Reviewerly/exHarmony huggingface dataset:
USAD: undetectable steganographic approach in DCT domain

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.
A fine granularity based user collaboration algorithm for location privacy protection

As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms.
Modifying Optimal SAT-Based Approach to Multi-Agent Path-Finding Problem to Suboptimal Variants

In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. Recently, a SAT-based approach was developed to solve this problem and proved beneficial in many cases when compared to other search-based solvers. In this paper, we introduce SAT-based unbounded- and bounded-suboptimal algorithms and compare them to relevant search-based algorithms.
An Architecture Model for a Distributed Virtualization System

The Thesis is about an architecture model for a Distributed Virtualization System, which could expand a virtual execution environment from a single physical machine to several nodes of a cluster. With current virtualization technologies, computing power and resource usage of Virtual Machines (or Containers) are limited to the physical machine where they run. To deliver high levels of performance and scalability, cloud applications are usually partitioned in several Virtual Machines (or Containers) located on different nodes of a virtualization cluster. Developers often use that processing model because the same instance of the operating system is not available on each node where their components run. The proposed architecture model is suitable for new trends in software development because it is inherently distributed. It combines and integrates Virtualization and Distributed Operating Systems technologies with the benefits of both worlds, providing the same isolated instance of a Virtual Operating System on each cluster node. Although it requires the introduction of changes in existing operating systems, thousands of legacy applications would not require modifications to obtain their benefits. A Distributed Virtualization System is suitable to deliver high-performance cloud services with provider-class features, such as high-availability, replication, migration, and load balancing. Furthermore, it is able to concurrently run several isolated instances of different guest Virtual Operating Systems, allocating a subset of nodes for each instance and sharing nodes between them. Currently, a prototype is running on a cluster of commodity hardware provided with two kinds of Virtual Operating Systems tailored for internet services (web server) as a proof of concept.
Web-based E-diagnostic for Digestive System Disorders in Mumans using the Demster Shafer Method

Digestive system disorders are dangerous diseases.Lack of awareness of public health is still low, life habits, behavior and mindset that want to live practically, a means of delivering information about diseases that are still lacking, and the lack of medical personnel is a problem, therefore an expert system application is needed to diagnose disease on the web-based digestive system.By providing certainty in the form of a percentage, then using the calculation through symptoms chosen by the user of each symptom has a density value, the density value is obtained from the results of interviews with doctors.Web-based E-Diagnostics for digestive system disorders uses the Dempster Shafer method which is expected to help users by providing information on disease diagnosis and solutions that can be done to help cure it, the Dempster Shafer Method has the ability to provide a high level of accuracy or certainty, which method this has characteristics that are in accordance with the way an expert thinks.This web-based expert system application will display symptoms that can be selected by the user to get the final results in the form of rapid disease diagnoses and suggestions for prevention in order to find out information in the form of diagnoses of diseases of the digestive system.Based on the calculation of accuracy that has been done in this study, it can be seen that the Dempster Shafer Method is the method that has the highest value with 85% confidence compared to the Certainty Factor Method with a value of 60%.
Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG

To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected by the image owner has one reference pixel and eight target pixels. The n least significant bits (LSBs) of the reference pixel and all bits of target pixel are self-embedded into other parts of the image by a method of predictive error expansion (PEE). To avoid overflowing when embedding data, the n LSBs of the reference pixel are reset to zero before encryption. Then, the pixel values of the image are encrypted after being encoded onto the points of the elliptic curve. The encrypted reference pixel replaces the encrypted target pixels surrounding it, thereby constructing the mirroring central ciphertext (MCC). In a set of MCC, the data hider embeds the encrypted additional data into the n LSBs of the target pixels by homomorphic addition in ciphertexts, while the reference pixel remains unchanged. The receiver can directly extract additional data by homomorphic subtraction in ciphertexts between the target pixels and the corresponding reference pixel; extract the additional data by subtraction in plaintexts with the directly decrypted image; and restore the cover image without loss. The experimental results show that the proposed scheme has higher security than the similar algorithms, and the average embedding rate of the scheme is 0.25 bpp under the premise of ensuring the quality of the directly decrypted image.
DESIGN THINKING IN HIGHER EDUCATION

1 University of Library Studies and Information Technologies (BULGARIA)2 University of Deusto (SPAIN)
What is... a Markov Basis?

This short piece defines a Markov basis. The aim is to introduce the statistical concept to mathematicians.
Case Study on Survival Analysis: Prediction of Cardiovascular Events

Survival is an important long-term outcome in prognostic research, including medical areas such as cardiovascular disease and oncology. We consider a model for the occurrence of vascular events in patients with symptomatic cardiovascular disease. Patient data were from the Second Manifestations of ARTerial disease (SMART) study. We go through the seven steps of the checklist for developing valid prediction models, as presented in Part II. Specific focus is on the combination of models specification and estimation with the LASSO in combination with multiple imputation of missing values. The data set and R code are made available at the book’s website (www.clinicalpredictionmodels.org).
Potentials of Chatbots for Spell Check among Youngsters

Chatbots are already being used successfully in many areas. This publication deals with the development and programming of a chatbot prototype to support learning processes. This Chatbot prototype is designed to help pupils in order to correct their spelling mistakes by providing correction proposals to them. Especially orthographic spelling mistake should be recognized by the chatbot and should be replaced by correction suggestions stored in test data.
InterSpot: Interactive Spammer Detection in Social Media

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.
Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention

The growth of online stores nowadays is very rapid. This is supported by faster and better internet infrastructure. The increasing growth of online stores makes the competition more difficult in this business field. It is necessary for online stores to have a website or an application that is able to measure and classify consumersâ€™ spending intentions, so that the consumers will have eyes on things on the sites and applications to make purchases eventually. Classification of online shoppersâ€™ intentions can be done by using several algorithms, such as NaÃ¯ve Bayes, Multi-Layer Perceptron, Support Vector Machine, Random Forest and J48 Decision Trees. In this case, the comparison of algorithms is done with two tools, WEKA and Sci-Kit Learn by comparing the values of F1-Score, accuracy, Kappa Statistic and mean absolute error. There is a difference between the test results using WEKA and Sci-Kit Learn on the Support Vector Machine algorithm. Based on this research, the Random Forest algorithm is the most appropriate algorithm to be used as an algorithm for classifying online shoppersâ€™ intentions.
HDI-Forest: Highest Density Interval Regression Forest

By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest. HDI-Forest does not require additional model training, and directly reuses the trees learned in a standard Random Forest model. By utilizing the special properties of Random Forest, HDI-Forest could efficiently and more directly optimize the PI quality metrics. Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability.
Generating and Sampling Orbits for Lifted Probabilistic Inference

A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable. Lifted inference algorithms identify symmetry as a property that enables efficient inference and seek to scale with the degree of symmetry of a probability model. A limitation of existing exact lifted inference techniques is that they do not apply to non-relational representations like factor graphs. In this work we provide the first example of an exact lifted inference algorithm for arbitrary discrete factor graphs. In addition we describe a lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the degree of symmetry of the distribution.
Learning Topic Models by Neighborhood Aggregation

Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks.
Advances in Argument Mining

This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months.Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just what people think, but why they hold the opinions they do.From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019.The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.
Exchangeability and Kernel Invariance in Trained MLPs

In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as Stochastic Gradient Descent. A relaxation of the IID condition is a probabilistic symmetry known as exchangeability. We show the sense in which the weights in MLPs are exchangeable. This yields the result that in certain instances, the layer-wise kernel of fully-connected layers remains approximately constant during training. Our results shed light on such kernel properties throughout training while limiting the use of unrealistic assumptions.
Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators

Wasserstein Generative Adversarial Nets~(GANs) are newly proposed GAN algorithms and widely used in computer vision, web mining, information retrieval, etc. However, the existing algorithms with approximated Wasserstein loss converge slowly due to heavy computation cost and usually generate unstable results as well. In this paper, we solve the computation cost problem by speeding up the Wasserstein GANs from a well-designed communication efficient parallel architecture. Specifically, we develop a new problem formulation targeting the accurate evaluation of Wasserstein distance and propose an easily parallel optimization algorithm to train the Wasserstein GANs. Compared to traditional parallel architecture, our proposed framework is designed explicitly for the skew parameter updates between the generator network and discriminator network. Rigorous experiments reveal that our proposed framework achieves a significant improvement regarding convergence speed with comparable stability on generating images, compared to the state-of-the-art of Wasserstein GANs algorithms.
Understanding Actors and Evaluating Personae with Gaussian Embeddings

Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors’ versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O’Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.
Metric-based Measurement and Selection for Software Product Quality Assessment: Qualitative Expert Interviews

A systematic and efficient measurement process can assist towards the production of quality software product.Metric-based measurement method often used to assess the product quality.Currently several hundreds of metrics have been proposed by previous researchers.However, there is no specific and structured mechanism for metrics selection process.Lack of awareness, knowledge and experience lead to selecting inappropriate and unsuitable metrics for assessment of software product quality done by the practitioners and stakeholders in the industry.Literature study found that the existing selection models are irrelevant and insufficient for assisting and supporting metrics selection process in which it should consists of criteria, and systematic and practical methods of selection process.A qualitative interview was conducted involving 12 experts and practitioners to reveal current issues in software measurement, to identify elements relevant for software metric selection process and to identify the appropriate and valid software metric selection criteria.Finding from this expert interview revealed important input from industry which are: Five main issues in software measurement, six elements associated with metric selection process and 13 criteria relevant for software metric selection.
Local Trend Inconsistency: A Prediction-driven Approach to Unsupervised Anomaly Detection in Multi-seasonal Time Series.

On-line detection of anomalies in time series is a key technique in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and demands are making this task more challenging than ever. First, the rapid increase of unlabeled data makes supervised learning no longer suitable in many cases. Second, a great portion of time series have complex seasonality features. Third, on-line anomaly detection needs to be fast and reliable. In view of this, we in this paper adopt an unsupervised prediction-driven approach on the basis of a backbone model combining a series decomposition part and an inference part. We then propose a novel metric, Local Trend Inconsistency (LTI), along with a detection algorithm that efficiently computes LTI chronologically along the series and marks each data point with a score indicating its probability of being anomalous. We experimentally evaluated our algorithm on datasets from UCI public repository and a production environment. The result shows that our scheme outperforms several representative anomaly detection algorithms in Area Under Curve (AUC) metric with decent time efficiency.
SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Machine Learning is considered as a subfield of Artificial Intelligence.Machine Learning is concerned with the development of techniques and design the methods which enable the computer to learn.The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience.In today's machine learning applications, support vector machines are considered (SVM) one of the most robust and accurate methods among all well-known algorithms and also being developed at a fast space.The aim of SVM is to find the best classification function, in a two-class learning task, and to distinguish between members of the two classes in the training data.Hence, the goal of machine learning is to find the output hypothesis that performed the correct classification of the training data, but the other earlier algorithms to find the hypothesis that accurate fit to the data.SVM requires that each data instance is represented as a vector of real numbers.Hence, if there are categorical attributes, convert them into numerical data, then we using m numbers to represent an m-category attribute.Only one of the m numbers is one, and others are zero.Machine learning has been applied in various field such as medical diagnosis, bioinformatics, detecting credit card fraud, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, and robot locomotion.The objective is algorithmic approach for non parametric methods to tractable for high dimensional massive datasets.