Reviewerly/exHarmony huggingface dataset:
Image–speech combination for interactive computer assisted transcription of handwritten documents

Handwritten document transcription aims to obtain the contents of a document to provide efficient information access to, among other, digitised historical documents. The increasing number of historical documents published by libraries and archives makes this an important task. In this context, the use of image processing and understanding techniques in conjunction with assistive technologies reduces the time and human effort required for obtaining the final perfect transcription. The assistive transcription system proposes a hypothesis, usually derived from a recognition process of the handwritten text image. Then, the professional transcriber feedback can be used to obtain an improved hypothesis and speed-up the final transcription. In this framework, a speech signal corresponding to the dictation of the handwritten text can be used as an additional source of information. This multimodal approach, that combines the image of the handwritten text with the speech of the dictation of its contents, could make better the hypotheses (initial and improved) offered to the transcriber. In this paper we study the feasibility of a multimodal interactive transcription system for an assistive paradigm known as Computer Assisted Transcription of Text Images. Different techniques are tested for obtaining the multimodal combination in this framework. The use of the proposed multimodal approach reveals a significant reduction of transcription effort with some multimodal combination techniques, allowing for a faster transcription process.
Reasoning and querying web-scale open data based on DL-Lite<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e7743" altimg="si91.gif"><mml:msub><mml:mrow /><mml:mrow><mml:mi mathvariant="script">A</mml:mi></mml:mrow></mml:msub></mml:math>in a divide-and-conquer way

We propose to use DL-LiteA techniques to reason and query the Web-scale Open Data (knowledge bases) described by Semantic Web standards like RDF and OWL due to the low reasoning complexity and suitable expressivity of the language. When facing the real-life scalability challenge, the actual reasoning and query answering may become infeasible by the following two factors. Firstly, for both satisfiability checking and conjunctive query answering, a polynomial size of queries may need to be answered over the data layers of the corresponding knowledge bases (KBs) w.r.t. the size of the schema knowledge of these KBs. Secondly, for KBs with massive individual assertions, evaluating a single query over the data layers may be highly time-consuming. This impels us to seek for a divide-and-conquer reasoning and query answering approach for DL-LiteA, with the basic idea of partitioning both KBs and queries into smaller chunks and decomposing the original reasoning and query answering tasks into a group of independent sub-tasks such that the overall performance can be improved by taking advantage of parallelization and distribution techniques. The challenge for designing such an approach lies in how to carry out partitioning and reasoning reduction in a sound and complete way. Motivated by hash partitioning of RDF graphs, we expect the smaller KB chunks to have the local feature for both satisfiability checking and simple-query answering. Here simple-queries are the conjunctive queries whose query atoms share a common variable or individual. For query answering, we expect to partition a query into smaller simple-queries and evaluate them over smaller KB chunks. Under these expectations, our divide-and-conquer approach is constructed from both theoretical and practical perspectives. Theoretically, definitions of KB partitions and query partitions are presented, and the sufficient and necessary conditions are identified to determine whether a KB partition holds the desired features. Practically, based on the theoretical results, the concrete ways of partitioning KBs and queries as well as evaluating query partitions over KB partitions are described. Moreover, a strategy of optimizing the procedure of evaluating query partitions over KB partitions is provided to improve the overall query answering performance. To verify our approach, two Web-scale open datasets, DBpedia and BTC 2012 dataset, have been chosen. The empirical results indicate that the provided approach opens new possibilities for realizing performance-critical applications on the Web with both high expressivity and scalability.
Levelling, Nudging, and Easing

