Reviewerly/exHarmony huggingface dataset:
Deep neural network model construction with interactive code reuse and automatic code transformation

Summary Many application developers have been recently interested in applying deep learning techniques to their works but have little knowledge and experience on them. This paper presents the methods for a GUI‐based modeling tool to easily build deep learning models and automatically transform them into executable program codes. For reuse of existing deep learning codes, it introduces a method that imports such a program code, extracts the deep learning model architecture, and transforms it into a graphical representation that can be modified on a graphical interface. Meanwhile, a deep learning model with many layers is difficult to be visualized on a small display screen. To handle the difficulty, it proposes a method to identify the submodules called articulable subgraphs in a deep learning model and to organize the deep learning model into a hierarchical architecture using the nesting relationships of articulable subgraphs. It introduces a method to identify frequent substractures as well as articulable subgraphs as building blocks for deep learning models for levelwise views. The hierarchical representation easily enables to build deep learning models with many layers. The GUI‐based modeling tool employing the proposed methods makes nonexpert developers easily use deep learning techniques in their practical applications.
Causation-Driven Visualizations for Insurance Recommendation

Recommender systems provide insurance enterprises with insights on customer behaviors and demands, thereby serving as an important source of revenue generation. Key success factors for a recommender include incorporation of multi-dimensional data from diverse media, clear and interpretable presentation of analytical results, and accurate recommendation of insurance products. However, most existing methods in the industry produce merely a list of recommendations without effectively visualizing them, and meanwhile are based on correlation-driven algorithms that can omit significant causal patterns in both customer and product data. In this paper, we propose a causation-driven visualization system that fundamentally transforms cross-media insurance data into network diagrams and performs recommendation reasoning. The system leverages data retrieved from both local insurance service providers and resources across the Web, and utilizes a multi-valued confounder balancing algorithm to compute the causal effect between customer and products, aiming to explore the reasons behind customers' purchasing behaviors and make appropriate recommendations accordingly. Furthermore, we conduct domain expert studies to evaluate the effectiveness of knowledge discovery by our causation-driven visualization system and correlation-driven tools currently adopted in the insurance industry. Results show that our system considerably outperforms others in terms of all evaluation metrics, and can potentially help insurers devise practical decision-making strategies.
Towards Democratizing E-health Under Constrained ICT Infrastructure Through ‘A- REaLiST’ Solution

The primary need for mass e-health service is, the remote doctor should be able to examine the patient through video communication over public Information and Communication Technology (ICT) infrastructure. However, poor ICT infrastructure creates a key obstacle for rural India due to low bandwidth and intermittent Internet connectivity. This paper critically examines the state of the public ICT infrastructure in India and proposes an end-to-end solution such that the Quality of Experience (QoE) for remote visual examination can be ensured over IP connectivity even in a 2G network. We extend previously proposed video streaming solution for Internet of Things (IoT) called A-REaLiST (Adaptive-RESTful Real time Streaming for Things). Thus we extend the design principles of IoT communication in order to achieve an acceptable QoE by effectively harnessing the latent potential in the existing infrastructure. Efficacy of the solution is determined through benchmarking experiments under realistic network conditions and comparing with existing state of the art.
Semi-Unsupervised Lifelong Learning for Sentiment Classification

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Naïve Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focus on how to accumulate knowledge during learning and leverage them for the further tasks. Meanwhile, the demand for labeled data for training also be significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labeled data and computational cost to achieve the performance as well as or even better than the supervised learning.
Hub companies shaping the future: the ethicality and corporate social responsibility of platform economy giants

Hub companies (e.g. Amazon, Facebook, Apple, Twitter and Google) rule the internet. They are de facto monopolies in their area of operations. They shape the future in which we live. And, it seems there is nothing we can do about that, as traditional economies of extreme scale – in which eventually the size of a corporation starts to be a hindrance, rather than an advantage – do not apply to them. On the contrary, they keep growing and growing and thus gaining stronger and stronger strangle hold over their respective areas of commerce and influence. This leads to unethical results, where the corporations spin out of any control, national or international. In this paper we give reasons to this phenomenon and lament the future of the internet – unless something drastic is done to change this.
Adversarial Attack, Defense, and Applications with Deep Learning Frameworks

