Reviewerly/exHarmony huggingface dataset:
The Development of News Text-Based Problems Teaching Materials in 12th Grade Students of Vocational High School at TR Sinar Husni of 2019/2020

This research aims to develop the material teaching of News text based Problem in 12th grade Students of Vocational High School at TR Sinar Husni. The methods used is quantitative method. It also use random sampling system. The product that was developed was in the form of a problem-based module on writing news text material entitled "News Text -Based Problem" for 12th grade students of Vocational High School at TR Sinar Husni qualified and suitable for use as individual teaching materials based on the assessment of material experts and learning design experts. The feasibility of teaching materials by material experts included in the criteria is excellent with aspects of the feasibility of the contents of 90.1%, the feasibility of the presentation of 90.3%, and the feasibility of aspects of the language of 93.2%. The feasibility of the design by the design experts in the criteria is excellent with an average score of 89.5%, the module size aspect with an average score of 89%, the module cover design (cover) with an average score of 91.6%, and the module design with an average score of 87.5%. The student news text test results after using the news text module increased by 9.47, with an average pretest learning outcomes or before using the module of 76.07 while the posttest learning outcomes or after using the module amounted to 85.54.
FAAL: a Feature-based Aligning ALgorithm

Abstract In this article I present FAAL , a new Feature-based Aligning ALgorithm that can be used for the alignment of phonetically transcribed lexical items according to the comparison of phonetic features. The algorithm can be run on any pair of phonetically transcribed words without requiring any specific setting or tuning, although various parameters of its implementation as .jar library can indeed be modified by the user, if needed. The structure and work-flow of the algorithm are described in the first part of the paper. In the second part FAAL is tested against previously proposed algorithms in a test case involving two different datasets. FAAL in its default configuration outperforms all of them. FAAL is distributed in the form of a .jar Java library. The description of this Java library and the details about the settings and configuration of its methods are provided on-line, in my github repository at https://github.com/MKilani/FAAL .
Signs of Appropriation: A Semiotic Account of Breakdowns with IoT Technology

The Internet of Things (IoT) is an emerging technology powered by smart devices that increasingly populate our environment and transform our practices. Understanding how technologies are appropriated is a key asset for designing them successfully. In this paper, we present findings from an in situ study where we observed how people use and adapt commercially available smart home technology, in real practice, and over time. By using semiotic theory and thematic analysis to investigate data from diaries, questionnaires and interviews collected from a group of eleven participants, we characterized IoT appropriation as interpretive states that can evolve overtime. We also identified eight classes of appropriation breakdowns and derived two semiotic qualities for IoT technologies. We propose that, together, they have the potential to support productive decision making by IoT designers, as well as to raise new research questions on the topic.
Combined Algorithm of Steganography with Matrix Pattern and Pixel Value Difference

Information hiding that different from encryption algorithm which needs to be encrypted and decrypted by complex mathematical models hides secret information in ordinary cover image and transmits it in open transmission channel in a way that is imperceptible to human eyes. LSB (least significant bit) algorithm is commonly used for information hiding, but it does not have good anti-detection ability. In this paper, a steganography combination of PVD (pixel value difference) with MP (matrix pattern) based on the difference of pixel texture is proposed, where the former is classified two portions and then the binary message is embedded in the edge portion of the blue layer of the image without original image in extracting, the latter algorithm generates matrix patterns representing 95 characters in the green layer of the cover image, and then revises the gray value of the pixels in the blue layer of the cover image. The 4th- 6th bits of the blue layer are used for MP algorithm, and the rest are used for PVD algorithm. The result had been proved that it has diverse embedded message forms, better detection resistance and high security capacity.
An Implemention of a Mechanism for Malicious URLs Detection

