Reviewerly/exHarmony huggingface dataset:
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go

This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go. The genetic fuzzy markup language (GFML)-based Robot Agent can work on various kinds of robots, including Palro, Pepper, and TMUs robots. We use the parameters of FAIR open source Darkforest and OpenGo AI bots to construct the knowledge base of Open Go Darkforest (OGD) cloud platform for student learning on the Internet. In addition, we adopt the data from AlphaGo Master sixty online games as the training data to construct the knowledge base and rule base of the co-learning system. First, the Darkforest predicts the win rate based on various simulation numbers and matching rates for each game on OGD platform, then the win rate of OpenGo is as the final desired output. The experimental results show that the proposed approach can improve knowledge base and rule base of the prediction ability based on Darkforest and OpenGo AI bot with various simulation numbers.
How to Train Your Dragon: Best Practices in Pedestrian Classifier Training

The present year witnesses another milestone in Pedestrian detection's journey. It has achieved remarkable progress in the course of the past 15 years, and experts foresee an everyday use of numerous stemming applications within the next 15 years. Standing on the tipping point between yesterday and tomorrow pushes to the field's retrospect. Features, context, the combination of approaches, and feature data are responsible for most of the breakthroughs in pedestrian detection. If the first three elements cover the bulk of the literature, much fewer efforts have been dedicated to featuring data. In many aspects, the construction of a training set remains similar to what it was at the birth of the domain, and some related problems are not well studied and sometimes still tackled empirically. This paper gets down to the study of pedestrian classifier training conditions. More than a survey of existing training classifiers or features, our goal is to highlight impactful parameters, potential new research directions, and combination dilemmas. Our findings are experimentally verified on two major datasets: the INRIA and Caltech-USA datasets.
Prospective Hybrid Consensus for Project PAI

PAI Coin's Proof-of-Work (PoW) consensus mechanism utilizes the double SHA-256 hashing protocol-- the same mechanism used by Bitcoin Core. This compatibility with classic Bitcoin-style mining provides low barrier to entry for PAI Coin mining, consequently rendering the PAI Coin network vulnerable to so-called 51% attacks, among others. To mitigate such risks, this paper proposes a hybrid Proof-of-Work, Proof-of-Stake (PoS) consensus mechanism and provides a detailed technical analysis of how such a mechanism would counter some of the PAI Coin network's inherent vulnerabilities, if successfully implemented. A detailed technical outline of blockchain-based PoW & PoS consensus, including their advantages and disadvantages, when used both independently and in the context of the hybrid model, is provided. An economic analysis of attacking a hybrid-powered PAI Coin network is presented, and a final recommendation for future development of PAI Coin consensus is made.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing.

Classifying semantic relations between entity pairs in sentences is an important task in Natural Language Processing (NLP). Most previous models for relation classification rely on the high-level lexical and syntactic features obtained by NLP tools such as WordNet, dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of entity that may be the most crucial features for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model which incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET. Experimental results on the SemEval-2010 Task 8, one of the most popular relation classification task, demonstrate that our model outperforms existing state-of-the-art models without any high-level features.
The CM Algorithm for the Maximum Mutual Information Classifications of Unseen Instances

The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduces the Channels Matching (CM) algorithm for the MMI classifications of unseen instances. It also introduces some semantic information methods, which base the CM algorithm. In the CM algorithm, label learning is to let the semantic channel match the Shannon channel (Matching I) whereas classifying is to let the Shannon channel match the semantic channel (Matching II). We can achieve the MMI classifications by repeating Matching I and II. For low-dimensional feature spaces, we only use parameters to construct n likelihood functions for n different classes (rather than to construct partitioning boundaries as gradient descent) and expresses the boundaries by numerical values. Without searching in parameter spaces, the computation of the CM algorithm for low-dimensional feature spaces is very simple and fast. Using a two-dimensional example, we test the speed and reliability of the CM algorithm by different initial partitions. For most initial partitions, two iterations can make the mutual information surpass 99% of the convergent MMI. The analysis indicates that for high-dimensional feature spaces, we may combine the CM algorithm with neural networks to improve the MMI classifications for faster and more reliable convergence.
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers

We present a simple hypothesis about a compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations. We also propose a new method for detecting when small input perturbations cause classifier errors, and show theoretical guarantees for the performance of this detection method. We present experimental results with a voice recognition system to demonstrate this method. The ideas in this paper are motivated by a simple analogy between AI classifiers and the standard Shannon model of a communication system.
SUBSTRA: Supervised Bayesian Patient Stratification

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.
Evolutionary active vision system: from 2D to 3D

Biological vision incorporates intelligent cooperation between the sensory and the motor systems, which is facilitated by the development of motor skills that help to shape visual information that is relevant to a specific vision task. In this article, we seek to explore an approach to active vision inspired by biological systems, which uses limited constraints for motor strategies through progressive adaptation via an evolutionary method. This type of approach gives freedom to artificial systems in the discovery of eye-movement strategies that may be useful to solve a given vision task but are not known to us. In the experiment sections of this article, we use this type of evolutionary active vision system for more complex natural images in both two-dimensional (2D) and three-dimensional (3D) environments. To further improve the results, we experiment with the use of pre-processing the visual input with both the uniform local binary patterns (ULBP) and the histogram of oriented gradients (HOG) for classification tasks in the 2D and 3D environments. The 3D experiments include application of the active vision system to object categorisation and indoor versus outdoor environment classification. Our experiments are conducted on the iCub humanoid robot simulator platform.
Many-Objective Cooperative Co-evolutionary Linear Genetic Programming Applied to the Automatic Microcontroller Program Generation

In this article, a methodology for the generation of programs in assembly language for microcontroller-based systems is proposed, applying a many-objective cooperative coevolutionary linear genetic programming based on the decomposition of a program into segments, which evolve simultaneously, collaborating with each other in the process.The starting point for the program generation is a table of input/output examples.Two methods of fitness evaluation are also proposed.When the objective is to find a binary combination, the authors propose fitness evaluation with an exhaustive search for the output of each bit of the binary combination in the genetic program.On the other hand, when the objective is to generate specific variations of the logical values in the pins of the microcontroller's port, the authors propose calculating the fitness, comparing the timing diagrams generated by the genetic program with the desired timing diagrams.The methodology was tested in the generation of drivers for the 4x4 matrix keyboard and character LCD module devices.The experimental results demonstrate that for certain tasks, the use of the proposed method allows for the generation of programs capable of competing with programs written by human programmers.
Modelling the Political Context in Requirements Engineering. The System is made for Man, not Man for the System

This paper describes the authors point of view on reaching a stage at which it is necessary to understand customer organizations better to identify their problems and how to address them. To resolve this issue we need a mechanism to capture and model how that organization actually operates by mapping organizations against the system to be developed, by including power and politics in their "too human" and even emotional dimension. We then describe here a notation by which to recognize and document power, politics and the emotional aspects of software requirements-related decision-making in customer organizations. We conclude by outlining that our suggested graphical notation would maybe not solve the problem: but even if it just raises awareness, this would make us closer to solving the problem. Given the sensibility of the political issue, it is assumed that the generated diagram using the above mentioned notations is only for the requirements engineer and his/her team, thus remaining strictly private.
May a Dissipative Environment Be Beneficial for Quantum Annealing?

We discuss the quantum annealing of the fully-connected ferromagnetic p-spin model in a dissipative environment at low temperature. This model, in the large p limit, encodes in its ground state the solution to the Grover’s problem of searching in unsorted databases. In the framework of the quantum circuit model, a quantum algorithm is known for this task, providing a quadratic speed-up with respect to its best classical counterpart. This improvement is not recovered in adiabatic quantum computation for an isolated quantum processor. We analyze the same problem in the presence of a low-temperature reservoir, using a Markovian quantum master equation in Lindblad form, and we show that a thermal enhancement is achieved in the presence of a zero temperature environment moderately coupled to the quantum annealer.
Corrections to “Requirements Socio-Technical Graphs for Managing Practitioners’ Traceability Questions”

In <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , Li Da Xu’s main affiliation should be Old Dominion University, Norfolk, VA 23529 USA.
Predicting Tomorrow's Headline using Today's Twitter Deliberations

