Reviewerly/exHarmony huggingface dataset:
Automated Journalism and Freedom of Information: Ethical and Juridical Problems Related to AI in the Press Field

Journalism and the Press have always been deeply influenced by technological changes, and so they are in the digital world: from the competition of new media and the challenges of the Web 2.0 to the creation of a new way to produce news, i.e. automated journalism. Between the different notions of the use of AI in the Press field (automated journalism, robot journalism, News-Writing Bots, algorithmic journalism) in this paper the wording “automated journalism” is preferred as long as it seems to describe in a better way the practice of this type of journalism and it seems more used by the scholars who have studied this topic. Automated journalism is the use of AI, i.e. software or algorithms, in order to automatically generate news stories without any contribution of human beings, apart from that of programmers who (eventually) have developed the algorithm. This paper aims to analyse the ethical and juridical problems of automated journalism, in particular, looking at the freedom of information and focusing on the issue of liability and responsibility. From a legal point of view, the analysis shall embrace and share the European concept of the freedom of information and media regulation, focusing in particular on the Italian legal system. Indeed in the range of European legal systems, the Italian system has more broadly developed the idea of freedom of information, and it has multiple approaches to the topic, which are partially explored here. The first paragraph of the paper shall explore the field of the media outputs in which automated journalism – as currently developed – could produce innovations and could be implemented. The utilization of the Italian model serves to understand how the pieces of automated journalism could be framed from a legal point of view. The second paragraph shall analyse the legal and ethical problem of automated journalism by looking at the problems of liability and data use. As a consequence, a first section shall be dedicated to the issue of liability and another one to that of data utilization. In the final remarks, some solutions and guidelines shall be proposed looking at the problems highlighted in the paper.
Compiled Obfuscation for Data Structures in Encrypted Computing

Encrypted computing is an emerging technology based on a processor that `works encrypted', taking encrypted inputs to encrypted outputs while data remains in encrypted form throughout. It aims to secure user data against possible insider attacks by the operator and operating system (who do not know the user's encryption key and cannot access it in the processor). Formally `obfuscating' compilation for encrypted computing is such that on each recompilation of the source code, machine code of the same structure is emitted for which runtime traces also all have the same structure but each word beneath the encryption differs from nominal with maximal possible entropy across recompilations. That generates classic cryptographic semantic security for data, relative to the security of the encryption, but it guarantees only single words and an adversary has more than that on which to base decryption attempts. This paper extends the existing integer-based technology to doubles, floats, arrays, structs and unions as data structures, covering ANSI C. A single principle drives compiler design and improves the existing security theory to quantitative results: every arithmetic instruction that writes must vary to the maximal extent possible.
Log-minor distributions and an application to estimating mean subsystem entropy

A common task in physics, information theory, and other fields is the analysis of properties of subsystems of a given system. Given the covariance matrix $M$ of a system of $n$ coupled variables, the covariance matrices of the subsystems are principal submatrices of $M$. The rapid growth with $n$ of the set of principal submatrices makes it impractical to exhaustively study each submatrix for even modestly-sized systems. It is therefore of great interest to derive methods for approximating the distributions of important submatrix properties for a given matrix. Motivated by the importance of differential entropy as a systemic measure of disorder, we study the distribution of log-determinants of principal $k\times k$ submatrices when the covariance matrix has bounded condition number. We derive upper bounds for the right tail and the variance of the distribution of minors, and we use these in turn to derive upper bounds on the standard error of the sample mean of subsystem entropy. Our results demonstrate that, despite the rapid growth of the set of subsystems with $n$, the number of samples that are needed to bound the sampling error is asymptotically independent of $n$. Instead, it is sufficient to increase the number of samples in linear proportion to $k$ to achieve a desired sampling accuracy.
Types of Sentence-final ‘-tako’ and Reported Evidentiality

