Reviewerly/exHarmony huggingface dataset:
Multisensory modulation of body ownership in mice

Abstract Body ownership is a fundamental aspect of self-consciousness that reflects more than the presence of physical body parts. As demonstrated by the rubber hand illusion (RHI), human brains construct body ownership experiences using available multisensory information. Experimental conditions similar to those that induce the RHI in humans have been recently adapted to induce the rubber tail illusion (RTI) in mice. Here, we show that the RTI is enhanced in both sexes of mice by repetitive synchronous stroking comprised of correlated visual and tactile stimulation of real and rubber tails compared to visual-only mimicked stroking conducted without tactile stimulation. The RTI also appears to be enhanced in female but not male mice by slow compared to fast stroking that reflects an interoceptive manipulation associated with affective touch in humans. Sex differences in slow stroking effects are exploratory and require replication in mice. Sex differences have not been reported for the RHI in healthy humans, but women rate slow stroking as more affectively pleasant compared to the ratings of men. Results suggest that the RHI in humans resembles aspects of the RTI in mice. Studies of mice may therefore provide neurobiological insights on evolutionarily conserved mechanisms of bodily self-consciousness in humans.
PMRS: A Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data

In cloud computing, data owners outsource their data to clouds for saving cost of data storage and computation. However, while enjoying the benefits of cloud computing, users have to face the risk that sensitive outsourced data could be leaked. This paper proposes a privacy-preserving multi-keyword ranked search scheme over encrypted cloud data, which adopts a novel two-layer complete binary tree index structure. The upper layer index is used to filter the candidate documents while the lower layer index is used to prune those unqualified documents, and then the search result is efficiently determined. Security analysis is presented which indicates that the proposed scheme is capable of preserving the privacy of outsourced data. Experiment results show that the proposed scheme has good performance in terms of search time cost.
Analysis SURF feature extraction and SVM classification for the facial image recognition from various angles

Abstract Biometric is a series of procedures used to measure the physical properties of a person based on your physical characteristics of a person’s behavior in identification and verification. One facial biometrics ie, feature extraction Speed Up Robust Feature (SURF) will be suitably used for extracting the characteristics of the face image. Support Vector Machine (SVM) will be used as a method of classification. The face data used in this study were obtained from the National Cheng Kung University (NKCU). SVM classification results with the help of SURF as a model feature extraction with the determination of the number of k = 50 gained 94.60% accuracy rate, k = 500 acquire a 100% accuracy rate and the number of k = 1000 classification results decreased with 93.70% accuracy rate.
Reversible Data Hiding in Homomorphic Encrypted Images Without Preprocessing

Recently, reversible data hiding in encrypted images (RDH-EI) has been developed to transmit additional data. Besides extracting the hidden data, the original or processed image should be obtained when needed. In this paper, a new RDH-EI method for homomorphic encrypted images is proposed by utilizing the additive homomorphism and self-blinding property in Paillier cryptosystem. Specifically, part of the hidden data may be extracted before image decryption while the rest can be extracted after image decryption. In addition, no preprocessing is required so that homomorphic processing can be performed before data embedding. The experimental results on test images validate that the proposed method is compatible with homomorphic image processing before data embedding. Compared with the state-of-the-art methods, higher embedding capacity can be obtained with the proposed method while the original or processed image can be correctly generated.
Application of Bio-pore to Increase Rainwater Absorption in Residential Areas

Permata Tembalang Housing is located in Kramas Sub-district Tembalang District is one of the new residential areas which is located next to Diponegoro University. Along with the better economic condition of the family and the need of space, the plots that initially still have a a large open space gradually began to decrease, because residents have started to expand the land built in the plot of their house. In the end, not even a bit of land was left open for the absorption of rainwater into the ground. The purpose of this paper is to determine the level of public awareness of the importance of open space as a means of absorbing rainwater through the application of biopores. The method of applying biopori is carried out in several stages: coordination, socialization, implementation, and monitoring-evaluation. To find out the level of awareness of residents, observations and interviews were conducted with residents who had participated in socialization and applied biopori in their lots. The results of observations and interviews showed that theresidents were quite enthusiastic in implementing this biopori, as evidenced by the excellent response from the coordination stage to the monitoring-evaluation stage. The final results show that residents can early understand on the importance of open land as rain water infiltration, so that they have consciously begun to be moved and are also encouraged to apply biopori in their respective land plots.
RESEPSI SISWA MADRASAH TSANAWIYAH KABUPATEN BANTUL TERHADAP CERPEN REMAJA PADA SURAT KABAR KEDAULATAN RAKYAT

