Reviewerly/exHarmony huggingface dataset:
Anti-Quantum Fast Authentication and Data Transmission Scheme for Massive Devices in 5G NB-IoT System

The narrowband Internet of Things (NB-IoT) system has become an integral part of the future fifth generation (5G) network. Although the NB-IoT system has gradually been improved in the traditional LTE network currently, the NB-IoT system does not have an effective access authentication scheme in the future 5G network. According to the current 3rd Generation Partnership Project (3GPP) standard, NB-IoT devices still use the traditional access authentication method to perform mutual authentication with the network, which may bring a large amount of signaling and communication overhead. This problem will be magnified in a large-scale device environment in the future 5G network. In this article, a quantum resistance access authentication and data distribution scheme is proposed for large-scale NB-IoT devices. The scheme can simultaneously implement access authentication and data transmission of a group of NB-IoT devices based on the lattice-based homomorphic encryption technology. Our scheme not only greatly reduce the network burden but also achieve strong security, including privacy protection and anti-quantum attacks. The performance analysis results show that our scheme has the ideal efficiency.
Pedestrian Detection in Thermal Images Using Saliency Maps

Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art techniques employ a fusion network that uses features from paired thermal and color images. Instead, we propose to augment thermal images with their saliency maps, to serve as an attention mechanism for the pedestrian detector especially during daytime. We investigate how such an approach results in improved performance for pedestrian detection using only thermal images, eliminating the need for paired color images. For our experiments, we train the Faster R-CNN for pedestrian detection and report the added effect of saliency maps generated using static and deep methods (PiCA-Net and R3-Net). Our best performing model results in an absolute reduction of miss rate by 13.4% and 19.4% over the baseline in day and night images respectively. We also annotate and release pixel level masks of pedestrians on a subset of the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly available dataset for salient pedestrian detection.
A Learned Representation for Scalable Vector Graphics

Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts crawled from the web and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for graphic designers to facilitate font design.
Secure image encryption scheme using double random-phase encoding and compressed sensing

A secure optical digital image encryption scheme with authentication capability is proposed using double random-phase encoding (DRPE) and compressed sensing (CS). Phase information of the plaintext image is obtained using DRPE and quantized to generate authentication information. Simultaneously, the plaintext image is compressed by CS and its measurements are quantized using the sigmoid map. Then the ciphertext image is obtained by permutation and diffusion after authentication information is embedded in quantified measurements. At receiving end, the authentication information is first extracted by inverse permutation and diffusion, and then the authentication image is obtained by inverse DRPE. Finally, the ciphertext image can be blindly authenticated using a nonlinear cross-correlation method with authentication image and reconstructed image. Experimental results demonstrate the effectiveness of our proposed scheme.
Intelligent Embedded Vision for Summarization of Multiview Videos in IIoT

Nowadays, video sensors are used on a large scale for various applications, including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging to process such heterogeneous nature of Big Data in real time. Multiview video summarization (MVS) enables us to suppress redundant data in distributed video sensors settings. The existing MVS approaches process video data in offline manner by transmitting them to the local or cloud server for analysis, which requires extra streaming to conduct summarization, huge bandwidth, and are not applicable for integration with industrial Internet of Things (IIoT). This article presents a light-weight convolutional neural network (CNN) and IIoT-based computationally intelligent (CI) MVS framework. Our method uses an IIoT network containing smart devices, Raspberry Pi (RPi) (clients and master) with embedded cameras to capture multiview video data. Each client RPi detects target in frames via light-weight CNN model, analyzes these targets for traffic and crowd density, and searches for suspicious objects to generate alert in the IIoT network. The frames of each client RPi are encoded and transmitted with approximately 17.02% smaller size of each frame to master RPi for final MVS. Empirical analysis shows that our proposed framework can be used in industrial environments for various applications such as security and smart transportation and can be proved beneficial for saving resources. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> [Online]. Available: https://github.com/tanveer-hussain/Embedded-Vision-for-MVS.
The Scene Perception &amp; Event Comprehension Theory (SPECT) Applied to Visual Narratives

Understanding how people comprehend visual narratives (including picture stories, comics, and film) requires the combination of traditionally separate theories that span the initial sensory and perceptual processing of complex visual scenes, the perception of events over time, and comprehension of narratives. Existing piecemeal approaches fail to capture the interplay between these levels of processing. Here, we propose the Scene Perception & Event Comprehension Theory (SPECT), as applied to visual narratives, which distinguishes between front-end and back-end cognitive processes. Front-end processes occur during single eye fixations and are comprised of attentional selection and information extraction. Back-end processes occur across multiple fixations and support the construction of event models, which reflect understanding of what is happening now in a narrative (stored in working memory) and over the course of the entire narrative (stored in long-term episodic memory). We describe relationships between front- and back-end processes, and medium-specific differences that likely produce variation in front-end and back-end processes across media (e.g., picture stories vs. film). We describe several novel research questions derived from SPECT that we have explored. By addressing these questions, we provide greater insight into how attention, information extraction, and event model processes are dynamically coordinated to perceive and understand complex naturalistic visual events in narratives and the real world.
Generalized Belief Entropy and Its Application in Identifying Conflict Evidence