Predicting the popularity of news article is a challenging task. Existing literature mostly focused on article contents and polarity to predict popularity. However, existing research has not considered the users' preference towards a particular article. Understanding users' preference is an important aspect for predicting the popularity of news articles. Hence, we consider the social media data, from the Twitter platform, to address this research gap. In our proposed model, we have considered the users' involvement as well as the users' reaction towards an article to predict the popularity of the article. In short, we are predicting tomorrow's headline by probing today's Twitter discussion. We have considered 300 political news article from the New York Post, and our proposed approach has outperformed other baseline models.
Meningkatkan Hasil Belajar PKN Pada Materi Nilai Dan Norma Sosial Melalui Pembelajaran CIRC (Cooperative, Integrated, Reading, And Composition) Pada Siswa Kelas X Multimedia SMK Negeri 3 Watampone Kabupaten Bone

This research is a classroom action research aimed at improving student learning outcomes in Civics lessons taught through CIRC learning models (Cooperative, Integrated, Reading and Composition) in Multimedia Class X Students of SMK Negeri 3 Watampone Kab. Bone with 35 students. The implementation of this action consists of two cycles, each cycle carried out through the stages of planning, action, observation, and reflection. Data collection techniques used are the provision of tests and observation sheets. The results of the analysis obtained were changes in behavior of students who experienced an increase with the percentage in the first cycle only 94.2% and in the second cycle increased to 95.71% there was also an increase in attention, interest and motivation of students towards the subject matter given by the teacher. From this analysis, it can be concluded that the learning outcomes of PKn students in Class X Multimedia at SMK Negeri 3 Watampone have increased after being given action through the CIRC learning model (Cooperative, Integrated, Reading and Composition).
PERFORMANCE OF IMAGE TATTOOING BY WAVELET DISCRETE TRANSFORM

This paper Digital image watermarking involves introducing a brand, signature, name or logo of the author into the image to protect it from counterfeit, various illegal copies and shares. There are several watermark methods, most of them based on mathematical transformations. In this document, we had compare two watermarking algorithm: the domain of wavelets and the domain of discrete cosines. By exploiting the results obtained, we have found that embedding the mark in the sub-layer A is the best against attacks
Deconstructing Generative Adversarial Networks

We deconstruct the performance of GANs into three components: 1. Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be viewed as a generalization of the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs, to provably recover meaningful low-dimensional generator approximations when the real distribution is high-dimensional and corrupted by outliers. 2. Generalization: given a population target of GANs, we design a systematic principle, projection under admissible distance, to design GANs to meet the population requirement using finite samples. We implement our principle in three cases to achieve polynomial and sometimes near-optimal sample complexities: (1) learning an arbitrary generator under an arbitrary pseudonorm; (2) learning a Gaussian location family under TV distance, where we utilize our principle provide a new proof for the optimality of Tukey median viewed as GANs; (3) learning a low-dimensional Gaussian approximation of a high-dimensional arbitrary distribution under Wasserstein distance. We demonstrate a fundamental trade-off in the approximation error and statistical error in GANs, and show how to apply our principle with empirical samples to predict how many samples are sufficient for GANs in order not to suffer from the discriminator winning problem. 3. Optimization: we demonstrate alternating gradient descent is provably not locally asymptotically stable in optimizing the GAN formulation of PCA. We diagnose the problem as the minimax duality gap being non-zero, and propose a new GAN architecture whose duality gap is zero, where the value of the game is equal to the previous minimax value (not the maximin value). We prove the new GAN architecture is globally asymptotically stable in optimization under alternating gradient descent.
Do We Preach What We Practice? Investigating the Practical Relevance of Requirements Engineering Syllabi - The IREB Case

Nowadays, there exist a plethora of different educational syllabi for Requirements Engineering (RE), all aiming at incorporating practically relevant educational units (EUs). Many of these syllabi are based, in one way or the other, on the syllabi provided by the International Requirements Engineering Board (IREB), a non-profit organisation devoted to standardised certification programs for RE. IREB syllabi are developed by RE experts and are, thus, based on the assumption that they address topics of practical relevance. However, little is known about to what extent practitioners actually perceive those contents as useful. We have started a study to investigate the relevance of the EUs included in the IREB Foundation Level certification programme. In a first phase reported in this paper, we have surveyed practitioners mainly from DACH countries (Germany, Austria and Switzerland) participating in the IREB certification. Later phases will widen the scope both by including other countries and by not requiring IREB-certified participants. The results shall foster a critical reflection on the practical relevance of EUs built upon the de-facto standard syllabus of IREB.
An Empirical Analysis of the Factors that Influence the Demand for Entrepreneurship in Cameroon

