Reviewerly/exHarmony huggingface dataset:
Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm

Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes' signal strength and evaluate the interference protection with high accuracy.
Semantic Zooming Approach to Semantic Link Network Visualization

In the paper, we described a semantic zooming approach to the visualization of special kind structures - semantic link networks (SLN), represented as a visual graph. The proposed approach allows decreasing semantic noise in SLN overview and navigation and also simplifies the process of understanding the domain studied with SLN by means of semantic zooming. We proposed priori importance levels of SLN items and semantic zooming scale to visualize the SLN with different details level. We designed an interactive SLN visualization process including the following SLN transformations: filtering SLN items, context collapse and expansion for SLN item, and changing the details in the visualized object representation in the geometric SLN graph. The transformation algorithms were developed, and also examples of SNL semantic zooming were described in details in the paper.
Introduction to 2019 ACM-BCB Highlights Session

2019 ACM-BCB highlights session is comprised of five significant advances in protein structure prediction, protein function analysis, and genomics data analysis that were recently published in several reputable journals in the field including Proteins, PLoS Computational Biology, Genome Research, and Journal of Mathematical Biology. In protein structure prediction, "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13" [1] reports the latest advance of applying deep learning and contact distance prediction to significantly improve both template-based and template-free protein structure prediction; and "How effective is contact-assisted protein threading?" [2] describes a new approach of using contact prediction to improve the template-based protein structure prediction (i.e. protein threading). In protein function analysis, "CoPhosK: a method for comprehensive kinase substrate annotation using co-phosphorylation analysis" [3] discusses a new method called CoPhoSK that uses co-phosphorylation analysis on mass spectrometry data to predict substrates of protein kinases. In genomics data analysis, "Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives" [4] presents a new method called CRISPRBetaBinomial that applies Beta Binomial model to recognize target genes from CRISPR pooled screen data; and "Low-dimensional representation of genomic sequences" [5] describes a multilateration-based embedding method for representing genomic sequences (e.g. DNA sequences) for classification and clustering analysis. These selected works have both significant technological innovations and valuable applications and therefore are appealing to the broad community of bioinformatics and computational biology.
Exploring the Advantages of Corpus in Neural Machine Translation of Agglutinative Language

This study solves the problem of mismatch between rigid model and varied morphology in machine translation of agglutinative language in two ways. (1) a free granularity preprocessing strategy is proposed to construct a multi-granularity mixed input. (2) the value iteration network is further added into the reinforcement learning model, and the rewards of each granularity input are converted into decision values, so that the model training has higher target and efficiency. The experimental results show that our approach has achieved significant improvement in the two representative agglutinative language machine translation tasks, including low-resource Mongolian $$\rightarrow $$ Chinese, and common Japanese $$\rightarrow $$ English, and has greatly shortened the training time.
PENINGKATAN KEMAMPUAN MENULIS CERITA FANTASI MENGGUNAKAN MODEL MIND MAPPING PADA SISWA KELAS VII A SMP NEGERI 24 TANJUNG JABUNG TIMUR TAHUN AJARAN 2018/2019

The purpose of this study was to describe the ability of students to write fantasy stories using the Mind Mapping model in class VII. A students at SMP Negeri 24 Tanjung Jabung Timur Academic Year 2018/2019. The type of research used was class action research and analyzed descriptively. The research subjects were all students of class VII.A with a total of 21 people consisting of 10 men and 11 women. The study uses total sampling. Data collection techniques are carried out with non-tests and tests. Data were analyzed using qualitative analysis techniques, using criteria scales and analyzed results presented in the form of numbers and explained in the form of a description. Based on the analysis carried out, it was obtained that the average value in cycle one was 69.28 enough but did not meet the minimum completeness criteria (KKM) Indonesian subjects for individual completeness were 73, while the average value in the second cycle was 80, 57 is good and has met the minimum completeness criteria (KKM) of Indonesian language subjects. Thus the Mind Mapping model can be used in learning to write fantasy stories. Keywords: writing, fantasy story, mind mapping model
Going Vegan

Previous attempts of the HCI community to reduce food waste or the carbon footprint, primarily relied on persuasive design. However, these approaches are criticized for not paying enough attention to everyday consumption practices and hence for not being successful in the long term. Therefore, we argue for a broader perspective on studying the role(s) of digital media supporting people in their transition towards more sustainable food consumption. To understand such roles, we conducted semi-structured interviews with 9 vegans. Our findings highlight an intensive use of (digital) media, especially in the early phase of changing consumption practices. Instead of gamification or persuasive design, media triggers initial irritation, provides information to develop a vegan-specific consumption knowledge and connects like-minded people.
Close-range image stitching based on depth information and moving DLT

