Reviewerly/exHarmony huggingface dataset:
ConVisQA: A Natural Language Interface for Visually Exploring Online Conversations

There has been an exponential growth of asynchronous online conversations thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. Traditional sites present a conversation in a paginated list view, making it very difficult to find comments of interests about a specific topic and/or opinions which may be scattered around a long thread of discussion. In this paper, we introduce a natural language interface that supports the user to quickly locate and browse through the comments that are relevant to her information needs. Our system takes a question asked by the reader about a conversation as input and then automatically finds the answer using natural language processing techniques. It then presents the results by highlighting in a visual interface, enabling the user to quickly navigate through the comments that match her information needs. Our case studies with three users suggest that the system can help the user to effectively fulfill her information needs by highlighting the relevant comments to their question.
Spam Detection and Spammer Behaviour Analysis in Twitter Using Content Based Filtering Approach

Abstract Twitter is one of the most popular social media networks and therefore it is prone to misuse. One of the ways in which people misuse Twitter is by spamming. Spam becomes an issue once a communication medium especially one, which enables global communication and handle huge amount of online data. Since Twitter is popular among so many people, it makes it easy for spammers to thrive. Spammers are people who send unwanted messages to people to either advertise a product or lure the victims into clicking malicious links, which may affect their user systems. The main objective of these spammers is usually to make money from their victims. In the last years, several systems has-been made with the aim of determining whether a user is a spammer or not. However, these systems cannot filter each spam message and a different account can be created by a spammer and used to send other messages. This paper proposes a content-based approach, which can be used to filter spam tweets. The approach involves using tweets in machine learning and compression algorithms in order to filter the undesired tweets.
Partitioned hybrid learning of Bayesian network structures

Abstract We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: Partitioned PC (pPC) accelerates skeleton learning via a divide-and-conquer strategy, p -value adjacency thresholding (PATH) effectively accomplishes parameter tuning with a single execution, and hybrid greedy initialization (HGI) maximally utilizes constraint-based information to obtain a high-scoring and well-performing initial graph for greedy search. We establish structure learning consistency of our algorithms in the large-sample limit, and empirically validate our methods individually and collectively through extensive numerical comparisons. The combined merits of pPC and PATH achieve significant computational reductions compared to the PC algorithm without sacrificing the accuracy of estimated structures, and our generally applicable HGI strategy reliably improves the estimation structural accuracy of popular hybrid algorithms with negligible additional computational expense. Our empirical results demonstrate the competitive empirical performance of pHGS against many state-of-the-art structure learning algorithms.
ColVis: Collaborative Visualization Design Workshops for Diverse User Groups

Understanding different types of users' needs can even be more critical in today's data visualization field, as exploratory visualizations for novice users are becoming more widespread with an increasing amount of data sources. The complexity of data-driven projects requires input from including interdisciplinary expert and novice users. Our work- shop framework helps taking design decisions collaboratively with experts and novice users, on different levels such as outlining users and goals, identifying tasks, structuring data, and creating data visualization ideas. We conducted workshops for two different data visualization projects. For each project, we conducted a workshop with project stakeholders who are domain experts, then a second workshop with novice users. We collected feedback from participants and used critical reflection on the process. Later on, we created recommendations on how this workshop structure can be used by others. Our main contributions are, (1) the workshop framework for designing data visualizations, (2) describing the outcomes and lessons learned from multiple workshops.
Smile! Positive Emojis Improve Reception and Intention to Use Constructive Feedback

Feedback is essential to creative work. In fact, feedback is so valuable that online crowdwork platforms are sometimes used to gather it quickly and repeatedly. However, when feedback contains negative content, the receiver’s mood may suffer, as well as his or her perceptions of the feedback and provider. In response, researchers have explored techniques to mitigate the negative impacts of constructive comments; surprisingly, few studies have investigated non-verbal communication such as images. We report an exploratory study of how the presence of positive emojis in a critique can affect the receivers’ reactions. We found that the positive emojis increased receiver positivity, also decreasing annoyance and frustration. The emojis also evoked more positive perceptions of feedback providers, and increased intentions to apply the feedback to future work. We discuss implications for designing feedback platforms in ways that might encourage the addition of message-appropriate emojis.
Building A Non-Personalized Recommender System by Learning Product and Basket Representation