Dempster-Shafer evidence theory has wide applications in many fields. Recently, A new entropy called Deng entropy was proposed in evidence theory. Some scholars have pointed out that Deng Entropy does not satisfy the additivity in uncertain measurements. However, irreducibility may have a huge effect. The derived entropy from complex systems is often irreducible. Inspired by this, generalized belief entropy is proposed. The belief entropy implies the relationship between Deng entropy, Rényi entropy, Tsallis entropy. In addition, numerical examples demonstrate the flexibility of the proposed Rényi-Deng (R-D) entropy to measure the uncertainty of basic probability assignment (BPA). Finally, a method for identifying contradictory evidence based on Rényi-Deng (R-D) entropy is proposed. The experiment show the effectiveness of the proposed method.
Combining 3D Morphable Models: A Large Scale Face-And-Head Model

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D surfaces of an object class. In this context, we identify an interesting question that has previously not received research attention: is it possible to combine two or more 3DMMs that (a) are built using different templates that perhaps only partly overlap, (b) have different representation capabilities and (c) are built from different datasets that may not be publicly-available? In answering this question, we make two contributions. First, we propose two methods for solving this problem: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Second, as an example application of our approach, we build a new head and face model that combines the variability and facial detail of the LSFM with the full head modelling of the LYHM. The resulting combined model achieves state-of-the-art performance and outperforms existing head models by a large margin. Finally, as an application experiment, we reconstruct full head representations from single, unconstrained images by utilizing our proposed large-scale model in conjunction with the Face-Warehouse blendshapes for handling expressions.
Eye tracking in developmental cognitive neuroscience – The good, the bad and the ugly

Eye tracking is a popular research tool in developmental cognitive neuroscience for studying the development of perceptual and cognitive processes. However, eye tracking in the context of development is also challenging. In this paper, we ask how knowledge on eye-tracking data quality can be used to improve eye-tracking recordings and analyses in longitudinal research so that valid conclusions about child development may be drawn. We answer this question by adopting the data-quality perspective and surveying the eye-tracking setup, training protocols, and data analysis of the YOUth study (investigating neurocognitive development of 6000 children). We first show how our eye-tracking setup has been optimized for recording high-quality eye-tracking data. Second, we show that eye-tracking data quality can be operator-dependent even after a thorough training protocol. Finally, we report distributions of eye-tracking data quality measures for four age groups (5 months, 10 months, 3 years, and 9 years), based on 1531 recordings. We end with advice for (prospective) developmental eye-tracking researchers and generalizations to other methodologies.
Hierarchical Attention Network for Visually-Aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. This work formulates the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, this work develops a dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, this work constructs a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference on food.
Quarantining online hate speech: technical and ethical perspectives

Abstract In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship.
Fast and general density peaks clustering

Density peaks is a popular clustering algorithm, used for many different applications, especially for non-spherical data. Although powerful, its use is limited by quadratic time complexity, which makes it slow for large datasets. In this work, we propose a fast density peaks algorithm that solves the time complexity problem. The proposed algorithm uses a fast and generic construction of approximate k-nearest neighbor graph both for density and for delta calculation. This approach maintains the generality of density peaks, which allows using it for all types of data, as long as a distance function is provided. For a dataset of size 100,000, our approach achieves a 91:1 speedup factor. The algorithm scales up for datasets up to 1 million in size, which could not be solved by the original algorithm at all. With the proposed method, time complexity is no longer a limiting factor of the density peaks clustering.
<i>BinFI</i>

As machine learning (ML) becomes pervasive in high performance computing, ML has found its way into safety-critical domains (e.g., autonomous vehicles). Thus the reliability of ML has grown in importance. Specifically, failures of ML systems can have catastrophic consequences, and can occur due to soft errors, which are increasing in frequency due to system scaling. Therefore, we need to evaluate ML systems in the presence of soft errors.
SparCML

Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data parallel" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is the single communication and thus scalability bottleneck for most machine learning workloads. We observe that frequently, many gradient values are (close to) zero, leading to sparse of sparsifyable communications. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute arbitrary sparse input data vectors. Our generic communication library, SparCML1, extends MPI to support additional features, such as non-blocking (asynchronous) operations and low-precision data representations. As such, SparCML and its techniques will form the basis of future highly-scalable machine learning frameworks.
A MATLAB based graphical user interface (GUI) for quickly producing widely used hydrogeochemical diagrams