In the image-based industrial inspection field, the imaging distance between the scene and the camera is relatively short, and the field-of-view of the imaging system is too small to meet the requirements of detection. So a close-range image stitching method is needed to get high quality and large field-of-view images. The traditional image stitching method uses a global homography transformation matrix for image stitching, which is stable, but only suitable for flat scenes, remote scenes or the scenes which are captured by the camera with rotation only. The As-Projective-As-Possible and Content- Preserving-Warping methods, which are realized by mesh optimization, improve the stitching result to a certain degree, but there will still have obvious ghost for the close-range scenes and images which have relatively large parallax. In this paper, an image stitching method which utilizes depth information and mesh optimization is proposed. The feature points are detected and clustered, and the depth information and grouping points are used to assign weights to each mesh to compute homography for each mesh respectively. Other state-of-the-art methods are compared with our method, it can be seen that the proposed method can get a better result.
Transferable Adversarial Cycle Alignment for Domain Adaption

Domain adaption is definitely critical for success in bridging source and target domains that data distribution shifts exist in domain or task. The state-of-the-art of the adversarial feature learning model named Bidirectional Generative Adversarial Networks (BiGAN), forces generative models to align with an arbitrarily complex distribution in a latent space. However, BiGAN only matches single data distribution without exploiting multi-domain structure, which means the learned latent representation could not transfer to related target domains. Recent research has proved that GANs combined with Cycle Consistent Constraints are effective at image translation. Therefore, we propose a novel framework named Transferable Bidirectional Generative Adversarial Networks combining with Cycle-Consistent Constraints (Cycle-TBiGAN) be applied in cross-domain translation, which aims at learning an alignment latent feature representation and achieving a mapping function between domains. Our framework is suitable for a wide variety of domain adaption scenarios. We show the surprising results in the task of image translation without prior ground-truth knowledge. Extensive experiments are presented on several public datasets. Quantitative comparisons demonstrate the superiority of our approach against previous methods.
Deep Domain Knowledge Distillation for Person Re-identification

Learning generic and robust representations with data from multiple domains is a big challenge in Person ReID. In this paper, we propose an end-to-end framework called Deep Domain Knowledge Distillation ( $$D^2KD$$ ) for leaning more generic and robust features with Convolutional Neural Networks (CNNs). Domain-specific knowledge learned by the auxiliary network is transferred to the domain-free subnetwork and guides the optimization of the feature extractor. While person identity information is transferred to the auxiliary network to further accurately identify domain classes. In the test period, just with a single base model as the feature extractor, we improve the Rank-1 and mAP by a clear margin. Experiments on Market-1501, CUHK03 and DukeMTMC-reID demonstrate the effectiveness of our method.
Optimal Thresholding in Direct Binary Search Visual Cryptography for Enhanced Bank Locker System

Visual cryptography (VC) is one of the strongest cryptographic method present. The main advantage of this system is that the decryption doesnot need any specific requirements for decoding other than human eyes. Using halftoning techniques binary images are obtained for grayscale and color images, this technique is applied in Halftone VC. In this paper, direct binary search (DBS) is implemented and initial images are modified for better quality of recovered images. The concept is proposed for bank locker systems. Comparison has been made using parameters like PSNR, Correlation, UQI and SSIM.
Point spread function measurement for projector based on Fourier single-pixel imaging

Point spread function (PSF) of projector plays an important role in coaxial projection-imaging profilometry and binary defocusing fringe projection profilometry. In the proposed method, the SPI (Single Pixel Imaging)-based PSF measurement method has been used to obtain the PSF of the projector point by point. We use the camera to capture the SPI patterns projected by the projector on a white plane. By considering each pixel of the camera as a single-pixel detector, we can apply SPI technology to the camera pixels and acquire the light transport coefficients between the object points on the white plane and image points of projector, which is the spatially varying blur. Owing to the characteristic of SPI, the proposed method could obtain the spatially varying blur of every pixel directly. The experiment also verified that the proposed method could provide a more accurate blur kernel than the traditional Gaussian blur kernel to fit the blur model of the camera lens.
Composing with Sounds as Images

