Reviewerly/exHarmony huggingface dataset:
CFM: convolutional factorization machines for context-aware recommendation

Factorization Machine (FM) is an effective solution for context-aware recommender systems (CARS) which models second-order feature interactions by inner product. However, it is insufficient to capture high-order and nonlinear interaction signals. While several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order interactions with outer product, resulting in images which capture correlations between embedding dimensions. Then all generated images are stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we also leverage a self-attention mechanism to perform the pooling of features to reduce time complexity. We conduct extensive experiments on three real-world datasets, demonstrating significant improvement of CFM over competing methods for context-aware top-k recommendation.
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision

Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The intuition behind this idea is to encourage the model to localize to regions which can explain some semantic property in the data, in our case, the property being the presence of a concept in a set of images. We present thorough quantitative and qualitative experiments to demonstrate the efficacy of our approach and show a 5.6% improvement over the current state of the art on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and comparable to state-of-art performance on the Flickr30k dataset.
Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

A key contributor to the success of keyword search systems is a ranking mechanism that considers the importance of the retrieved documents. The notion of importance in graphs is typically computed using centrality measures that highly depend on the degree of the nodes, such as PageRank. However, in RDF graphs, the notion of importance is not necessarily related to the node degree. Therefore, this paper addresses two problems: (1) how to define importance measures in RDF graphs; (2) how to use these measures to help compile and rank results of keyword queries over RDF graphs. To solve these problems, the paper proposes a novel family of measures, called InfoRank, and a keyword search system, called QUIRA, for RDF graphs. Finally, this paper concludes with experiments showing that the proposed solution improves the quality of results in two keyword search benchmarks.
Asymmetry of Quantum Correlations Decay in Nonlinear Bosonic System

We study the problem of the influence of one-sided different noisy channels to the quantum correlations decay in a symmetric bosonic system. We concentrate on one type of these correlations—the entanglement. The system under consideration is composed of two nonlinear oscillators coupled by two-boson interactions and externally driven by a continuous coherent field. Our low-dimensional system can be treated as 2-qutrit one. Two different noisy channels (the amplitude and the phase-damping reservoirs) are applied to both of the system’s modes. We show that there is a noticeable difference in the quantum entanglement in 2-qubit subspaces of the whole system decrease after swapping the reservoirs between the modes of the considered symmetric system. It appears also that the degree of obtained entanglement depends crucially on the position of the appropriate type of reservoir. The origin of the observed asymmetry is also explained.
A research on interaction between bitcoin and foreign exchange rates

Purpose -This study conducts an analysis to reveal the interaction between Bitcoin and Exchange Rates to find out whether Bitcoin is becoming a substitution for the exchange rates.Methodology -To investigate the mutually interaction between the exchange rates and the Bitcoin, the interaction (relationship) between daily closing price of both exchange rates and Bitcoin was analyzed through the Var model.Thus, it was tried to show the sensitivity of the values of Bitcoin to the changes occured in the exchange rates.Findings -Based on Variance Decomposition analysis, BITCOIN and Euro can be considered as largely external variables and their prices are not significantly affected by USD.An interesting result in this study is that the USD exchange rate was found to be significantly sensitive to the Euro.Conclusion -Findings obtained from analysis show that Bitcoin and Excange Rates have not become an alternative tools for each other yet.
Algorithmization of constrained monotonic maneuvers for an advanced driver assistant system in the intelligent urban buses

The paper presents a motion algorithmization subsystem used as a part of an advanced driver assistance system (ADAS) which is intended to help a human operator perform difficult maneuvers with intelligent urban buses in a more effective way. The algorithmization task, being a tight combination of motion planning and feedback control, is addressed in the presence of various constraints imposed on the nonholonomic vehicle motion, like limited motion curvature and curvature rate, obstacle collision avoidance, and maneuvering without a change of a longitudinal velocity sign (monotonic maneuvers). Description of the system is followed by exemplary results obtained on a laboratory testbed emulating a driver workplace with ADAS.
Eagle: a team practices audit framework for agile software development

