Reviewerly/exHarmony huggingface dataset:
A strict implication calculus for compact Hausdorff spaces

We introduce a simple modal calculus for compact Hausdorff spaces. The language of our system extends that of propositional logic with a strict implication connective, which, as shown in earlier work, algebraically corresponds to the notion of a subordination on Boolean algebras. Our base system is a strict implication calculus SIC, to which we associate a variety SIA of strict implication algebras. We also study the symmetric strict implication calculus S2IC, which is an extension of SIC, and prove that S2IC is strongly sound and complete with respect to de Vries algebras. By de Vries duality, this yields completeness of S2IC with respect to compact Hausdorff spaces. Since some of the defining axioms of de Vries algebras are Π2-sentences, we develop the corresponding theory of non-standard rules, which we term Π2-rules. We study the resulting inductive elementary classes of algebras, and give a general criterion of admissibility for Π2-rules. We also compare our approach to approaches in the literature that are related to our work.1
Adaptive ranking based constraint handling for explicitly constrained black-box optimization

A novel explicit constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint handling exhibits two invariance properties. One is the invariance to arbitrary element-wise increasing transformation of the objective and constraint functions. The other is the invariance to arbitrary affine transformation of the search space. The proposed technique virtually transforms a constrained optimization problem into an unconstrained optimization problem by considering an adaptive weighted sum of the ranking of the objective function values and the ranking of the constraint violations that are measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Simulation results are presented and show that the CMA-ES with the proposed constraint handling exhibits the affine invariance and performs similarly to the CMA-ES on unconstrained counterparts.
Auto-Suggesting Browsing Actions for Personalized Web Screen Reading

Web browsing has never been easy for blind people, primarily due to the serial press-and-listen interaction mode of screen readers -- their "go-to'' assistive technology. Even simple navigational browsing actions on a page require a multitude of shortcuts. Auto-suggesting the next browsing action has the potential to assist blind users in swiftly completing various tasks with minimal effort. Extant auto-suggest feature in web pages is limited to filling form fields; in this paper, we generalize it to any web screen-reading browsing action, e.g., navigation, selection, etc. Towards that, we introduce SuggestOmatic, a personalized and scalable unsupervised approach for predicting the most likely next browsing action of the user, and proactively suggesting it to the user so that the user can avoid pressing a lot of shortcuts to complete that action. SuggestOmatic rests on two key ideas. First, it exploits the user's Action History to identify and suggest a small set of browsing actions that will, with high likelihood, contain an action which the user will want to do next, and the chosen action is executed automatically. Second, the Action History is represented as an abstract temporal sequence of operations over semantic web entities called Logical Segments - a collection of related HTML elements, e.g., widgets, search results, menus, forms, etc.; this semantics-based abstract representation of browsing actions in the Action History makes SuggestOmatic scalable across websites, i.e., actions recorded in one website can be used to make suggestions for other similar websites. We also describe an interface that uses an off-the-shelf physical Dial as an input device that enables SuggestOmatic to work with any screen reader. The results of a user study with 12 blind participants indicate that SuggestOmatic can significantly reduce the browsing task times by as much as 29% when compared with a hand-crafted macro-based web automation solution.
Efficient arbitrated quantum signature scheme without entangled states

In the most arbitrated quantum signatures (AQSs), the signers and verifiers need to perform the quantum key distribution protocols or some other protocols to share secret keys before signing a signature. In some schemes, the entangle states, which are not easily implemented, have to be prepared and distributed among the partners. Based on single photon and one-way functions, a new AQS scheme without entangled states is proposed. In our scheme, the signer generates a quantum signature on the classical message with his/her private key and the one-way function. The arbitrator communicates with the signer through a classical unencrypted channel. The signer and verifier need not perform the key distribution protocol before signing a message. On the other hand, without entangled states, our scheme can reduce the complexity of implementation. Then, our scheme is more efficient than the similar schemes. At the same time, our scheme is secure against forgery attack and disavowal attack.
Tracking reasons with extensions of relevant logics