Digital media editing techniques have allowed the emergence of common technical approaches regardless of specific media. The non-linear approach to digital editing has encouraged a certain eclecticism in sound and music work to image. The practice of compositing, recombining images from different sources onto the visual frame, is echoed in the layering of audio samples in the soundtrack. This remix approach to audio creativity can be compared to DJ remix practice. Compositing sound to visual sequences is a task of finding meaning in musical building blocks, that can then be freely mixed in sync to picture. The resulting changes in professional practice are shaping a new kind of audio-visual composer: a polymath at ease in music, sound and image.
Pengaruh Pemberian Puding Buah Naga Merah (Hylocereus Polyrhizus) Terhadap Kadar Kolesterol Total Pasien Hiperkolesterolemia Rawat Jalan Di RSUD Kota Mataram

Background . Hypercholesterolemia is a condition of cholesterol levels increased in the blood. Hypercholesterolemia can cause atherosclerosis. Atherosclerosis can cause various cardiovascular diseases such as coronary heart disease and stroke . Research Method s. This type of research is using quasi-experimental study with pretest and posttest with control group design. The sample was given 150 g of red dragon fruit pudding every day for 14 days. Data obtained from this study were analyzed using paired test and independent t-test. Research Result . The average total cholesterol level of control group before the research was 290.20 mg / dL while the cholesterol level of intervention group before the research was 290.30 mg / dL. The average total cholesterol level of the control group after intervention was 289.0 mg / dL while the total cholesterol level of the intervention group was 234.20 mg / dL. The reduction in the total cholesterol level of the intervention group was 56.10 mg / dL . Conclusion. Red dragon fruit pudding affects the decrease of total cholesterol levels.
Phonological Game-based Module to Enhance Tamil School Linus Program

This study evaluates the relationship between phonological game based module to enhance reading skills among Tamil school Linus Program students. When one is considered a normal reader, he or she does not encounter many difficulties in reading and understanding texts. There are, however, children who have problems with phoneme identification and thus do not read well and they also have difficulties in comprehending texts. These learners should be identified and be assisted with the game module so that they can proceed with their learning lives. This study was guided by the following objectives: to identify phonological game based scores among the participants; to identify the significances of the phonological game based module in classroom practices. A sample of respondents from standard one from two selected schools in Perak state was used in this study. Phonological awareness skills of these respondents were tested to find out the nature of their phonological awareness. The results indicated that the phonological game based module has a significant role in enhancing the participants’ phonological awareness and reading skills.
Effects of Activity Recognition Window Size and Time Stabilization in the SHL Recognition Challenge

The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge considers the problem of human activity recognition from inertial sensor data collected at 100 Hz from an Android smartphone. We propose a data analysis pipeline that contains three stages: a pre-processing stage, a classification stage, and a time stabilization stage. We find that performing classification on “raw” data features (i.e. without feature extraction) over extremely short time windows (e.g. 0.1 s of data) and then stabilizing the activity predictions over longer time windows (e.g. 15 s) results in much higher accuracy than directly performing classification on the longer windows when evaluated on a 10% hold-out sample of the training data. However, this finding does not hold on the competition test data, where we find that accuracy drops with decreasing window size. Our submitted model uses a random forest classifier and attains a mean F1 score over all activities of about 0.97 on the hold-out sample, but only about 0.54 on the competition test data, indicating that our model does not generalize well despite the use of a hold-out sample to prevent test set leakage.
Natural Language Business Intelligence Question Answering Through SeqtoSeq Transfer Learning

Enterprise data is usually stored in the form of relational databases. Question Answering systems provides an easier way so that business analysts can get data insights without struggling with the syntax of SQL. However, building a supervised machine learning based question answering system is a challenging task involving large manual annotations for a specific domain. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., Training samples (NL questions) on IT enetrprize domain) is used to improve performance on a target task with fewer available annotations (e.g., Training samples (NL questions) on pharmaceutical domain). We examine the effects of transfer learning for deep recurrent networks across domains and show that significant improvement can often be obtained. Our question answering framework is based on a set of machine learning models that create an intermediate sketch from a natural language query. Using the intermediate sketch, we generate a final database query over a large knowledge graph. Our framework supports multiple queries such as aggregation, self joins, factoid and transnational.
Recommendation Method by Direct Setting of Preference Patterns Based on Interrelationship Mining

