Reviewerly/exHarmony huggingface dataset:
Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy

Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon.
Conditional Joint Model for Spoken Dialogue System

Spoken Language Understanding (SLU) and Dialogue Management (DM) are two core components of a spoken dialogue system. Traditional methods model SLU and DM separately. Recently, joint learning has made much progress in dialogue system research via taking full advantage of all supervised signals. In this paper, we propose an extension of joint model to a conditional setting. Our model does not only share knowledge between intent and slot, but also efficiently make use of intent as a condition to predict system action. We conduct experiments on popular benchmark DSTC4, which includes rich dialogues derived from real world. The results show that our model gives excellent performance and outperforms other popular methods significantly, including independent learning methods and joint models. This paper gives a new way for spoken dialogue system research.
Is the Policy Gradient a Gradient?

The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distribution and therefore do not optimize the discounted objective. What do they optimize instead? This has been an open question for several years, and this lack of theoretical clarity has lead to an abundance of misstatements in the literature. We answer this question by proving that the update direction approximated by most methods is not the gradient of any function. Further, we argue that algorithms that follow this direction are not guaranteed to converge to a "reasonable" fixed point by constructing a counterexample wherein the fixed point is globally pessimal with respect to both the discounted and undiscounted objectives. We motivate this work by surveying the literature and showing that there remains a widespread misunderstanding regarding discounted policy gradient methods, with errors present even in highly-cited papers published at top conferences.
University in a Small City Environment: Campus Vibes

The article explores the phenomenon of strong nexus between the development strategies of small cities, claiming for the status of supporting regional cities, and universities located there. The university community and urban public are inclined to the positioning of such cities as “university cities”. The authors dwell on the role of campuses in the implementation of the “third mission” by universities. Particular consideration is given to the specific component of the third mission and, accordingly, the campus infrastructure for the development of local communities, urban and regional environments. A model of the regional environment monitoring system is described, which is based on the system of sociological surveys including five subsystems (questionnaire of employers, questionnaire of schoolchildren aimed at vocational guidance, questionnaire of parents, questionnaire of communities, and internal monitoring). The article also focuses on the forms of using campus facilities for the implementation of various events and social projects involving the local community and aimed at the development of the urban and regional environment in the following areas: consolidation, education and community assistance, social mobilization and public branding.
A HYBRID SELF ORGANIZING MAP IMPUTATION (SOMI) WITH NAÏVE BAYES FOR IMPUTATION MISSING DATA CLASSIFICATION

This study proposes hybrid SOMI (Self Organizing Map Imputation) and Naive Bayes (NB) model on data, that contain missing values to improve the performance of the Naive Bayes Imputation (NBI) it has weaknesses for missing categories n ≤ 1. This new hybrid model, using imputation approach based on SOMI is used for prepossessing and NB classification for the classification process in multivariate data, so that it can improve performance. SOMI measurements use an average error with self-organizing feature map. The multivariate attribute is converted to numeric attributes to establish data uniformity. The SOMI learning results have used weight variations by combining the mechanism of distance hierarchical value representation with a new scheme to overcome mixed types. Hybrid SOMINB is used to classify mixed data to correct misclassification. The model has advantages because it can update weights with the probability of each attribute. Attribute values have produced a set of probabilities for each cluster using the Naive Bayes group. Outputs of the SOMI Method are used as learning machines to produce training data for the target class to be used in Naive Bayes machine learning. The results of this study used all missing scenarios at a random mechanism and various missing percentages. The results of the hybrid SOMINB model showed more results with an accuracy rate of 90.00% with other imputation analysis. Experimental results present that the proposed produces higher accuracy than general estimating values which established missing value treatment methods.
Public-Key applications in E-commerce

Abstract In this paper, we describe the application of data encryption technology in E-commerce, and introduce the public key encryption and public key encryption system in detail. Then, we propose a composite encryption model that nests the DES algorithm in the RSA algorithm. Finally, we analyzed two widely used Internet protocols, SET and SLL, which can improve the security of payment and customer trust, and then describe the development of e-commerce by ensuring the security and accuracy of the transaction process.
Motivation System for Students to Learn Control Engineering and Image Processing

Our aim was to motivate students to study control engineering and image processing. To this end, we designed a motivation system based on the ARCS model, developed by John M. Keller, to motivate learners. We used a drone as the control object. The ARCS model consists of four steps: attention, relevance, confidence, and satisfaction. The control process is performed by capturing images from the drone’s camera on a PC via Wi-Fi, and detecting a target color using an image processing technique. Then, the PC sends the control inputs from a PID controller back to the drone to track the target color by keeping it at the center of the images. With this system, students can gain knowledge on control engineering and image processing by tuning the parameters of PID controller and image processing, and observing the responses of the drone. To assess the effectiveness of our system, we requested 150 first-grade technical college students, who had no prior knowledge of control engineering and image processing, to attend our lecture. After the lecture, the students were asked to answer a questionnaire on their interests. The result demonstrated that over 80% of them expressed an interest in learning these two techniques.
PRNU based source camera attribution for image sets anonymized with patch-match algorithm