Literary reception encompasses reader’s role in making meaning from literary texts. Student’s reception of teenage short story can give an idea of student’s acceptance of this type of text. This is interesting because students as teenagers are rarely involved in responding to teenage short stories, particularly ones available in newspapers. This research aims to examine the reception of teen short stories in the Kedaulatan Rakyat Newspaper by students of Madrasah Tsanawiyah in Bantul Regency in terms of intellectual and emotional aspects. Respondents in this study are 128 students of MTsN 1 Bantul, MTsN 3 Bantul, MTsN 4 Bantul, MTs Al Falah, and MTs Hasyim Asy'ari. The data were collected using a reception questionnaire focusing on intellectual and emotional aspects. The results of the study are as follows. First, in terms of intellectual aspect, the students’ reception of teen short stories in Kedaulatan Rakyat is high (70.82%), moderate (15.62%), and low (13.58%). Second, in terms of emotional aspect, the students’ reception of teen short stories in this newspaper is high (38.86%), moderate (20.28%), and low (40.86%). Fourth, there is no significant difference between the reception of students from state madrasah and private madrasah. Based on the results of this study, it can be concluded that students of class IX of Madrasah Tsanawiyah, Bantul Regency can understand well the elements of story builders and the structure of short story, language, themes, and conflicts in the short story. Students also understand the logic of the story in the text and feel the tension of the conflict. The new values in the short stories and actions of the main characters are quite acceptable to students. Students are also interested enough to discuss the short stories further. Meanwhile, most students felt less emotional impact and do not feel the tension presented in the short story.
Deleterious Effects of Uncertainty in Color Imagery Streams on Classification Models

Image Classification (IC) is most prominent among other Artificial Intelligence (AI) domains. Mainly, IC participates rigorously for the development of society in a variety of application areas such as finance, marketing, health, industrial automation, education, and safety and security. Typically, an IC model takes image input data and tunes itself as per the required application task and classify accordingly. Among the various categories of images, color image category is better due to the capability of capturing more details, which are essential for classification purpose. However, the modern world demands Realtime or online image classification, which involves Imagery Streams. The highly likely uncertainty in Imagery Streams is due to non-stationary environment, for example, certain features or class boundaries which are valid at one-time step are not adequate for another time step. These uncertainties in Imagery Streams have deleterious effects on IC models, which causes performance degradation in terms of accuracy or make IC models, not in further use. Therefore, to overcome these issues, IC models need to adapt to changes caused by uncertainties in Imagery Streams. This paper focuses on the understanding the possible scenarios of such uncertainties in Color Imagery Streams, investigates the deleterious effects due to changes in Color Imagery Streams and provides the possible mitigation approach to overcome the issues in IC models. The contribution of this research is the first step towards an adaptive model development to mitigate the deleterious effects of uncertainty in Color Imagery Streams. This model will benefit many application areas and will directly contribute to the daily life of a society.
Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning.

We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset. The implementation of our algorithm is available on the project webpage: this https URL.
PENGGUNAAN PODCAST UNTUK MEMPERBAIKI PENGUCAPAN (PRONUNCIATION) MAHASISWA DALAM BERBICARA BAHASA INGGRIS (Sebuah Penelitian Tindakan Kelas pada Mahasiswa Pendidikan Bahasa Inggris Semester I Kelas 01, FKIP UNISRI pada Tahun Akademik 2019/2020 )

The objective of this research is to know whether Podcast can improve the students’ pronunciation in speaking English when it is implemented in teaching learning process in class 01 Semester I, English Education Program, Teacher Training and Education Faculty, UNISRI, Surakarta in 2019/2020 Academic Year.This research method used a Classroom Action Research which was implemented in two cycles. The subject of the research was class 01 Semester I, English Education Program, Teacher Training and Education Faculty, UNISRI, Surakarta in 2019/2020 Academic Year. This study was conducted in two cycles. The first cycle consisted of four meetings, and the second cycle consisted of three meetings. The procedure of the action research consisted of planning, acting, observing and reflecting. The data were collected through questionnaire, interview, field notes, and test. To analyze the quantitative data, the researcher applied descriptive statistics. It was used to compare the scores and means of pre-test and post-test. The pre-test was conducted in the preliminary research while post-test was conducted at the end of cycle 1 and 2. The result of the test was used to know how well the students understand the text of listening. To analyze the qualitative data, the researcher analyzed the improvement of the teaching learning process based on the results of questionnaire, interview, and field notes.The result of the research showed that the use of Podcast has helped students to get exposed to correct pronunciation. By constantly listening to Podcast of native speakers of English, students develop their skill not only in listening but also in pronouncing the words in English correctly. It also has helped the students in improving the students’ vocabulary mastery. In conclusion, by listening to Podcast, students can improve their pronunciation and vocabulary mastery so that they also can speak more during the speaking activity.Based on the result of the study, it can be concluded that students’ pronunciation in English can be improved by the use of Podcast. Therefore, it is recommended that teachers and students can use Podcast as one of the alternative techniques in teaching and learning speaking.Keywords : Podcast, Proununciation, Speaking Skill, Action Research
Building Visual Anomaly Dataset from Satellite Data using ADS-B