In this paper, we addressed the problem of learning product and basket representation for a non-personalized recommendation system where the baskets do not have a specific owner. The recommendation models tend to exploit as much information as possible along with basket patterns to improve performance. We focus on the representation problem for the baskets without any customer information. Deep learning-based architectures have solved many representation problems such as natural language processing (NLP) and computer vision (CV) so far. While the NLP model takes a bag of words as input, the recommendation models take a basket of products as input. The learning algorithm uses co-occurrence information and therefore exploits the idea that the things that appear in a similar environment share similar meaning. But traditional representation approaches such as one-hot encoding have dimensionality problems when the number of entities increases. On the other hand, neural models can solve this dimensionality curse and transform each entity into a short and dense vector, namely embeddings. We successfully designed unsupervised and super-vised architectures to solve the product and basket embeddings for a recommendation engine. Our experiments show that the proposed deep learning architecture showed better performance than baseline approaches in terms of many metrics. We also discussed and addressed many product representation related problems throughout the paper.
A Hybrid Semantic Segmentation Based on Level-Set Evolution Driven by Fully Convolutional Networks

Semantic segmentation network is one successful deep architecture for understanding key foregrounds.The fully convolutional networks (FCNs) can learn the regional masks by composing layered representations coarse-to-fine.Nevertheless, these increasing feature maps often fail to improve significantly the discrimination to segmentation details, due to the lack of accurate spatial representations and prior on salient foreground boundaries.This paper presents a hybrid semantic segmentation network based on a differentiable level-set evolving on multiple feature channels, which can be driven by the deep architectures of FCNs.To the level-set iterations, the re-initialization and Gaussian smoothing operations have been integrated to achieve an uniform training procedure.Also, this proposed wrapping level-set layer can be easily generalized to other existing deep segmentation architectures.Experimental results on various open datasets verify that, compared with related baselines, this hybrid framework significantly improves the spatial details and accuracy rates of the segmenting foreground boundaries.
Handwritten Text Recognition using Fully Convolutional Network

Abstract Handwritten text recognition from images is challenging because there are many variations in handwriting as each person has a different writing style. This research implements multilevel recognition to solve this problem. In the first level, a Lexicon Convolutional Neural Network (CNN) model is used to recognize words (containing a set of word) that often appear in the text. If a word is not recognized by the Lexicon CNN model, which is designed for a limited number of words, then it goes to the next level. A character sequence recognition consisting of predicting the number of characters using CNN, cropping each character using a sliding window, and character recognition using Fully Convolutional Network (FCN), is applied at the second level. Experiments show that the system performance is promising. The experiment conducted using NIST Special Database 19 as a training dataset and a handwritten text on screen as a testing dataset. The best accuracy of word recognition, character number prediction, and character recognition is 99.98%, 98.56%, and 83.52%, respectively.
Epistasis Analysis: Classification Through Machine Learning Methods

Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.
Quality of Information in Gathering Information via Video Analytics for Military Networks

Military networks, and especially tactical networks, have become crucial in conducting military actions. With images and videos being rich sources of information, a primary challenge in military networks is to be able to issue a query over a (large) distributed set of devices to find concrete actions or objects of interest. The problem inherent to this scenario is how to gather this information in an optimal way, depending on the objective, for further processing. To this end, in this work we present three different approaches, with different objectives, for using video analytics to label and collect the right information that is stored on mobile devices. We first present a solution for crowdsourcing devices to label videos that contain objects and actions of interest in a minimum amount of time. We then present a solution for gathering all images that contain objects of interest at a central server where a complete search of images stored on the mobile devices is completed as fast as possible. Finally, we consider the case in which we desire to collect images that contain objects of interest as fast as possible, perhaps at the expense of the overall search time taking longer. In all three cases, we divide processing responsibilities between mobile devices where the videos/images are stored and the edge cloud.
Visualization of similarity queries with trajectory estimation in complex data