Abstract In relevant logics, necessary truths need not imply each other. In justification logic, necessary truths need not all be justified by the same reason. There is an affinity to these two approaches that suggests their pairing will provide good logics for tracking reasons in a fine-grained way. In this paper, I will show how to extend relevant logics with some of the basic operators of justification logic in order to track justifications or reasons. I will define and study three kinds of frames for these logics. For the first kind of frame, I show soundness and highlight a difficulty in proving completeness. This motivates two alternative kinds of frames, with respect to which completeness results are obtained. Axioms to strengthen the justification logic portions of these logics are considered. I close by developing an analogy between the dot operator of justification logic and theory fusion in relevant logics.
Automatic GPU memory management for large neural models in TensorFlow

Deep learning models are becoming larger and will not fit in the limited memory of accelerators such as GPUs for training. Though many methods have been proposed to solve this problem, they are rather ad-hoc in nature and difficult to extend and integrate with other techniques. In this paper, we tackle the problem in a formal way to provide a strong foundation for supporting large models. We propose a method of formally rewriting the computational graph of a model where swap-out and swap-in operations are inserted to temporarily store intermediate results on CPU memory. By introducing a categorized topological ordering for simulating graph execution, the memory consumption of a model can be easily analyzed by using operation distances in the ordering. As a result, the problem of fitting a large model into a memory-limited accelerator is reduced to the problem of reducing operation distances in a categorized topological ordering. We then show how to formally derive swap-out and swap-in operations from an existing graph and present rules to optimize the graph. Finally, we propose a simulation-based auto-tuning to automatically find suitable graph-rewriting parameters for the best performance. We developed a module in TensorFlow, called LMS, by which we successfully trained ResNet-50 with a 4.9x larger mini-batch size and 3D U-Net with a 5.6x larger image resolution.
Analysis of Customer Reviews for Product Service System Design based on Cloud Computing

Designing a product service system begins with understanding customer voices. Compared to the traditional methods such as survey, interview, customer review represents a particular kind of big data that contain rich information that is useful for the design of product service system. This paper presents a new framework that integrates a variety of artificial intelligence and machine learning techniques. All proposed operations of the framework can be realized based on the Google Cloud Platform. A case study is conducted to showcase the practical applicability of some key operations such as opinion mining. More than 40,000 customer reviews about the product Kindle White E-reader were analyzed.
CaDET: interpretable parametric conditional density estimation with decision trees and forests

We introduce CaDET, an algorithm for parametric Conditional Density Estimation (CDE) based on decision trees and random forests. CaDET uses the empirical cross entropy impurity criterion for tree growth, which incentivizes splits that improve predictive accuracy more than the regression criteria or estimated mean-integrated-square-error used in previous works. CaDET also admits more efficient training and query procedures than existing tree-based CDE approaches, and stores only a bounded amount of information at each tree leaf, by using sufficient statistics for all computations. Previous tree-based CDE techniques produce complicated uninterpretable distribution objects, whereas CaDET may be instantiated with easily interpretable distribution families, making every part of the model easy to understand. Our experimental evaluation on real datasets shows that CaDET usually learns more accurate, smaller, and more interpretable models, and is less prone to overfitting than existing tree-based CDE approaches.
SPI is Dead, isn't it? Clear the Stage for Continuous Learning!

Software process improvement (SPI) is around for decades, but it is a critically discussed topic. In several waves, different aspects of SPI have been discussed in the past, e.g., large-scale company-level SPI programs, maturity models, success factors, and in-project SPI. It is hard to find new streams or a consensus in the community, but there is a trend coming along with agile and lean software development. Apparently, practitioners reject extensive and prescriptive maturity models and move towards smaller, faster and continuous project-integrated SPI. Based on data from two survey studies conducted in Germany (2012) and Europe (2016), we analyze the process customization for projects and practices for implementing SPI in the participating companies. Our findings indicate that, even in regulated industry sectors, companies increasingly adopt in-project SPI activities, primarily with the goal to continuously optimize specific processes. Therefore, with this paper, we want to stimulate a discussion on how to evolve traditional SPI towards a continuous learning environment.
Bridging Gaps

