Reviewerly/exHarmony huggingface dataset:
Secure Communication System Based on Synchronized 3D Spherical Chaotic Systems

This article presents a 3-dimensional chaotic system that generates spherical attractor. The basis of the chaotic system is the Shilnikov criterion. For the first time in literature and which is the first contribution, this investigation shows the synchronization of two of the chaotic spherical systems in a master-slave topology using Hamiltonian forms and observer approach. As a second contribution, the article demonstrates further the implementation of a secure communication system using the synchronized master-slave system. To verify the secure communication system, the transmission system uses states x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> as transmission variables to send and receive grayscale and RGB images. The performance analyses of the results, which examine the security and reliability which the transmission system, show that the system is very effective, robust against exhaustive attack, and removes relativity between the original and encrypted images with correlation coefficient very close to zero. Variables x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and x <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> give the best result in transmission of the grayscale and RGB images, respectively, based on the correlation coefficient between the original and encrypted images.
Towards Adversarially Robust DDoS-Attack Classification

On the frontier of cybersecurity are a class of emergent security threats that learn to find vulnerabilities in machine learning systems. A supervised machine learning classifier learns a mapping from x to y where x is the input features and y is a vector of associated labels. Neural Networks are state of the art performers on most vision, audio, and natural language processing tasks. Neural Networks have been shown to be vulnerable to adversarial perturbations of the input, which cause them to misclassify with high confidence. Adversarial perturbations are small but targeted modifications to the input often undetectable by the human eye. Adversarial perturbations pose risk to applications that rely on machine learning models. Neural Networks have been shown to be able to classify distributed denial of service (DDoS) attacks by learning a dataset of attack characteristics visualized using three-axis hive plots. In this work we present a novel application of a classifier trained to classify DDoS attacks that is robust to some of the most common, known, classes of gradient-based and gradient-free adversarial attacks.
Multimodal Conversational Agent for Older Adults' Behavioral Change

Recently, research in the multimodal interaction area has shown a rapid development and users have been embracing their experience of multimodal technologies. In fact, having a multimodal system means allowing the user to choose one or more communication channel to access the system. While, on the contrary, unimodal systems do not have this option and can only settle to one-way of communication. Particularly, conversational agents are also in rapid increment and older adults are becoming more and more exposed to such agents. NESTORE is a virtual coach that aims to follow older adults in their wellbeing journey. It comes in two forms of interfaces: (i) a chatbot which is a text-based messaging application and (ii) a tangible coach which is a vocal assistant. This virtual coach is multimodal, due to the fact that it can depict or send information and even receive the user's input through two different interfaces which are the chatbot and the tangible coach. Our aim is then to explore the modality that the user prefers in terms of user experience. We experimented with older adults five different types of scenarios where the virtual coach was interacting with a user through different modalities, used individually or combined. The virtual coach was asking a set of questions derived from a behavioral change model called HAPA. We measured the perceived user experience for each scenario with the UEQ-S questionnaire, and asked to rank the scenarios according to the users? preferences.
Artificial Intelligence, LLC: Corporate Personhood for AI

Our legal system has long tried to fit the square peg of artificial intelligence (AI) technologies into the round hole of the current tort regime, overlooking the inability of traditional liability schemes to address the nuances of how AI technology creates harms. The current tort regime deals out rough justice—using strict liability for some AI products and using the negligence rule for other AI services—both of which are insufficiently tailored to achieve public policy objectives. Under a strict liability regime where manufacturers are always held liable for the faults of their technology regardless of knowledge or precautionary measures, firms are incentivized to play it safe and stifle innovation. But even with this cautionary stance, the goals of strict liability cannot be met due to the unique nature of AI technology: its mistakes are merely “efficient errors”—they appropriately surpass the human baseline, they are game theory problems intended for a jury, they are necessary to train a robust system, or they are harmless but misclassified.Under a negligence liability regime where the onus falls entirely on consumers to prove the element of causation, victimized consumers must surmount the difficult hurdle of tracing the vectors of causation through the “black box” of algorithms. Unable to do so, many are left without sufficient recourse or compensation. This Article proposes a new framework to regulate AI technologies: bestowing corporate personhood to AI systems. First, the corporate personality trait of “limited liability” strikes an optimal balance in determining liability—it would both compensate victims (for instance, through obligations to carry insurance and a straightforward burden of causation) while holding manufacturers responsible only when the infraction is egregious (for instance, through veil-piercing). Second, corporate personhood is “divisible”—meaning not all corporate personality traits need to be granted—which circumvents many of the philosophical criticisms of giving AI the complete set of rights of full legal personhood. Third, innovation incentives weigh heavily in favor of shifting to a tort regime better tailored to the potential promises and unique harms of AI.
Case studies: learning strategy incorporating Lego Education in the seventh-grade math course at the school

