Reviewerly/exHarmony huggingface dataset:
Building Semantic Metadata for Historical Archives through an Ontology-driven User Interface

Historical archives represent an immense wealth, the potential of which is endangered by the lack of effective management and access tools. We believe that this issue can be faced by providing archive catalogs with a semantic layer , containing rich semantic metadata, representing the content of documents in a full-fledged formal machine-readable format. In this article, we present the contribution offered in this direction by the PRiSMHA project , in which the conceptual vocabulary of the semantic layer is represented by computational ontologies. However, acquiring semantic knowledge represents a well-known bottleneck for knowledge-based systems; to solve this problem, PRiSMHA relies on a crowdsourcing collaborative model , i.e., an online community of users who collaborate in building semantic representations of the content of archival documents. In this perspective, this article aims at answering the following research question : Starting from the axioms characterizing concepts in the computational ontology underlying the system, how can we derive a user interface enabling users to formally represent the content of archival documents by exploiting the conceptual vocabulary provided by the ontology? Our solution includes the following steps: (a) a manually defined configuration, acting as a pre-filter, to hide “unsuited” classes, properties, and relations; (b) an algorithm, combining heuristics and reasoning, which extracts from the ontology all and only the “compatible” properties and relations, given an entity (event) type; and (c) a set of strategies to rank, group, and present the entity (event) properties and relations, based on the results of a study with users. This integrated solution enabled us to design an ontology-driven user interface enabling users to characterize entities, and in particular (historical) events, on the basis of the vocabulary provided by the ontology.
VR Chiriyakhana: A Virtual Zoo Using Google Cardboard

The application of virtual reality in entertainment and education industry is growing rapidly day by day. The proposed method offers a new experience for animal lovers and zoo visitors by gaining a new experience of surfing at the virtual zoo using the Google cardboard. Additionally, the study provides informative view to the students and creates possibilities for unable people to visit zoo virtually which delivers 3D computer generated animal objects with virtual surroundings using animations and videos from the real environment which includes vocal expression and scientific description of different animals available in traditional zoos of Bangladesh. An android-based mobile application developed using unity platform which consist of eight different animals with their different activity. The major findings of the study, virtual zoo provides the visiting opportunity regardless of seasonal weather and pandemic situation. AR based zoo helps the visitors to watch the animals more precisely and more interesting way by getting 360 video of the animals. The proposed method supports the early age students to know the physical view of different animals and their scientific name which benefits to identify different animals in real life situation. Qualitative feedback collected from the 25 participants who had experience visiting the traditional zoo where participants showed positive attitude in experiencing virtual zoo. The findings from the participants’ feedback, the implication of proposed system creates impressive contribution in education and entertainment industry.
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference.
Application design phase risk assessment framework using cloud security domains

Security risk assessment is done to identify the vulnerabilities of a client’s application and develop strong security measures within budgetary constraints. However, while migrating to the Cloud platform, a generic notion of their publicly available security policies make it challenging for clients to assess the security threats solely relevant to their applications. Additionally, traditional risk assessment techniques cannot address these challenges as they neither consider cloud security domains as assessment criteria nor identifies critical system resources that need to be protected in the likelihood of a successful attack. In order to address these challenges, this paper presents a risk assessment framework for clients’ applications that is characterized by the inclusion of cloud security metrics to perform risk assessment during the design phase of an application by incorporating the techniques of cloud misuse patterns. It also helps improve the security requirements phase that precedes risk assessment, by illustrating clients how different attack scenarios can spread through the applications by using the concepts of percolation centrality and probabilistic state transition diagrams. One of the key findings this work address is how to systematically gain a distinction between multiple system resources belonging to the same security defense priority level.
Fonts Like This but Happier

Fonts carry strong emotional and social signals, and can affect user engagement in significant ways. Hence, selecting the right font is a very important step in the design of a multimodal artifact with text. Currently, font exploration is frequently carried out via associated social tags. Users are expected to browse through thousands of fonts tagged with certain concepts to find the one that works best for their use case. In this study, we propose a new multimodal font discovery method in which users provide a reference font together with the changes they wish to obtain in order to get closer to their ideal font. This allows for efficient and goal-driven navigation of the font space, and discovery of fonts that would otherwise likely be missed. We achieve this by learning cross-modal vector representations that connect fonts and query words.
Modeling the effects of contact-tracing apps on the spread of the coronavirus disease: Mechanisms, conditions, and efficiency