Patch-Match is an efficient algorithm used for structural image editing and available as a tool on popular commercial photo-editing software. The tool allows users to insert or remove objects from photos using information from similar scene content. Recently, a modified version of this algorithm was proposed as a counter-measure against Photo-Response Non-Uniformity (PRNU) based Source Camera Identification (SCI). The algorithm can provide anonymity at a great rate (97\%) and impede PRNU based SCI without the need of any other information, hence leaving no-known recourse for the PRNU-based SCI. In this paper, we propose a method to identify sources of the Patch-Match-applied images by using randomized subsets of images and the traditional PRNU based SCI methods. We evaluate the proposed method on two forensics scenarios in which an adversary makes use of the Patch-Match algorithm and distorts the PRNU noise pattern in the incriminating images he took with his camera. Our results show that it is possible to link sets of Patch-Match-applied images back to their source camera even in the presence of images that come from unknown cameras. To our best knowledge, the proposed method represents the very first counter-measure against the usage of of Patch-Match in the digital forensics literature.
The Light Will Be with You. Always -- A Novel Continuous Mobile Authentication with the Light Sensor (poster)

Existing continuous authentication proposals tend to have two major drawbacks. First, touch-based smartphone authentication approaches typically require explicit user interactions with the smartphone to collect sufficient touch data. These approaches may provide an attacker the opportunity to steal a victim's sensitive data before the system detects the attacker's intrusion. Likewise, an attacker may disable the continuous authentication scheme itself before detection. Second, sensor-based continuous authentication approaches inherently suffer from high energy consumption due to the constant usage of multiple sensors. In this paper, we present a novel continuous authentication system that collects light sensor data from a user's smartphone and analyzes them to authenticate users using support vector machines. We focus on the possibility of collecting light sensor data from users' smartphones while they are conducting daily behaviors to develop an anomaly detection system.
Filtering in Gaussian Linear Systems With Fuzzy Switches

This paper presents recent results on conditionally Gaussian observed Markov switching models by incorporating fuzzy switches in the model, instead of hard ones. This kind of generalization is of interest for applications involving continuous switching regimes, such as tracking an object using cameras in intermittent sunlight and shadow conditions. The filter developed hereby is recursive, optimal, and exact, up to an approximation of integrals according to some fuzzy measures. Experiences on simulated and on real data-dealing with outdoor air temperature and power consumption of a building-confirm the accuracy and effectiveness of the proposed filter compared with the hard filter with “crisp” switches.
Sampling Representation Contexts with Attribute Exploration

We tackle the problem of constructing the representation context of a pattern structure. First, we present a naive algorithm that computes the representation context a pattern structure. Then, we add a sampling technique in order to reduce the size of the output. We show that these techniques reduce significantly the size of the representation context for interval pattern structures.
Accelerating Deep Learning Inference on Mobile Systems

Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart devices that rely on operations with real-time constraints. We present PolimiDL, a framework for the acceleration of Deep Learning on mobile and embedded systems with limited resources and heterogeneous architectures. Experimental results show competitive results with respect to TensorFlow Lite for the execution of small models.
Improved SVRG for finite sum structure optimization with application to binary classification

This paper looks at a stochastic variance reduced gradient (SVRG) method for minimizing the sum of a finite number of smooth convex functions, which has been involved widely in the field of machine learning and data mining. Inspired by the excellent performance of two-point stepsize gradient method in batch learning, in this paper we present an improved SVRG algorithm, named stochastic two-point stepsize gradient method. Under some mild conditions, the proposed method achieves a linear convergence rate $ O(\rho^k) $ for smooth and strongly convex functions, where $ \rho\in(0.68, 1) $. Simulation experiments on several benchmark data sets are reported to demonstrate the performance of the proposed method.
Development of Quiz Card Media to Improve Reading Skills and Critical Thinking on Student

This study aims to develop and test the feasibility, practicality, and effectiveness of learning media in&#x0D; the form of quiz card-based learning media to improve reading skills of understanding and critical&#x0D; thinking. The research model used is the Borg and Gall research and development (R &amp;amp; D) model.&#x0D; The feasibility of learning media is based on the results of validation. The practicality of the media is&#x0D; based on the results of observations on the implementation of learning, student activities, and media&#x0D; practicality questionnaires. The effectiveness of the media was tested by nonequivalent control&#x0D; group design. The results showed that quiz card-based learning media were feasible, practical, and&#x0D; effective. Quiz card based learning media has an influence on student learning outcomes. This can be&#x0D; seen from the results of the t-test showing that tcount which is 2.671&amp;gt; than ttable 2.093. The use of&#x0D; quiz card-based learning media also received a very good response from students, which amounted&#x0D; to 90.5%. Based on the results of these studies it can be concluded that the quiz card-based learning&#x0D; media is feasible, practical, and effective to improve reading skills in the fifth grade students'&#x0D; understanding and critical thinking in Tuban State Elementary School
Personalized employment recommendation method based on semantic matching of requirements

