Reviewerly/exHarmony huggingface dataset:
A Two-layer Mechanism Identification Method for VIN Adversarial Examples

Abstract With the rapid development of machine learning algorithms, the security problems are gradually emerging. Most existing machine learning algorithms may be attacked by adversarial examples. Especially in the domain of path planning, the adversarial maps may result in multiple harmful consequences on the paths predicted by Deep Reinforcement Learning (DRL) algorithms. However, there is no suitable approach to automatically identify them. To our knowledge, all previous work used manual observation method to identify the adversarial maps, which is time-consuming. Therefore, this paper explores a two-layer mechanism method to automatically identify the adversarial examples in Value Iteration Networks (VIN). We define the four categories of attack results and identify them by combining path feature comparison and path image classification. Experiments show that this method can achieve an effective identification on VIN adversarial examples.
Dirichlet Process, Ewens Sampling Formula, and Chinese Restaurant Process

The Dirichlet process is a random probability measure and its realization is discrete almost surely. Therefore, there may be duplications among a sample from a distribution having the Dirichlet process. The distribution of this duplication is well known as the Ewens sampling formula. This formula is derived by another model, for example, the Chinese restaurant process. The Ewens sampling formula is related with the Donnelly–Tavaré–Griffiths formula I and II, the GEM distribution, and the Poisson–Dirichlet distribution. The Donnelly–Tavaré–Griffiths formula II is related with the Yule distribution and the Waring distribution. The distribution of the number of distinct components of the Ewens sampling formula asymptotically converges to normal distribution. It converges also to the shifted Poisson distribution under the condition resembled to that of Poisson law of small number. As a formula related to the Ewens sampling formula, the Pitman sampling formula is well known. It is also derived by the Chinese restaurant process.
Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies

Anomaly estimation, or the problem of finding a subset of a dataset that differs from the rest of the dataset, is a classic problem in machine learning and data mining. In both theoretical work and in applications, the anomaly is assumed to have a specific structure defined by membership in an $\textit{anomaly family}$. For example, in temporal data the anomaly family may be time intervals, while in network data the anomaly family may be connected subgraphs. The most prominent approach for anomaly estimation is to compute the Maximum Likelihood Estimator (MLE) of the anomaly; however, it was recently observed that for normally distributed data, the MLE is a $\textit{biased}$ estimator for some anomaly families. In this work, we demonstrate that in the normal means setting, the bias of the MLE depends on the size of the anomaly family. We prove that if the number of sets in the anomaly family that contain the anomaly is sub-exponential, then the MLE is asymptotically unbiased. We also provide empirical evidence that the converse is true: if the number of such sets is exponential, then the MLE is asymptotically biased. Our analysis unifies a number of earlier results on the bias of the MLE for specific anomaly families. Next, we derive a new anomaly estimator using a mixture model, and we prove that our anomaly estimator is asymptotically unbiased regardless of the size of the anomaly family. We illustrate the advantages of our estimator versus the MLE on disease outbreak and highway traffic data.
The relationship between the GDP, FDI, and non-oil exports in the Saudi economy - 1970-2019: Evidence from (VECM) and (ARDL) assessment - according to Vision 2030

Abstract. This study examines the long-term and short-term balance relationship of GDP, Foreign Direct Investment to the performance of nonoil exports in KSA within the framework of the export-led growth (ELG) hypothesis: Evidence from ARDL, VECM and a smaller evaluation according to Vision 2030. We performed an analysis for the period from 1970 to 2019 by an autoregressive distributed lag (ARDL) model and checked the robustness of the results in the vector error correction (VECM) model. The co-integration and Toda - Yamamoto causality analysis are conducted by using two techniques of vector error correction model (VECM) and autoregressive distributed lag (ARDL). The main findings are: Foreign direct investment can increase GDP growth rates by increasing non-oil exports in the Saudi economy according to the results of the Toda - Yamamoto Causality Test; and the GDP in the Saudi economy are affected by FDI and the rates of non-oil exports, in the long and short term, and the reason is the strength of the reserves of the Saudi economy. The contribution of this research is that the outcomes found by means of econometric models can be used for predicting and measuring GDP in upcoming years, can get a guideline from this research To the economic policy makers in Saudi Arabia. Also, the dynamic interaction among FDI, non-oil exports, and economic growth is investigated using the ARDL. The ARDL co-integration results showed that GDP, FDI and non-oil exports are co-integrated, indicating the presence of a long-run equilibrium relationship between them. Besides, the results for the relationship between GDP, FDI and Non-Oil Exports are interesting and indicate that there is no significant from variables and vice-versa using Toda-Yamamoto causality. Keywords. GDP, FDI, Non-oil exports, Stationary, Toda-Yamamoto Test, VECM, ARDL. JEL. D42, D43, H21, L12, L13.
“Judging Extreme Hardship”: An in-class activity for teaching critical interrogation of discursive frames in U.S. im/migration law

