Reviewerly/exHarmony huggingface dataset:
HiTR: Hierarchical Topic Model Re-Estimation for Measuring Topical Diversity of Documents

A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies three distributions for assessing the diversity of documents: distributions of words within documents, words within topics, and topics within documents. Topic models play a central role in this approach and, hence, their quality is crucial to the efficacy of measuring topical diversity. The quality of topic models is affected by two causes: generality and impurity of topics. General topics only include common information of a background corpus and are assigned to most of the documents. Impure topics contain words that are not related to the topic. Impurity lowers the interpretability of topic models. Impure topics are likely to get assigned to documents erroneously. We propose a hierarchical re-estimation process aimed at removing generality and impurity. Our approach has three re-estimation components: (1) document re-estimation, which removes general words from the documents; (2) topic re-estimation, which re-estimates the distribution over words of each topic; and (3) topic assignment re-estimation, which re-estimates for each document its distributions over topics. For measuring topical diversity of text documents, our HiTR approach improves over the state-of-the-art measured on PubMed dataset.
Fully private auctions for the highest bid

We present a secure auction scheme with affordable computations and communication complexity. In this scheme, we use blockchain to publish commitment and some public messages only. All bidders are forced to perform protocol honestly and are not permitted to change or retract their bids. To preserve privacy, we use insulated ciphertext comparison protocol to compare the bids. Our scheme is more efficient than traditional auctions using MPC/ZKP primitives. The total number of interaction rounds for N bidders is 3(N - 1). Furthermore, when the comparisons is performed in parallel, the number of rounds dramatically decreases to 3 log N.
Learning to Rank in Theory and Practice

This tutorial aims to weave together diverse strands of modern Learning to Rank (LtR) research, and present them in a unified full-day tutorial. First, we will introduce the fundamentals of LtR, and an overview of its various sub-fields. Then, we will discuss some recent advances in gradient boosting methods such as LambdaMART by focusing on their efficiency/effectiveness trade-offs and optimizations. Subsequently, we will then present TF-Ranking, a new open source TensorFlow package for neural LtR models, and how it can be used for modeling sparse textual features. Finally, we will conclude the tutorial by covering unbiased LtR -- a new research field aiming at learning from biased implicit user feedback. The tutorial will consist of three two-hour sessions, each focusing on one of the topics described above. It will provide a mix of theoretical and hands-on sessions, and should benefit both academics interested in learning more about the current state-of-the-art in LtR, as well as practitioners who want to use LtR techniques in their applications.
Wheelchair control by head motion using a noncontact method in relation to the pacient

As the EU population is getting older, the number of Europeans with disabilities is rising significantly. Among them, a consistent percentage is represented by persons with severe mobility impairment, making them dependent of wheelchairs. This paper proposes a human - wheelchair command interface based on head movement tracking. The head movements are tracked using an artificial vision-based system. The wheelchair user can select between two control modes - in a first control mode, simple commands (as left, right, forward, stop) are given; in the second control mode, the wheelchair movements are correlated with both amplitude and speed of head movement. For the both control modes, the wheelchair movement commands are assisted by a 3D cartography analysis system, having the role to prevent the collision with near-by obstacles.
Style-Analyzer: Fixing Code Style Inconsistencies with Interpretable Unsupervised Algorithms

Source code reviews are manual, time-consuming, and expensive. Human involvement should be focused on analyzing the most relevant aspects of the program, such as logic and maintainability, rather than amending style, syntax, or formatting defects. Some tools with linting capabilities can format code automatically and report various stylistic violations for supported programming languages. They are based on rules written by domain experts, hence, their configuration is often tedious, and it is impractical for the given set of rules to cover all possible corner cases. Some machine learning-based solutions exist, but they remain uninterpretable black boxes. This paper introduces style-analyzer, a new open source tool to automatically fix code formatting violations using the decision tree forest model which adapts to each codebase and is fully unsupervised. style-analyzer is built on top of our novel assisted code review framework, Lookout. It accurately mines the formatting style of each analyzed Git repository and expresses the found format patterns with compact human-readable rules. style-analyzer can then suggest style inconsistency fixes in the form of code review comments. We evaluate the output quality and practical relevance of style-analyzer by demonstrating that it can reproduce the original style with high precision, measured on 19 popular JavaScript projects, and by showing that it yields promising results in fixing real style mistakes. style-analyzer includes a web application to visualize how the rules are triggered. We release style-analyzer as a reusable and extendable open source software package on GitHub for the benefit of the community.
Crime control in the sphere of information technologies in the Republic of Turkey