Hydrogeochemical diagrams are powerful tools facilitating hydrogeochemical research, among which Piper diagram, Gibbs diagrams, USSL diagram, Wilcox diagram and PI classification diagram are widely applied. However, it is usually tedious and time-consuming to draw these diagrams, not only because of the complex procedures to treat input data, but also due to the different software used. It is always required that several pieces of software should be used for a single hydrochemical study. Therefore, this study reported a MATLAB based graphical user interface (GUI) to overcome these shortages. Three simple steps are included in the GUI to draw a diagram. The frame of the diagram to be drawn is first selected from the database of the GUI, after which the input data (water sample data) are imported into the GUI and the properties of the points such as the sizes, colors and shapes are set. Finally, the diagrams produced can be exported from the GUI as images. This MATLAB based GUI is capable of generating quickly five types of diagrams that are commonly used in hydrogeochemical research. The codes of the GUI and related functions can be adjusted according to the user's needs. It is helpful to reduce the time spent in drawing these hydrochemical diagrams.
MatRiCT

We introduce MatRiCT, an efficient RingCT protocol for blockchain confidential transactions, whose security is based on "post-quantum'' (module) lattice assumptions. The proof length of the protocol is around two orders of magnitude shorter than the existing post-quantum proposal, and scales efficiently to large anonymity sets, unlike the existing proposal. Further, we provide the first full implementation of a post-quantum RingCT, demonstrating the practicality of our scheme. In particular, a typical transaction can be generated in a fraction of a second and verified in about 23 ms on a standard PC. Moreover, we show how our scheme can be extended to provide auditability, where a user can select a particular authority from a set of authorities to reveal her identity. The user also has the ability to select no auditing and all these auditing options may co-exist in the same environment. The key ingredients, introduced in this work, of MatRiCT are 1) the shortest to date scalable ring signature from standard lattice assumptions with no Gaussian sampling required, 2) a novel balance zero-knowledge proof and 3) a novel extractable commitment scheme from (module) lattices. We believe these ingredients to be of independent interest for other privacy-preserving applications such as secure e-voting. Despite allowing 64-bit precision for transaction amounts, our new balance proof, and thus our protocol, does not require a range proof on a wide range (such as 32- or 64-bit ranges), which has been a major obstacle against efficient lattice-based solutions. Further, we provide new formal definitions for RingCT-like protocols, where the real-world blockchain setting is captured more closely. The definitions are applicable in a generic setting, and thus are believed to contribute to the development of future confidential transaction protocols in general (not only in the lattice setting).
Verifiable Delay Functions from Supersingular Isogenies and Pairings

We present two new Verifiable Delay Functions (VDF) based on assumptions from elliptic curve cryptography. We discuss both the advantages and drawbacks of our constructions, we study their security and we demonstrate their practicality with a proof-of-concept implementation.
Artificial Intelligence (AI) Chatbot as Language Learning Medium: An inquiry

Abstract In facing industry revolution 4.0, utilizing advanced information and computer technology in educational environment is crucial. One of the advanced computation technologies that can be used for learning, especially language learning, is chatbot. Chatbot is a computer program based on artificial intelligence that can carry out conversations through audio or text. This study intends to find out and analyze the types of artificial intelligence in the form of chatbots and the possibility of their use as language learning medium. The data in this study obtained from literature review on chatbot researches, and from observation results on chatbot -based language learning medium developed by the author. The results indicated that chatbots have a high potential to be used as a language learning medium, both as tutor in practicing language, and as independent learning medium. Moreover, research results revealed that language learners are interested in using chatbots because they can be used anytime and anywhere, and they are more confident in learning languages using chatbots than when dealing directly with human tutors.
Clinical Human Gait Classification: Extreme Learning Machine Approach

This study reports a novel approach for biometric gait pattern classification using Extreme Learning Machine (ELM) algorithm. Clinical gait analysis can be used for early detection of gait abnormality in brain or neurological disorder subjects. In many cases gait abnormality cannot be detected through visual observation alone, but becomes apparent only in a quantitative analysis of subject's gait. This can also help us understand the neuro-muscular mechanics associated with brain disorders. Human gait is also of profound interest to the research community in the field of biometric identification and bipedal robot locomotion due to its uniqueness and efficiency. This paper explores multi-class gait classification using four machine learning methods (KNN, SVM, ELM, MLP) and evaluates their performance for multi class gait classification. The proposed method achieves very good results. TheELM is used first time in to analyses the neuromuscular of patients suffering from multiple sclerosis and stroke.