A key element in teaching the anthropology of im/migration is fostering critical analysis of the discursive frames used in conversations about im/migrants.In this article I describe an in-class activity I use to foster critical thinking about discursive frames on im/migration-specifically those which are embedded into U.S. immigration law.Students are asked to play the role of an immigration judge deciding on a de-identified version of an actual "hardship waiver" case-a petition for relief from deportation.By putting themselves in the shoes of an immigration judge, students must work to disconnect from their own biases and assumptions in order to attempt to apply immigration law.In the process, students learn about the inner workings of the immigration system and interrogate how discursive frames shape the application of immigration law.
Learning with Privileged Information for Efficient Image Super-Resolution

Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on achieving performance gains in terms of quality metrics, such as PSNR and SSIM, over classical approaches. They typically require a large amount of memory and computational units. FSRCNN, consisting of few numbers of convolutional layers, has shown promising results, while using an extremely small number of network parameters. We introduce in this paper a novel distillation framework, consisting of teacher and student networks, that allows to boost the performance of FSRCNN drastically. To this end, we propose to use ground-truth high-resolution (HR) images as privileged information. The encoder in the teacher learns the degradation process, subsampling of HR images, using an imitation loss. The student and the decoder in the teacher, having the same network architecture as FSRCNN, try to reconstruct HR images. Intermediate features in the decoder, affordable for the student to learn, are transferred to the student through feature distillation. Experimental results on standard benchmarks demonstrate the effectiveness and the generalization ability of our framework, which significantly boosts the performance of FSRCNN as well as other SR methods. Our code and model are available online: https://cvlab.yonsei.ac.kr/projects/PISR.
THE USE OF FOUR-SQUARE WRITING TO IMPROVE THE WRITING ABILITY OF DESCRIPTIVE TEXTS BY THE FIRST YEAR (X.1) STUDENTS OF SMA NEGERI 4 PEKANBARU

This research aims to know whether Four-Square Writing Method could improve the students' ability in writing descriptive texts and also the factors that contribute to the improvement. The writer collected the quantitative data by using writing tests and qualitative by using observation sheets and field notes. The minimum criteria of the successful action were 75. Before applying the Four-Square Writing Method, the students were given a pre-test to know their writing ability or their base score. After applying the Four-Square Writing Method, the students' score increased from pre-test to post-test 1 and post-test 2. Based on the data analysis, the students' score improved from 68,97 in the pre-test, to 72,77 in the post-test 1 and 75,40 in post-test 2. Furthermore, based on the data analysis of students' observation sheet, there is a significant functional improvement of students' activities during teaching and learning process from Cycle 1 (58,05% ) to Cycle 2 (82,18%). Besides, based on the data analysis of teacher’s observation sheet, it also proved that there was a good improvement from teacher’s activities in Cycle 1 (72,22%) to Cycle 2 (91,67% ). Therefore, the use of Four-Square Writing Method can improve the ability to write descriptive texts by the first year (X.1) students of SMA Negeri 4 Pekanbaru.
Menumbuhkembangkan Kecerdasan Majemuk Siswa Madrasah Ibtidaiyah Melalui Metode Pembelajaran Quantum Teaching Dan Learning

Education aims to educate the nation and develop Indonesian people, the meaning of educating is a process of intelligent life of the nation that has not become a nation of intelligent life. Every human being is born with the potential and intelligence are different and not infrequently they have a lot of intelligence, or more commonly referred to as multiple intelligences. Thus as a teacher in charge understand the intelligences that exist in the students then develop and facilitate student learning by applying quantum teaching and learning hopefully can develop the intelligence of multiple intelligences that exist so that students can form fine Keywords: Multiple Intelligences, Quantum Teaching Learning
Postponement of UIC-KORAIL Training Session on Railway Passenger Service based on IT Technology in Seoul, Korea to 8 – 12 June 2020 – UIC Communications

Register here: https://uic.org/events/uic-korail-training-session-2020 Due to an unexpected situation, we would like to inform you that the UIC-KORAIL Training Session 2020 on Railway Passenger…
Sensor numerical prediction based on long-term and short-term memory neural network

Many sensor nodes are scattered in the sensor network,which are used in all aspects of life due to their small size, low power consumption, and multiple functions. With the advent of the Internet o ...
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MRI.

