Reviewerly/exHarmony huggingface dataset:
Methods of Multi-Modal Data Exploration

Techniques and tools designed for information retrieval, data exploration or data analytical tasks are based on the relational and text-search model, and cannot be easily applied to unstructured data such as images or videos. Researcher communities have been trying to reveal the semantics of multimedia in the last decades with ever-improving results in various tasks, dominated by the latest success of deep learning. Limits of object retrieval models drive the need for data exploration methods that support multi-modal data, like multimedia surrounded by structured attributes. In this paper, we describe, implement and evaluate exploration methods using multiple modalities and retrieval models in the context of multimedia. We apply the techniques in e-commerce product search and recommending, and demonstrate benefit for different retrieval scenarios. Lastly, we propose a method for extending database schema by latent visual attributes learned from image data. This enables closing the loop by going back to relational data, and potentially benefiting a range of industrial applications.
Virtual Inpainting for Dazu Rock Carvings Based on a Sample Dataset

Numerous image inpainting algorithms are guided by a basic assumption that the known region in the original image itself can provide sufficient prior information for the guess recovery of the unknown part, which is not often the case in actual art-image inpainting. Sometimes, the art image that needs to be inpainted is so badly damaged that there is little prior information to serve as a good model to infer the appearance of the unknown fragment. Focusing on the lookup strategy for optimal patches, a novel semi-automatic exemplar-based inpainting framework based on a sample dataset is proposed in this article to solve such a problem with three steps: (1) reference images selection from the dataset using deep convolutional network, (2) sample image creation based on reference images with melding algorithm, and (3) exemplar-based inpainting according to the created sample image. Several comparative experiments over Dazu Rock Carvings with the state-of-the-art image completion approaches demonstrate the effectiveness of our contributions. First, the search space for candidate patches is extended from the known region to a sample image. It performs effectively for the inpainting case of little prior information existing in the original image itself. Furthermore, sample image creation is added to reduce the complexity of inpainting via multiple images and avoid the taboo of complete duplication in art restoration. Moreover, Poisson blending is used for post-procedure to improve the visual harmony between the reconstructed fragment and the known region in both color and illumination. Last but not least, our method is successfully applied in the virtual inpainting of Dazu Buddhist face images. The inpainted proposals can be a reference for the final actual artificial inpainting as well as a base for VR show.
Heavy Metal Pollution Assessment in Sediment of Mussel Habitats UsingGeochemical Indices

The heavy metals Pb, Cd, Cu, Zn, and Ni were analyzed in the soft tissue of mussels (Anodonta cygnea) as well as in sediments at five regions of Anzali Wetland, on the southern bank of the Caspian Sea during October and November 2016. The data were applied for assessing the pollution levels in emphasizing the mussel habitats by geochemical indices and furthermore answering the inquiry whether these indices can predict the contaminant level in the swan mussel soft tissue.The highest geochemical indices in the sediment: EF, Igeo, CF, and PLI revealed moderately to seriously contamination level of Cd and Pb in sampling sites. The sheijan and Bahambar also showed a higher contamination level of metals than in other regions. According to the ecological risk index (RI) Mahrouzeh, Hendekhaleh, Abkenar, and Bahambar areas were presented within the range of moderate risk level. Additionally, multivariate statistics showed that Ni is controlled by parent rocks and at the same time Pb, Cd, Cu, and Zn originated from anthropogenic sources.The highest geochemical data for Pb, Cd, Cu, Zn, and Ni recorded in Bahambar, Abkenar, Mahrouzeh, Sheijan, and Mahrouzeh respectively. But the highest accumulation of Pb, Cd, and Cu in mussel measured in Bahambar, Abkenar, and Mahrouzeh respectively, and also Zn and Ni in Hendekhaleh. Therefore, the geochemical index data does not always predict heavy metal contaminants into the soft tissue of mussels in sampling sites.
Optical inspection of noncooperative copper surface structures using multi- and hyperspectral acquisition systems

