Reviewerly/exHarmony huggingface dataset:
Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

Big data ecosystems are increasingly important for the daily activities of any type of company. They are decisive elements in the organization, so any malfunction of this environment can have a great impact on the normal functioning of the company; security is therefore a crucial aspect of this type of ecosystem. When approaching security in big data as an issue, it must be considered not only during the creation and implementation of the big data ecosystem, but also throughout its entire lifecycle, including operation, and especially when managing and responding to incidents that occur. To this end, this paper proposes an incident response process supported by a private blockchain network that allows the recording of the different events and incidents that occur in the big data ecosystem. The use of blockchain enables the security of the stored data to be improved, increasing its immutability and traceability. In addition, the stored records can help manage incidents and anticipate them, thereby minimizing the costs of investigating their causes; that facilitates forensic readiness. This proposal integrates with previous research work, seeking to improve the security of big data by creating a process of secure analysis, design, and implementation, supported by a security reference architecture that serves as a guide in defining the different elements of this type of ecosystem. Moreover, this paper presents a case study in which the proposal is being implemented by using big data and blockchain technologies, such as Apache Spark or Hyperledger Fabric.
Crops reclamation management based on hybrid neuro-fuzzy systems

Abstract The article deals the technique of application of neuro-fuzzy systems in problems of management of melioration of agricultural crops is considered. In particular, an adaptive network based on the fuzzy inference system is developed, a method for predicting data flows based on the neuro-fuzzy network is proposed, which is implemented in the MATLAB ANFIS module.
Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks

Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of users' hands that may look as target gestures, and be able to work online. In this paper, we address these challenges with a recurrent neural architecture for online finger gesture spotting. We propose a multi-stream network merging hand and hand-location features, which help to discriminate target gestures from natural movements of the hand, since these may not happen in the same 3D spatial location. Our multi-stream recurrent neural network (RNN) recurrently learns semantic information, allowing to spot gestures online in long untrimmed video sequences. In order to validate our method, we collect a finger gesture dataset in an in-vehicle scenario of an autonomous car. 226 videos with more than 2100 continuous instances were captured with a depth sensor. On this dataset, our gesture spotting approach outperforms state-of-the-art methods with an improvement of about 10% and 15% of recall and precision, respectively. Furthermore, we demonstrated that by combining with an existing gesture classifier (a 3D Convolutional Neural Network), our proposal achieves better performance than previous hand gesture recognition methods.
Logistic Tan Map Based Audio Steganography

Steganography is the art of concealing a message within an unremarkable cover media in order to avoid detection. This paper proposes using both cryptography for scrambling the secret message and steganography techniques to provide a more secure communication. In this paper a cryptographic scheme employing AES-128 is first applied to the secret message in order to add an extra layer of security. Then, an audio cover file is used in the steganographic layer. The scrambled message is embedded using Least Significant Bit (LSB) of the audio file using a Tan Logistic Map generated Sequence. The metrics used to evaluate the performance of the algorithm are hearing test, mean square error (MSE), Peak signal to noise ratio (PSNR) and waveform plots.
Learning Perceived Emotion Using Affective and Deep Features for Mental Health Applications

Virtual agents are being increasingly used in the areas of healthcare, treatments, and therapy. Virtual agents with emotional intelligence have shown the potential to be applicable to deliver mental health intervention and facilitate therapies for children on the autism spectrum. To build an emotionally intelligent agent, automatic recognition of emotions is an essential building block. For mental health therapies and treatments, detecting rapidly changing emotions of individuals can be useful to know whether the patients are showing appropriate emotional response or not. To this end, we present a new data-driven approach to identify the perceived emotions of individuals based on their walking styles. We extract an individual's walking gait in the form of a sequence of 3D poses given an RGB video of him/her walking. We leverage the gait features to classify the perceived emotional state of the individual into one of four categories: happy, sad, angry, or neutral. First, we use an LSTM network to extract deep features of the gait using labeled emotion datasets. Next, we compute the affective features of the gaits using posture and movement cues. We combine these affective features with the deep features and classify them using a Random Forest Classifier. We observe that this approach provides an accuracy of 80:07% in identifying the perceived emotions. Additionally, we present a new dataset consisting of videos of walking individuals, their extracted gaits, and perceived emotion labels associated with each gait. We refer to this dataset as the `EWalk (Emotion Walk)” dataset.
A Relational Approach to Designing Social Technologies that Foster Use of the Kuku Yalanji Language