The success and efficiency of Deep Learning based models for computer vision applications require large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this problem by learning meaningful features from unlabeled image or video data. In this paper, we propose a self-supervised learning approach to learn transferable features from MRI clips by enforcing the model to learn anatomical features. The pretext task models are designed to predict the correct ordering of the jumbled image patches that the MRI frames are divided into. To the best of our knowledge, none of the supervised learning models performing injury classification task from MRI frames, provide any explanations for the decisions made by the models, making our work the first of its kind on MRI data. Experiments on the pretext task show that this proposed approach enables the model to learn spatial context invariant features which helps in reliable and explainable performance in downstream tasks like classification of ACL tear injury from knee MRI. The efficiency of the novel Convolutional Neural Network proposed in this paper is reflected in the experimental results obtained in the downstream task.
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection

One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images. Most image reconstruction based AD methods rely on the discriminative capability of reconstruction error. This is heuristic as image reconstruction is unsupervised without incorporating normal-class-specific information. In this paper, we propose an AD method called dual deep reconstruction networks based image decomposition (DDR-ID). The networks are trained by jointly optimizing for three losses: the one-class loss, the latent space constrain loss and the reconstruction loss. After training, DDR-ID can decompose an unseen image into its normal class and the residual components, respectively. Two anomaly scores are calculated to quantify the anomalous degree of the image in either normal class latent space or reconstruction image space. Thereby, anomaly detection can be performed via thresholding the anomaly score. The experiments demonstrate that DDR-ID outperforms multiple related benchmarking methods in image anomaly detection using MNIST, CIFAR-10 and Endosome datasets and adversarial attack detection using GTSRB dataset.
Evaluation of Drainage Channels on Residential Area

The incompatibility of construction of urban or residential drainage channels with design criteria is often found due to difficulties in designing drainage channels.  designers often make design mistakes when designing drainage channels. This study aims to evaluate the drainage system and designs a drainage channel that can collect runoff discharge on Residential Area. The results of flood discharge obtained using the Gumbel method for the 10 and 25 year return periods are 298.78 mm and 370.38 mm. From the calculation of the channel capacity in the field and the discharge plan most of the existing channel capacity cannot accommodate the current planned discharge of 2.261 m3s-1 for the primary channel and 1.824 m3s-1 for the secondary channel. The current channel cannot accept runoff discharge during rain, so the channel rehabilitation plan is carried out by expanding and extracting the dimensions of the drainage channel.
Controllable Image Synthesis via SegVAE

Flexible user controls are desirable for content creation and image editing. A semantic map is commonly used intermediate representation for conditional image generation. Compared to the operation on raw RGB pixels, the semantic map enables simpler user modification. In this work, we specifically target at generating semantic maps given a label-set consisting of desired categories. The proposed framework, SegVAE, synthesizes semantic maps in an iterative manner using conditional variational autoencoder. Quantitative and qualitative experiments demonstrate that the proposed model can generate realistic and diverse semantic maps. We also apply an off-the-shelf image-to-image translation model to generate realistic RGB images to better understand the quality of the synthesized semantic maps. Furthermore, we showcase several real-world image-editing applications including object removal, object insertion, and object replacement.
Educational System for Augmented Reality Applications

In this article, we present a mechatronic education system need it by students to learn the application of the augmented reality. Using a tablet, the user can identify any equipment and can control and modify the working parameters. Using the software which use augmented reality on our demo mechatronic education system, the tablet identifies the motor, the switches, and the converters. Also, on the tablet screen are displayed the working parameters and some of them can be modified by user using a simple touch on the parameter.
A Review on Security Techniques for Privacy-Preserving in Data Mining

with the increase in the data mining algorithm knowledge extraction from the large data is getting easy. But at the same time this lead to new problem of Privacy of the knowledge from the stored data at various servers. So it is required to provide privacy of the sensitive data from the data miners. This paper focuses on various approaches implemented by the miners for preserving of information at individual level, class level, etc. A detail description with limitation of different techniques security of privacy preserving is explained.
User Assisted Clustering Based Key Frame Extraction