This paper aims to identify reported evidentials among various types of "-tako"s that occur in sentence final position in spoken Korean. "-Tako"s have been treated as sentence final endings and can be classified into five types: "emphasis", "negative assertion", "verification of wrong prediction", "confirmative question", and "repeated answer". Careful examination reveals that only those of "emphasis" and "confirmative question" are sentence final endings of which grammaticalization has completed. In the aspect of reported evidentiality, "-tako"s that function as "emphasis" are not reported evidentials because they do not convey the meaning of speech report. "-Tako"s of "confirmative question" can be divided into two types according to source of information: one from the hearer"s speech and the other from the third person"s speech. Of these two types, "-tako"s that are used to verify information from the third person are identified as sentence final endings that convey the meaning of speech report. In conclusion, "-tako"s that are qualified as reported evidentials are of "confirmative question" whose source comes from the third person.
US-net for robust and efficient nuclei instance segmentation

We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and segmentation networks by sharing their outputs through the same foundation network, and thus enhancing the performance of both. The detection network takes into account the high-level semantic cues with contextual information, while the segmentation network focuses more on the low-level details like the edges. Extensive experiments reveal that our proposed framework can strengthen the performance of both branch networks in an integrated architecture and outperforms most of the state-of-the-art nuclei detection and segmentation networks.
Face Detection and Face Recognition In the Wild Using Off-the-Shelf Freely Available Components

This paper presents an easy and efficient face detection and face recognition approach using free software components from the internet. Face detection and face recognition problems have wide applications in home and office security. Therefore this work will helpful for those searching for a free face off-the-shelf face detection system. Using this system, faces can be detected in uncontrolled environments. In the detection phase, every individual face is detected and in the recognition phase the detected faces are compared with the faces in a given data set and recognized.
Bayesian method for evaluation an airline profitability on the base components of Airline Route Planning

Airline route planning takes into account the factors of commercial and customer preferences, safety, and should allow a flexibility given the tremendous uncertainty about market conditions. The mathematical model on the Bayes formula allows optimizing the interaction of the base factors.
A Vecm Analysis on the Determinants of FDI Inflows: The Case of Yemen

This study investigates the impact of foreign direct investment on economic growth in Yemen during the period 1990 to 2015. This study revealed that foreign direct investment positively affects the real GDP in the long term and short term. While the exchange rate, fixed capital formation, and trade openness negatively affect long-term and short-term economic growth, inflation adversely affects the economic growth in the long term but positively affects the short term. Conclusions and policy recommendations were made using these results.
Testing Design Value of CC with Reflective SEM Line Application Data Resistance Design Medical Safety Reserve in Airport Abdul Rachman Saleh Malang, Indonesia

In-flight safety services are currently a top priority that cannot be separated from in-flight service components.Each airline has in-flight safety services, to provide services in accordance with Aviation Safety Law Regulations.Management of aviation safety management services ranging from information submitted to airline users with accurate and accountable information on the truth of the data provided.The purpose of this study is to provide an overview Web-based application design data passenger medical record of in-flight safety services using ID Hostinger.Medical record data that is known by the public is only located in the hospital and is the patient's medical record data that is vital in the management of medical record data as a whole.An in-flight safety service includes: 1) providing passenger medical record information when departing and departing according to the intended flight destination.2) Provide ease of passenger data to the airlines used, to provide in-flight safety services with medical record data of passenger data to obtain.The health service is in accordance with the intended Hospital and in accordance with the condition of the last complaint of the passenger.3) Provide ease of passengers to claim health insurance in case of an accident with medical record data that has been integrated with the intended hospital.4) Provide insurance claim information to the airlines in accordance with the medical record data and the intended hospital passengers.So it can be in the number of insurance claims that will be issued by the airlines in the event of an accident.The limitations of the problem in this study only describe how the in-flight safety service system uses airline passenger medical record data.By using airplane ticket purchasing system at each of the designated airline ticket counter and in cooperation with related Hospital and insurance party to claim medical expenses in accordance with the diagnosis of passenger medical record and patient's medical history at the time of input by medical record officer in each counter integrated with the Hospital and passenger insurance.Web-based application prototype of the in-flight safety medical record data is run on the Server ID Hosting server.
Deeper & Sparser Exploration.