Cybercrime is considered an issue of both local and global concern. Therefore, this study focuses on the local experience in cybercrime control of different countries, including the Republic of Turkey. The article discusses issues in cybersecurity policy and analyzes the legislative framework of the Republic of Turkey on cybercrime issues. The findings underlie the continuing education policy for cybersecurity employees. The study concludes that Turkey handles the current cybercrime situation with efficiency.
From Dependency to Causality: A Machine Learning Approach

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with n > 2 variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n > 2 variate distributions.
A Pretrained DenseNet Encoder for Brain Tumor Segmentation

This article presents a convolutional neural network for the automatic segmentation of brain tumors in multimodal 3D MR images based on a U-net architecture. We evaluate the use of a densely connected convolutional network encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two network architectures that can take into account multiple 3D images as inputs. This work aims to identify if a generic pretrained network can be used for very specific medical applications where the target data differ both in the number of spatial dimensions as well as in the number of inputs channels. Moreover in order to regularize this transfer learning task we only train the decoder part of the U-net architecture. We evaluate the effectiveness of the proposed approach on the BRATS 2018 segmentation challenge [1–5] where we obtained dice scores of 0.79, 0.90, 0.85 and 95% Hausdorff distance of 2.9 mm, 3.95 mm, and 6.48 mm for enhanced tumor core, whole tumor and tumor core respectively on the validation set. This scores degrades to 0.77, 0.88, 0.78 and 95% Hausdorff distance of 3.6 mm, 5.72 mm, and 5.83 mm on the testing set [1].
On potentials of regularized Wasserstein generative adversarial networks for realistic hallucination of tiny faces

Super-resolution of facial images, a.k.a. face hallucination, has been intensively studied in the past decades due to the increasingly emerging analysis demands in video surveillance, e.g., face detection, verification, identification. However, the actual performance of most previous hallucination approaches will drop dramatically when a very low-res tiny face is provided, due to the challenging multimodality of the problem as well as lack of an informative prior as a strong semantic guidance. Inspired by the latest progress in deep unsupervised learning, this paper focuses on tiny faces of size 16 × 16 pixels, hallucinating them to their 8 × upsampling versions by exploring the potentials of Wasserstein generative adversarial networks (WGAN). Besides a pixel-wise L2 regularization term imposed to the generative model, it is found that our advocated autoencoding generator with both residual and skip connections is a critical component for WGAN representing the facial contour and semantic content to a reasonable precision. With the additional Lipschitz penalty and architectural considerations for the critic in WGAN, the proposed approach finally achieves state-of-the-art hallucination performance in terms of both visual perception and objective assessment. The cropped CelebA face dataset is primarily used to aid the tuning and analysis of the new method, termed as tfh-WGAN. Experimental results demonstrate that the proposed approach not only achieves realistic hallucination of tiny faces, but also adapts to pose, expression, illuminance and occluded variations to a great degree.
Using Stack Overflow content to assist in code review

Summary An essential goal for programmers is to minimize the cost of identifying and correcting defects in source code. Code review is commonly used for identifying programming defects. However, manual code review has some shortcomings: (1) it is time‐consuming and (2) outcomes are subjective and depend on the skills of reviewers. An automated approach for assisting in code reviews is thus highly desirable. We present a tool for assisting in code review and results from our experiments evaluating the tool in different scenarios. The tool leveraged content available from professional programmer support forums (eg, StackOverflow.com) to determine potential defectiveness of a given piece of source code. The defectiveness is expressed on the scale of { Likely defective, neutral, unlikely to be defective }. The basic idea employed in the tool is (1) to identify a set P of discussion posts on Stack Overflow such that each p ∈ P contains source code fragment(s), which sufficiently resemble the input code C being reviewed, and (2) to determine the likelihood of C being defective by considering all p ∈ P . A novel aspect of our approach is to use document fingerprinting for comparing two pieces of source code. Our choice of document fingerprinting technique is inspired by source code plagiarism detection tools where it has proven to be very successful. In the experiments that we performed to verify the effectiveness of our approach, source code samples from more than 300 GitHub open‐source repositories were taken as input. An F1 score of 0.94 has been achieved in identifying correct/relevant results.
Glove puppetry cloud theater through a virtual reality network