Social and collaborative benefits are a major motivator for developing tangible and embodied interaction, but if, how, and why such approaches may successfully facilitate collocated encounters between strangers is not well established. We have been conducting an iterative, and participatory design space review of diverse design examples that may help foster social icebreaking in urban settings. Qualitative data was collected through workshops and group sessions, analysed thematically and compared against insights from social science literature. As a result, clusters of diverse design examples were connected with explanations of specific difficulties with initiating new encounters that they might support. In this paper, we focus on how different aspects of the distances between people may impede new encounters. We contribute three inspirational design patterns that offer strategies to make social interaction more likely through enabling, encouraging or excusing people to move closer together. These articulations of possible approaches for increasing conviviality may broaden the repertoire of developers concerned with social settings and collaboration.
Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Efficient harbor extraction is essential due to the strategic importance of this target in economic and military construction. However, there are few studies on harbor extraction. In this article, a new harbor extraction algorithm based on edge preservation and edge categories (EC) is proposed for high spatial resolution remote-sensing images. In the preprocessing stage, we propose a local edge preservation algorithm (LEPA) to remove redundant details and reduce useless edges. After acquiring the local edge-preserve images, in order to reduce the redundant matched keypoints and improve the accuracy of the target candidate extraction method, we propose a scale-invariant feature transform (SIFT) keypoints extraction method based on edge categories (EC-SIFT): this method greatly reduces the redundancy of SIFT keypoint and improves the computational complexity of the target extraction system. Finally, the harbor extraction algorithm uses the Support Vector Machine (SVM) classifier to identify the harbor target. The experimental results show that the proposed algorithm effectively removes redundant details and improves the accuracy and efficiency of harbor target extraction.
Doubly Half-Injective PRGs for Incompressible White-Box Cryptography

White-box cryptography was originally introduced in the setting of digital rights management with the goal of preventing a user from illegally re-distributing their software decryption program. In recent years, mobile payment has become a popular new application for white-box cryptography. Here, white-box cryptography is used to increase the robustness against external adversaries (i.e., not the user) who aim to misuse/attack the cryptographic functionalities of the payment application. A necessary requirement for secure white-box cryptography is that an adversary cannot extract the embedded secret key from the implementation. However, a white-box implementation needs to fulfill further security properties in order to provide useful protection of an application. In this paper we focus on the popular property incompressibility that is a mitigation technique against code-lifting attacks. We provide an incompressible white-box encryption scheme based on the standard-assumption of one-way permutations whereas previous work used either public-key type assumptions or non-standard symmetric-type assumptions.
A Toolkit for Creating Cross-Reality Serious Games

In this paper, we present a toolkit that aims at facilitating the design and implementation of serious games played in cross-reality environments. This type of systems interconnects a physical space with a virtual world. Its use as a platform for serious games will open the door to the design of collaborative learning experiences based on games played simultaneously by real and virtual players. However, due the wide range of technologies they require to integrate, their implementation could be complex and expensive. The toolkit aims at lowering the cost of building cross-reality serious games, so that the opportunities that this type of artefacts might offer in educational contexts can be more easily explored.
A comparison and evaluation of variants in the coupling between objects metric

The Coupling Between Objects metric (CBO) is a widely-used metric but, in practice, ambiguities in its correct implementation have led to different values being computed by different metric tools and studies. CBO has often been shown to correlate with defect occurrence in software systems, but the use of different calculations is commonly overlooked. This paper investigates the varying interpretations of CBO used by those metrics tools and researchers and defines a set of metrics representing the different computational approaches used. These metrics are calculated for a large-scale Java system and logistic regression used to correlate them with defect data obtained by analysing the system's version tracking records. The different variations of CBO are shown to have significantly different correlations to defects. Regarding results, a clear binary divide was found between CBO values which, on the one hand, predicted a defect and, on the other, those that did not. The results, therefore, show that a clarification or unambiguous re-definition of CBO is both desirable and essential for a general consensus on its use. Moreover, applications of the metric must pay close attention to the actual method of calculation being used and, conclusions and comparisons made as a result.
Black-Box Hyperparameter Optimization for Nuclei Segmentation in Prostate Tissue Images

Segmentation of cell nuclei is essential for analyzing highcontent histological screens. Often, parameters of automatic approaches need to be optimized, which is tedious and difficult to perform manually. We propose a novel hyperparameter optimization framework, which formulates optimization as a combination of candidate sampling and an optimization strategy. We present a clustering based and a deep neural network based pipeline for nuclei segmentation, for which the parameters are optimized using state of the art optimizers as well as a novel optimizer. The pipelines were applied to challenging prostate cancer tissue images. We performed a quantitative evaluation using 28,388 parameter settings. It turned out that the deep neural network outperforms the clustering based pipeline, while the results for different optimizers vary slightly.
Single- and Multi-Step Image Enlargement for Medical Image Coding