The users accessing the internet is increasing day by day, therefore, there is also a significant increase in criminal activities. Therefore, to check the credibility of website user uses traditional blacklist approach. Most antivirus, spam filter and intrusion detection system use a Blacklisting Method. However, this method is inconsequential because the behaviour of the website changes frequently. Hence, it is difficult to differentiate between the site which is benign or malicious. In our paper, we proposed a machine learning approach for detecting malicious URLs in place of the traditional blacklisting approach. In the proposed paper, we compared different supervised machine learning algorithms namely Random Forest, K-Nearest Neighbor, and Support Vector Machine and Decision Tree. The Artificial Neural Network from the domain of deep learning would also be used along with these classification algorithms. The dataset is going to have multiple features and we will use the most significant and impactful features to train the classifier model. All these algorithms are binary classifier accustomed to building a predictive model to detect a high number of malicious URLs. Since many people use Google Chrome browser most frequently hence, we plan to create a chrome extension which will give result whether the site is malicious or not.
Mobile-based Augmented Reality for Historic- Building Lessons in West Java

Learning media is currently important in education at the elementary and higher education level. Teacher should choose the right learning media with a good curriculum for students. Current technology can be utilized to support learning media. New education material, such as video or audio can be added. One kind of learnings that can be delivered with the video and audio media is about historic building. Historic buildings are cultural and historic heritage that must be preserved for the need for students to find information about historic buildings, names, locations and their historic story. The study proposed the mobile-based learning media of historic building. The development method used in this study was Rapid Application Development (RAD). A blueprint for learning media of historic buildings in west Java in this study might help the local governments to share historic buildings.
A semi-supervised learning model based on convolutional autoencoder and convolutional neural network for image classification

Deep learning has achieved the state-of-the-art performance in image classification. But, the model with supervised learning approach should be trained with large parameters and completely labeled datasets. Therefore, we proposed a semi-supervised learning model based on a convolutional auto-encoder and a complementary convolutional neural network to assist image classification. Experimental results show that in the proposed model, the number of labelled data can be reduced by more than half, and the classification accuracy continues to have the same performance. The results show that the effectiveness and feasibility of our model with a limited number of labeled data.
Open Source Software (OSS) for Big Data

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.
Scalable and Practical Natural Gradient for Large-Scale Deep Learning

Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization. We propose Scalable and Practical Natural Gradient Descent (SP-NGD), a principled approach for training models that allows them to attain similar generalization performance to models trained with first-order optimization methods, but with accelerated convergence. Furthermore, SP-NGD scales to large mini-batch sizes with a negligible computational overhead as compared to first-order methods. We evaluated SP-NGD on a benchmark task where highly optimized first-order methods are available as references: training a ResNet-50 model for image classification on ImageNet. We demonstrate convergence to a top-1 validation accuracy of 75.4% in 5.5 minutes using a mini-batch size of 32,768 with 1,024 GPUs, as well as an accuracy of 74.9% with an extremely large mini-batch size of 131,072 in 873 steps of SP-NGD.
International Cooperation and Legal Response to Cybercrime in Pakistan

According to Internet World Stats (IWS), the total number of internet users in in Pakistan during October 2018 was 44,608,065, which is 22.2% of the total population. More than 30 million of Pakistan's 212 million people use the internet via mobile devices. The literacy rate of the country is quite low as compared to other countries (i.e., 58%). This research focuses on the common patterns of cyber criminals and the required legislation and enforcement of existing laws along with the need of international cooperation to counter global cyber threat.
Architectural Patterns for Cross-Domain Personalised Automotive Functions

Context: Future automotive customer functions will be highly personalisable and adapt their settings proactively in an intelligent way. Aim: We aim at designing generic architectural patterns for functional architectures containing machine learning components. Method: We first formalise a new architectural model. Based on this model, we present and discuss three alternative architectural patterns: (1) concurrent learning, (2) end-to-end learning, and (3) user shadow learning. For these patterns, three alternative integration approaches are discussed: (i) centralised holistic approach, (ii) domain-specific approach, and (iii) dedicated approach. Moreover, we conduct an evaluation using real car data for different customer functions. Conclusion: We propose the use of the user shadow learning pattern in the dynamic architectural model. The user shadow learning pattern is not affected by safety constraints, as is usually the case for integrating artificial intelligence, as it only models user behaviour while leaving the original function intact. To integrate the multitude of models, we propose a domain-specific approach. This approach provides a balance between the trade-offs in the dedicated approach and the holistic approach, being high computational overhead and design complexity, respectively.
On the Value of Target Data in Transfer Learning