Australia has a rich array of Aboriginal and Torres Strait Islander languages, but they face decline along with many valued aspects of culture unless they are passed down to, and used by, younger generations. Prior work on designing technologies for language learning has often taken particular language skills, learning theories, and technologies as their starting point. Our empirical work with a remote Aboriginal community illustrates four ways in which this community's language practices intersect with family relations and are deeply enmeshed with family histories and stories, Indigenous Knowledges, and activities on and about country. Thus, we argue for a relational approach that instead takes family communication and social activities as the basis for designing technologies that foster everyday language use. We outline the guiding principles of this design orientation, and illustrate how they have been taken up in the co-design of a talking soft toy called the 'Crocodile Language Friend.' Finally, we identify opportunities and open issues in taking a relational approach to designing technologies for language communities with similar needs and aspirations.
Is It Cold in Here or Is It Just Me? Analysis of Augmented Reality Temperature Visualization for Computer-Mediated Thermoception

Modern augmented reality (AR) head-mounted displays comprise a multitude of sensors that allow them to sense the environment around them. We have extended these capabilities by mounting two heat-wavelength infrared cameras to a Microsoft HoloLens, facilitating the acquisition of thermal data and enabling stereoscopic thermal overlays in the user's augmented view. The ability to visualize live thermal information opens several avenues of investigation on how that thermal awareness may affect a user's thermoception. We present a human-subject study, in which we simulated different temperature shifts using either heat vision overlays or 3D AR virtual effects associated with thermal cause-effect relationships (e.g., flames burn and ice cools). We further investigated differences in estimated temperatures when the stimuli were applied to either the user's body or their environment. Our analysis showed significant effects and first trends for the AR virtual effects and heat vision, respectively, on participants' temperature estimates for their body and the environment though with different strengths and characteristics, which we discuss in this paper.
Color Image Reversible Data Hiding With Double-Layer Embedding

Reversible data hiding (RDH) in color image is an important topic of data hiding.This paper presents an efficient RDH algorithm for color image via double-layer embedding.The key contribution is the proposed double-layer embedding technique based on histogram shifting (HS).This technique exploits image interpolation to generate prediction error matrices for HS in the first-layer embedding and uses local pixel similarity to calculate difference matrices for HS in the second-layer embedding.It inherits reversibility from HS and makes high embedding capacity due to the use of double layers in data embedding.In addition, inter-channel correlation is incorporated into the first-layer embedding and the second-layer embedding for generating histograms with high peaks, so as to improve embedding capacity.Experiments with open standard datasets are done to validate performance of the proposed RDH algorithm.Comparison results show that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in terms of embedding capacity and visual quality.
Cyclic quantum teleportation of an unknown multi-particle high-dimension state

In this paper, via 12-particle entangled states as quantum channels, two schemes for cyclic quantum teleportation are proposed by using two different measurement methods, one is the Bell state measurement, the other is single-particle measurement. Besides, these schemes can be generalized to realize the probabilistic teleportation of an unknown multi-particle high-dimension state. Furthermore, feasibility for the schemes and some conclusions are discussed at the end of this paper.
A deep learning approach to handwritten text recognition in the presence of struck-out text

The accuracy of handwritten text recognition may be affected by the presence of struck-out text in the handwritten manuscript. This paper investigates and improves the performance of a widely used handwritten text recognition approach Convolutional Recurrent Neural Network (CRNN) on handwritten lines containing struck out words. For this purpose, some common types of struck-out strokes were superimposed on words in a text line. A model, trained on the IAM line database was tested on lines containing struck-out words. The Character Error Rate (CER) increased from 0.09 to 0.11. This model was re-trained on dataset containing struck-out text. The model performed well in terms of struck-out text detection. We found that after providing an adequate number of training examples, the model can deal with learning struck-out patterns in a way that does not affect the overall recognition accuracy.
Knowledge embedding towards the recommendation with sparse user-item interactions

