Reviewerly/exHarmony huggingface dataset:
Development of Hybrid Extreme Learning Machine for Classification of Brain MRIs

The current biomedical research focuses on automated classification of brain magnetic resonance images into normal or pathological. This paper presents an Extreme Learning Machine model hybridized with one of the recently developed swarm intelligence based optimization algorithm, i.e., Crow Search Algorithm (CSA) for the classification of brain MRIs. This work is broadly divided into three different processes, i.e., Extraction of features from the images, Reduction of features, and classification task. For feature extraction from MR images, Discrete Wavelet Transformation (DWT) is used. Some of the features are not useful for the classification model, so Principal Component Analysis (PCA) is used for reducing the features, thus decreases the computational complexity and memory requirements of the classification model. CSA is used for the optimal determination of the weights between the input layer and the hidden layer of the ELM. The proposed CSA-ELM model is used for the classification of the processed images. Different performance metrics, i.e., classification accuracy, specificity, sensitivity are considered for measuring the performances of the proposed model.
3D Learning and Reasoning in Link Prediction Over Knowledge Graphs

Knowledge Graph Embeddings (KGE) are used for representation learning in Knowledge Graphs (KGs) by measuring the likelihood of a relation between nodes. Rotation-based approaches, specially axis-angle representations, were shown to improve the performance of many Machine Learning (ML)-based models in different tasks including link prediction. There is a perceived disconnect between the topics of KGE models and axis-angle rotation-based approaches. This is particularly visible when considering the ability of KGEs to learn relational patterns such as symmetry, inversion, implication, equivalence, composition, and reflexivity considering axis-angle rotation-based approaches. In this article, we propose RodE, a new KGE model which employs an axis-angle representation for rotations based on Rodrigues' formula. RodE inherits the main advantages of 3-dimensional rotation from angle, orientation and distance preservation in the embedding space. Thus, the model efficiently captures the similarity between the nodes in a graph in the vector space. Our experiments show that RodE outperforms state-of-the-art models on standard datasets.
MOVE

The interest in sharing the Data-Information-Knowledge-Wisdom (DIKW) continuum has been amplified by the latest multi-scale social changes including but not limited to pandemics, economic crises, climate change, and racial issues. This workshop aims to inspire research and discussion on measuring sharing of the DIKW continuum, including through computer-mediated methods, represented by its ontologies. The implied suggestion is that there are ways to improve human adaptation by social technologies that enable rapidly finding solutions for complex global situations. We therefore invite research on (1) ontologies as a medium that enables comparing and measuring the DIKW continuum, (2) ontologies and their convergence or divergence with the values that motivate and determine DIKW sharing, (3) properties and dynamics of ontologies shared via social technologies in their relation to human adaptation.
Random Occlusion Recovery with Noise Channel for Person Re-identification

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective.
The Case for Better Integrating Scalable Data Stores and Stream-Processing Systems

Scalable stream processing systems require external storage systems for long-term storage of non-emphemeral state. Recent research have pointed to scalable in-memory key-value stores, such as Redis, as an efficient solution to external management of state [1], [2]. While such data stores have been interconnected with scalable streaming systems, they are currently managed independently, missing opportunities for optimizations, such as exploiting locality between stream partitions and table shards, as well as coordinating elasticity actions.
Genius Loci and Cultural Heritage

One of the main challenges of cultural heritage is to enhance the visit to ancient ruins, in which what remains is difficult to interpret. For this reason, the authors designed an alternate reality game by making use of tangible devices to improve the experience of wandering around the ruins of Egnatia (Fasano, Brindisi, Italy). They tried to evoke the “Genius Loci,” the sacral spirit of a territory, that is analysed according to its dimensions in the field of human-computer interaction. Those dimensions are enchantment, ambiguity, bodily experience, topology of the place, the dialogue between the past and the present, and the perceptual gestalt.
Gallery-sensitive single sample face recognition based on domain adaptation