We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy. To this aim, we establish the first minimax-rates in terms of both source and target sample sizes, and show that performance limits are captured by new notions of discrepancy between source and target, which we refer to as transfer exponents.
A study of water quality modelling with the artificial neural network method in Surabaya river

Abstract Decreasing river water quality due to the environmental conditions around the river can cause changes in the value of river benefits and endanger the environment. Thus, it becomes necessary to improve river water quality management by monitoring water quality. The purpose of this study is to model predictions of water quality conditions. The method is by analysing the parameters of water quality (BOD, COD, DO, pH, temperature) influenced by rainfall, catchment area, and land use. Data analysis was performed by the artificial neural network (ANN) method, using the Matlab R2014b software. In the process of network development, the most effective model was obtained with a variation of 75% training data with 5 hidden layers and a maximum epoch of 2000. From the results of the model, the relative errors (RE) for BOD, COD, DO, pH, and temperature were found to be 7.80%, 6.33%, 6.83%, 1.92% and 1.05% respectively, with an overall average of 4.79%. Because this RE value is quite small and less than 5%, it can be concluded that the artificial neural network method is quite effective for monitoring water quality conditions in the study area.
Implementation of the Regulatory Mechanism for Developing the Educational Potential of Senior Citizens in Russia and Abroad

The article analyses the experience of implementing the regulatory and legal mechanism for the development of the educational potential of senior citizens in Russia and abroad; it substantiates the need for its application in social practice.The purpose of the study is to determine directions, features and forms of implementing the regulatory mechanism for developing the educational potential of senior citizens in Russia and abroad.Results: the comparative analysis of the experience of different countries has revealed the success of implementing the regulatory mechanism for public management of developing the educational potential of senior citizens; it has determined its most effective form, such as the public-private partnership (Denmark, Holland, Sweden).In Russia a sufficient legislative framework has been formed; it allows the effective implementation of the regulatory mechanism for the development of the educational potential of senior citizens.The main directions of implementing this mechanism in Russia and abroad are creating universities of the third age, as well as developing programs of additional professional education.The main feature is the joint training of young people and senior citizens at the educational programs in order to transfer some social and professional experience to the new generation (France, China).
Research on a Digital Watermarking Algorithm for Color Image Based on Dual-Tree Complex Wavelet Transform (DTCWT)

This paper proposes a digital watermarking algorithm for color image based on Dual-Tree Complex Wavelet Transform (DTCWT). It decomposes the color watermark image M in the RGB color space into three channels, i.e. R, G and B, and then applies Arnold transform on them respectively. It then applies DCT (Discrete Cosine Transform) and DFT (Discrete Fourier Transform) on the scrambled watermarks in the frequency domain. The algorithm applies Dual-Tree Complex Wavelet Transform (DTCWT) on the color carrier image, and uses the third-level low-frequency sub-bands of its real part as the embedded sub-bands. It then embeds all the above-mentioned watermarks into these sub-bands. Finally, it applies inverse Dual-Tree Complex Wavelet Transform on the color carrier image embedded with the color watermark to embed the color watermark into it. Watermark extraction is the inverse process of watermark embedding. Experimental results show that this algorithm features high imperceptibility and robustness.
Object spatial localization by fusing 3D point clouds and instance segmentation

Real-time detection and acquisition of localization information of instance targets in real three-dimensional space plays an important role in application scenarios such as virtual reality simulation and digital twinning. The existing spatial localization methods without the aid of lidar and other equipment often have problems in restoring the real scale. In order to overcome this problem and achieve more accurate object spatial localization, an object spatial localization by fusing 3D point clouds and instance segmentation is proposed. This method obtains sparse 3D point cloud data by binocular stereo matching, which is used to describe the real scale and spatial location information of the object. Then uses deep learning method to perform monocular instance segmentation on the specific category target of interest, and the segmentation result is used as the front/background prior information to complete the coordinate correction and densification of the 3D point cloud data inside and outside the object contour. Compared with the unsupervised depth estimation methods based on deep learning, our method can quickly and accurately achieve the three-dimensional precise localization of the instance target and its various components in real-world scenes, and the accuracy in the indoor scene is more than 90%.
A Better Approach to Generating Random Numbers

