Reviewerly/exHarmony huggingface dataset:
Software Complexity Reduction by Automated Refactoring Schema

As the scale of software systems is growing rapidly, software complexity is becoming one of the main problems in software engineering. Higher complexity in software increases the potential risk and defects of software system, which makes it more difficult to analyze the correctness and improve the quality of software. In this paper, we present an automated refactoring schema to reduce the complexity of the component-based software. The main idea of our approach is to search a hierarchical software with a minimum hierarchical complexity and refactor the original software into it by reassembling several subcomponents into tightly coupled hierarchical ones. Besides, our approach can be easily adjusted to deal with some new situations, in which several types of constraints on partition of software components are given. Finally, we conduct a case study with Battery Management System (BMS) and the result demonstrates our approach can automatically and effectively reduce the structural complexity of software system.
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, deep learning approaches are vulnerable to adversarial attacks, which, in a crowd-counting context, can lead to serious security issues. However, attack and defense mechanisms have been virtually unexplored in regression tasks, let alone for crowd density estimation. In this paper, we investigate the effectiveness of existing attack strategies on crowd-counting networks, and introduce a simple yet effective pixel-wise detection mechanism. It builds on the intuition that, when attacking a multitask network, in our case estimating crowd density and scene depth, both outputs will be perturbed, and thus the second one can be used for detection purposes. We will demonstrate that this significantly outperforms heuristic and uncertainty-based strategies.
Natural Language Generation Using Reinforcement Learning with External Rewards

We propose an approach towards natural language generation using a bidirectional encoder-decoder which incorporates external rewards through reinforcement learning (RL). We use attention mechanism and maximum mutual information as an initial objective function using RL. Using a two-part training scheme, we train an external reward analyzer to predict the external rewards and then use the predicted rewards to maximize the expected rewards (both internal and external). We evaluate the system on two standard dialogue corpora - Cornell Movie Dialog Corpus and Yelp Restaurant Review Corpus. We report standard evaluation metrics including BLEU, ROUGE-L, and perplexity as well as human evaluation to validate our approach.
Implementation of Minority Language Translation System Based on Android

This paper designs a minority language translation system based on Android, which combines the development technology of TensorFlow building neural network. In addition, the Python-based flask framework helps us achieve data transmission, so that the translation of minority languages into Chinese or Chinese into minority languages, and in the form of deep learning, the above system can be used in practical applications to solve the communication barriers between ethnic minority areas and language lovers. We try to use the LSTM algorithm to realize the background translation of the system, and by comparing the translation results with the original text, we find that the translation results have a certain quality improvement over the traditional machine translation, rather than rigid mechanical translation. Therefore, it is hoped that a good translation system of minority languages can be realized.
PREDICT &amp; CLUSTER: Unsupervised Skeleton Based Action Recognition

We propose a novel system for unsupervised skeleton-based action recognition. Given inputs of body keypoints sequences obtained during various movements, our system associates the sequences with actions. Our system is based on an encoder-decoder recurrent neural network, where the encoder learns a separable feature representation within its hidden states formed by training the model to perform prediction task. We show that according to such unsupervised training the decoder and the encoder self-organize their hidden states into a feature space which clusters similar movements into the same cluster and distinct movements into distant clusters. Current state-of-the-art methods for action recognition are strongly supervised, i.e., rely on providing labels for training. Unsupervised methods have been proposed, however, they require camera and depth inputs (RGB+D) at each time step. In contrast, our system is fully unsupervised, does not require labels of actions at any stage, and can operate with body keypoints input only. Furthermore, the method can perform on various dimensions of body keypoints (2D or 3D) and include additional cues describing movements. We evaluate our system on three extensive action recognition benchmarks with different number of actions and examples. Our results outperform prior unsupervised skeleton-based methods, unsupervised RGB+D based methods on cross-view tests and while being unsupervised have similar performance to supervised skeleton-based action recognition.
Speech to Text Processing for Interactive Agent of Virtual Tour Navigation

The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time
An Approach to Optimize the Management of Information Security in Public Organizations of Ecuador

The problems of information security in public organizations in Ecuador are evident, which, as a result, have led to corruptions that are present at all levels of operational, tactical and strategic management. The objective of this chapter is to analyze the available information found in different media, written, spoken, among others. The deductive method was used for the collection of information and observation techniques. It turned out the improve in the administrative processes, prototype diagram of sequence of access of users and services, prototype of integration of technologies of security of the information for public organizations of Ecuador. It was concluded that to avoid corruption in a country change should happen at all levels: the way of thinking and culture of the inhabitants, laws, penalties to politicians without parliamentary immunity, application of information and communications technologies (ICT) in an appropriate manner, and complying with international standards in information security. To improve information security, administrative policies on information security must be changed, and technologies related to immutable security algorithms, Ledger, Hyperledger, etc., must be used.
PENGEMBANGAN SISTEM COMPUTER BASED TEST (CBT) TINGKAT SEKOLAH