In this paper, we present a novel way of obtaining extremely challenging image dataset for the purpose of benchmarking image anomaly detection methods. By definition, anomalies are rare occurrences, and therefore, annotation of anomalies using human workforce is difficult and costly, as large amounts of mostly non-anomalous data need to be checked. To alleviate this problem, we use satellite images from Planet.com as the source of visual data, and combine them with ADS-B data to detect airplanes in a semi-automatic way. This way, our definition of anomaly is an appearance of an airplane on mostly airplane-free images. This not only speeds up annotation, but also provides the exact specification of what constitutes an anomaly, in an objective way. The resulting meta-dataset, containing references to Planet.com imagery and accurate annotations will be published in the near future. It will include locations of nearly 100 positions of airplanes on satellite images and the corresponding references to satellite images, captured in vicinity of large airports in different parts of the world, in different climate zones.
SENTiVENT Economic Event Annotation Guidelines

Annotation Guidelines for economic Events in the SENTiVENT project for economic news text mining.The goal of this annotation scheme is to produce a gold-standard labeled dataset for enabling supervised event extraction in the company-specific news text domain. The guidelines are based on the [Rich-ERE Guidelines][1] for [Events][2] and [Argument Fillers][3] but adapted to a corpus of business and financial news articles. We exclusively annotate event structures, unlike Rich ERE which annotates Entities and Relations separately.
An Ultra-Short-Term PV Power Prediction Model Based on Path Space Distance Cross-Similar Clustering and STL Decomposition

With energy crises and environmental issues going serious, the development of renewable energy is increasingly crucial. Photovoltaic (PV) power generation has become a new growth point for renewable energy generation after wind power generation. However, the randomness of PV power generation will threaten the stability of the power grid, and the research of PV power prediction has great significance for mitigating the negative impact of PV power fluctuation. The research carried out in this paper is aimed at the ultra-short-term prediction of PV power, which is mainly divided into two parts. The first part is similarity clustering of curve space trajectories. Facing the unfavorable effects of traditional Euclidean distance based clustering, the K-mediod clustering algorithm based on Dynamic time warping is proposed to perform path-space distance cross-similar clustering. In the second part, for the volatility of power, in order to better predict the PV power, seasonal and trend decomposition based on loess is performed on the similar time period data after clustering. Then, according to the decomposed components, which have different characteristics, autoregressive moving average model, long-term and short-term memory model and convolutional neural network model are proposed to predict the final prediction result. Simulation verifies the feasibility of proposed model and shows that the path space distance cross-similar clustering can provide a more reasonable data classification for the following decomposition prediction models.
Humor As a Means of Manipulating a Social Group’s Opinion in Modern Online Communities (A Case-Study on ‘the Houses of’ 2ch and Pikabu)

The Internet is a communication space where newly formed communities are searching for ways to reflect on their social nature. We provide a theoretical framework to demonstrate how humor was used to manipulate social groups before and after the emergence of the media. We use Critical Discourse Analysis and pragmatics to study several cases of social manipulation through humor. The two Internet communities, 2ch and Pikabu, being among the largest Russian-speaking entertainment communities, often compete and use humor as a way to manipulate their representatives for social purposes: to consolidate, fight back, reflect on the norms and values of their community. Our research shows that these communities follow the old traditions of humor and laughter in order to organize a poorly regulated information space. Although 2chers tend to use trolling more often, there is no general difference between these communities in the way they use humor to manipulate their social group.&#x0D; Keywords: humor, laughter, Internet, social cognition, Critical Discourse Analysis, pragmatics, speech act
Adaptive cache pre-forwarding policy for distributed deep learning