Agile/XP (Extreme Programming) software teams are expected to follow a number of specific practices in each iteration, such as estimating the effort (”points”) required to complete user stories, properly using branches and pull requests to coordinate merging multiple contributors’ code, having frequent ”standups” to keep all team members in sync, and conducting retrospectives to identify areas of improvement for future iterations. We combine two observations in developing a methodology and tools to help teams monitor their performance on these practices. On the one hand, many Agile practices are increasingly supported by web-based tools whose ”data exhaust” can provide insight into how closely the teams are following the practices. On the other hand, some of the practices can be expressed in terms similar to those developed for expressing service level objectives (SLO) in software as a service; as an example, a typical SLO for an interactive Web site might be ”over any 5-minute window, 99% of requests to the main page must be delivered within 200ms” and, analogously, a potential Team Practice (TP) for an Agile/XP team might be ”over any 2-week iteration, 75% of stories should be ’1-point’ stories”. Following this similarity, we adapt a system originally developed for monitoring and visualizing service level agreement (SLA) compliance to monitor selected TPs for Agile/XP software teams. Specifically, the system consumes and analyzes the data exhaust from widely-used tools such as GitHub and Pivotal Tracker and provides team(s) and coach(es) a ”dashboard” summarizing the teams’ adherence to various practices. As a qualitative initial investigation of its usefulness, we deployed it to twenty student teams in a four-sprint software engineering project course. We find an improvement of the adherence to team practice and a positive students’ self-evaluations of their team practices when using the tool, compared to previous experiences using an Agile/XP methodology. The demo video is located at <a>https://youtu.be/A4xwJMEQh9c</a> and a landing page with a live demo at <a>https://isa-group.github.io/2019-05-eagle-demo/</a>.
Smart Phone-Based Intelligent Invoice Classification Method Using Deep Learning

Conventional invoice reimbursement methods often require excessive human resources. Intelligent invoice reimbursement (IIR) has been received strongly attentions since it can improve process efficiency and also save manpower cost. Among these procedures, invoice classification is considered one of most important steps. This paper aims to improve IIR performance capabilities by proposing a deep learning based method to intelligently classify invoices photographed by smart phones. The end-to-end method can directly identify the category of input various invoice images in a short time. Experimental results indicate that the proposed method can achieve both high classification accuracy 99.05 % and fast running speed 18.16 seconds for 105 invoice images.
Challenges in Rule Based Machine Translation from Marathi to English

Machine translation is being carried out by the researchers from quite a long time.However, it is still a dream to materialize flawless Machine Translator and the small numbers of researchers has focussed at translating Marathi Text to English.Perfect Machine Translation Systems have not yet been fully built because the fact that languages differ syntactically as well as morphologically.Majority of the researchers have opted for Statistical Machine translation whereas in this paper we have addressed the challenges of Rule based Machine Translation.The paper describes the major divergences observed in language Marathi and English and many challenges encountered while attempting to build machine translation system form Marathi to English using rule based approach.As there are exceptions to the rules and limit to the feasibility of maintaining knowledgebase, the practical machine translation from Marathi to English is a complex task.
Pan-Africanism as a laughing matter: (Funny) expressions of African identity on Twitter

Pan-Africanism, a concept that attempts to capture the essence of being an African, needs to be reconsidered in the age of social media. In this article, we examine how Twitter users negotiate the question of African identity through humorous hashtagdriven conversations. We specifically question whether a new kind of Pan-Africanism is emerging on Africa’s Twitterverse through the use of a popular hashtag in 2015, #IfAfricaWasABar. In our analysis of tweets linked to #IfAfricaWasABar, we conclude that Twitter provides temporary solidarity by engaging users in humorous exchanges regarding the sociocultural, political and economic issues that define the African continental condition today.
On Combinatorial Proofs for Modal Logic

In this paper we extend Hughes’ combinatorial proofs to modal logics. The crucial ingredient for modeling the modalities is the use of a self-dual non-commutative operator that has first been observed by Retoré through pomset logic. Consequently, we had to generalize the notion of skew fibration from cographs to Guglielmi’s relation webs. Our main result is a sound and complete system of combinatorial proofs for all normal and non-normal modal logics in the $$\mathsf {S4}$$ -tesseract. The proof of soundness and completeness is based on the sequent calculus with some added features from deep inference.
Automatic Taxonomy Induction and Expansion

TheKnowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system.One of its main capabilities is to automatically induce taxonomies 1 from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks.KGIS allows the user to semiautomatically curate and expand the induced taxonomy through a component called smart spreadsheet by exploiting distributional semantics.In this paper, we describe these taxonomy induction and expansion features of KGIS.A screencast video demonstrating the system is available in https://ibm.box.com/v/emnlp-2019-demo .
English Teaching Platform Based on Online Education Technology and Licensure and Master's Program Technology

This paper attempts to build a web-based English teaching platform that improves students’ efficiency in English learning. After reviewing the current theories on teaching platforms, the author combined online education technology and the Licensure and Master’s Program (LAMP) technology into a mixed teaching mode, which inherits the advantages of traditional teaching and network technologies. In addition, an English teaching platform was designed for the instruction and exercises for English courses. The platform is operated jointly by students, teachers and administrators. The research shows that the English teaching system, through the integration between information technology and LAMP, could be turned into a resource module to be used as a plug-in for online education; the proposed English teaching platform is a new-generation system for the teaching and learning of English reading, grammar, speaking and listening. The proposed platform greatly improves the teaching methods and the learning efficiency, laying a theoretical basis for the construction of the new teaching system platform.
Metaphors in Text Simplification: To change or not to change, that is the question

We present an analysis of metaphors in news text simplification.Using features that capture general and metaphor specific characteristics, we test whether we can automatically identify which metaphors will be changed or preserved, and whether there are features that have different predictive power for metaphors or literal words.The experiments show that the Age of Acquisition is the most distinctive feature for both metaphors and literal words.Features that capture Imageability and Concreteness are useful when used alone, but within the full set of features they lose their impact.Frequency of use seems to be the best feature to differentiate metaphors that should be changed and those to be preserved.
Construction and Annotation of the Jordan Comprehensive Contemporary Arabic Corpus (JCCA)

To compile a modern dictionary that catalogues the words in currency, and to study linguistic patterns in the contemporary language, it is necessary to have a corpus of authentic texts that reflect current usage of the language.Although there are numerous Arabic corpora, none claims to be representative of the language in terms of the combination of geographical region, genre, subject matter, mode, and medium.This paper describes a 100-million-word corpus that takes the British National Corpus (BNC) as a model.The aim of the corpus is to be balanced, annotated, comprehensive, and representative of contemporary Arabic as written and spoken in Arab countries today.It will be different from most others in not being heavily-dominated by the news or in mixing the classical with the modern.In this paper is an outline of the methodology adopted for the design, construction, and annotation of this corpus.DIWAN (Al-Shargi and Rambow, 2015) was used to annotate a one-million-word snapshot of the corpus.DI-WAN is a dialectal word annotation tool, but we upgraded it by adding a new tag-set that is based on traditional Arabic grammar and by adding the roots and morphological patterns of nouns and verbs.Moreover, the corpus we constructed covers the major spoken varieties of Arabic.
High-Dimensional Optimization in Adaptive Random Subspaces

We propose a new randomized optimization method for high-dimensional problems which can be seen as a generalization of coordinate descent to random subspaces. We show that an adaptive sampling strategy for the random subspace significantly outperforms the oblivious sampling method, which is the common choice in the recent literature. The adaptive subspace can be efficiently generated by a correlated random matrix ensemble whose statistics mimic the input data. We prove that the improvement in the relative error of the solution can be tightly characterized in terms of the spectrum of the data matrix, and provide probabilistic upper-bounds. We then illustrate the consequences of our theory with data matrices of different spectral decay. Extensive experimental results show that the proposed approach offers significant speed ups in machine learning problems including logistic regression, kernel classification with random convolution layers and shallow neural networks with rectified linear units. Our analysis is based on convex analysis and Fenchel duality, and establishes connections to sketching and randomized matrix decompositions.
ARTALIVE: AN ANDROID APPLICATION FOR AUGMENTED REALITY WITHOUT MARKERS, BASED ON ANAMORPHIC IMAGES