Background: Some interpolators cannot be used in an image magnification problem in a freely scalable form. For instance, when we want to magnify an image to a 16-time bigger scale, some interpolators have to do this process in two steps including two 4-time magnification steps, however, some are able to do it directly. Materials and Methods: For generating data of this study, MATLAB as a simulator has been used. Bi-; Linear (BL) and Cubic Convolution (CC) interpolators are the two applied re-samplers in the reconstruction of digital images. Results: Data shows that the performance of both free-size interpolators (BL and CC) is obviously different in both direct and indirect pixel reconstruction. Conclusion: The acquired data shows a less error in the condition of direct interpolation. The relative results of experiments are different from the type of core interpolators (BL and CC).
Selective multi-descriptor fusion for face identification

Over the last 2 decades, face identification has been an active field of research in computer vision. As an important class of image representation methods for face identification, fused descriptor-based methods are known to lack sufficient discriminant information, especially when compared with deep learning-based methods. This paper presents a new face representation method, multi-descriptor fusion (MDF), which represents face images through a combination of multiple descriptors, resulting in hyper-high dimensional fused descriptor features. MDF enables excellent performance in face identification, exceeding the state-of-the-art, but it comes with high memory and computational costs. As a solution to the high cost problem, this paper also presents an optimisation method, discriminant ability-based multi-descriptor selection (DAMS), to select a subset of descriptors from the set of 65 initial descriptors whilst maximising the discriminant ability. The MDF face representation, after being refined by DAMS, is named selective multi-descriptor fusion (SMDF). Compared with MDF, SMDF has much smaller feature dimension and is thus usable on an ordinary PC, but still has similar performance. Various experiments are conducted on the CAS-PEAL-R1 and LFW datasets to demonstrate the performance of the proposed methods.
An Efficient Private Evaluation of a Decision Graph

A decision graph is a well-studied classifier and has been used to solve many real-world problems. We assumed a typical scenario between two parties in this study, in which one holds a decision graph and the other wants to know the class label of his/her query without disclosing the graph and query to the other. We propose a novel protocol for this scenario that can obliviously evaluate a graph that is designed by an efficient data structure called the graph level order unary degree sequence (GLOUDS). The time and communication complexities of this protocol are linear to the number of nodes in the graph and do not include any exponential factors. The experiment results revealed that the actual runtime and communication size were well concordant with theoretical complexities. Our method can process a graph with approximately 500 nodes in only 11 s on a standard laptop computer. We also compared the runtime of our method with that of previous methods and confirmed that it was one order of magnitude faster than the previous methods.
A Skip-Connected 3D DenseNet Networks with Adversarial Training for Volumetric Segmentation

In this paper, we propose a novel end-to-end adversarial training on volumetric brain segmentation architecture that allows to enforce long-range spatial label contiguity and label consistency. The proposed network consists of two networks: generator and discriminator. The generator network allows to take volumetric image as input and provides a volumetric probability map for each tissue. Then, the discriminator network learns to differentiate ground-truth maps from the probability maps of generator network. We design a discriminator in a fully convolutional manner to differentiate the predicted probability maps from the ground-truth segmentation distribution with the consideration of the spatial information on voxel level, which makes it difficult to learn the discriminator. In order to overcome it, the proposed discriminator provides a 3D confidence map which indicates corresponding regions of the probability maps close to the ground-truth. Based on the 3D confidence map information, the generator network will refine prediction output close to the ground-truth maps in a high-order structure.
A Fast Sparse Coding Method for Image Classification

Image classification is an important problem in computer vision. The sparse coding spatial pyramid matching (ScSPM) framework is widely used in this field. However, the sparse coding cannot effectively handle very large training sets because of its high computational complexity, and ignoring the mutual dependence among local features results in highly variable sparse codes even for similar features. To overcome the shortcomings of previous sparse coding algorithm, we present an image classification method, which replaces the sparse dictionary with a stable dictionary learned via low computational complexity clustering, more specifically, a k-medoids cluster method optimized by k-means++. The proposed method can reduce the learning complexity and improve the feature’s stability. In the experiments, we compared the effectiveness of our method with the existing ScSPM method and its improved versions. We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase the accuracy of spatial pyramid matching, which suggests that our method is capable of improving performance of sparse coding features.
Human vs. AI