This study simulates the spread of the coronavirus disease (COVID-19) using a detailed agent-based model and the census data of Japan to provide a comprehensive analysis of the effects of contact-tracing apps. The model assumes two types of response to the app notification: the notified individuals quarantine themselves (type-Q response) or they get tested (type-T response). The results reveal some crucial characteristics of the apps. First, type-Q response is successful in achieving containment; however, type-T response has a limited curve-flattening effect. Second, type-Q response performs better than type-T response because it involves quarantine of those who are infected but have not become infectious yet, and the current testing technology cannot detect the virus in these individuals. Third, if the download rate of the apps is extremely high, type-Q response can achieve virus containment with a small number of quarantined people and thereby high efficiency. Finally, given a fixed download rate, increasing the number of tests per day enhances the effectiveness of the apps, although the degree of improved effectiveness is not proportional to the change in the number of tests.
On the universal steganography of optimal rate

In this work, we present the first secure stegosystem in the common computational model which, for any communication channel, is provably secure, reliable, and has nearly optimal bandwidth, but needs super-polynomial time. This solves several open problems about secret-key steganography in the computational model. In particular, our result answers affirmatively the question whether there exists a secure and reliable universal system of rate asymptotically larger than log⁡κ, where κ is the security parameter. Next, we prove a lower bound on the query complexity of stegosystems showing that our construction is optimal. This lower bound extends the results by Hopper et al. (2009) [19] and by Dedić et al. (2009) [22]. We also discuss universal steganography of optimal rate in the information-theoretic setting. We prove that an exponential number of samples is needed to embed messages in documents of high min-entropy. Our results, together with the result by Cachin (2004) [16], show that the situation of universal steganography in the computational and in the information-theoretic model is analogous: optimal universal steganography exists, but the protocols need super-polynomial time.
New Interactive Methods for Image Registration with Applications in Repeat Photography

We present two methods to interactively register images, which can not be registered automatically due to the large differences between them. Those difference may occur with images which are multitemporal, multisensory, or multipositional. Our methods are used to register repeat photography compilations consisting of a historic image and a contemporary image of the same scene. Method I iteratively computes a rigid transformation registering the two images based on user added point pairs. More complex transformations are generated with more added point pairs starting from a translation with one point pair to an optimized perspective transformation with more than four user added point pairs. Method II allows users to grab and drag one of the images to be registered and pin it to the second image. Depending on the number of positions at which the images are pinned together more complex transformations are computed. Further we present a third method aiding already at the creation of the second image. Method III allows the user to compare a life camera stream (e.g. from a smartphone camera or webcam) with a first image using different composition forms such allowing users to check if they reached the camera position of the first image. All methods give direct feedback to the user during the registration process and run on client side on a web browser without additional software. Further, they are planned to be integrated into our repeat photography web portal.
Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography

Real-coded evolutionary algorithms have solved numerous real-world optimization problems. In this work, we aim to analyze the behavior and robustness of several real-coded evolutionary algorithms from the state of the art in a challenging real world optimization problem. This optimization problem consists on the superimposition of 3D and 2D images of skeletal structures (i.e. bones and cavities) based on their silhouette. This task is required for the automation of a forensic identification technique known as comparative radiography, via the generation of the best projection of the 3D image with respect to the 2D image. This superimposition problem was tackled in a recent proposal using an evolutionary 3D–2D image registration method based on differential evolution. However, the results obtained were insufficient for its use in real scenarios, due to: (1) the complexity and multi-modality of search space, despite the reduced number of parameters to be optimized (7 in its simple version and 9 in a more complex one, proposed in this work); and (2) the high computational cost of generating and evaluating a superimposition. Particularly, we have performed a rigorous comparative study of six state-of-the-art real-coded evolutionary algorithms (DE, L-SHADE, CMA-ES, BIPOP-CMA-ES, CRO-SL, and MVMO-SH) with synthetic images of three forensic anatomical structures (frontal sinuses, clavicles, and patellae), showing that the best results are always obtained by MVMO-SH in terms of precision, robustness and computational cost. Furthermore, we have validated the quality of the superimpositions obtained by the evolutionary image registration method using MVMO-SH with real images of frontal sinuses. We have performed the comparison of 50 head radiographs and 50 3D images, resulting in 2,500 cross-comparisons (50 positive and 2,450 negatives). The obtained results are promising since the superimpositions obtained allowed us to filter out 88% of the possible candidates with 0 error rate in a fully automatic manner, showing the high quality of the superimposition obtained.
Adversarial Image Attacks Using Multi-Sample and Most-Likely Ensemble Methods