Abstract. The project that we present develops a mobile application (ArtAlive®) for the generation of augmented reality experiences on museum sculptural objects. The novelty of our application is that we eliminate the markers that other applications need to run. We do this turning the sculptures into their own markers, based on the recognition of anamorphic images of these sculptures: two-dimensional images of three-dimensional objects. So we eliminate the intermediate elements of the process making the experience easier. The aim is to transform the experience of visiting a museum into a more interactive and intuitive experience. The possibility of accessing the information contained in the augmented reality experiences linked specifically to each work, from the comfort of the smartphone, gives the process greater accessibility and flexibility. The recognition of the objects is developed with Vuforia, an augmented reality software development kit (SDK) for mobile devices. ArtAlive® has been registered in the Spanish Intellectual Property Registry, and a possible business model has been defined in a very basic way.
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis

The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers. The phenomenon is actively inspected in the 1990s and found to be due to the fundamental hierarchical structure of neural network models. Then the phenomenon has been thought as inevitable. However, the phenomenon seldom occurs in the context of recent deep learning. There is a gap between theory and reality. In this paper, using statistical mechanical formulation, we clarified the relationship between the plateau phenomenon and the statistical property of the data learned. It is shown that the data whose covariance has small and dispersed eigenvalues tend to make the plateau phenomenon inconspicuous.
Performance analysis of transformation and Bogdonov chaotic substitution based image cryptosystem

&lt;span lang="EN-US"&gt;In this article, a combined Pseudo Hadamard transformation and modified Bogdonav chaotic generator based image encryption technique is proposed. Pixel position transformation is performed using Pseudo Hadamard transformation and pixel value variation is made using Bogdonav chaotic substitution. Bogdonav chaotic generator produces random sequences and it is observed that very less correlation between the adjacent elements in the sequence. The cipher image obtained from the transformation stage is subjected for substitution using Bogdonav chaotic sequence to break correlation between adjacent pixels. The cipher image is subjected for various security tests under noisy conditions and very high degree of similarity is observed after deciphering process between original and decrypted images.&lt;/span&gt;
Explainable AI: A Neurally-Inspired Decision Stack Framework

European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called “decision stacks” that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs

We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location points but at regions. Existing regression-based models can only utilize the sufficiently fine-grained auxiliary data sets on the same domain (e.g., a city). With the proposed model, the functions for respective areal data sets are assumed to be a multivariate dependent Gaussian process (GP) that is modeled as a linear mixing of independent latent GPs. Sharing of latent GPs across multiple areal data sets allows us to effectively estimate the spatial correlation for each areal data set; moreover it can easily be extended to transfer learning across multiple domains. To handle the multivariate areal data, we design an observation model with a spatial aggregation process for each areal data set, which is an integral of the mixed GP over the corresponding region. By deriving the posterior GP, we can predict the data value at any location point by considering the spatial correlations and the dependences between areal data sets, simultaneously. Our experiments on real-world data sets demonstrate that our model can 1) accurately refine coarse-grained areal data, and 2) offer performance improvements by using the areal data sets from multiple domains.
ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects

In this paper, we present our systems for the MADAR Shared Task: Arabic Fine-Grained Dialect Identification.The shared task consists of two subtasks.The goal of Subtask-1 (S-1) is to detect an Arabic city dialect in a given text and the goal of Subtask-2 (S-2) is to predict the country of origin of a Twitter user by using tweets posted by the user.In S-1, our proposed systems are based on language modelling.We use language models to extract features that are later used as an input for other machine learning algorithms.We also experiment with recurrent neural networks (RNN), but these experiments showed that simpler machine learning algorithms are more successful.Our system achieves 0.658 macro F 1 -score and our rank is 6 th out of 19 teams in S-1 and 7 th in S-2 with 0.475 macro F 1 -score.
Exploring the Scoring Function Space

In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the chemical space is newer, later 1990s. With the progress of computational methodologies to create machine-learning models to predict the ligand-binding affinity, clearly there is a need for novel approaches to the problem of protein-ligand interactions. New abstractions are required to guide the conceptual analysis of the molecular recognition problem. Using a systems approach, we proposed to address protein-ligand scoring functions using the modern idea of the scoring function space. In this chapter, we describe the fundamental concept behind the scoring function space and how it has been applied to develop the new generation of targeted-scoring functions.