Reviewerly/exHarmony huggingface dataset:
Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas

A salient scene is an area within an image that contains visual elements that stand out from surrounding areas. They are important for distinguishing landmarks in first-person-view (FPV) applications and determining spatial relations in images. The relative spatial relation between salient scenes acts as a visual guide that is easily accepted and understood by users in FPV applications. However, current digitally navigable maps and location-based services fall short of providing information on visual spatial relations for users. This shortcoming has a critical influence on the popularity and innovation of FPV applications. This paper addresses the issue by proposing a method for detecting visually salient scene areas (SSAs) and deriving their relative spatial relationships from continuous panoramas. This method includes three critical steps. First, an SSA detection approach is introduced by fusing region-based saliency derived from super-pixel segmentation and the frequency-tuned saliency model. The method focuses on a segmented landmark area in a panorama. Secondly, a street-view-oriented SSA generation method is introduced by matching and merging the visual SSAs from continuous panoramas. Thirdly, a continuous geotagged panorama-based referencing approach is introduced to derive the relative spatial relationships of SSAs from continuous panoramas. This information includes the relative azimuth, elevation angle, and the relative distance. Experiment results show that the error for the SSA relative azimuth angle is approximately ± 6° (with an average error of 2.67°), and the SSA relative elevation angle is approximately ± 4° (with an average error of 1.32°) when using Baidu street-view panoramas. These results demonstrate the feasibility of the proposed approach. The method proposed in this study can facilitate the development of FPV applications such as augmented reality (AR) and pedestrian navigation using proper spatial relation.
Optimization of Advanced Encryption Standard Algorithm (AES) on Field Programmable Gate Array (FPGA)

In this paper, we have proposed high speed Advanced Encryption Standard (AES) hardware architecture by using parallelism in the process. National Institute of Standards and Technology (NIST) cases are predefined inside the module will be selected by test case bits, according to test case bit selection, the key and input will be selected, and module generate corresponding output and clock cycles reduces to 44 cycles. AES Algorithm synthesized using VHDL Code and targeted into FPGA. For Synthesis and Simulation Xilinx Design Suit Version 14.7 is used. The design has been Successfully tested on ARTIX-7 FPGA.
Framework Blockchain Education

We live in a dynamic and evolutionary world. Concepts such as internet of things, blockchain, convergence, transformations, disruptions, robotics, artificial intelligence, and learning analytics are increasingly becoming part of our lives. In the present chapter, the authors call attention to the higher education sector, which needs to go beyond the pedagogical and material limits in order to give to the student the protagonism of his/her life story. Rupture means to abstract the methods and models for living together in an environment of strong interactions and relationships, whose transactional cost becomes evident. Thus, this chapter presents a framework, which the authors titled framework blockchain education, in an ecosystem of changes, feeds, analyses, diagnostics, and intelligence, which contributes to the formation of the student according to his/her expectations, that is, to contribute in an effective manner in his/her achievement, while offering a sustainable education.
Investigating Input and Output Units in Diacritic Restoration

Diacritic restoration is the task of assigning diacritics (accents) for each character in a given segment. The typical input levels that have been previously used in diacritic restoration models are word and/or character units. In this paper, we investigate the use of subwords as input units along with their diacritic patterns (combinations of adjacent diacritics) as output, as an alternative to word or character-based models. Our experiments show that characters provide the optimal level of information for sequence-based diacritic restoration models across different languages. We additionally improved our diacritic restoration model by maximizing over the output diacritic sequence using a Conditional Random Field (CRF). Adding a CRF layer improves the performance on observed and unobserved words substantially for Arabic and marginally for Yoruba.
Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets

Synchrophasor technology or phasor measurement units (PMUs) are known to detect multiple type of oscillations or faults better than Supervisory Control and Data Acquisition (SCADA) systems, but the volume of Bigdata (e.g., 30-120 samples per second on a single PMU) generated by these sensors at the aggregator level (e.g., several PMUs) requires special handling. Conventional machine learning or data mining methods are not suitable to handle such larger streaming real-time data. This is primarily due to latencies associated with cloud environments (e.g., at an aggregator or PDC level), and thus necessitates the need for local computing to move the data on the edge (or locally at the PMU level) for processing. This requires faster real-time streaming algorithms to be processed at the local level (e.g., typically by a Field Programmable Gate Array (FPGA) based controllers). This paper proposes a transfer learning-based hoeffding tree with ADWIN (THAT) method to detect anomalous synchrophasor signatures. The proposed algorithm is trained and tested with the OzaBag method. The preliminary results with transfer learning indicate that a computational time saving of 0. 7ms is achieved with THAT algorithm (0.34ms) over Ozabag (1.04ms), while the accuracy of both methods in detecting fault events remains at 94% for four signatures.
Standardization of Gram Matrix for Improved 3D Neural Style Transfer