Interactive information retrieval (IIR) researchers often conduct laboratory studies to understand the relationship between people seeking information and information retrieval systems. They develop extensive data collection methods and tools create new understanding about the relationship between observable behaviors, searcher context, and underlying cognition, to better support people's information seeking. Yet aside from the problems of data size, realism, and demographics, laboratory studies are limited in the number and nature of phenomena they can study. Hence, data collected in laboratories contains different searcher populations and collects non-overlapping user and task characteristics. While research analyses and collection methods are isolated, how can we further IIR's mission of broad understanding? We approach this as a structure learning problem on incomplete data, determining the extent to which incomplete data can be used to predict user and task characteristics from interactions. In particular, we examine whether combining heterogeneous data sets is more effective than using a single data set alone in prediction. Our results indicate that adding external data significantly improves predictions of searcher characteristics, task characteristics, and behaviors, even when the data does not contain identical information about searchers.
Fostering interpretability of data mining models through data perturbation

With the widespread adoption of data mining models to solve real-world problems, the scientific community is facing the need of increasing their interpretability and comprehensibility. This is especially relevant in the case of black box models, in which inputs and outputs are usually connected by highly complex and nonlinear functions; in applications requiring an interaction between the user and the model; and when the machine’s solution disagrees with the human experience. In this contribution we present a new methodology that allows to simplify the process of understanding the rules behind a classification model, even in the case of black box ones. It is based on the perturbation of the features describing one instance, and on finding the minimal variation required to change the forecasted class. It thus yields simplified rules describing under which circumstances would the solution have been different, and allows to compare these with the human expectation. We show how such methodology is well defined, model-agnostic, easy to implement and modular; and demonstrate its usefulness with several synthetic and real-world data sets.
The European Union as a child molester: sex education on pro-Russian websites

As a part of education fostering diversity, human rights and gender equality, sex education has proved an ideal target for disinformation. This article offers an analysis of articles on sex education published between 2007 and 2016 on pro-Russian websites in Czech and Slovak. The extreme and manipulated representations of sex education on these websites are part of a Russian disinformation campaign that activates social anxiety by triggering emotions and utilising regionally sensitive issues to foment feelings of inferiority in post-socialist countries. The Russian Federation strives to be perceived as a saviour of traditional values, actively feeding a conservative backlash and anti-gender and anti-LGBT movements. It therefore recruits conservative allies across Europe to expand its sphere of influence.
An Augmented Reality App to Learn to Interpret the Nutritional Information on Labels of Real Packaged Foods

Healthy eating habits involve controlling your diet. It is important to know how to interpret the nutritional information of the packaged foods that you consume. These packaged foods are usually processed and contain carbohydrates and fats. Monitoring carbohydrates intake is particularly important for weight-loss diets and for some pathologies such as diabetes. In this paper, we present an augmented reality app for helping interpret the nutritional information about carbohydrates in real packaged foods with the shape of boxes or cans. The app tracks the full object and guides the user in finding the surface or area of the real package where the information about carbohydrates is located using augmented reality and helps the user to interpret this information. The portions of carbohydrates (also called carb choices or carb servings) that correspond to the visualized food are shown. We carried out a study to check the effectiveness of our app regarding learning outcomes, usability, and perceived satisfaction. A total of 40 people participated in the study (20 men and 20 women). The participants were between 14 and 55 years old. The results reported that their initial knowledge about carb choices was very low. This indicates that education about nutritional information in packaged foods is needed. An analysis of the pre-knowledge and post-knowledge questionnaires showed that the users had a statistically significant increase in knowledge about carb choices using our app. Gender and age did not influence the knowledge acquired. The participants were highly satisfied with our app. In conclusion, our app and similar apps could be used to effectively learn how to interpret the nutritional information on the labels of real packaged foods and thus help users acquire healthy life habits.
TAPSTROKE: A novel intelligent authentication system using tap frequencies

Emerging security requirements lead to new validation protocols to be implemented to recent authentication systems by employing biometric traits instead of regular passwords. If an additional security is required in authentication phase, keystroke recognition and classification systems and related interfaces are very promising for collecting and classifying biometric traits. These systems generally operate in time-domain; however, the conventional time-domain solutions could be inadequate if a touchscreen is so small to enter any kind of alphanumeric passwords or a password consists of one single character like a tap to the screen. Therefore, we propose a novel frequency-based authentication system, TAPSTROKE, as a prospective protocol for small touchscreens and an alternative authentication methodology for existing devices. We firstly analyzed the binary train signals formed by tap passwords consisting of taps instead of alphanumeric digits by the regular (STFT) and modified short time Fourier transformations (mSTFT). The unique biometric feature extracted from a tap signal is the frequency-time localization achieved by the spectrograms which are generated by these transformations. The touch signals, generated from the same tap-password, create significantly different spectrograms for predetermined window sizes. Finally, we conducted several experiments to distinguish future attempts by one-class support vector machines (SVM) with a simple linear kernel for Hamming and Blackman window functions. The experiments are greatly encouraging that we achieved 1.40%–2.12% and 2.01%–3.21% equal error rates (EER) with mSTFT; while with regular STFT the classifiers produced quite higher EER, 7.49%–11.95% and 6.93%–10.12%, with Hamming and Blackman window functions, separately. The whole methodology, as an expert system for protecting the users from fraud attacks sheds light on new era of authentication systems for future smart gears and watches.
3D No-Reference Image Quality Assessment via Transfer Learning and Saliency-Guided Feature Consolidation