Abstract Aiming at the lack of personalized recommendation method in existing employment application system, a personalized employment recommendation method based on semantic matching of requirements(PERM-SMR) was proposed in the paper.Firstly, the web crawler technology is used to collect the recruitment information and clean the data.Secondly, word2vec method was selected for corpus training to obtain the word vector model.Thirdly, we performed semantic analysis on the demand information of both sides, calculated the weight of the feature words in the text and generated text vector, and then carried out the requirement information matching and generated the recommendation list.Finally, it is verified by experiments that the PERM-SMR is more accurate and effective, and provides better employment guidance for graduates.
Constructions and necessities of some permutation polynomials

Permutation polynomials over finite fields have important applications in many areas of science and engineering such as coding theory, cryptography, combinatorial design, etc. In this paper, we construct several new classes of permutation polynomials, and the necessities of some permutation polynomials are studied.
Data Poisoning Attack against Knowledge Graph Embedding

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE' robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension.

We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our dataset contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM dataset show the effectiveness of dialogue structure and general world knowledge. DREAM will be available at this https URL.
Domain adaptation on graphs by learning graph topologies: theoretical analysisand an algorithm

Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Domain adaptation methods take into account the deviations in data distribution. In this work, we study the problem of domain adaptation on graphs. We consider a source graph and a target graph constructed with samples drawn from data manifolds. We study the problem of estimating the unknown class labels on the target graph using the label information on the source graph and the similarity between the two graphs. We particularly focus on a setting where the target label function is learned such that its spectrum is similar to that of the source label function. We first propose a theoretical analysis of domain adaptation on graphs and present performance bounds that characterize the target classification error in terms of the properties of the graphs and the data manifolds. We show that the classification performance improves as the topologies of the graphs get more balanced, i.e. as the numbers of neighbors of different graph nodes become more proportionate, and weak edges with small weights are avoided. Our results also suggest that graph edges between too distant data samples should be avoided for good generalization performance. We then propose a graph domain adaptation algorithm inspired by our theoretical findings, which estimates the label functions while learning the source and target graph topologies at the same time. The joint graph learning and label estimation problem is formulated through an objective function relying on our performance bounds, which is minimized with an alternating optimization scheme. Experiments on synthetic and real data sets suggest that the proposed method outperforms baseline approaches.
Lightweight Linearly-typed Programming with Lenses and Monads

This paper shows an encoding of linear types in OCaml and its applications.The encoding enables to write correct OCaml programs based on safe resource access guided by linear types.Linear types ensure that every variable is used exactly once, and, thus, they can be used to check the behavioural aspects of programs such as resource access and communication protocols in a static way.However, linear types require significant effort to be integrated into existing programming languages.Our encoding allows the vanilla OCaml typechecker to enforce linearity by using lenses and a parameterised monad.Parameterised monads are monads with a pre-and a post-condition, and we use them to track the creation and consumption of resources at the type level.Lenses, which point at parts of a data type, are used to refer to a resource in pre-and post-conditions.To handle comfortably structured data such as linearly typed lists, we further propose an extension to pattern matching based on the syntax-extension mechanism of OCaml.We show an application to static checking of communication protocols in OCaml.
Perceptron Partition Model to Minimize Input Matrix

Abstract Implementation of Neuron Network model using Perceptron has not given optimal result in real time learning. The large number of inputs expressed in matrix form makes the process slower in pattern recognition. So, it takes characteristic to represent all the input matrices by using the partition method. By partitioning each input and with the best weight and using the activation function will produce an output value. And learning is valid in recognizing patterns only 1 iteration only. Further validation is done on the water mill control module with dissolved oxygen input, water pH, salinity and water temperature. With Perceptron Partition learning algorithm more real-time than perceptron model. Testing on the waterwheel input whether rotating or stopping by Matrix Laboratory software simulation.
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating high-resolution images using GANs is nontrivial, and often produces problematic images with incomplete objects. To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details. In our network, we use an autoencoder to learn the intrinsic high-level structure of real images and design a novel denoiser network to provide photo-realistic details for the generated images. In the experiments, we are able to produce 512x512 images of promising quality directly from the input noise. The resultant images exhibit better perceptual photo-realism, i.e., with sharper structure and richer details, than other baselines on several datasets, including Oxford-102 Flowers, Caltech-UCSD Birds (CUB), High-Quality Large-scale CelebFaces Attributes (CelebA-HQ), Large-scale Scene Understanding (LSUN) and ImageNet.
DDFlow: Learning Optical Flow with Unlabeled Data Distillation

We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on hand-crafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.