In order to enhance the efficiency of quality inspection of Direct Copper Bonded (DCB) structures an optical inspection using a 3D measuring system is conceivable. However, it is a challenging task to use 3D optical measurement techniques for diffuse reflective copper surfaces. This work deals with the optical detection of defects of the copper surface, using multi- and hyperspectral acquisition devices. Over a broad spectral range from the visual spectrum to the short-wave infrared (400 nm - 1700 nm) it is analysed which wavelengths provide good contrast ratios for the detection of flaws. For the inspection of the sample back side, the push-broom imager, operating in the VIS and NIR range (400 nm - 1000 nm), provides the best contrast ratio. An outstanding contrast is reached around 400 nm. Deposited particles on the front side of the DCB substrates are best detected by the filter wheel camera, which is sensitive in the visual and near infrared range. Outstanding contrast is reached with wavelengths around 640 nm. After evaluating the standard deviations of the gray values, it can be shown that defects differ clearly from flawless substrate areas under investigation with light of wavelengths 577 nm, 640 nm and 950 nm. Furthermore, the comparison between certain pixel spectra confirms that significant differences appear at the same three wavelengths. Regarding an automated inspection of defects, it is advisable to shift the pattern projection for the 3D correspondence analysis to the spectral ranges mentioned.
Cognitive chronometric assessment of constructive knowledge: an empirical approach to support formative assessment

A cognitive system using mental chronometry to innovate assessment of students learning outcomes is presented. High school and bachelor students from several knowledge domains are required to use a natural semantic technique to provide mental representations of course schemata contents at the beginning and end of the course. At the end of course, schemata related word pairs due to learning obtained from students´ semantic nets are used to compare recognition times of these concepts against other semantic related concepts. Results showed that specific priming effects are obtained from schemata related concepts after successful learning. Implications to formative constructive knowledge assessment are presented.
Optical measurement of ground cylinder lead angle

The functional behavior of sealing surfaces and bearings depends on the texture direction with respect to the axis of rotation of a machined part, known as the twist or lead angle. We present our lead angle measurement solution using interference microscopy, multi-axis staging, and advanced software for determining surface texture direction and cylinder rotation axis.
Order-preserving pattern matching indeterminate strings

Given a pattern p of size m and a text t, the problem of order-preserving pattern matching (OPPM) is to find all substrings of t that satisfy one of the orderings defined by p. This problem has applications on time series analysis. However given its strict nature this model is unable to deal with indetermination, thus limiting its application to noisy time series. In this paper we introduce indeterminate characters to alleviate this limitation. We then propose two polynomial time algorithms. If the indetermination is limited to p confirming one occurrence can be computed in O(rmlg⁡r) time, where r is a bound on the number of uncertain characters per position. If the indetermination alternates, but does not occur at the same position in t and p, we present an algorithm that requires O(rm(m+log⁡r)) time. We also show that the general problem is NP-hard and provide a polynomial size boolean formula.
(Core) common ground once more: Response to Liu and Liu

It is argued that contraposition is valid for a class of natural language conditionals, if some modifications are allowed to preserve the meaning of the original conditional. In many cases, implicit temporal indices must be considered, making a change in verb tense necessary. A suitable contrapositive for implicative counterfactual conditionals can also usually be found. In some cases, the addition of certain words is necessary to preserve meaning that is present in the original sentence and would be lost or changed in the contrapositive without them. A distinction is made between adding new meaning and adding new words to preserve existing meaning. For concessive conditionals and relevance conditionals, however, no valid contrapositive can be found. They do not belong to the class of contraposable conditionals, which can be independently defined. Difficult cases are also discussed in which the contradictory of the consequent semantically entails the truth of the antecedent. In such cases the content of the antecedent is implicit in the meaning of the consequent. Contraposition becomes possible if what is implicit in the original consequent is made explicit in the contrapositive antecedent.
Towards the advanced predictive modelling in epidemiology

Abstract Data-driven prediction systems used in epidemiological studies are still unsatisfactory from a practical point of view. Different pitfalls should be considered while transferring technologies from research to practice. The proposed k-Nearest Neighbors approach is designed to make disease-related predictions in a more holistic manner: we detect cases of novelty among unobserved subjects to identify situations when model predictions are not reasonably valid. Moreover, it copes with overlapping classes, finds new examples which cannot be labelled with the high confidence and reveals healthy subjects in the training data who might be at risk. Additionally, variable selection is built-in to select relevant predictors. The approach was applied to predict cardiovascular diseases based on the data collected within an ongoing follow-up study undertaken in Eastern Finland. According to the experimental results, our proposal allows increasing the accuracy of predictions made.
Impact of Advent of Electronic Information Resources on Research

