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support

The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS).We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors.Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network.The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition.Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.
Filtering of Noisy Parallel Corpora Based on Hypothesis Generation

The filtering task of noisy parallel corpora in WMT2019 aims to challenge participants to create filtering methods to be useful for training machine translation systems.In this work, we introduce a noisy parallel corpora filtering system based on generating hypotheses by means of a translation model.We train translation models in both language pairs: Nepali-English and Sinhala-English using provided parallel corpora.To create the best possible translation model, we first join all provided parallel corpora (Nepali, Sinhala and Hindi to English) and after that, we applied bilingual cross-entropy selection for both language pairs (Nepali-English and Sinhala-English).Once the translation models are trained, we translate the noisy corpora and generate a hypothesis for each sentence pair.We compute the smoothed BLEU score between the target sentence and generated hypothesis.In addition, we apply several rules to discard very noisy or inadequate sentences which can lower the translation score.These heuristics are based on sentence length, source and target similarity and source language detection.We compare our results with the baseline published on the shared task website, which uses the Zipporah model, over which we achieve significant improvements in one of the conditions in the shared task.The designed filtering system is domain independent and all experiments are conducted using neural machine translation.
DBMS-KU Interpolation for WMT19 News Translation Task

This paper presents the participation of DBMS-KU Interpolation system in WMT19 shared task, namely, Kazakh-English language pair.We examine the use of interpolation method using a different language model order.Our Interpolation system combines a direct translation with Russian as a pivot language.We use 3-gram and 5-gram language model orders to perform the language translation in this work.To reduce noise in the pivot translation process, we prune the phrase table of source-pivot and pivot-target.Our experimental results show that our Interpolation system outperforms the Baseline in terms of BLEU-cased score by +0.5 and +0.1 points in Kazakh-English and English-Kazakh, respectively.In particular, using the 5-gram language model order in our system could obtain better BLEU-cased score than utilizing the 3-gram one.Interestingly, we found that by employing the Interpolation system could reduce the perplexity score of English-Kazakh when using 3-gram language model order.
Feature Aligned Recurrent Network for Causal Video Object Detection

Recently, object detection in static images has achieved great success thanks to the application of deep convolutional neural networks. Nevertheless, extending object detectors from image to video is not trivial. Video object detection often suffers from deteriorated object appearances, such as motion blur, video defocus, rare poses or partial occlusion. This paper presents a novel end-to-end Feature Aligned Recurrent Network (FARN) for video object detection with the following two key modules. The first one is a motion guided feature alignment module, which aligns feature maps of past frames with that of the current frame using optical flow fields derived by a subnetwork. The second one is a spatial aware recurrent module, which consists of an attention unit to adaptively weight feature maps from different frames according to learned image quality, and a recurrent unit to leverage spatio-temporal coherence. FARN works causally in the sense that no future frame is used for object detection in the current frame. As evaluated on the ImageNet VID dataset, FARN demonstrated promising performance for causal video object detection compared to state-of-the-art non-causal detectors.
The Synchronisation Of Motion Capture Results In The Animation Character Reinforcement Process

Animation is a collection of frames that express a motion activity. The animation consists of actors, characters and performance components that present a story — one of the technologies in making an animation with motion capture. The results of catching motion in the form of motion record data are then synchronised to animated characters. However, the problem is that there are difficulties in synchronising animated characters with the results of catching motion so that the motion can be more subtle. On capture, this motion uses Kinect as a motion capture sensor for actors. The results of this capture then become motion data. The synchronisation of motion data from the motion capture results with Kinect then adjusts the joint body character points, frames, time with three-dimensional (3D) animated characters according to the flow of character actors. This synchronisation is adjusted to the recording of character movements starting from the motion of humans, animals and objects that invite motion activity with motion capture methods both marked and without markers. In making 3D, there are models of giving a body or bone frame (rigging) after giving the body frames is done by giving visual effects, lighting, rendering, and compositing. Rigging is the installation of bones to characters used to place and manipulate animated controls on characters that will be animated to produce the desired gesture. Animation synchronisation is to create animated characters manually; the file is used for. The synchronisation is Biovision Hierarchy (BVH) with gesture giving movement without having to save the keyboard in every animation movement manually.
POLAR SORT: COMBINING MULTIDIMENSIONAL SCALING AND POLAR COORDINATES FOR MATRIX REORDERING

Matrix reordering algorithms aim to permute rows and columns of a matrix (or a matrix-based visualization, such as a heatmap) in order to reveal data patterns that could not be perceivable in some orderings of its columns and rows.Finding a good permutation, according to some evaluation criteria, is not straightforward, and many approaches try to find out a good tradeoff between algorithm execution time and output quality.This work argues that using multidimensional scaling and polar coordinates helps to find row and column orderings that reveal Band and Circumplex patterns if they are present in some permutation of a matrix.The proposed O(n 3 ) method -Polar Sortuses classical MDS to find an initial 2-dimensional projection of rows (or columns) of the input matrix.After that, the method sorts the projected points according to their angular coefficients in a polar coordinate system whose center is the barycenter of these points.The algorithm then replicates this order to the correspondent rows (or columns).Experiments with synthetic data indicates that Polar Sort produces high-quality results for uncovering Band and Circumplex patterns, and that its mean execution time is as fast as the fastest tested methods for matrices with size 200 × 200.This paper also presents real-world examples in which Polar Sort shows those patterns.