&lt;p&gt;&lt;em&gt;Population expansion and resource availability have been the basis for many entrepreneurial activities around the world. The presence of resources and the continuous drive to provide the ever growing needs of man has been a fortune for many entrepreneurs. Unfortunately, this is not a general rule as there are resources rich areas with little or no entrepreneurial activities. Within the context of this lack of standard rule for the development of entrepreneurship, this paper investigates the determinants of the demand for entrepreneurship in Cameroon using the&lt;strong&gt; &lt;/strong&gt;&lt;/em&gt;&lt;em&gt;Johansen co-integration procedures and the Error Correction Mechanism (ECM) based on data collected from the &lt;/em&gt;&lt;em&gt;World Development Indicators, WDI (2016) and the KOF globalisation index database between 1980 and 2017. After testing for the short and long run relationship, the study found out that population growth, technology and globalization negatively affects the demand for entrepreneurship in Cameroon while economic development positively and significantly determines the demand for entrepreneurship. On the basis of these findings, the study suggests that government should develop and enact policies that will sufficiently regulate the market and create the right business environment for the sustainability of entrepreneurial activities in Cameroon. While recognizing the role of population expansion in providing a market for business, overpopulation should not be tolerated as it becomes a liability in struggling entrepreneurialism. &lt;/em&gt;&lt;/p&gt;
P027 Machine learning approaches to identify prognosis indicators from microbiome data

Inflammatory bowel disease (IBD) has been shown to associate with alterations in intestinal microbiome. However, the precise nature of these microbial changes remains unclear. With the vast number of microbes present within the gut, novel and powerful computational techniques are required to distinguish between important microbial changes and noise. Machine learning (ML) allows for a data-driven approach to identify these discrete dynamic changes within the microbiome, while systems biology (SB) gives mechanisms to the findings of the ML algorithms. By combining ML and SB approaches, we aim to characterise key microbial factors in ulcerative colitis (UC) pathogenesis. Interpreting the functional and mechanistic importance of microbiome features requires higher resolution than 16S rRNA sequencing. However, the lack of Whole Genome Shotgun (WGS) data at a scale required for ML-based classification is a bottleneck. To overcome this and to develop the ML pipeline, we generated a large artificial patient cohort using the SMOTE algorithm to oversample a small UC WGS cohort. The artificial dataset was created by preserving the complexity and distribution functions observed in real WGS datasets. This generated enough samples to be able to train a deep learning model. We utilised the power of Artificial Neural Networks (ANNs) to obtain discrete underlying data structures from the microbiome data, thus eliminating noise from the feature space. Dynamic changes within the patient's microbiome are predicted by employing a heterogeneous ensemble (Random Forest, Gradient Boosting, etc.) to match the complexity of underlying relations of the microbiome. Using our ANN to encode the data, we identified potential candidate prognosis indicators from this artificial dataset. The ML pipeline was able to recover top-performing features from the synthetic dataset, thus determining the underlying structure of the dataset. As a next step, we have collected and interrogated publicly available microbiome data (NIH Integrative Human Microbiome Project) to enable the ML model to be applicable to actual UC cohorts. We have developed an integrated ML-based microbiome pipeline to identify prognostic indicators for UC from artificial data. Furthermore, using SB approaches, we were able to interpret the predicted key microbial features and communities by inferring connections between microbial and host proteins relevant in UC. This pipeline will enable us to analyse and assess real UC patient microbiome data, and identify prognostic indicators for disease subtypes and personalised treatments.
Metoda Autoregressive untuk Peramalan Jangka Panjang

Changes in seasonal patterns in Indonesia are closely related to rainfall. Various forecasting techniques were developed to produce better accuracy. In this study ARIMA linear forecasting techniques were used. The data used is secondary data from BMKG Kalianget Station, Sumenep from January 2008 - December 2017 with a monthly rainfall research variable. To measure the accuracy of the forecast results used by RMSE.From the result of this study, ARIMA ([1,6],0,0)(0,1,1)12providing better accuracy than ARIMA (1,0,0)(0,1,1)12 for predicting the next 1 month or 12 months (a year ahead).