Chinese glove puppetry is a traditional art with a long history and widely spread in the folk ever. This paper presents a collaborative work in multi-user virtual reality system for puppetry's opera. According to our developed system, each user has a unique perspective on any shared virtual world and interaction through our virtual reality network. This system achieves human-computer interaction and realizes the interaction between people. In addition, it brings an entertaining experience to users and easy to operate for all ages. In order to cultural preservation, we can record a grandmaster's puppet show performance in our system. Our research not only delivers a balance of art and technology in culture creativity, but also preserve folk art.
The role of the atomic decoherence-free subalgebra in the study of quantum Markov semigroups

We show that for a Quantum Markov Semigroup (QMS) with a faithful normal invariant state, atomicity of the decoherence-free subalgebra and environmental decoherence are equivalent. Moreover, we prove that the predual of the decoherence-free subalgebra is isometrically isomorphic to the subspace of reversible states. We also describe, in an explicit and constructive way, the relationship between the decoherence-free subalgebra and the fixed point subalgebra.
Persistent hidden states and nonlinear transformation for long short-term memory

Recurrent neural networks (RNNs) have been drawing much attention with great success in many applications like speech recognition and neural machine translation. Long short-term memory (LSTM) is one of the most popular RNN units in deep learning applications. LSTM transforms the input and the previous hidden states to the next states with the affine transformation, multiplication operations and a nonlinear activation function, which makes a good data representation for a given task. The affine transformation includes rotation and reflection, which change the semantic or syntactic information of dimensions in the hidden states. However, considering that a model interprets the output sequence of LSTM over the whole input sequence, the dimensions of the states need to keep the same type of semantic or syntactic information regardless of the location in the sequence. In this paper, we propose a simple variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension of hidden states keeps persistent information across time, so that the space keeps the same meaning over the whole sequence. In addition, to improve the nonlinear transformation power, we add a feedforward layer in the PRU structure. In the experiment, we evaluate our proposed methods with three different tasks, and the results confirm that our methods have better performance than the conventional LSTM.
The achievement of higher flexibility in multiple-choice-based tests using image classification techniques

In spite of the high accuracy of the existing optical mark reading (OMR) systems and devices, a few restrictions remain existent. In this work, we aim to reduce the restrictions of multiple-choice questions (MCQ) within tests. We use an image registration technique to extract the answer boxes from answer sheets. Unlike other systems that rely on simple image processing steps to recognize the extracted answer boxes, we address the problem from another perspective by training a machine learning classifier to recognize the class of each answer box (i.e., confirmed, crossed out or blank answer). This gives us the ability to deal with a variety of shading and mark patterns, and distinguish between chosen (i.e., confirmed) and canceled answers (i.e., crossed out). All existing machine learning techniques require a large number of examples in order to train a model for classification; therefore, we present a dataset including six real MCQ assessments with different answer sheet templates. We evaluate two strategies of classification: a straightforward approach and a two-stage classifier approach. We test two handcrafted feature methods and a convolutional neural network. At the end, we present an easy-to-use graphical user interface of the proposed system. Compared with existing OMR systems, the proposed system has the least constraints and achieves a high accuracy. We believe that the presented work will further direct the development of OMR systems toward reducing the restrictions of the MCQ tests.
Implementing Mediators with Asynchronous Cheap Talk