Neural Style Transfer based on convolutional neural networks has produced visually appealing results for image and video data in the recent years where e.g. the content of a photo and the style of a painting are merged to a novel piece of digital art. In practical engineering development, we utilize 3D objects as standard for optimizing digital shapes. Since these objects can be represented as binary 3D voxel representation, we propose to extend the Neural Style Transfer method to 3D geometries in analogy to 2D pixel representations. In a series of experiments, we first evaluate traditional Neural Style Transfer on 2D binary monochromatic images. We show that this method produces reasonable results on binary images lacking color information and even improve them by introducing a standardized Gram matrix based loss function for style. For an application of Neural Style Transfer on 3D voxel primitives, we trained several classifier networks demonstrating the importance of a meaningful convolutional network architecture. The standardization of the Gram matrix again strongly contributes to visually improved, less noisy results. We conclude that Neural Style Transfer extended by a standardization of the Gram matrix is a promising approach for generating novel 3D voxelized objects and expect future improvements with increasing graphics memory availability for finer object resolutions.
Role of Bell-CHSH violation and local filtering in quantum key distribution

In this article, we analyse the relationship between the Bell violation and the secure key rate of entanglement assisted quantum key distribution (QKD) protocols. Specifically, we address the question whether Bell violation is necessary or sufficient for secure communication. We construct a class of states which do not show Bell violation, however, which can be used for secure communication after local filtering. Similarly, we identify another class of states which show Bell violation but can not be used for generating secure key even after local filtering. The existence of these two classes of states demonstrates that Bell violation as an initial resource is neither necessary nor sufficient for QKD. Our work therefore forces a departure from traditional thinking that the degree of Bell violation is a key resource for quantum communication and brings out the role of local filtering.
Blind Audio Watermarking Based on Parametric Slant-Hadamard Transform and Hessenberg Decomposition

Digital watermarking has been widely utilized for ownership protection of multimedia contents. This paper introduces a blind symmetric audio watermarking algorithm based on parametric Slant-Hadamard transform (PSHT) and Hessenberg decomposition (HD). In our proposed algorithm, at first watermark image is preprocessed to enhance the security. Then, host signal is divided into non-overlapping frames and the samples of each frame are reshaped into a square matrix. Next, PSHT is performed on each square matrix individually and a part of this transformed matrix of size m×m is selected and HD is applied to it. Euclidean normalization is calculated from the 1st column of the Hessenberg matrix, which is further used for embedding and extracting the watermark. Simulation results ensure the imperceptibility of the proposed method for watermarked audios. Moreover, it is demonstrated that the proposed algorithm is highly robust against numerous attacks. Furthermore, comparative analysis substantiates its superiority among other state-of-the-art methods.
Blockchain technology for electoral process in Africa: a short review

Abstract The electoral process has suffered from deep political instability following the post-colonial independence of most African nations. Moreover, the electoral process in many countries is characterized by massive rigging, high cost of electoral materials, and declaration of false results. In this paper, we will present a review of the blockchain Technology and some of the potential roles to play in conducting a transparent election. This paper opines that with the emergence of the blockchain technology, African Nations should tap from it and build a reliable, secure, and convenient electoral voting system. It further suggests that a blockchain electoral voting system will eliminate most of the challenges faced by African nations in conducting a free, fair and transparent election with low cost and total security. The issue of election rigging is almost completely eradicated with this technology (if properly installed). An attempt to alter/manipulate records (votes) in the system’s database can be spotted easily, because of its rigorous consensus rules, such an attempt is considered void and denied permission to access, alter, or destroy any of the previously saved votes. However, the paper argues that there are institutional challenges to implementing this technology within the continent. Specifically, there is a need to educate the masses as well as create robust policies that can accommodate this technology within the continent. Failure to acknowledge these challenges may well prevent the application of blockchain technology in African electoral process in the foreseeable future.
A comprehensive tool for creating and evaluating privacy-preserving biomedical prediction models