Taking advantage of labeled auxiliary training data whose distribution is similar to the distribution of the gallery, single sample face recognition (SSFR) has achieved encouraging performance. However, in many real-world applications, it is difficult to collect such an auxiliary training dataset, while it may be easier to collect an unlabeled target training dataset whose distribution is similar to the distribution of the gallery and a labeled source training dataset whose distribution may be different to the distribution of the gallery. How can these three datasets be effectively leveraged to handle SSFR? To address this issue, this paper proposes a new method of Gallery-Sensitive Single Sample Face Recognition based on Domain Adaptation (GS-DA). First, GS-DA employs the method of TSD (targetize the source domain) to construct a common subspace and a targetized source domain. Secondly, it projects each gallery image into the common subspace and obtains the sparse representation of each gallery image in the common subspace. Thirdly, it reconstructs each gallery image from the targetized source domain to estimate the within-class scatter matrix and the between-class scatter matrix of the gallery. Lastly, it learns a discriminant model by maximizing the sum of the traces of the between-class scatter matrix of the gallery and the between-class scatter matrix of the targetized source domain as well as minimizing the sum of the traces of the total scatter matrix of the gallery and the total scatter matrix of the target training data. The experimental results on five datasets illustrate the superiority of GS-DA in leveraging these three datasets for SSFR.
An Evolutionary Orthogonal Component Analysis Method for Incremental Dimensionality Reduction

In order to quickly discover the low-dimensional representation of high-dimensional noisy data in online environments, we transform the linear dimensionality reduction problem into the problem of learning the bases of linear feature subspaces. Based on that, we propose a fast and robust dimensionality reduction framework for incremental subspace learning named evolutionary orthogonal component analysis (EOCA). By setting adaptive thresholds to automatically determine the target dimensionality, the proposed method extracts the orthogonal subspace bases of data incrementally to realize dimensionality reduction and avoids complex computations. Besides, EOCA can merge two learned subspaces that are represented by their orthonormal bases to a new one to eliminate the outlier effects, and the new subspace is proved to be unique. Extensive experiments and analysis demonstrate that EOCA is fast and achieves competitive results, especially for noisy data.
Copyright issues related to reproduction rights arising from streaming

Abstract With the rapid development of streaming media technology, more and more people have begun to watch films on streaming websites. However, it has been noted that some of them are streaming unauthorized content, which has posed a major challenge to the entertainment sector's traditional business model. Against this background, legal issues arising from the streaming of media online is an area of huge current interest, one issue being the exact liability of providers and users of streaming services and whether they are infringing on the reproduction right of the copyright holders. Since the liability of platforms has been extensively discussed in the academic literature, in this article we mainly focus on the liability of users who stream unauthorized works. To this end, the article analyses the reproduction act of streaming platform users under the UK and European Union approaches and examines Article 5(1) of the Information Society Directive in the context of streaming. In addition, we explore the liability of users who stream unauthorized content via virtual private network services.
Multi-cavity Heart Segmentation in Non-contrast Non-ECG Gated CT Scans with F-CNN

The anatomical delineation of heart cavities in CT scans is a well-known problem with extended applications in cardiopulmonary diseases. A common approach to provide multi-cavity segmentations is the use of atlas-based statistical shape models. This methodology is of particular interest when just non-contrast and non-gated scans are available since the non-observable internal structures of the heart can be inferred from the fitting of the pericardium provided by the shape model. These inferred cavities have shown predictive power in clinical studies when compared to reference standards. However, although promising, the shape model is limited to the fitting of the pericardium and cannot take advantage of the geometrical inter-relations of internal cavities. This leads to inaccurate segmentations of the smaller structures such as the atria and wrong estimations of the internal cavities. In this work, we study the potential of CNNs to learn the inter-relations of a multicavity active shape model to provide accurate estimations of the internal cavities in non-gated and non-contrast CT scans. We propose an architecture that is able to learn the appearance model of the heart from an inaccurate training dataset. Results demonstrate that our segmentation method improves the correlation of ventricular volume estimations when compared against a semiautomatic active shape approach using cardiac MRI as the reference standard.
Classification of Skin Diseases Types using Naïve Bayes Classifier based on Local Binary Pattern Features

This study aims to analyze the Naïve Bayes classifier (NBC) method and feature extraction of Local Binary Pattern (LBP) for the classification of skin diseases. NBC was chosen because it is reliable for small datasets. Whereas LBP is suitable for feature extraction because every skin disease has a distinctive texture. The combination of these two algorithms is proven to produce good accuracy in small datasets. Based on four experiments with a total of images used are 225, 180, 135 and 90 images on nine types of skin diseases, with a composition of 80% for training data and 20% for testing data resulted in an accuracy of 82.20%, 91.67%, 85.18%, and 94.44%. The best accuracy obtained with the total image used is 90, this proves that the Naïve Bayes classifier has good performance for classifying images in small datasets and with a small number of datasets can save time to do the training process.
A Discriminative Dual-Stream Model With a Novel Sustained Attention Mechanism for Skeleton-Based Human Action Recognition