In recent years, deep learning frameworks have been applied in many domains and achieved promising performance. However, recent work have demonstrated that deep learning frameworks are vulnerable to adversarial attacks. A trained neural network can be manipulated by small perturbations added to legitimate samples. In computer vision domain, these small perturbations could be imperceptible to human. As deep learning techniques have become the core part for many security-critical applications including identity recognition camera, malware detection software, self-driving cars, adversarial attacks have become one crucial security threat to many deep learning applications in real world. In this chapter, we first review some state-of-the-art adversarial attack techniques for deep learning frameworks in both white-box and black-box settings. We then discuss recent methods to defend against adversarial attacks on deep learning frameworks. Finally, we explore recent work applying adversarial attack techniques to some popular commercial deep learning applications, such as image classification, speech recognition and malware detection. These projects demonstrate that many commercial deep learning frameworks are vulnerable to malicious cyber security attacks.
Comparison of dimensional reduction using the Singular Value Decomposition Algorithm and the Self Organizing Map Algorithm in clustering result of text documents

Abstract Dimension reduction has two methods namely feature selection and feature extraction. Dimension reduction using the feature selection method has a better influence than the feature extraction method on the cluster results. However, there is still a need for feature extraction methods to reduce dimensions. For this reason, an alternative algorithm is needed from the feature extraction method. Self Organizing Map (SOM) is one of the artificial neural network models that has a special nature that is effectively able to create spatial internal representations of input data, or in general to create smaller data dimensions. This research was examining the capability of SOM compared to Singular Value Decomposition (SVD) in reducing data dimension of text documents before there were clustered by k-Means. Results show that SVD still better than SOM in cluster quality index but SOM faster than SVD in computation times.
AskCO: A Multi-language and Extensible Smart Virtual Assistant

This paper discusses a language-independent approach for building smart and easily-extensible personal assistants and chatbots. Given a natural language utterance, the system extracts a formal meaning representation that enables mapping the user intent to the execution of a Java source code. This allows building both smart personal assistants and chatbots that can be easily extended by overriding and implementing simple Java methods. The matching process between the natural language input and the output source code relies on an unsupervised algorithm. Therefore, adding new features to the final system does not require training on large datasets mapping natural language queries to a formal meaning representation.
Optimal manifold neighborhood and kernel width for robust non-linear dimensionality reduction

The graph Laplacian approximation of the Laplace–Beltrami operator on the Riemann manifold requires estimation of the kernel width and the neighborhood which is usually obtained through cross-validation. In this work, we propose a method based on local tangent space alignment to determine the optimal neighborhood. The method works by selecting the most distant point with minimum acceptable misalignment in every local region to define the neighborhood size. The work also proposes to use local kernel width which is represented as a function of the number of data points in the optimal neighborhood and maximum neighborhood distance. Extensive experiments on synthetic and real-world data including brain computer interface readings confirm that the proposed method is able to extract the intrinsic representation and outperforms the related existing state-of-the-art methods in the area.
Higher order statistics for modulation and STBC recognition in MIMO systems

Identification of modulation and space-time block code (STBC) is an important task of receivers in applications such as military, civilian, and commercial communications. Here, we consider multiple-input multiple-output (MIMO) systems. We propose two methods for STBC identification when the modulation is known. We also introduce a method for joint identification of code and modulation. Additionally, we present an enhanced zero-forcing (ZF) equaliser to improve the separation between the features of different classes. Higher order cumulants are used as the statistical features. In the first method of STBC identification, after the proposed equalisation, received data samples are segmented, and then using the mean and Frobenius norm of the covariance matrix of extracted cumulants as threshold values, the STBCs are detected. This method requires the knowledge of the threshold values for each modulation type and noise power. In the second method, the knowledge of noise power and the type of modulation are not needed. After the proposed ZF equalisation, feature vector based on the cumulants is calculated, and then support vector machine (SVM) classifier is used to classify different STBCs. In the third method, joint detection of STBC and modulation type is performed using the proposed ZF equaliser and mapping the received samples. This method deals with the theoretical values calculated from the cumulants of four STBCs and four modulations, where there is no need to know the noise power. The results indicate that the proposed methods perform well even at low signal-to-noise ratios (SNRs).
How Well Current Saliency Prediction Models Perform on UAVs Videos?