As one of the most important applications of AI, machine translation has always been the hot topic among scholars in linguistics, computer science, cognitive science and other areas. This article made an assessment of translations of 4 selected major online machine translation platforms from perspectives of efficiency, operating mode and condition. The outputs of machine and human were compared by employing new “6-4” table and comprehensive error rate. The assessment shows that although the quality of machine translation is improving, the gap still exists between the quality of machine translation and human translation. Based on the research findings, the author predicts that machine translation cannot possibly replace human translation and the two will continue to coexist in the foreseeable future.
Quality Assurance of Solar Radiation Measurements

Solar radiation measurements are necessary for every solar energy project to evaluate solar resource assessment studies. Quality assurance of solar radiation measurements is essential in all the stages of solar resource analysis. Model development and assessment, improvement of models and characterisation of the uncertainty, among others features, depend strongly on the accuracy and quality efforts in designing and operating the solar radiation ground station. This chapter summarises several aspects involved in ensuring the quality of solar radiation measurements, addressing the requirements for instrument selection and the quality methods applied to solar radiation data. This chapter has been written intended to be useful for project or group leaders involved in solar resource assessment and not a rigorous scientist text since the chapter presents a summary of many manuals for quality control.
Sensors for Seamless Learning

The chapter highlights the role of sensors for supporting seamless learning experiences. In the first part, the relation between sensor tracking of learning activities and research around real-time feedback in educational situations is introduced. The authors present an overview of the kinds of sensor data that have been used for educational purposes in the literature. Secondly, the authors introduce the link between sensor data and educational interventions, and especially the role of building expert models from real-world expert tracking. The third part of the paper illustrates how educational AR applications have used sensor data for different forms of learning support. The authors present 15 design patterns that have been implemented in different educational AR applications that build on our analysis of sensor tracking. For future AR applications, the authors propose that the use of sensors for building expert performance models is essential for a variety of educational interventions.
A Somewhat Homomorphic Encryption Scheme based on Multivariate Polynomial Evaluation

We propose a symmetric key homomorphic encryption scheme based on the evaluation of multivariate polynomials over a finite field. The proposed scheme is somewhat homomorphic with respect to addition and multiplication. Further, we define a generalization of the Learning with Errors problem called the Hidden Subspace Membership problem and show that the semantic security of the proposed scheme can be reduced to the hardness of this problem.
The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>F</mml:mi></mml:math>-score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.
A realistic simulation platform for multi-quadcopter search using downward facing cameras

This paper presents a cross platform simulation environment, in a hybrid centralized-decentralized architecture, for multi-quadcopter search problem developed using MATLAB and Gazebo simulator in Robot Operating System (ROS) environment. Multiple quadcopters equipped with downward facing camera are deployed in a search area to gather information such as presence of targets of interest. Search is modeled as reducing uncertainty density, a metric of lack of information about the presence (or absence) of the targets of interest. The simulation platform developed will be a very useful tool for conducting realistic simulation experiments to validate the proposed search strategy and to make a comparative study of its performance in terms of time for the search process, with parameters such as camera search effectiveness models, sensor range, the number of robots, to decide on the parameters best suited for a given situation. We provide simulation results demonstrating the proposed search strategy using the developed simulation environment.
Visualisation Model Based on Phishing Features

The numbers of online purchases and electronic banking transactions have increased substantially in the era of electronic business and mobile commerce. These online financial activities have attracted a special web threat called “phishing” that targets Internet users by seeking their credentials in order to access their financial information. Phishing involves impersonating a legitimate website by creating a visually similar fake website to deceive users. In the last decade different solutions to fight phishing that are primarily based on educating users, user’s experience, search methods, machine learning and features similarity have been developed. This paper combines computational intelligence along with user’s experience approaches to develop an anti-phishing visualisation method. Our method employs effective features chosen following thorough analysis on features scores generated by Correlation Feature Set and Information Gain processing techniques. We validate our anti-phishing features using classification systems produced by rule induction data mining approach. False positives, false negatives and phishing detection rate are the basis of evaluating the classification systems to measure our anti-phishing methods features’ integrity.