The development of RGB-D sensors that have been widely applied in human motion collection is driving research in skeleton-based human action recognition. In recent works, most models based on the attention mechanism are proposed to assign the same weights to all body joints for spatiotemporal feature modeling. However, they fail to consider the fact that the differential contribution of joint points to the human movement, which is a challenge to obtain the high-level performance skeleton presentations. Therefore, in this article, we propose a novel sustained attention model based on the above fact, which adaptively assigns corresponding weights to all the body joints to extract the key skeleton part in the global input sequence. We design a two-stream network based on RNNs and CNNs and integrate the sustained attention mechanism into each subnetwork, in which both the body joint weights and the input frame weights are learned effectively and thus resulting in superior performance. Next, in the training process, the skeleton is randomly transformed to enhance the robustness of this model and reduce overfitting. A group of ablation studies and visualization analyses are conducted to prove the validity and robustness of the proposed model. Extensive experiments on four benchmark datasets included the challenging interaction datasets demonstrate that our proposed model outperforms recent state-of-the-art works.
Removal of Unwanted Objects from Still Photographs

In this paper we present a method for removing unwanted objects from still photographs. The method is based on taking several shoots of the same picture by either moving the camera or holding the camera fixed if the unwanted object is moving, so that afterwards, by careful comparisons between macroblocks from different shoots of the same picture, we are able to reconstruct the original picture without the unwanted object in it. The method operates in two scenarios, one when the unwanted object is moving in front of the camera (e.g. like a passing car) and another when the unwanted object is fixed (e.g. like electrical or communication wires). In the evaluation section we perform several tests that show our method is viable.
TRANSPR—Transforming Public Accountability Through Blockchain Technology

To ameliorate the deplorable state of public infrastructure construction in growing and underdeveloped countries, and eliminating the rampant corruption involved in them is the primary objective of this research work. We propose to achieve this objective by bringing enhanced transparency in public sector projects. This project aims to deploy a system that would capture all bids for public infrastructure projects on the blockchain. All activities on the project, from the call for tender, procurement of material, choice of contractor, the status of work will be stored on the blockchain. By opening up the blockchain to the public it would be a fully transparent interface. Since the data is stored on the blockchain, it is immutable and every update can be traced to the person who made the update and can be independently verified. By publicly disclosing the status of infrastructure and government projects, a greater degree of accountability can be unlocked. To facilitate this, we propose a methodology called TRANSPR, a concord-based blockchain network. The design of the system is laid out, and the mechanism by which the system maintains state would be elaborated. The coin distribution process will also be detailed, and the formulae would be laid out. As the system is consensus—based, new blocks are established by consensus decisions and not mining. Since there is no mining or documentation of execution involved, the energy needs of TRANSPR are minimal, and it is environmentally sustainable. The primary difficulty in the rollout of TRANSPR would be in the adoption phase, as it involves an overhaul of existing processes.
Backtracking Group Search Optimization: A Hybrid Approach for Automatic Data Clustering

Data clustering is one of the most primitive tasks in pattern recognition, although it is known to be a NP-hard grouping task. Given its complexity, standard clustering methods, such as the partitional data clustering algorithms, are easily trapped in local minimum solutions, due to their lack of good global searching mechanisms. Evolutionary Algorithms (EAs) and Swarm Intelligence (SIs) methods, such as Group Search Optimization (GSO) and Backtracking Search Optimization (BSA), are commonly employed to deal with clustering task, given their capabilities to handle global search problems. In this work, a new hybrid evolutionary algorithm between GSO and BSA is presented, named BGSO, to tackle clustering problem, which combines the best features of GSO and the historical mechanisms of BSA. Also, BGSO is developed in the context of Automatic Clustering approach, which means that it is able to predict the best number of final clusters, so no prior assumption about the data set at hand is required. The proposed approach is compared to standard GSO, BSA and other three EAs and SIs from the literature by means of nine real-world problems, showing promising results considering four clustering metrics.
VR SandScape: Working with Multiple Perspectives in a Hybrid VR/SAR Collaborative Play Space

We present our informal observations of children and adults playing together with our VR SandScape, a hybrid system with a Spatial Augmented Reality (SAR) sandbox and a Virtual Reality (VR) perspective. We discuss our preliminary findings of how the players, with access to multiple shared immersive VR and SAR views, navigate between what they see from their natural viewpoint while considering what other players see, and how the process of such negotiations influences their ongoing play and virtual creation. We argue for the design of a play space that can support sharing and negotiation of diverse perspectives.
Physical Adversarial Attacks Against Deep Learning Based Channel Decoding Systems