It is exciting to witness the fast development of Unmanned Aerial Vehicle (UAV) imaging which opens the door to many new applications. In view of developing rich and efficient services, we wonder which strategy should be adopted to predict salience in UAV videos. To that end, we introduce here a benchmark of off-the-shelf state-of-the-art models for saliency prediction. This benchmark monitors two challenging aspects related to salience, namely the peculiar characteristics of UAV contents and the temporal dimension of videos. This paper enables to identify the strengths and weaknesses of current static, dynamic, supervised and unsupervised models for drone videos. Eventually, we highlight several strategies for the development of visual attention in UAV videos.
Tomographic Reconstruction of Imaging Diagnostics with a Generative Adversarial Network

We have developed a tomographic reconstruction method using a conditional Generative Adversarial Network to obtain local-intensity profiles from imaging-diagnostic data. To train the network we prepared pairs of local-emissivity and line-integrated images that simulate the experimental system. After validating the accuracy of the trained network, we used it to reconstruct a local image from a measured line-integrated image. We applied this procedure to the He II-emission imaging diagnostic for RT-1 magnetospheric plasmas, including the effects of stray light within the measured image to remove reflections from the chamber walls in the reconstruction. The local intensity profiles we obtain clearly elucidate the effect of ion-cyclotron-resonance heating. This method is a powerful tool for systems where it is difficult to solve the inversion problem due to the involved contributions of nonlocal optical effects or measurement restrictions.
A Hybrid Method for Vietnamese Text Normalization

This paper presents a hybrid method for normalizing written text often found on newspapers to its spoken form. To normalize raw text with a number of non-standard words (NSWs), a two-step model is proposed. The first step involves classifying NSWs into different categories using Random Forest. The latter one is to expand them, depending on their NSW types, into pronounceable syllables using a hybrid method. Most of numeric types can be expanded by well-defined rules while most of alphabetic ones must be expanded by a deep learning (i.e. sequence-to-sequence) model and a post adjustment. The experiment on a Vietnamese corpus with proposed NSW categories shows that the most ambiguous cases of the classification model are for abbreviation and read-as-sequence types, hence combined into one category for the latter expansion with more complex model and better context. The classification model gives an enhanced result of 99.20% with the category combination and the feature optimization. In the expansion, the sequence-to-sequence model shows a good result of 96.53% for abbreviations and 96.25% for loanwords with a post-adjustment for some completely wrong cases. This model can predict effectively the expansions of abbreviations in context.
The Complexity of the Consistency Problem in the Probabilistic Description Logic $$\mathcal {ALC} ^\mathsf {ME}$$

The probabilistic Description Logic $$\mathcal {ALC} ^\mathsf {ME}$$ is an extension of the Description Logic $$\mathcal {ALC} $$ that allows for uncertain conditional statements of the form “if C holds, then D holds with probability p,” together with probabilistic assertions about individuals. In $$\mathcal {ALC} ^\mathsf {ME}$$ , probabilities are understood as an agent’s degree of belief. Probabilistic conditionals are formally interpreted based on the so-called aggregating semantics, which combines a statistical interpretation of probabilities with a subjective one. Knowledge bases of $$\mathcal {ALC} ^\mathsf {ME}$$ are interpreted over a fixed finite domain and based on their maximum entropy ( $$\mathsf {ME}$$ ) model. We prove that checking consistency of such knowledge bases can be done in time polynomial in the cardinality of the domain, and in exponential time in the size of a binary encoding of this cardinality. If the size of the knowledge base is also taken into account, the combined complexity of the consistency problem is NP-complete for unary encoding of the domain cardinality and NExpTime-complete for binary encoding.
On decidability and complexity of low-dimensional robot games

A robot game, also known as a Z-VAS game, is a two-player vector addition game played on the integer lattice Zn, where one of the players, Adam, aims to avoid the origin while the other player, Eve, aims to reach the origin. The problem is to decide whether or not Eve has a winning strategy. In this paper we prove undecidability of the two-dimensional robot game closing the gap between undecidable and decidable cases. We also prove that deciding the winner in a robot game with states in dimension one is EXPSPACE-complete and study a subclass of robot games where deciding the winner is in EXPTIME.
A Two-Step System Based on Deep Transfer Learning for Writer Identification in Medieval Books

In digital paleography, recent technology advancements are used to support paleographers in the study and analysis of ancient documents. One main goal of paleographers is to identify the different scribes (writers) who wrote a given manuscript. Deep learning has recently been applied to many domains. However, in order to overcome its requirement of large amount of labeled data, transfer learning have been used. This approach typically uses previously trained large deep networks as starting points to solve specific classification problems. In this paper, we present a two step deep transfer learning based tool to help paleographers identify the parts of a manuscript that were written by the same writer. The suggested approach has been tested on a set of digital images from a Bible of the XII century. The achieved results confirmed the effectiveness of the proposed approach.
Advanced Stereo Seam Carving by Considering Occlusions on Both Sides