Modern data driven medical research promises to provide new insights into the development and course of disease and to enable novel methods of clinical decision support. To realize this, machine learning models can be trained to make predictions from clinical, paraclinical and biomolecular data. In this process, privacy protection and regulatory requirements need careful consideration, as the resulting models may leak sensitive personal information. To counter this threat, a wide range of methods for integrating machine learning with formal methods of privacy protection have been proposed. However, there is a significant lack of practical tools to create and evaluate such privacy-preserving models. In this software article, we report on our ongoing efforts to bridge this gap.We have extended the well-known ARX anonymization tool for biomedical data with machine learning techniques to support the creation of privacy-preserving prediction models. Our methods are particularly well suited for applications in biomedicine, as they preserve the truthfulness of data (e.g. no noise is added) and they are intuitive and relatively easy to explain to non-experts. Moreover, our implementation is highly versatile, as it supports binomial and multinomial target variables, different types of prediction models and a wide range of privacy protection techniques. All methods have been integrated into a sound framework that supports the creation, evaluation and refinement of models through intuitive graphical user interfaces. To demonstrate the broad applicability of our solution, we present three case studies in which we created and evaluated different types of privacy-preserving prediction models for breast cancer diagnosis, diagnosis of acute inflammation of the urinary system and prediction of the contraceptive method used by women. In this process, we also used a wide range of different privacy models (k-anonymity, differential privacy and a game-theoretic approach) as well as different data transformation techniques.With the tool presented in this article, accurate prediction models can be created that preserve the privacy of individuals represented in the training set in a variety of threat scenarios. Our implementation is available as open source software.
Evaluation of Image Inpainting for Classification and Retrieval

A common approach to censoring digital image content is masking the region(s) of interest with a solid color or pattern. In the case where the masked image will be used as input for classification or matching, the mask itself may impact the results. Recent work in image inpainting provides an alternative to masking by replacing the foreground with predicted background. In this paper, we perform an extensive evaluation of inpainting approaches to understand how well inpainted images can serve as proxies for the original in classification and retrieval. Results indicate that the metrics typically used to evaluate inpainting performance (e.g., reconstruction accuracy) do not necessarily correspond to improved classification or retrieval, especially in the case of person-shaped masked regions.
Dynamic Prototype Selection by Fusing Attention Mechanism for Few-Shot Relation Classification

In a relation classification task, few-shot learning is an effective method when the number of training instances decreases. The prototypical network is a few-shot classification model that generates a point to represent each class, and this point is called a prototype. The mean is used to select prototypes for each class from a support set in a prototypical network. This method is fixed and static, and will lose some information at the sentence level. Therefore, we treat the mean selection as a special attention mechanism, then we expand the mean selection to dynamic prototype selection by fusing a self-attention mechanism. We also propose a query-attention mechanism to more accurately select prototypes. Experimental results on the FewRel dataset show that our model achieves significant and consistent improvements to baselines on few-shot relation classification.
Unmanned Vehicle Route Planning Based on Improved Artificial Potential Field Method

Abstract Aiming at the defect of target unreachable and local minimum in the unmanned path planning by traditional artificial potential field method, an improved artificial potential field method is proposed in this paper. By rotating the repulsive force by a certain angle, the unmanned vehicle jumps out of the local minimum point, and adopts the safety distance and the adjustment factor in the repulsive field to solve the problem of the target unreachability, and also ensures the safety of unmanned vehicles during obstacle avoidance. The experimental results show that the improved artificial potential field method can better achieve the safe obstacle avoidance of driverless vehicles and effectively plan the optimal target path.
Chimera: A New Efficient Transform for High Quality Lossy Image Compression

A novel scheme is presented for image compression using a compatible form called Chimera. This form represents a new transformation for the image pixels. The compression methods generally look for image division to obtain small parts of an image called blocks. These blocks contain limited predicted patterns such as flat area, simple slope, and single edge inside images. The block content of these images represent a special form of data which be reformed using simple masks to obtain a compressed representation. The compression representation is different according to the type of transform function which represents the preprocessing operation prior the coding step. The cost of any image transformation is represented by two main parameters which are the size of compressed block and the error in reconstructed block. Our proposed Chimera Transform (CT) shows a robustness against other transform such as Discrete Cosine Transform (DCT), Wavelet Transform (WT) and Karhunen-Loeve Transform (KLT). The suggested approach is designed to compress a specific data type which are the images, and this represents the first powerful characteristic of this transform. Additionally, the reconstructed image using Chimera transform has a small size with low error which could be considered as the second characteristic of the suggested approach. Our results show a Peak Signal to Noise Ratio (PSNR) enhancement of 2.0272 for DCT, 1.179 for WT and 4.301 for KLT. In addition, a Structural Similarity Index Measure (SSIM) enhancement of 0.1108 for DCT, 0.051 for WT and 0.175 for KLT.
Improving taxonomic relation learning via incorporating relation descriptions into word embeddings