The study has been conducted with an objective to assess the impact of e-resources on seven aspects of research. Seven agricultural universities of North India were surveyed while using questionnaire as a data collection tool. The majority of respondents are of the opinion that due to advent of e-resources and in view of the ICT developments, they ‘feel much comfortable and competent to search information' and the changed scenario has enabled them to ‘generate new research ideas'. The quantity as well as quality of research has improved. A majority of the respondents are also of the opinion that their ‘research output finds more citations'. A majority of users are of the opinion that the problem of ‘duplication of research' has diminished while in contrast the menace of ‘plagiarism has increased'. A majority of the users agree that advent of e-resources has laid a positive impact except for the fact that ‘plagiarism' has enhanced.
XINA: Explainable Instance Alignment using Dominance Relationship (Extended Abstract)

In this extended abstract, we present an instance alignment framework, namely XINA, for KB integration. We then show its effectiveness and efficiency on real-world KBs.
Digital model sample—Scanning and processing protocol

This chapter is a brief introduction to the virtual environment for working with three-dimensional data. The chapter will introduce terminology, software and file formats, various alignment methods, surface scanner data collection, a discussion of source of error on data collection on surface scan, and processing of the mesh model. Software topics include open source software and commercial software for model processing and data management. Because the methods of data capture and model processing varies between technologies and different equipment, this chapter also discusses how to capture surface data in cross-compatible formats, data management, processing mesh data, cleaning data, and decimation of the data for open source software and commercial software. To tie this together for research design, this chapter also suggests “workflow” for surface scan data collection for the beginners, as well as model processing protocols.
How Notification Queues Contain Hidden Natural Experiments

Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneously assigned to treatment and control. However, it is not always feasible or desirable to run an experiment due to engineering costs, or concerns related to the user experience or ethics of randomizing the presence of a feature. Naturally occurring exogenous variation, or 'natural experiments,' allow researchers to recover causal estimates of peer effects from observational data in the absence of experimental manipulation. Natural experiments trade off the engineering costs and some of the ethical concerns associated with randomization with the search costs of finding instrumental variables. To mitigate the search costs associated with discovering natural counterfactuals, we identify a common engineering requirement used to scale massive online systems, in which natural exogenous variation is likely to exist: notification queuing. We identify a natural experiment on the LinkedIn platform based on the order of notification queues to estimate the causal peer effect of a received message on the engagement of a message recipient (based on work anniversary announcements distributed to a member's connections). We show that receiving a message from another member significantly increases a member's engagement, but that some popular observational specifications, such as fixed-effects estimators, overestimate this effect by as much as 2.7x. The study points to the benefits of using notification queues to discover naturally occurring counterfactuals for the estimation of causal effects without experimenter intervention or the engineering costs associated with online randomization to different feature sets. It also implies a potential benefit of involving data scientists in the system design process to maximize the informational benefits of software systems.
Ping - A customizable, open-source information filtering system for textual data

Information filtering has emerged as a prominent paradigm of timely information delivery; in such a setup, users submit profiles that express their information needs to a server which is responsible for notifying them when information that matches their profiles becomes available. Traditionally, information filtering research has focused mainly on providing algorithmic solutions that enhance the efficiency and effectiveness of the filtering process, and has largely neglected the development of tools/systems that showcase the usefulness of the paradigm. In this work, we put forward Ping, a fully-functional content-based information filtering system aiming (i) to showcase the realisability of information filtering and (ii) to explore and test the suitability of the existing technological arsenal for information filtering tasks. The proposed system is entirely based upon open-source tools and components, is customisable enough to be adapted for different textual information filtering tasks, and puts emphasis in user profile expressivity, intuitive UIs, and timely information delivery. To assess the customisability of Ping, we deployed it in two distinct application scenarios, and assessed its performance under both scenarios.
Chronoscope

With the massive proliferation of digital photos, new approaches are needed to enable people to engage with their vast photo archives over time and into the future. Our demo will feature Chronoscope - an interactive near-eye photo viewer ('scope') that uses temporal metadata embedded in digital photos as a design material to encourage curious and temporally diverse explorations of one's personal photo archive. Our demo will enable users to experience alternative ways of engaging with a large digital photo archive that emphasizes interactions through time and across time to encourage rich, open-ended experiences of curiosity, exploration, and self-reflection. This demo paper briefly introduces the motivation, rationale, and implementation of Chronoscope.
Sealed computation: a mechanism to support privacy-aware trustworthy cloud service