We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal policy, with a lower computational complexity. The main novelty is the use of a candidate policy generator, to generate long-term options in the planning tree (over beliefs), which allows us to create much sparser and deeper trees. Experimental results on different environments show that in comparison to the state-of-the-art, our algorithm is both computationally more efficient, and obtains significantly higher reward in discrete environments.
Real-Time Freespace Segmentation on Autonomous Robots for Detection of Obstacles and Drop-Offs

Mobile robots navigating in indoor and outdoor environments must be able to identify and avoid unsafe terrain. Although a significant amount of work has been done on the detection of standing obstacles (solid obstructions), not much work has been done on the detection of negative obstacles (e.g. dropoffs, ledges, downward stairs). We propose a method of terrain safety segmentation using deep convolutional networks. Our custom semantic segmentation architecture uses a single camera as input and creates a freespace map distinguishing safe terrain and obstacles. We then show how this freespace map can be used for real-time navigation on an indoor robot. The results show that our system generalizes well, is suitable for real-time operation, and runs at around 55 fps on a small indoor robot powered by a low-power embedded GPU.
Efficiently factoring polynomials modulo $p^4$

Polynomial factoring has famous practical algorithms over fields-- finite, rational \& $p$-adic. However, modulo prime powers it gets hard as there is non-unique factorization and a combinatorial blowup ensues. For example, $x^2+p \bmod p^2$ is irreducible, but $x^2+px \bmod p^2$ has exponentially many factors! We present the first randomized poly(deg $f, \log p$) time algorithm to factor a given univariate integral $f(x)$ modulo $p^k$, for a prime $p$ and $k \leq 4$. Thus, we solve the open question of factoring modulo $p^3$ posed in (Sircana, ISSAC'17). Our method reduces the general problem of factoring $f(x) \bmod p^k$ to that of {\em root finding} in a related polynomial $E(y) \bmod\langle p^k, \varphi(x)^\ell \rangle$ for some irreducible $\varphi \bmod p$. We could efficiently solve the latter for $k\le4$, by incrementally transforming $E(y)$. Moreover, we discover an efficient and strong generalization of Hensel lifting to lift factors of $f(x) \bmod p$ to those $\bmod p^4$ (if possible). This was previously unknown, as the case of repeated factors of $f(x) \bmod p$ forbids classical Hensel lifting.
Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective

Biased stochastic estimators, such as finite-differences for noisy gradient estimation, often contain parameters that need to be properly chosen to balance impacts from the bias and the variance. While the optimal order of these parameters in terms of the simulation budget can be readily established, the precise best values depend on model characteristics that are typically unknown in advance. We introduce a framework to construct new classes of estimators, based on judicious combinations of simulation runs on sequences of tuning parameter values, such that the estimators consistently outperform a given tuning parameter choice in the conventional approach, regardless of the unknown model characteristics. We argue the outperformance via what we call the asymptotic minimax risk ratio, obtained by minimizing the worst-case asymptotic ratio between the mean square errors of our estimators and the conventional one, where the worst case is over any possible values of the model unknowns. In particular, when the minimax ratio is less than 1, the calibrated estimator is guaranteed to perform better asymptotically. We identify this minimax ratio for general classes of weighted estimators, and the regimes where this ratio is less than 1. Moreover, we show that the best weighting scheme is characterized by a sum of two components with distinct decay rates. We explain how this arises from bias-variance balancing that combats the adversarial selection of the model constants, which can be analyzed via a tractable reformulation of a non-convex optimization problem.
PENGARUH PENERAPAN PEMBELAJARAN MOBILE LEARNING TERHADAP PENINGKATAN HASIL BELAJAR EKONOMI

The Information and Communication Technology is growing rapidly at this time. So, it sues all levels of society to follow that growth, so does education world should take advantage of current developments. The research objective wassee the effect ofmobile learning that utilizesadvances in information technology and communication on that improvement in learning outcomes on economic subjects using social media Edmodo. This research was conducted in SMA Negeri 4 Kota Tangerang Selatan with quasi experimental designby two sample groups which are all experimental class. The results of this study showed a significant improvement on the learning outcomes of the two experimental classes shown in the n-gain test, whereas the hypothesis test results explained that the improvement of the two experimental classes was the same, and based on the triangulation method of observation, the questionnaire and the interview showed the results which is positive.
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training. When evaluated extensively on benchmark datasets from systems of both deterministic and stochastic dynamics. NAOMI demonstrates significant improvement in imputation accuracy (reducing average prediction error by 60% compared to autoregressive counterparts) and generalization for long range sequences.
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning

In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1) classical multi-agent reinforcement learning with direct message communication among agents and 2) centralized training with decentralized execution. In the first application scenario of multi-agent systems in which direct message communication among agents is allowed, the message-dropout technique drops out the received messages from other agents in a block-wise manner with a certain probability in the training phase and compensates for this effect by multiplying the weights of the dropped-out block units with a correction probability. The applied message-dropout technique effectively handles the increased input dimension in multi-agent reinforcement learning with communication and makes learning robust against communication errors in the execution phase. In the second application scenario of centralized training with decentralized execution, we particularly consider the application of the proposed message-dropout to Multi-Agent Deep Deterministic Policy Gradient (MADDPG), which uses a centralized critic to train a decentralized actor for each agent. We evaluate the proposed message-dropout technique for several games, and numerical results show that the proposed message-dropout technique with proper dropout rate improves the reinforcement learning performance significantly in terms of the training speed and the steady-state performance in the execution phase.
PENINGKATAN KETERAMPILAN BERCERITA DENGAN MENGGUNAKAN MEDIA WAYANG KARTUN PADA SISWA VII A SMP NEGERI 4 PURBALINGGA KABUPATEN PURBALINGGA TAHUN PELAJARAN 2017/2018

This study aims to (1) describe the improvement skills of VII A SMP Negeri 4 Purbalingga students in a story telling activity after using wayang cartoon media and (2) describing behavior changes of VII A SMP Negeri 4 Purbalingga students in a storytelling activity after using wayang cartoon media.This research method is quantitative descriptive and qualitative descriptive.The research design used classroom action research (CAR).The results of this study are (1) improvement of students' storytelling skills in cycle 1 by 61.87% and cycle 2 by 76.31% and (2) changes in student behavior in cycle 1 by 48% and cycle 2 by 76%.This study can be concluded that in learning to tell stories using wayang cartoons can improve students' storytelling skills and change student behavior.
cartoee: Publication quality maps using Earth Engine

Google Earth Engine (Gorelick et al., 2017) is a cloud-based geoprocessing platform that provides easy access to petabytes of Earth science and remote sensing datasets that is colocated with computational resources for scientific algorithm development.Access to Earth Engine is provided through a simple application programming interface (API) in JavaScript and Python programming languages.An online development environment is provided for rapid prototyping and visualization using the JavaScript API (Google, 2019).Additionally, Earth Engine allows for easy dissemination of algorithms and results through their online code editor and interactive applications backed by Earth Engine's resources, without needing to be an expert in application development, web programming.Although there are great resources to use, visualize, and distribute result from Earth Engine through the web, there are limited pathways for scientists to easily and quickly create publication quality figures using Earth Engine.A typical workflow would require the data be processed on Earth Engine, the data exported to the users local computer, and then the user creates a map or figure.
Enhancing the accuracy of digit recognition using machine learning algorithms

Handwritten character recognition is one of the important issues in pattern recognition applications. The applications of digit recognition include postal mail sorting, bank check processing, data entry form, pin code identification, identify the doctors' prescriptions etc. The conversion of an image based on the digit into letter codes for further use in a computer or text processing application is the first step in an off-line handwriting recognition system. This paper presents an approach of (MLP) Multilayer Perceptron neural network to recognize and predict handwritten digits from 0 to 9. MLP is a class of feed-forward artificial neural network. It consists of at least three layers of nodes having an input layer, a hidden layer, and an output layer. Each node is a neuron that uses a nonlinear activation function. The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems contain 60,000 training images and 10,000 testing images. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden layers. Various algorithms used for image processing have been discussed. The main objective of this paper is to ensure effective and reliable approaches for recognition of handwritten digits.
College English Teaching Mode Based on Intelligent Robot

Using the traditional method to design college English teaching mode, normally the students' satisfaction is not high, which leads to the situation where the students' performance is not ideal. Therefore, a research method of college English teaching mode based on intelligent robots is proposed. Firstly, in order to realise the stable control of the intelligent robot in college English teaching, a motion planning model of the intelligent robot in college English teaching is constructed. Secondly, the control design of college English teaching machine is optimised. A specific improvement method to the college English teaching mode based on intelligent robots is proposed. Finally, the college English teaching mode based on intelligent robot is realised. According to the experimental results, the proposed method is significantly superior to the traditional method in terms of enhancement effect of students satisfaction and performance, indicating that the proposed method has high application value and certain reference value.