The information in a database can change over time and requires specific support for operations that process the temporal characteristics of the data. New instances of objects present in the database are inserted frequently, requiring management methods capable of handling temporal information. This paper aims to extend the delimitation techniques for visualization of similarity queries of images using estimates of their trajectories. The study of temporal evolution in complex data can benefit several areas of knowledge, such as agriculture, medicine, civil engineering and others. The use of visualization to understand data changes over time improves the knowledge of the distribution of the dataset, enables the recognition of data patterns more quickly and favors the acquisition of new useful information. In this sense, the visualization of queries with trajectory estimation in a three-dimensional space allows to study how the data are organized in the estimated regions. To perform this visual analysis, we use images at different time points of the same object in the database to estimate what an image of that object would look like at a different time.
Match matrix aggregation enhanced transition-based neural network for SQL parsing

Nowadays many neural networks have been widely employed for semantic parsing problems especially for Structured Query Language (SQL) parsing, which aims at transforming natural language sentences into SQL representations. Selecting proper table headers in SQL tasks is extremely important, and the main cause of performance drop is that attention mechanism in neural models sometimes obtains wrong word-level distribution over headers. In order to obtain better header selection, we propose a match matrix aggregation enhanced SQL parser to consider the outer character-level ROUGE-L match information between the question and headers, then dynamically combine it with the inner generated attention matrix. We also introduce customized BERT and extra semantic information of the question and headers to further improve the performance. The results on two SQL datasets demonstrate that our method achieves an inspiring performance and highly outperforms other state-of-the-art alternatives.
Technical Challenges and Vision of Slovak National Telepresence Infrastructure during the COVID-19 Pandemic

This paper introduces technical challenges with National Telepresence Infrastructure in Slovakia during COVID-19 Pandemic. Rapidly increasing demand for videoconference technology from academic and research institutions is calling for quick sometimes not yet stable solutions during these days. Paper should be a summary of operational and innovative rapid development activities of large telepresence infrastructure. There will be also a vision for transforming telepresence technology regarding current situation into more effective digital tool not only for communication, but also for remote work, digital traveling etc.
Complementary Recommendations Using Deep Multi-modal Embeddings For Online Retail

Recommendation systems have been crucial in driving revenue in e-commerce especially in online retail. Complementary item recommendation is a challenging problem within this field due to the inherent difficulty in defining how products relate to each other. In this paper we design and propose a complementary item recommendation system that uses multi-modal embeddings (both text-based and image-based). Our system is carefully designed and validated by domain experts. We provide extensive insights into our design choices and what does not work in terms of model choice and features used. We successfully demonstrate our system on two challenging datasets from a home improvement retailer: one involving outdoor furniture (patio) and the second involving bathroom products. When deployed live on the website, our model resulted in +170% increase in engaged visits to the recommendation container.
Quasi-optimal Data Placement for Secure Multi-tenant Data Federation on the Cloud

As it is difficult to directly share data among different organizations, data federation brings new opportunities to the data-related cooperation among different organizations by providing abstract data interfaces. With the development of Cloud computing, organizations store data on the Cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either communication cost or time cost, and do not consider the features of jobs that process the data. In this paper, we propose an approach to enable secure data processing on the Cloud with the data from different organizations. The approach consists of a data federation platform for secure data processing on the Cloud named FedCube and a greedy data placement algorithm that creates a plan to store data on the Cloud in order to achieve multiple objectives based on a cost model. The cost model is composed of two objectives, i.e., reducing both monetary cost and execution time. We present an experimental evaluation by comparing our data placement algorithm with the existing methods based on the data federation platform. The experiments show that our proposed algorithm significantly reduce the total cost (up to 69.8%).
Gaussian processes for shock test emulation

Certifying performance of mechanical components with experimental tests is time consuming and expensive, which motivates the development of efficient approaches for predicting the outcomes from such testing. We propose two methods based on Gaussian processes (GP) to estimate the probability that new components will pass future certification tests, while assessing our prediction confidence. The first method processes a set of Bernoulli trials into a suitable machine learning dataset and subsequently infers the probability of performing satisfactorily for new components using heteroscedastic bounded GP regression. The second method uses GP classification with linear kernels. We demonstrate that linear kernels are well suited for datasets representing snapshots of mechanical system responses by accurately reproducing the underlying physical trends in the data. This yields consistent probabilities of passing and provides high labeling accuracy, even with small datasets. We demonstrate these techniques on synthetic datasets consistent with ship cabinet certification tests. We achieve up to 100% accuracy using all of the training data, and at least 92% with only 10% of the available data. With a corrupted training set, we obtain at least 93% accuracy. In the regression framework, we demonstrate that introducing heteroscedasticity helps achieve significantly better accuracy than frequentist machine learning methods.
Quantum machine learning of graph-structured data

Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that exploits the quantum source's graph structure to improve learning via an arbitrary quantum neural network (QNN) ansatz. In particular, we devise and optimize a self-supervised objective to capture the information-theoretic closeness of the quantum states in the training of a QNN. Numerical simulations show that our approach improves the learning efficiency and the generalization behavior of the base QNN. On a practical note, scalable quantum implementations of the learning procedure described in this paper are likely feasible on the next generation of quantum computing devices.
Machine Learning as Key Technology of AI: Automated Workforce Planning

The level of digitization is increasing globally on a daily basis. This change is not only met with enthusiasm, because it fuels people’s fundamental fear of being substituted. Many unpleasant and usually monotonous tasks are already being performed by machines—and this more accurately, more cost-effectively, and faster. At the same time, we are facing one of the most significant social challenges with the shortage of skilled workers. With the realization that technical solutions should be tailored to the needs of future users, the urgent question of what optimal staff deployment planning can look like also arises in the healthcare sector. This chapter answers the question with a solution approach from digitization. It outlines a harmonious interaction between human and machine. In addition, it aims to address productivity considerations and frequent challenges.
Driven Qubit by Train of Gaussian-Pulses

The simplest non-dissipative 2-level atom system, a qubit, excited by a train of resonant n-Gaussian laser pulses is investigated. This concerns examination of the averaged atomic variables, the intensity-intensity correlation function, and the transient fluorescent radiation. Analytical formulas for the above expressions are obtained. Computational results show that the transient spectra with the initial ground and coherent atomic states exhibit asymmetric Mollow structure with dip structure, dense oscillation, and narrowing, and depends on the pulse number (n), the repetition time (τR), and the observed time.
The Chiral Domain of a Camera Arrangement

We introduce the chiral domain of an arrangement of cameras $$\mathcal {A} = \{A_1,..., A_m\}$$ which is the subset of $$\mathbb {P}^3$$ visible in $$\mathcal {A}$$ . It generalizes the classical definition of chirality to include all of $$\mathbb {P}^3$$ and offers a unifying framework for studying multiview chirality. We give an algebraic description of the chiral domain which allows us to define and describe the chiral version of Triggs’ joint image. We then use the chiral domain to re-derive and extend prior results on chirality due to Hartley.
Textual Evidence Mining via Spherical Heterogeneous Information Network Embedding

Scientific literature, as one of the major knowledge resources, provides abundant textual evidence that has great potential to support high-quality scientific hypothesis validation. In this paper, we study the problem of textual evidence mining in scientific literature: given a scientific hypothesis as a query triplet, find the textual evidence sentences in scientific literature that support the input query. A critical challenge for textual evidence mining in scientific literature is to retrieve high-quality textual evidence without human supervision. Because it is non-trivial to obtain a large set of human-annotated articles containing evidence sentences in scientific literature. To tackle this challenge, we propose EvidenceMiner, a high-quality textual evidence retrieval method for scientific literature without human-annotated training examples. To achieve high-quality textual evidence retrieval, we leverage heterogeneous information from both existing knowledge bases and massive unstructured text. We propose to construct a large heterogeneous information network (HIN) to build connections between the user-input queries and the candidate evidence sentences. Based on the constructed HIN, we propose a novel HIN embedding method that directly embeds the nodes onto a spherical space to improve the retrieval performance. Quantitative experiments on a huge biomedical literature corpus (over 4 million sentences) demonstrate that EvidenceMiner significantly outperforms baseline methods for unsupervised textual evidence retrieval. Case studies also demonstrate that our HIN construction and embedding greatly benefit many downstream applications such as textual evidence interpretation and synonym meta-pattern discovery.