A mediator can help non-cooperative agents obtain an equilibrium that may otherwise not be possible. We study the ability of players to obtain the same equilibrium without a mediator, using only cheap talk, that is, nonbinding pre-play communication. Previous work has considered this problem in a synchronous setting. Here we consider the effect of asynchrony on the problem, and provide upper bounds for implementing mediators. Considering asynchronous environments introduces new subtleties, including exactly what solution concept is most appropriate and determining what move is played if the cheap talk goes on forever. Different results are obtained depending on whether the move after such "infinite play'' is under the control of the players or part of the description of the game.
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains

Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation.To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks.As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts.We create and publish a new dataset covering two domains and carefully analyse the suggested annotations.We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases.Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.
Existence of infinite Viterbi path for pairwise Markov models

For hidden Markov models one of the most popular estimates of the hidden chain is the Viterbi path — the path maximizing the posterior probability. We consider a more general setting, called the pairwise Markov model, where the joint process consisting of finite-state hidden regime and observation process is assumed to be a Markov chain. We prove that under some conditions it is possible to extend the Viterbi path to infinity for almost every observation sequence which in turn enables to define an infinite Viterbi decoding of the observation process, called the Viterbi process. This is done by constructing a block of observations, called a barrier, which ensures that the Viterbi path goes through a given state whenever this block occurs in the observation sequence.
Spatial Knowledge Distillation to Aid Visual Reasoning

For tasks involving language and vision, the current state-of-the-art methods tend not to leverage any additional information that might be present to gather relevant (commonsense) knowledge. A representative task is Visual Question Answering where large diagnostic datasets have been proposed to test a system's capability of answering questions about images. The training data is often accompanied by annotations of individual object properties and spatial locations. In this work, we take a step towards integrating this additional privileged information in the form of spatial knowledge to aid in visual reasoning. We propose a framework that combines recent advances in knowledge distillation (teacher-student framework), relational reasoning and probabilistic logical languages to incorporate such knowledge in existing neural networks for the task of Visual Question Answering. Specifically, for a question posed against an image, we use a probabilistic logical language to encode the spatial knowledge and the spatial understanding about the question in the form of a mask that is directly provided to the teacher network. The student network learns from the ground-truth information as well as the teachers prediction via distillation. We also demonstrate the impact of predicting such a mask inside the teachers network using attention. Empirically, we show that both the methods improve the test accuracy over a state-of-the-art approach on a publicly available dataset.
Automated N-way Program Merging for Facilitating Family-based Analyses of Variant-rich Software

Nowadays software tends to come in many different, yet similar variants, often derived from a common code base via clone-and-own. Family-based-analysis strategies have recently shown very promising potential for improving efficiency in applying quality-assurance techniques to such variant-rich programs, as compared to variant-by-variant approaches. Unfortunately, these strategies require a single program representation superimposing all program variants in a syntactically well-formed, semantically sound, and variant-preserving manner, which is usually not available and manually hard to obtain in practice. In this article, we present a novel methodology, called S i MPOSE, for automatically generating superimpositions of existing program variants to facilitate family-based analyses of variant-rich software. To this end, we propose a novel N-way model-merging methodology to integrate the control-flow automaton (CFA) representations of N given variants of a C program into one unified CFA representation. CFA constitute a unified program abstraction used by many recent software-analysis tools for automated quality assurance. To cope with the inherent complexity of N-way model-merging, our approach (1) utilizes principles of similarity-propagation to reduce the number of potential N-way matches, and (2) enables us to decompose a set of N variants into arbitrary subsets and to incrementally derive an N-way superimposition from partial superimpositions. We apply our tool implementation of S i MPOSE to a selection of realistic C programs, frequently considered for experimental evaluation of program-analysis techniques. In particular, we investigate applicability and efficiency/effectiveness trade-offs of our approach by applying S i MPOSE in the context of family-based unit-test generation as well as model-checking as sample program-analysis techniques. Our experimental results reveal very impressive efficiency improvements by an average factor of up to 2.6 for test-generation and up to 2.4 for model-checking under stable effectiveness, as compared to variant-by-variant approaches, thus amortizing the additional effort required for merging. In addition, our results show that merging all N variants at once produces, in almost all cases, clearly more precise results than incremental step-wise 2-way merging. Finally, our comparison with major existing N-way merging techniques shows that S i MPOSE constitutes, in most cases, the best efficiency/effectiveness trade-off.