The term random number has been used by many scholars to explain the behaviour of a stochastic system. Many of such scholars with statistical or mathematical background view it as an organized set of numbers produced by a function in a numerical way in which the next number to be produced is unknown or unpredictable. This paper produced software that generates a sequence of random number and also compared the algorithm with the commonly used method of random number generator. The three most common methods selected were the Mid Square method, Fibonacci method and Linear Congruential Generator Method (LCG). The result shows that the LCG provides a more acceptable result in terms of speed, long cycle, uniformity and independence Applications of this random numbers can be seen in Monte Carlo simulations, simulation or modelling, password generation, cryptography and online games.
MNN: A Universal and Efficient Inference Engine

Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. In this paper, the contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2)deliveringthorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight. Extensive benchmark experiments demonstrate that MNN performs favorably against other popular lightweight deep learning frameworks. MNN is available to public at: https://github.com/alibaba/MNN.
Handling Position Bias for Unbiased Learning to Rank in Hotels Search

Nowadays, search ranking and recommendation systems rely on a lot of data to train machine learning models such as Learning-to-Rank (LTR) models to rank results for a given query, and implicit user feedbacks (e.g. click data) have become the dominant source of data collection due to its abundance and low cost, especially for major Internet companies. However, a drawback of this data collection approach is the data could be highly biased, and one of the most significant biases is the position bias, where users are biased towards clicking on higher ranked results. In this work, we will investigate the marginal importance of properly handling the position bias in an online test environment in Tripadvisor Hotels search. We propose an empirically effective method of handling the position bias that fully leverages the user action data. We take advantage of the fact that when user clicks a result, he has almost certainly observed all the results above, and the propensities of the results below the clicked result will be estimated by a simple but effective position bias model. The online A/B test results show that this method leads to an improved search ranking model.
KEBUTUHAN MAHASISWA AKAN MATERI AJAR MENULIS ARTIKEL DALAM BAHASA INGGRIS BERBASIS KRITERIA JURNAL-JURNAL BEREPUTASI

Good teaching material can be defined as a material that is based on the results of needs analysis, so that it is not only about the significant effect on improving students' ability to write scientific articles but also the increasement of students’ knowledge and understanding on writing research articles. The purpose of this study was to analyze the level of students’ needs related to teaching material about how to write research articles based on the criteria of reputable journals. The research design used was survey research. Data was collected using a questionnaire related to the students’ needs toward teaching materials. The questionnaires were distributed to 120 students of English Language Teaching study programs who had learned article writing course. The results showed that the level of student needs regarding abstract and conclusion section are in the High category. Besides, the introduction, literature review, and methodological section are in the Very High category. From these findings, it can be concluded that the students' needs for teaching material related to English writing research articles based on the criteria of reputable journals is very high. Thus, teaching material writing research articles in English based on the criteria of reputable journals really needs to be developed.
When Explanations Lie: Why Many Modified BP Attributions Fail

Attribution methods aim to explain a neural network's prediction by highlighting the most relevant image areas. A popular approach is to backpropagate (BP) a custom relevance score using modified rules, rather than the gradient. We analyze an extensive set of modified BP methods: Deep Taylor Decomposition, Layer-wise Relevance Propagation (LRP), Excitation BP, PatternAttribution, DeepLIFT, Deconv, RectGrad, and Guided BP. We find empirically that the explanations of all mentioned methods, except for DeepLIFT, are independent of the parameters of later layers. We provide theoretical insights for this surprising behavior and also analyze why DeepLIFT does not suffer from this limitation. Empirically, we measure how information of later layers is ignored by using our new metric, cosine similarity convergence (CSC). The paper provides a framework to assess the faithfulness of new and existing modified BP methods theoretically and empirically. For code see: https://github.com/berleon/when-explanations-lie
State-Aware Tracker for Real-Time Video Object Segmentation

In this work, we address the task of semi-supervised video object segmentation(VOS) and explore how to make efficient use of video property to tackle the challenge of semi-supervision. We propose a novel pipeline called State-Aware Tracker(SAT), which can produce accurate segmentation results with real-time speed. For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation. SAT achieves a promising result of 72.3% J&F mean with 39 FPS on DAVIS2017-Val dataset, which shows a decent trade-off between efficiency and accuracy. Code will be released at github.com/MegviiDetection/video_analyst.