Recently, many researchers in recommender systems have realized that encoding user-item interactions based on deep neural networks (DNNs) promotes collaborative-filtering (CF)'s performance. Nonetheless, those DNN-based models' performance is still limited when observed user-item interactions are very less because the training samples distilled from these interactions are critical for deep learning models. To address this problem, we resort to plenty features distilled from knowledge graphs (KGs), to profile users and items precisely and sufficiently rather than observed user-item interactions. In this paper, we propose a knowledge embedding based recommendation framework to alleviate the problem of sparse user-item interactions in recommendation. In our framework, each user and each item are both represented by the combination of an item embedding and a tag embedding at first. Specifically, item embeddings are learned by Metapath2Vec which is a graph embedding model qualified to embedding heterogeneous information networks. Tag embeddings are learned by a Skip-gram model similar to word embedding. We regarded these embeddings as knowledge embeddings because they both indicate knowledge about the latent relationships of movie-movie and user-movie. At last, a target user's representation and a candidate movie's representation are both fed into a multi-layer perceptron to output the probability that the user likes the item. The probability can be further used to achieve top-n recommendation. The extensive experiments on a movie recommendation dataset demonstrate our framework's superiority over some state-of-the-art recommendation models, especially in the scenario of sparse user-movie interactions.
An Equalized Margin Loss for Face Recognition

In this paper, we propose a new loss function, termed the equalized margin (EqM) loss, which is designed to make both intra-class scopes and inter-class margins similar over all classes, such that all the classes can be evenly distributed on the hypersphere of the feature space. The EqM loss controls both the lower limit of intra-class similarity by exploiting hard-sample mining and the upper limit of inter-class similarity by assuring equalized margins. Therefore, using the EqM loss, we can not only obtain more discriminative features, but also overcome the negative impacts from the data imbalance on the inter-class margins. We also observe that the EqM loss is stable with the variation of the scale in normalized Softmax. Furthermore, by conducting extensive experiments on LFW, YTF, CFP, MegaFace and IJB-B, we are able to verify the effectiveness and superiority of the EqM loss, compared with other state-of-the-art loss functions for face recognition.
Smart contracts: will Fintech be the catalyst for the next global financial crisis?

Purpose This paper aims to examine the risks associated with smart contracts, a disruptive financial technology (FinTech) innovation, and assesses how in the future they could threaten the integrity of the global financial system. Design/methodology/approach A qualitative approach is used to identify risk factors related to the use of new financial innovations, by examining how over-the-counter (OTC) derivatives contributed to the Global Financial Crisis (GFC) which occurred during 2007 and 2008. Based on this analysis, the potential for similar concerns with smart contracts are evaluated, drawing on the failure of The DAO on the Ethereum blockchain, which involved the loss of over $60m of digital currency. Findings Extensive use of bilateral agreements, complexity and lack of standardization, lack of transparency, misuse and speed of contagion were factors that contributed to the GFC that could also become material concerns for smart contract technology as its adoption grows. These concerns, combined with other contextual factors, such as the risk of defects in smart contracts and cyberattacks, could lead to potential destabilization of the broader financial system. Practical implications The paper’s findings provide insights to help make the design, management and monitoring of smart contract technology more robust. They also provide guidance for key stakeholders on proactive steps that can be taken with smart contract technology to avoid repeating the types of oversights that contributed to the GFC. Originality/value This paper draws attention to the risks associated with the adoption of disruptive FinTech. It also suggests steps that regulators and other key stakeholders can take to help mitigate those risks.
Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road

This paper introduces an approach to produce accurate 3D detection boxes for objects on the ground using single monocular images. We do so by merging 2D visual cues, 3D object dimensions, and ground plane constraints to produce boxes that are robust against small errors and incorrect predictions. First, we train a single-shot convolutional neural network (CNN) that produces multiple visual and geometric cues of interest: 2D bounding boxes, 2D keypoints of interest, coarse object orientations and object dimensions. Subsets of these cues are then used to poll probable ground planes from a pre-computed database of ground planes, to identify the “best fit” plane with highest consensus. Once identified, the “best fit” plane provides enough constraints to successfully construct the desired 3D detection box, without directly predicting the 6DoF pose of the object. The entire ground plane polling (GPP) procedure is constructed as a non-parametrized layer of the CNN that outputs the desired “best fit” plane and the corresponding 3D keypoints, which together define the final 3D bounding box. Doing so allows us to poll thousands of different ground plane configurations without adding considerable overhead, while also creating a single CNN that directly produces the desired output without the need for post processing. We evaluate our method on the 2D detection and orientation estimation benchmark from the challenging KITTI dataset, and provide additional comparisons for 3D metrics of importance. This single-stage, single-pass CNN results in superior localization and orientation estimation compared to more complex and computationally expensive monocular approaches.
Blockchain Technology: Applications, Benefits and Challenges