Many studies on deep neural networks have shown very promising results for most image recognition tasks. However, these networks can often be fooled by adversarial examples that simply add small but powerful distortions to the original input. Recent works have demonstrated the vulnerability of deep learning systems to adversarial examples, but most such works directly manipulate and attack the digital images for a specific classifier only, and cannot attack the physical images in real world. In this paper, we propose the multi-sample ensemble method (MSEM) and most-likely ensemble method (MLEM) to generate adversarial attacks that successfully fool the classifier for images in both the digital and real worlds. The proposed adaptive norm algorithm can craft faster and smaller perturbation than other state-of-the-art attack methods. Besides, the proposed MLEM extended with weighted objective function can generate robust adversarial attacks that can mislead multiple classifiers (Inception-v3, Inception-v4, Resnet-v2, Ince-res-v2) simultaneously for physical images in real world. Compared with other methods, experiments show that our adversarial attack methods not only can achieve higher success rates but also can survive in the multi-model defense tests.
Parallel advanced iterative algorithm for phase extraction with unknown phase-shifts

Phase-extraction is important in various fields of optical metrology, for which, many phase-shifting algorithms have been developed. Among them, the advanced iterative algorithm (AIA) can accurately extract phase from fringe patterns with random unknown phase-shifts by iteratively estimating the phase and phase-shifts. However, these iterations make the AIA much slower than traditional phase-shifting algorithms. This problem is severer when both the pixel number and the frame number are large for high resolution and accuracy, restricting AIA's wide application. In this paper, based on the detailed analysis of the algorithm's structure, a fully parallelized GPU-based AIA (gAIA) is proposed for the first time. Without scarifying the phase extraction accuracy, the gAIA achieves 500 × speedup comparing with the sequential implementation on a single-core-CPU, and 10 × speedup comparing with the state-of-the-art partial GPU implementation which has a potential convergence issue. Also, for the first time, the real-time phase extraction with AIA is achieved by using a normal NVIDIA RTX 2080 Ti GPU, i.e., the proposed gAIA only takes 26.55 ms to extract phase from 13 frames of fringe patterns with 2048 × 2048 pixels per frame. Finally, through the implementation and testing of the gAIA, it is discovered that increasing the frame number has little effect on the speed performance, which is against our intuition. As a consequence, more frames can be used for gAIA to increase the phase extraction accuracy with little influence on the speed.
Distributed Multi-agent Video Fast-forwarding

In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness. As these agents often have limited resources, it could be greatly beneficial to identify the content overlapping among camera views from different agents and leverage it for reducing the processing, transmission and storage of redundant/unimportant video frames. This paper presents a consensus-based distributed multi-agent video fast-forwarding framework, named DMVF, that fast-forwards multi-view video streams collaboratively and adaptively. In our framework, each camera view is addressed by a reinforcement learning based fast-forwarding agent, which periodically chooses from multiple strategies to selectively process video frames and transmits the selected frames at adjustable paces. During every adaptation period, each agent communicates with a number of neighboring agents, evaluates the importance of the selected frames from itself and those from its neighbors, refines such evaluation together with other agents via a system-wide consensus algorithm, and uses such evaluation to decide their strategy for the next period. Compared with approaches in the literature on a real-world surveillance video dataset VideoWeb, our method significantly improves the coverage of important frames and also reduces the number of frames processed in the system.
Chinese Microblog Sentiment Detection Based on CNN-BiGRU and Multihead Attention Mechanism