Stereo image retargeting plays a significant role in the field of image processing, which aims at making major objects as prominent as possible when the resolution of an image is changed, including maintaining disparity and depth information at the same time. Some seam carving methods are proposed to preserve the geometric consistency of the images. However, the regions of occlusion on both sides are not considered properly. In this article, we propose a solution to solve this problem. A new strategy of seams finding is designed by considering occluded and occluding regions on both of the input images, and leaving geometric consistency in both images intact. We also introduced the method of line segment detection and superpixel segmentation to further improve the quality of the images. Imaging effects are optimized in the process and visual comfort, which is also influenced by other factors, can be boosted as well.
FAME(Q): An Automated Tool for Forgetting in Description Logics with Qualified Number Restrictions

In this paper, we describe FAME(Q), a Java-based implementation of a forgetting method developed for eliminating concept and role names from $$\mathcal {ALCOQH}$$ -ontologies. FAME(Q) is presently the only tool for concept forgetting in description logics with qualified number restrictions and nominals, and the only tool for role forgetting in description logics with qualified number restrictions. FAME(Q) can be used as a stand-alone tool or a Java library for forgetting, or related tasks. An evaluation of FAME(Q) on a large corpus of biomedical ontologies shows that the tool is able to compute forgetting solutions in 90% of the test cases; in most cases, the solutions are computed within a few seconds.
On fused dimension reduction in multivariate regression

High-dimensional data analysis often suffers the so-called curse of dimensionality, and various data reduction methods are adopted in order to avoid it in practice. Consequently, in multivariate regression, high-dimensional predictors should be reduced to lower-dimensional ones without the loss of information, following a notion of sufficient dimension reduction. In this paper, a fused clustered seeded reduction approach is proposed for multivariate regression. The proposed method utilizes two types of information: supervised learning between the responses and the predictors, and unsupervised learning of the predictors alone. Fusing all the information has a potential advantage in the accuracy of the reduction of predictors. Numerical studies and a real data analysis confirm the practical usefulness of the proposed approach over existing methods.
Dueto: Accessible, Gaze-Operated Musical Expression

Gaze-tracking technologies can enable computer access for users who are unable to use standard input devices. However, using gaze as input poses challenges for interactions that require visual planning, like playing a digital instrument. We explore how multimodality can support eye-controlled musical expression by designing different multi-modal gaze interactions around a digital instrument we call Dueto. We tackle three design goals: creating an instrument that is explorable, easy to learn, and allows feature controllability. We showcase three different multimodal interactions for music playing such as eye gaze only, gaze + switch, and gaze + partner mode.
Graph comparison via nonlinear quantum search

Graph comparison is an established NP-hard problem. In this paper, we present an efficiently scaling quantum algorithm which finds the size of the maximum common edge subgraph for any pair of unlabelled graphs and thus provides a meaningful measure of graph similarity. The algorithm makes use of a two-part quantum dynamic process: in the first part, we obtain information crucial for the comparison of two graphs through linear quantum computation. However, this information is hidden in the quantum system with such a vanishingly small amplitude that even quantum algorithms such as Grover’s search are not fast enough to distil it efficiently. In order to extract the information, we call upon techniques in nonlinear quantum computing to provide the speed-up necessary for an efficient algorithm. The linear quantum circuit requires $$\mathcal {O}(n^3 \log ^3 (n) \log \log (n))$$ elementary quantum gates, and the nonlinear evolution under the Gross–Pitaevskii equation has a time scaling of $$\mathcal {O}(\frac{1}{g} n^2 \log ^3 (n) \log \log (n))$$ , where n is the number of vertices in each graph and g is the strength of the Gross–Pitaevskii nonlinearity. Through this example, we demonstrate the power of nonlinear quantum search techniques to solve a subset of NP-hard problems.
BArt: Trading digital contents through digital assets

Summary Since digital artworks are indeed digital content, they face the inherent problem digital content has: the link between content and its original author is very difficult to keep. Additionally, retaining control over digital copies of the content is also a challenging task. Digital coins have solved this very same problem through cryptocurrencies (for instance, Bitcoin) and have opened the door to apply the same techniques to other similar scenarios where ownership of digital assets needs to be preserved. In this paper, we propose BArt , a transparent and distributed mechanism for artists to commercialize their digital artwork, keeping control of the copies, monetizing its usage, and maintaining ownership. BArt allows artists to publicly register their work in the Bitcoin blockchain and sell usage rights in exchange for bitcoins. Buyers are allowed to exert the acquired rights. Proper behavior from all parties is enforced by the system with cryptography and economic incentives.