Deep Learning (DL), in spite of its huge success in many new fields, is extremely vulnerable to adversarial attacks. We demonstrate how an attacker applies physical white-box and black-box adversarial attacks to Channel decoding systems based on DL. We show that these attacks can affect the systems and decrease performance. We uncover that these attacks are more effective than conventional jamming attacks. Additionally, we show that classical decoding schemes are more robust than the deep learning channel decoding systems in the presence of both adversarial and jamming attacks.
Polynomial least squares multiple-model estimation: simple, optimal, adaptive, practical

This paper presents a polynomial least squares (LS) method of overcoming numerous weaknesses and deficiencies in the interacting multiple model (IMM), which comprises multiple Kalman filter models as the state-of-the-art algorithm for tracking maneuvering targets. The paper also addresses several polynomial LS misunderstandings and flaws applied in econometrics. These aims are achieved by first uniquely deriving a very simple version of conventional LS, which fits discrete deterministic data by minimizing the sum of squared deviations of data from an assumed polynomial (often called data-fitting). The contrasting contemporary polynomial LS method filters out corrupting statistical noise from already existing known polynomials, as in target tracking. Contemporary polynomial LS is developed from the conventional LS method derived by Gauss. Polynomial LS conventionally applied to data-fitting and contemporarily applied to noise filtering are shown to be different problems that are not to be conflated as done in econometrics, where data-fitting is described as regression using a 1st degree polynomial called ordinary LS. Most significantly, analogous to the IMM, the polynomial LS multiple model (LSMM) is derived. Contrary to the IMM, which is not optimized; the LSMM is optimized with a trade-off between the statistical variance and the deterministic bias-squared to minimize the mean-square-error (variance plus bias-squared). A sequence of optimal LSMMs matched to accelerations covering the spectrum of acceleration between zero and the assumed maximum are derived and applied in designing an adaptive algorithm. Results demonstrate improved target tracking and accuracy by the LSMM of maneuvering targets compared with published IMM performances.
Design and Implementation of Number Plate Recognition System Based on Cortex-M3

This design is based on the FPGA platform and builds an automatic number plate recognition system with an on-chip Cortex-M3 core, and collects the number plate photo through the OV5640 camera, and converts it into a digital signal for the image processing module. The image processing module can obtain a clear binary image after gray-scale processing, binarization processing and median filtering. Then it determines the position and character information of the number through vertical and horizontal projection and numerical recognition. The numerical result obtained by the recognition is input to Cortex-M3 through the AHB bus, and the program can be programmed and debugged using keil software on the PC side, and finally the number plate recognition result is displayed on the LCD. The recognition speed of the number plate recognition system reaches 0.4 seconds per picture, and the average correct rate reaches more than 98.5%, achieving the overall performance goal of the system.
Improving energy efficiency in colocation data centers for demand response

Colocation data centers (colocations, for short) are developing rapidly and have become ideal participants for emergency demand response (EDR) programs. However, even colocation operators wish to save energy, they cannot achieve energy reduction without coordination from the tenants, because the servers in the colocations are owned and operated by the tenants. To solve the “uncoordinated relationship” issue between operators and tenants, some works have been done by way of incentivizing the tenants to reduce the energy consumption of their servers. However, apart from servers, the power consumption of the cooling system also accounts for a large portion in a colocation, which should be optimized as well. Unfortunately, the servers and the cooling system are controlled by tenants and operators separately in colocations, and thus “uncoordinated relationship” issue also exists between IT and cooling systems. In this paper, coarse-grained and fine-grained incentive mechanisms are proposed to solve these problems. Approximation algorithms are developed to optimize the energy-saving problems. Furthermore, Vickrey–Clarke–Groves (VCG) theory is introduced into our incentive mechanism design to guarantee the feasibility and truthfulness of the two mechanisms. Trace-driven simulations are performed to validate the effectiveness of the two incentive mechanisms. The results show that compared with the existing incentive mechanisms, up to 20.50% of the energy-saving cost can be reduced in the coarse-grained mechanism and 28.34% of the cost reduction can be achieved in the fine-grained mechanism.
Diversified Top-k Querying in Knowledge Graphs

The existing literatures of the query processing on knowledge graphs focus on an exhaustive enumeration of all matches, which is time-consuming. Users are often interested in diversified top-k matches, rather than the entire match set. Motivated by these, this paper formalizes the diversified top-k querying (DTQ) problem in the context of RDF/SPARQL and proposes a diversification function to balance importance and diversity. We first prove that the decision problem of DTQ is NP-complete, and give a baseline algorithm with an approximation ratio of 2. Secondly, an index-based algorithm with the early termination property is proposed. The index is adept in parallel diversified top-k selection in multicore architectures. Using real-world and synthetic data, we experimentally verify that our algorithms are efficient and effective in computing meaningful diversified top-k matches.