With the rapid development of the Internet, Weibo has gradually become one of the commonly used social tools in society at present. We can express our opinions on Weibo anytime and anywhere. Weibo is widely used and people can express themselves freely on it; thus, the amount of comments on Weibo has become extremely large. In order to count up the attitudes of users towards a certain event, Weibo managers often need to evaluate the position of a certain microblog in an appropriate way. In traditional position detection tasks, researchers mainly mine text semantic features through constructing feature engineering and sentiment dictionary, but it takes a large amount of manpower in feature selection and design. However, it is an effective method to analyze the sentiment state of microblog comments. Deep learning is developing in an increasingly mature direction, and the utilization of deep learning methods for sentiment detection has become increasingly popular. The application of convolutional neural networks (CNN), bidirectional GRU (BiGRU), and multihead attention mechanism- (multihead attention-) combined method CNN-BiGRU-MAttention (CBMA) to conduct Chinese microblog sentiment detection was proposed in this paper. Firstly, CNN were applied to extract local features of text vectors. Afterward, BiGRU networks were applied to extract the global features of the text to solve the problem that the single CNN cannot obtain global semantic information and the disappearance of the traditional recurrent neural network (RNN) gradient. At last, it was concluded that the CBMA algorithm is more accurate for Chinese microblog sentiment detection through a variety of algorithm experiments.
Photo Stand-Out: Photography with Virtual Character

In this paper, we propose a novel optimization framework to synthesize an aesthetic pose for the virtual character with respect to the presented user's pose. Our approach applies aesthetic evaluation that exploits fully connected neural networks trained on example images. The aesthetic pose of the virtual character is obtained by optimizing a cost function that guides the rotation of each body joint angles. In our experiments, we demonstrate the proposed approach can synthesize poses for virtual characters according to user pose inputs. We also conducted objective and subjective experiments of the synthesized results to validate the efficacy of our approach.
The parent–subsidiary knowledge transfer efficiency of Chinese–African multinational enterprises based on a metafrontier epsilon‐based measure model

To investigate knowledge transfer efficiency, this study applies a metafrontier epsilon‐based measure (EBM) model to survey data for 102 Chinese and African multinational enterprises (C–A MNEs). The results are as follows. (1) Only 14 of the 102 C–A MNEs have an efficiency score of 1. (2) The scores for different indicators of the same enterprise vary significantly. (3) Technical support input is inefficient, with all major industries showing obvious disadvantages. (4) Team performance efficiency scores significantly lag behind the other output variables' efficiency scores, presenting significant room for improvement. (5) There are differences in the efficiency of knowledge transfer among different industries.
Uncertain distance-based outlier detection with arbitrarily shaped data objects

Abstract Enabling information systems to face anomalies in the presence of uncertainty is a compelling and challenging task. In this work the problem of unsupervised outlier detection in large collections of data objects modeled by means of arbitrary multidimensional probability density functions is considered. We present a novel definition of uncertain distance-based outlier under the attribute level uncertainty model, according to which an uncertain object is an object that always exists but its actual value is modeled by a multivariate pdf. According to this definition an uncertain object is declared to be an outlier on the basis of the expected number of its neighbors in the dataset. To the best of our knowledge this is the first work that considers the unsupervised outlier detection problem on data objects modeled by means of arbitrarily shaped multidimensional distribution functions. We present the UDBOD algorithm which efficiently detects the outliers in an input uncertain dataset by taking advantages of three optimized phases, that are parameter estimation, candidate selection, and the candidate filtering. An experimental campaign is presented, including a sensitivity analysis, a study of the effectiveness of the technique, a comparison with related algorithms, also in presence of high dimensional data, and a discussion about the behavior of our technique in real case scenarios.
Real-Time Hierarchical Supervoxel Segmentation via a Minimum Spanning Tree

Supervoxel segmentation algorithm has been applied as a preprocessing step for many vision tasks. However, existing supervoxel segmentation algorithms cannot generate hierarchical supervoxel segmentation well preserving the spatiotemporal boundaries in real time, which prevents the downstream applications from accurate and efficient processing. In this paper, we propose a real-time hierarchical supervoxel segmentation algorithm based on the minimum spanning tree (MST), which achieves state-of-the-art accuracy meanwhile at least 11× faster than existing methods. In particular, we present a dynamic graph updating operation into the iterative construction process of the MST, which can geometrically decrease the numbers of vertices and edges. In this way, the proposed method is able to generate arbitrary scales of supervoxels on the fly. We prove the efficiency of our algorithm that can produce hierarchical supervoxels in the time complexity of O(n) , where n denotes the number of voxels in the input video. Quantitative and qualitative evaluations on public benchmarks demonstrate that our proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of supervoxel segmentation accuracy and computational efficiency. Furthermore, we demonstrate the effectiveness of the proposed method on a downstream application of video object segmentation.