Evaluation activities are part of the learning process at every level of education. The evaluation model or method also varies, from written tests to practical tests. Of the many evaluation methods, written evaluation or written test is an evaluation method that takes up a lot of paper, which is about 166 million pieces of paper for one national examination throughout Indonesia. The purpose of this study is to use a Computer Based Test (CBT) system that can be used as an alternative to overcome the problem of excessive paper use. The research method used in this study is R &amp;amp; D combined with ADDIE learning design concepts. This CBT system uses the PHP (Persolan Hypertext Preprocessor) programming language with a MySQL database connection to manage the content of the CBT system itself. This CBT system is intended for junior high / high school / vocational / equivalent levels with a trial percentage value of 83.34% for product validation test, 100% for operator user test and 88.94% for user test.
Exploiting Operation Importance for Differentiable Neural Architecture Search

Recently, differentiable neural architecture search methods significantly reduce the search cost by constructing a super network and relax the architecture representation by assigning architecture weights to the candidate operations. All the existing methods determine the importance of each operation directly by architecture weights. However, architecture weights cannot accurately reflect the importance of each operation; that is, the operation with the highest weight might not related to the best performance. To alleviate this deficiency, we propose a simple yet effective solution to neural architecture search, termed as exploiting operation importance for effective neural architecture search (EoiNAS), in which a new indicator is proposed to fully exploit the operation importance and guide the model search. Based on this new indicator, we propose a gradual operation pruning strategy to further improve the search efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.50\% on CIFAR-10, which significantly outperforms state-of-the-art methods. When transferred to ImageNet, it achieves the top-1 error of 25.6\%, comparable to the state-of-the-art performance under the mobile setting.
FairyTED: A Fair Rating Predictor for TED Talk Data

With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the www.ted.com website when deciding to view a talk.
Outsourcing Computation: The Minimal Refereed Mechanism

We consider a setting where a verifier with limited computation power delegates a resource intensive computation task---which requires a $T\times S$ computation tableau---to two provers where the provers are rational in that each prover maximizes their own payoff---taking into account losses incurred by the cost of computation. We design a mechanism called the Minimal Refereed Mechanism (MRM) such that if the verifier has $O(\log S + \log T)$ time and $O(\log S + \log T)$ space computation power, then both provers will provide a honest result without the verifier putting any effort to verify the results. The amount of computation required for the provers (and thus the cost) is a multiplicative $\log S$-factor more than the computation itself, making this schema efficient especially for low-space computations.
LIVE FORENSIK UNTUK ANALISA ANTI FORENSIK PADA WEB BROWSER STUDI KASUS BROWZAR

Cybercrime continues to increase and innovate along with the rapid development of internet and more easily accessible everywhere. Most business organizations have used the internet for its operations so that the use of browsers is a necessity to support work. So that the browser also adjusts to improve security on the user's side so that information accessed by users cannot be known by other users. Browzar is a browser that answers these challenges, where Browzar can run without having to be installed on the computer and automatically deletes information generated by the use of the browser itself. However, these advantages become a challenge for investigators because these advantages can be exploited by cybercriminals to eliminate, minimize existing digital evidence. This study intends to analyze and find digital evidence in criminal cases using Browzar with Live Forensic. Digital evidence is obtained using dumpit for data acquisition and forensic volatility memory and winhex to analyze data and information on RAM. Results of the study were able to obtain information that could be used for digital evidence on Browzar web browser, namely URL history, account used log in, namely username and password, timestamp, that is, the user access time to a web page.
Peningkatan Kapasitas Teknik Penulisan Jurnal Ilmiah pada Guru Sekolah

Teachers as professional educators should have some competencies, such as pedagogical competence, personal competence, social competence, and professional competence. The focus of this workshop is on the professional competence which its definition is the teacher's ability in mastering broad and in-depth subject matter that allows guiding students to meet the competency standards set in the National Education Standards. There are many activities that can support or enhance the professional competence of teachers, such as writing. By writing, teachers have the opportunity to develop themselves in order to become more creative, active, innovative and professional teachers. The module of X applications usage for vocational teachers can be used to support teacher activities in writing scientific papers. This module will be useful to facilitate the writing of citations in a scientific work. Using the Mendeley application in this workshop, make the teacher to be more insightful which support their research activity, also can improve their motivation to know how the important thing of research in their field. Therefore, this service activity want to give the teacher a knowledge of tips and tricks to facilitate journal writing. There are tools that can be used to make citations in the bibliography, such as the reference feature, Endnote, or Mendeley. So that the teacher's writing will be known and it is easier for teacher to convey the results of his research in relevant journals, especially vocational teachers in Malang.
A Note on Computational Complexity of Kill-all Go

Kill-all Go is a variant of Go in which Black tries to capture all white stones, while White tries to survive. We consider computational complexity of Kill-all Go with two rulesets, Chinese rules and Japanese rules. We prove that: (i) Kill-all Go with Chinese rules is PSPACE-hard, and (ii) Kill-all Go with Japanese rules is EXPTIME-complete.
Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging

Abstract Biostatistic applications often require to collect and analyze a massive amount of data. Hence, it has become necessary to consider new statistical paradigms that perform well in characterizing complex data. Nonparametric Bayesian methods provide a widely used framework that offers the key advantages of a fully model-based probabilistic framework, while being highly flexible and adaptable. The goal of this paper is to provide a motivation of Bayesian nonparametrics (BNP) through a particular biomedical application, namely Positron Emission Tomography (PET) imaging reconstruction.
Frequency Separation for Real-World Super-Resolution.