Our study proposes a novel method of key frame extraction, useful for video data. Video summarization indicates condensing the amount of data that must be examined to retrieve any noteworthy information from the video. Video summarization [1] proves to be a challenging problem as the content of video varies significantly from each other. Further significant human labor is required to manually summarize video. To tackle this issue, this paper proposes an algorithm that summarizes video without prior knowledge. Video summarization is not only useful in saving time but might represent some features which may not be caught by a human at first sight. A significant difficulty is the lack of a pre-defined dataset as well as a metric to evaluate the performance of a given algorithm. We propose a modified version of the harvesting representative frames of a video sequence for abstraction. The concept is to quantitatively measure the difference between successive frames by computing the respective statistics including mean, variation and multiple standard deviations. Then only those frames are considered that are above a predefined threshold of standard deviation. The proposed methodology is further enhanced by making it user interactive, so a user will enter the keyword about the type of frames he desires. Based on input keyword, frames are extracted from the Google Search API and compared with video frames to get desired frames.
English Language Education Students’ Intercultural Sensitivity in Cross Cultural Understanding Subject

Understanding cultural differences is one of the affective aspects in education. In English Language Education Program, Department of Language Education, Universitas Brawijaya, students are facilitated by learning the meaning of respecting cultural differences through the CCU subject course. This study aims at describing the intercultural sensitivity level of English education students who have taken the CCU course. It also tries to find out what factor that affects the students’ intercultural sensitivity level. This study used quantitative research design for measuring, assessing, and calculating the result based on numerical data. The questionnaires were distributed to 142 participants who from second, third, and fourth-year students and who have taken CCU course. The result of the study showed that 25% of students have ethnocentric level and 75% of ethno-relative level. The factor that affects both levels was the interaction engagement. It means that the students’ sensitivity level of cultural differences is very sufficient. However, there are still 25% students have no posttive perceprion about cultural differences. Therefor, faculty members still need to give innovation to the introduction of culture such as giving them insight into Indonesian culture.
CROSSOVER TASK PLANNING METHOD IN CLOUD COMPUTING USING ARTIFICIAL BEE COLONYALGORITHM PAPER TITLE

AbstractCloud computing has brought a revolution in the domain of computing. Numerous algorithms are proposed toperform it more effectively. In cloud computing, Virtualization plays a significant role and entire performance ofcloud depends on VM allocation and Migration. As many of energy is absorbed in this technology so differentalgorithms are used to save energy and enhance the efficiency of proposed work known as Green algorithms. In thisresearch work, a green algorithm for VM Migration is introduced using met heuristic algorithm named as ArtificialBee Colony (ABC) optimization algorithm. Every server has to perform different or same functions. A cloudcomputing infrastructure can be model as PM is a set of physical Servers/host PM1, PM2, PM3 PMn. Theresources of cloud infrastructure can be used by the virtualization technology, which allows one to create severalVMs on a physical server/host and therefore, reduces amount of hardware in use and improves the utilization ofresources. The computing resource/node in cloud is used through the virtual machine. To address this problem, datacentre resources need to be managed in resource -efficient manner to drive Green Cloud computing has beenproposed in this work using Virtual machine concept with Artificial Bee Colony (ABC). The simulation have beencarried out in CLOUDSIM environment and the parameters like Energy consumption, make span, Computation costratio (CCR) are measured.
Target Tracking Based on Deep Optimization Features

Abstract Target tracking algorithm based on deep learning generally adopts offline trained deep network to extract features, but with unsatisfactory separation effect of the target and the background. A target tracking algorithm based on deep optimization features is proposed to solve this problem. First, the Conv3-1 and Conv4-3 layers are selected from the offline trained convolutional neural network model VGGNet16 for basic feature extraction. Then the loss function is used to quantitatively describe the effectiveness of different filter positioning target in the basic feature layer. Finally, the selected high-efficiency filters are fused to extract deep optimization features and achieve effective tracking under the correlation filtering framework. The experimental results show that compared with other related algorithms, this algorithm can better recognize the target and present better results.
Analysis of body movements and acoustic parameters when playing the clarinet

Background Body movements while playing an instrument contain functional movements required for the sound production and ancillary movements with more expressive elements. Using 3D Motion Capture ancillary movements can be recorded and analysed. In this study, clarinettists were recorded visually and acoustically during performance and were analysed regarding specific movement patterns and interrelations with acoustic parameters.