Purpose The purpose of this study is to propose an approach to avoid having to trust a single entity in cloud-based applications. In cloud computing, data processing is delegated to a remote party for efficiency and flexibility reasons. A practical user requirement usually is data privacy; hence, the confidentiality and integrity of data processing needs to be protected. In the common scenarios of cloud computing today, this can only be achieved by assuming that the remote party does not in any form act maliciously. Design/methodology/approach An approach that avoids having to trust a single entity is proposed. This approach is based on two concepts: the technical abstraction of sealed computation, i.e. a technical mechanism to confine a privacy-aware processing of data within a tamper-proof hardware container, and the role of an auditing party that itself cannot add functionality to the system but is able to check whether the system (including the mechanism for sealed computation) works as expected. Findings Discussion and analysis of the abstract, technical and procedural requirements of these concepts and how they can be applied in practice are explained. Originality/value A preliminary version of this paper was published in the proceedings of the second International Workshop on SECurity and Privacy Requirements Engineering (SECPRE, 2018).
Optimum Color and Contrast Enhancement for Online Ferrography Image Restoration

Online ferrography, because of its nondestructive and real-time capability, has been increasingly applied in monitoring machine wear states. However, online ferrography images are usually degraded as a result of undesirable image acquisition conditions, which eventually lead to inaccurate identifications. A restoration method focusing on color correction and contrast enhancement is developed to provide high-quality images for subsequent processing. Based on the formation of a degraded image, a model describing the degradation is constructed. Then, cost functions consisting of colorfulness, contrast, and information loss are formulated. An optimal restored image is obtained by minimizing the cost functions, in which parameters are properly determined using the Lagrange multiplier. Experiments are carried out on a collection of online ferrography images, and results show that the proposed method can effectively improve the image both qualitatively and quantitatively.
The Primary Geo-electromagnetic Data Preprocessing Received from a Modified Geophysical Automatic Station

The results of preliminary processing of the primary data obtained from the geophysical automatic station are presented. Descriptive statistics and simulation of time series were used as processing methods. The presentation of the results by multidimensional graphs allowed to reveal the phenomenon of coincidence in the first indicators of descriptive statistics, and in the second, the coincidence of the model’s coefficients for the daily measurements of the natural electric field. This phenomenon represents that the days with practically identical values of indicators and coefficients of the model are manifested. However, the essence of this phenomenon needs new additional research.
Combining external prior and internal features: toward a robust foreground seed selection method

Graph-based salient object detection has been widely applied in many applications, because of its excellent performance and strong theoretical basis. Basically, the performance of this type of methods depends on the correctness in foreground seed selection. In research aiming to exactly identify the seeds on foreground objects, an external prior has been defined in recent work as having an image boundary that is mostly background (called boundary prior), so the foreground seeds must locate around the image center. However, this is not the case when salient objects are spatially close to the image boundary. This problem will cause a severe error in salient object detection, because background noises are likely mixed in foreground seeds. To solve this problem, we propose a robust foreground seed selection method for salient object detection. In our method, the external prior and multiple internal image features are combined for foreground seed selection. Our method can relax the limitation of the external prior and make the foreground seed selection more adaptive and robust to diverse samples. As a result, the proposed method can generate satisfying results, no matter where the salient object is located. This advantage is demonstrated by experimental comparisons with several state-of-art methods.
2D-Convolution Based Feature Fusion for Cross-Modal Correlation Learning

Cross-modal information retrieval (CMIR) enables users to search for semantically relevant data of various modalities from a given query of one modality. The predominant challenge is to alleviate the “heterogeneous gap” between different modalities. For text-image retrieval, the typical solution is to project text features and image features into a common semantic space and measure the cross-modal similarity. However, semantically relevant data from different modalities usually contains imbalanced information. Aligning all the modalities in the same space will weaken modal-specific semantics and introduce unexpected noise. In this paper, we propose a novel CMIR framework based on multi-modal feature fusion. In this framework, the cross-modal similarity is measured by directly analyzing the fine-grained correlations between the text features and image features without common semantic space learning. Specifically, we preliminarily construct a cross-modal feature matrix to fuse the original visual and textural features. Then the 2D-convolutional networks are proposed to reason about inner-group relationships among features across modalities, resulting in fine-grained text-image representations. The cross-modal similarity is measured by a multi-layer perception based on the fused feature representations. We conduct extensive experiments on two representative CMIR datasets, i.e. English Wikipedia and TVGraz. Experimental results indicate that our model outperforms state-of-the-art methods significantly. Meanwhile, the proposed cross-modal feature fusion approach is more effective in the CMIR tasks compared with other feature fusion approaches.