Motivated by the success of convolutional neural networks (CNNs) in image-related applications, in this paper, we design an effective method for no-reference 3D image quality assessment (3D IQA) through CNN-based feature extraction and consolidation strategy. In the first and most vital stage, quality-aware features, which reflect the inherent quality of images, are extracted by a fine-tuned CNN model exploiting the concept of transfer learning. This fine-tuning strategy solves the large-scale training data dependence existing in current deep-learning-based IQA algorithms. In the second stage, features from the left and right view are consolidated by linear weighted fusion where the weight for each image is obtained from its saliency map. In addition, the statistical characteristics of the disparity map are also considered in a multi-scale manner as additional features. In the final stage of quality mapping, the objective score for each stereoscopic pair is gained by support vector regression. The experimental results on the public databases show that our approach outperforms many existing no-reference and even full-reference methods.
ENRE: A Tool Framework for Extensible eNtity Relation Extraction

Understanding the dependencies among code entities is fundamental to many software analysis tools and techniques. However, with the emergence of new programming languages and paradigms, the increasingly common practice of writing systems in multiple languages, and the increasing popularity of dynamic languages, no existing framework can reliably extract this information. That is, no tools exist to accurately extract dependencies from systems written in multiple and dynamic languages. To address this problem, we have designed and implemented the Extensible eNtity Relation Extraction (ENRE) framework. ENRE supports the extraction of entities and their dependencies from systems written in multiple languages, enables the customization of dependencies of interest to the user, and makes implicit dependencies explicit. To demonstrate feasibility of this framework, we developed two ENRE instances for analyzing Python and Golang programs. Our experiments on 12 Python and Golang projects demonstrated the effectiveness and flexibility of ENRE. By comparing with a commercial static analysis tool, we show that we can extract dependencies from Golang programs which are not supported by existing tools and we can reveal implicit dependencies in Python. (Demo Video: https://youtu.be/BfXp5bb1yqc).
A Novel Stochastic Gradient Descent Algorithm Based on Grouping over Heterogeneous Cluster Systems for Distributed Deep Learning

On heterogeneous cluster systems, the convergence performances of neural network models are greatly troubled by the different performances of machines. In this paper, we propose a novel distributed Stochastic Gradient Descent (SGD) algorithm named Grouping-SGD for distributed deep learning, which converges faster than Sync-SGD, Async-SGD, and Stale-SGD. In Grouping-SGD, machines are partitioned into multiple groups, ensuring that machines in the same group have similar performances. Machines in the same group update the models synchronously, while different groups update the models asynchronously. To improve the performance of Grouping-SGD further, the parameter servers are arranged from fast to slow, and they are responsible for updating the model parameters from the lower layer to the higher layer respectively. The experimental results indicate that Grouping-SGD can achieve 1.2~3.7 times speedups using popular image classification benchmarks: MNIST, Cifar10, Cifar100, and ImageNet, compared to Sync-SGD, Async-SGD, and Stale-SGD.
A Systematic Comparison of Methods for Finding Good Premises for Claims

Research on computational argumentation has recently become very popular. An argument consists of a claim that is supported or attacked by at least one premise. Its intention is the persuasion of others. An important problem in this field is retrieving good premises for a designated claim from a corpus of arguments. Given a claim, oftentimes existing approaches' first step is finding textually similar claims. In this paper we compare 196 methods systematically for determining similar claims by textual similarity, using a large corpus of (claim, premise) pairs crawled from debate portals. We also evaluate how well textual similarity of claims can predict relevance of the associated premises.