Most of the recent literature on image super-resolution (SR) assumes the availability of training data in the form of paired low resolution (LR) and high resolution (HR) images or the knowledge of the downgrading operator (usually bicubic downscaling). While the proposed methods perform well on standard benchmarks, they often fail to produce convincing results in real-world settings. This is because real-world images can be subject to corruptions such as sensor noise, which are severely altered by bicubic downscaling. Therefore, the models never see a real-world image during training, which limits their generalization capabilities. Moreover, it is cumbersome to collect paired LR and HR images in the same source domain. To address this problem, we propose DSGAN to introduce natural image characteristics in bicubically downscaled images. It can be trained in an unsupervised fashion on HR images, thereby generating LR images with the same characteristics as the original images. We then use the generated data to train a SR model, which greatly improves its performance on real-world images. Furthermore, we propose to separate the low and high image frequencies and treat them differently during training. Since the low frequencies are preserved by downsampling operations, we only require adversarial training to modify the high frequencies. This idea is applied to our DSGAN model as well as the SR model. We demonstrate the effectiveness of our method in several experiments through quantitative and qualitative analysis. Our solution is the winner of the AIM Challenge on Real World SR at ICCV 2019.
Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition. We demonstrate that LRP modifications could provide more sensible visual explanations to an otherwise highly noise-skewed saliency map. We also link amplitude of modified signals to useful information content. High amplitude localized signals appear to constitute the noise that undermines the interpretability capacity of LRP. Furthermore, mathematical framework for possible analysis of function approximation is developed by analogy.
Barcodes as summary of objective function's topology

We apply the canonical forms (barcodes) of gradient Morse complexes to explore topology of loss surfaces. We present a novel algorithm for calculations of the objective function's barcodes of local minima. We have conducted experiments for calculating barcodes of local minima for benchmark functions and for loss surfaces of neural networks. Our experiments confirm two principal observations for loss surfaces of neural networks. First, the barcodes of local minima are located in a small lower part of the range of values of loss function of neural networks. Second, increase of the neural network's depth brings down the barcodes of local minima. This has natural implications for the neural network learning and the generalization ability.
Dynamically Protecting Privacy, under Uncertainty.

We propose and analyze the $\varepsilon$-Noisy Goal Prediction Game to study a fundamental privacy versus efficiency tradeoff in dynamic decision-making under uncertainty. An agent wants to quickly reach a final goal in a network through a sequence of actions, while the effects of these actions are subject to random noise and perturbation. Meanwhile, an overseeing adversary observes the effects of the agent's past actions and tries to predict the goal. We are interested in understanding the probability that the adversary predicts the goal correctly (prediction risk) as a function of the time it takes the agent to reach her goal (delay). Our main results characterize the prediction risk versus delay tradeoff under various network topologies. First, we establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level, and thus incur an asymptotically negligible effect on system performance. We further apply these insights to studying network topologies generated by random graphs, and to designing private networks. In both cases, we show how to achieve an additive overhead even for relatively sparse, non-complete networks. Finally, for general graphs, we construct a private agent strategy that can operate under any level of intrinsic uncertainty. Our analysis is centered around a new class of noise-harvesting agent strategies which adaptively combines intrinsic uncertainty with additional artificial randomization to achieve efficient obfuscation.
Development of Electronic Module Text Writing Procedure Discovery Based Learning in High School

The purpose of this study was to describe the process of developing an elearning module to write text-based procedures of discovery learning is valid (in terms of content, presentation, linguistic and kegrafikaan), practical (in terms of ease of use and compliance with time), and effective ( visits from student activity, learning outcomes, and affective students) used in class XI student of SMA.This type of research is the research and development (research and development).Development model used is the model 4-D (defining, Design, development and deployment.The subjects were students class XI.IPA 1 SMAN 7 Padang totaling 29 people.The research data consists of quantitative and qualitative data.Qualitative data were collected through questionnaires filling e-module validation, questionnaire practicalities of e-module, and student activity observation sheet.Quantitative data obtained from the students in learning writing procedure text.The results of this study indicate that the e-learning modules for writing text-based discovery learning procedures generated are valid, practical, and effective.This was evidenced from the results validity, practicalities, and the effectiveness of the e-module.
Financial stock application using websocket in real time application

&lt;p&gt;Real time web application involves clients which is also known as a browser getting updates from the server as they happen. The finance world is moving fast, and human brains cannot sustain processing data at that speed, so algorithms are used with regards to the limit observed in old-style real-time communication which include long polling, polling server-sent events and comets, this paper uses WebSocket technology when dealing with real time applications. The Web Socket offers improved result as compared to the conventional approaches that are considered to be good solution of providing real time information and lessens overhead acquired while communicating over the internet and offers stateful, efficient communication among web servers and clients. When there is need to track companies worth/stock using a dashboard immediately and not some seconds ago, WebSocket can be used to stream data without delays.&lt;/p&gt;