With the rapid growth of deep learning algorithms, several high-accuracy models have been developed and applied to many real-world domains. Deep learning is parallel and suitable for distributed computing, which can significantly improve the system throughput. However, there is a bottleneck for cross-machine training, that is, network latency. Nodes frequently need to wait for synchronization, and the content of each synchronization may range from several megabytes to hundred megabytes. Thus, network communication takes considerable time in the training process, which reduces system performance. Therefore, many computing architectures have been proposed. This paper proposes a type of distributed computing system for deep learning. Our design aims to reduce synchronization times and network blocking times by using a new cache mechanism, called cache pre-forwarding. The design concept of cache pre-forwarding aims to exploit reinforcement learning to train a pre-forwarding policy to increase the cache hit rate. Because of the features of reinforcement learning, our policy is adaptive and applicable to different computing environments. Finally, we experimentally demonstrate that our system is feasible.
An Algorithm for Unmanned Aerial Vehicle Landing Scene Recognition based on Deep Learning and Computational Verbs

Safe landing is a very important part of making the Unmanned Aerial Vehicle (UAV) truly capable of autonomous flight in the field. In order to determine whether the landing site is suitable for UAV landing, it is necessary to study the scene recognition problem. Aiming at the problem, this paper proposes a learning rate attenuation method based on computational verb theory, which can effectively improve the recognition accuracy. The parameters of the four exponential decay learning rate curves were selected to calculate the verb similarity (that is, four computational verb rules are constructed), then the verb similarity was used to derive the learning rate used for each round of optimization, finally, we trained the network with the obtained learning rate. Compared with the usual exponential decay method, the proposed algorithm can effectively improve the recognition accuracy of landing scenes.
An Efficient VLSI Design of Residue to Binary Converter Circuit for a New Moduli Set {22n, 22n–1 – 1, 22n–1 + 1}

This paper paper presents a new 6n bit moduli set $\{2^{2n}, 2^{2n-1}-1, 2^{2n-1}+1\}, n \geq 2$ with its associated Residue representaion to binary representation arithmetic scheme. After demonstrating that the proposed moduli set is valid for the construction of a Residue Number System Processor, a reverse converter algorithm is presented using the New Chinese Remainder Theorem (CRT) I. Closed forms of multiplicative inverses required for the implementation of the New CRT I are introduced. The resulting architecture is further reduced to obtain an adder based and memory free scheme. Our investigations after implementing the proposed converter and other best known similar state of the art schemes on Xilinx xc6s1x45-3fgg484 FPGA shows that on the average, the converter circuit for the new moduli set $\{2^{2n}, 2^{2n-1}-1, 2^{2n-1}+1\}$ is more efficient than other similar state of the arts converters in terms of area resource requirement and conversion time.
Optimal Rating Prediction in Recommender Systems

Recommendation systems are best choice to cope with the problem of information overload. These systems are commonly used in recent years help to match users with different items. The increasing amount of available data on internet in recent year’s pretenses some great challenges in the field of recommender systems. Main challenge is to predict the user preference and provide favorable recommendations. In this article, we present a new mechanism to improve the prediction accuracy in recommendations. Our method includes a discretization step and chi-square algorithm for attribute selection. Results on MovieLens dataset show that our technique performs well and minimize the error ratio.
Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints

This study proposes a strategy to refine optical flow based on the estimated reliability maps. These maps are firstly estimated a posteriori after the motion estimation by the well-known Kanade–Lucas–Tomasi (KLT). With two new defined criteria based, respectively, on the optical flow local variance and the temporal evolution of the KLT residuals, a global refinement of the motion map is then carried out through two stages under the control of the reliability measures and the colour local homogeneousness. According to the experiments performed on the Middlebury dataset, the authors' reliability measures prove to be a good indicator for the quality of the estimation. Indeed, the correction process increases the global reliability measures and reduces the global errors in a significant way. The experiments show that the quality is higher than classical estimation methods and ranked at 88/168 on Middlebury website.
Smoothed Deep Neural Networks for Marine Sensor Data Prediction

Deep learning has become a mainstream method in marine data processing field. However, the raw marine data, characterized by fluctuations, outliers and noise, is a serious obstacle to its performance improvement. To address this problem, a hybrid deep computing model is developed for marine sensor data prediction, combining smoothing and deep belief echo state network (DBEN). The proposed structure adopts a two-stage data processing mode to deal with the complex characteristics of marine time series. In the preprocessing stage, four smoothing methods are considered for fluctuation reduction and outlier handling, so that purer data conducive to achieving a higher prediction accuracy can be gained. In the prediction stage, DBEN equipped with efficient feature learning serves as a nonlinear approximator. The effectiveness of the constructed hybrid model is tested on real-world marine time series of different sources and traits, and its superiority in prediction accuracy is demonstrated by readout comparisons and statistical measures.