This chapter introduces a recommendation method by directly setting of users’ preference patterns of items for similarity evaluation between users. Yamawaki et al. proposed a recommendation method based on comparing users’ preference patterns instead of directly comparing users’ rating scores. However, Yamawaki et al.’s method for extraction of preference patterns has limitation of representation ability and this method is not applicable in the case that a user has “cyclic” preference patterns. Our method for extraction of preference patterns is based on partial pairwise comparison of items and it is applicable to represent “cyclic” preference patterns. A tourist spot recommender system is implemented as a prototype of recommender system based on the proposed approach and the implemented recommender system is evaluated by experiments.
Research on Data Synchronization of Cement Intelligent Control Cloud Platform

Based on the data category of cement intelligent control cloud platform, this paper deeply studies the three types of replication of SQL Server replication technology for the synchronization of data between cement enterprise server and cloud platform data center. After analyzing and comparing the three replication types, a data synchronization solution is proposed. SQL Server transaction replication technology can be used to realize real-time data synchronization, meet the real-time demand of local enterprise and cloud platform data synchronization, and provide necessary real-time data guarantee for the stable operation of cloud platform.
Soft Sensor for Ball Mill Load Based on Multi-view Domain Adaptation Learning

In the operation process of wet ball mill, there are often multi-modal and multi-condition problems. In this paper, a multi-view based domain adaptive extreme learning machine (MVDAELM) was used to measure the mill load. Firstly, the correlation relationship between the load parameters and the two views (vibration and acoustic signals of the ball mill) was obtained by Canonical Correlation Analysis (CCA) respectively. Secondly, a small number of labeled data from the target domain were introduced to construct a Domain Adaptation Extreme Learning Machine (DAELM) model under manifold constraints, which solve the mismatch problem caused by the change of working conditions in the multi-condition grinding process. Finally, based on the correlation coefficient obtained before, the two views domain adaptive load parameter soft sensor model was integrated to solve the uncertainty problem in single-modal data modeling. The experimental results show that the proposed method can effectively improve the learning accuracy of the soft sensor model under multi-modal conditions.
Relative Pose Estimation Method for Unmanned Helicopter Landing Based on Vision

In the process of unmanned helicopter landing based on vision, a new pose estimation algorithm based on orthogonal iteration was proposed to solve the problem of relative pose estimation by selecting the angular feature of the landing image. Aiming at the requirements of accuracy, robustness and efficiency of pose estimation results, the objective space error was used as the error function. The initial value of iteration which affected the orthogonal iteration algorithm greatly was given based on the perspective projection model. Compared with the initial value which was random given, the number of iterations was reduced and the convergence speed was improved. The optimal value of spatial collinearity error was solved by absolute orientation method in the iterative process. The simulation results showed that the method has a good real-time performance, high precision and global convergence.
THE EDUCATIONAL PLATFORM OF CABARET PROJECT (WP7)

The CABARET (Capacity Building in Asia for Resilience EducaTion) Project is funded by the European Union under the Erasmus+ program, to foster regional cooperation for more effective multihazard early warnings and increased disaster resilience among coastal communities. The goal of the Project is to strengthen the evidence-base in support of the implementation of the new framework. The participants constructed of a consortium of 14 European and Asian higher education institutions from nine countries - four from Europe and five from Asia. The Project covers three years period and intend many meetings among participants for data and knowledge exchange. The MGU (Mining and Geology University, Sofia, Bulgaria) and MNU (Maldives National University, Male, Maldives) participations are active as co-chairs of the WP7 – “Learning and teaching tools methodologies and approaches to the MHEW (Multihazards Early Warning Systems)” and sustainable development of the resilience. The progress and achievements of the WP7 co-chaired by MGU and MNU are presented and the developments of MOOC’s (Major Online Open Courses) are under discussion. The new concept about the Educational Platform targeted to the wider audience is proposed. In conclusion several topics are underlined:  The international bilateral cooperation between MGU and MNU is established in the frame of the co-chairmanship of the WP7 – CABARET Project.  Project is displayed including educational platform development and MOOC’s skeleton and schedule.  First results of MOOC’s are available. Educational platform includes curriculum of Philippines for De La Sale University (DLSU) and Ateneo De Manila University (ADMU).  New concept for further development of the structure of the Educational Platform of the CABARET Projects is proposed. Intended international cooperation among participants of the Project is an essential topic for the coastal resilience educational platform development. Keywords: Educational Platform, new concept, hazards, warnings.