Summary Taxonomic relations play an important role in various Natural Language Processing (NLP) tasks (eg, information extraction, question answering and knowledge inference). Existing approaches on embedding‐based taxonomic relation learning mainly rely on the word embeddings trained using co‐occurrence‐based similarity learning. However, the performance of these approaches is not quite satisfactory due to the lack of sufficient taxonomic semantic knowledge within word embeddings. To solve this problem, we propose an improved embedding‐based approach to learn taxonomic relations via incorporating relation descriptions into word embeddings. First, to capture additional taxonomic semantic knowledge, we train special word embeddings using not only co‐occurrence information of words but also relation descriptions (eg, taxonomic seed relations and their contextual triples). Then, using the trained word embeddings as features, we employ two learning models to identify and predict taxonomic relations, namely, offset‐based classification model and offset‐based similarity model. Experimental results on four real‐world domain datasets demonstrate that our proposed approach can capture additional taxonomic semantic knowledge and reduce dependence on the training dataset, outperforming the state‐of‐the‐art compared approaches on the taxonomic relation learning task.
Organization of the Quantum Cryptographic Keys Distribution System for Transportation Infrastructure Users

For operation of cryptographic protocols, auxiliary systems performing the key delivery to users are needed. Using quantum systems as a key provider for the devices of intelligent transportation systems is proposed. The possibility of using the isogeny protocols for transferring quantum keys was investigated.
Comparative Analysis of Semantic Similarity Techniques for Medical Text

In medical domain, physicians frequently consult information and knowledge sources for learning relevant piece of information for their clinical decisions. The sources are either internal such as electronic medical records databases or external sources such as biomedical literature. The key challenge for medical information extraction systems is finding required information, considering the explicit and implicit clinical context, with high precision and recall. Previously, many approaches were presented to exploit different aspects of information in the user query and medical text for obtaining semantic similarity at a concept, sentence, and document level, however, there doesn't exist a comparative analysis of different semantic similarity approaches for biomedical domain. This paper aims to compare different techniques used for finding semantic similarity by performing empirical analysis. We have implemented various modern semantic similarity techniques including ontological based matching and contextual word embedding.
Numerical solution to the equation of transfer and diffusion of harmful substances distribution in atmosphere

Research, prediction and monitoring of air masses in the atmosphere of industrial regions are described in the paper. A mathematical model, an effective numerical algorithm based on the method of splitting into physical processes and analysis of numerical calculations on a computer are developed.
Towards Content-Dependent Social Media Platform Preference Analysis

Social media is one of the major outcomes of progressive changes in the world of technology. The various social webs and mobile technologies have accelerated the rate at which information sharing is done, how relationships developed, and influences are held. Social media is increasingly being used by the people to help and shape the world's events and cultures with the ability to share pictures, ideas, events, etc. Further, it has transformed the way the authors interpret life and the way business is done. This article presents a decision system for selecting an appropriate social media platform (such as Facebook or Twitter) to post content with the objective to maximize the reachability of the post. The decision is made considering the domain or subject of the post and retrieving data associated with it from the web at regular time intervals. The retrieved data has been trained using logistics and K-NN regression to classify a particular instance of data and identify the platform which can provide the most reachability. The system also suggests keywords related to the topic of the post which has been mostly used in recent times.
Quality Assessment Approaches for Ambient Assisted Living Systems: A Systematic Review

Ambient Assisted Living (AAL) aims to improve people’s quality of life through the use of information technologies. Due to the critical nature of AAL systems, quality is a priority. However, as AAL is a relatively new domain, its main limitation is the lack of consensus and standardization in quality assessment. This work presents a systematic review to determine the state of the art on the quality assessment of AAL systems from a multidimensional vision (software product, in use, data and context). Initially, 1308 primary studies were extracted, from them 21 relevant studies related to models, frameworks, taxonomies and other approaches of quality assessment were selected after applying the corresponding inclusion and exclusion criteria. The selected studies were subject to a comparative analysis that determined the most recurrent and critical quality attributes for AAL systems, being an important contribution to generate consensus in the construction of more complete quality models. Furthermore, this work allowed to recognize the strengths and limitations of the quality proposals studied and to identify research gaps and challenges.