Blockchain is the name given to the technology that allows any data set to be stored in a distributed manner. It provides a secure structure with its distributed structure and provides a transparent system with the data set easily accessible by each user. Bitcoin application using blockchain infrastructure has gained attention with its reliability, robustness and performance. Thus, the blockchain is provided to reach large masses. Although the blockchain stands out as the technology of the future, it presents several disadvantages according to the application fields. In this study, blockchains performed on application basis were compared. The advantages and disadvantages of this system, which is called the technology of the future, are examined and compared in detail.
Pragmatic Aspects of Discourse Production for the Automatic Identification of Alzheimer's Disease

Clinical literature provides convincing evidence that language deficits in Alzheimer's disease (AD) allow for distinguishing patients with dementia from healthy subjects. Currently, computational approaches have widely investigated lexicosemantic aspects of discourse production, while pragmatic aspects like cohesion and coherence, are still mostly unexplored. In this article, we aim at providing a more comprehensive characterization of language abilities for the automatic identification of AD in narrative description tasks by also incorporating pragmatic aspects of speech production. To this end, we investigate the relevance of a recently proposed set of pragmatic features extracted from an automatically generated topic hierarchy graph in combination with a complementary set of state-of-the-art features encoding lexical, syntactic and semantic cues. Experimental results on the DementiaBank corpus show an accuracy improvement from 82.6% to 85.5% in identifying AD patients when pragmatic features are incorporated to the set of lexicosemantic features. Nevertheless, these results are obtained relying on manual transcriptions, which strongly limits the applicability of computational analysis to clinical settings. Thus, in this work we additionally carry out an analysis of the errors introduced by a speech recognition system and the way in which they impact the performance of the proposed method. In spite of the high word error rates obtained on these data (~40%), automatic AD identification accuracy decreased only to 79.7%, which is considered a remarkable result when compared with solutions based on manual transcriptions.
RF-OSFBLS: An RFID reader-fault-adaptive localization system based on online sequential fuzzy broad learning system

Indoor localization technology has recently attracted an increasing attention in research, among which radio frequency identification (RFID) technology has become a preferred solution due to its advantages in non-line of sight, non-contact and rapid identification. However, in the practical RFID indoor localization application scenarios, the RFID readers need to keep working for an extensive time. When some readers malfunction and fail to be repaired in time, the existing algorithms usually cannot maintain the accuracy of the original localization system. In this paper, we propose an RFID reader-fault-adaptive localization algorithm based on online sequential fuzzy broad learning system, called RF-OSFBLS algorithm. The RF-OSFBLS algorithm improves the fuzzy broad learning system (FBLS) with the ability of online sequential learning (OSFBLS) by using the updating algorithm, so that it can process data streams that continue to arrive in the environment. Meanwhile, RF-OSFBLS algorithm proposes RFID reader-fault-adaptive strategy by introducing a transformation matrix, which can process subsequent data streams when reader fault occurs. We have carried out experiments to study the influence factors and validate the performance, both the simulation and realistic experiment results show that our proposed RF-OSFBLS algorithm can achieve better positioning effect and maintain a relatively high accuracy in the dynamically changing and reader-fault environment.
Feature-Weighted Possibilistic <i>c</i>-Means Clustering With a Feature-Reduction Framework

In 1993, Krishnapuram and Keller proposed possibilistic c-means (PCM) clustering, where the PCM had various extensions in the literature. However, the PCM algorithm with its extensions treats data points under equal importance for features. In real applications, different features in a dataset should take different importance with different weights. In this article, we first propose a feature-weighted PCM (FW-PCM). We then construct a feature-reduction framework. Therefore, we give a feature-weighted possibilistic c-means clustering with a feature-reduction framework, termed as a feature-weighted reduction PCM (FW-R-PCM) algorithm. The proposed FW-R-PCM can improve the clustering performance of PCM by calculating feature weights to identify important features, and so it can consequently eliminate these irrelevant features to reduce feature dimension. Its theoretical behavior and computational complexity are also analyzed. The effectiveness and usefulness of FW-R-PCM are demonstrated through experimental results using synthetic and real datasets, where comparisons of FW-R-PCM with PCM, FW-PCM, and some existing feature-weighted clustering algorithms are also made.