Abstract This article presents reflections on the use of case studies that revolve around the student’s environment to use it as an input in the development of the mathematics course in the seventh grades at the “Isidro Caballero Delgado” school in Floridablanca, Santander, Colombia. With this experience, it can be determined that the use of robotics prototypes in mathematics is a support tool as a pedagogical strategy in the student’s learning process, strengthening not only the academic but also their comprehensive training. That is why students use the construction of the prototypes not only to learn mathematical concepts for better understanding, analysis, and critical reasoning but also to develop technical language in an empirical way giving answers to the laws of physics in the prototype or its function. This work is part of doctoral research where 60 students of the experimental group participated. The structure of the document contains a first section with the introduction, second section where the methodology and the applied strategy are detailed; in section third the results obtained are mentioned; and finally, in fourth section, the conclusions and possible recommendations are presented.
Syncphonia: Understanding the value of participatory design in developing music technology to support musical ensembles that use notation

The benefits of ensemble performance are well recognized; notation supports group performance, but cuts in music education and changing musical cultures mean that notation is increasingly perceived as a barrier to entry. In an extended participatory design (PD) project, we co-designed and developed a software system for networked notation called Syncphonia with the aim of enhancing access to and experience of notation-based ensemble performance. In previous work, our formal evaluation and informal observations and feedback revealed a wide range of benefits. In this article, we are concerned with articulating the knowledge generated and insights gained through this extended PD process. To do so, we employ a framework for systematic reflection that has been designed to support investigation into the tacit knowledge generated in participatory design. Through this method, we focus inward and share three insights into the value of networked notation in contemporary musical cultures; we also look outward and articulate five approaches to PD with musical ensembles that might benefit others adopting this rich research method. A pluralistic and inclusive vision of notation is espoused and speculation is submitted that a dynamic, networked notation might ameliorate the boundaries between composing, improvising and performing to the benefit of all three.
Cypher Social Contracts A Novel Protocol Specification for Cyber Physical Smart Contracts

Social interaction is the foundation of the Internet. Nevertheless, contract drafting is a tedious task and often too complex for normal social interactions between individuals or small service providers, which has led to the rise of centralized services to manage contracts, purchases or other arrangements. In order to give regular users more privacy and a place for self-organization, this paper describes the definition of a novel protocol that allows the creation of generic smart contracts which can be processed by machines or create social interaction between humans while maintaining a high level of privacy and anonymity.
Gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data

The phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing.This is more evident in multilingual societies.In this paper, we have developed a system for SemEval 2020: Task 9 on Sentiment Analysis for Code-Mixed Social Media Text.Our system first generates two types of embeddings for the social media text.In those, the first one is character level embeddings to encode the character level information and to handle the out-of-vocabulary entries and the second one is FastText word embeddings for capturing morphology and semantics.These two embeddings were passed to the LSTM network and the system outperformed the baseline model.
A Model for Pointing at Targets with Different Clickable and Visual Widths and with Distractors

In this study, we extend Fitts' law to enable it to predict the movement time of pointing operations in interfaces, such as those in navigation bars whose items have different clickable and visual widths and intervals between a target and distractors. For this, we conduct two experiments to investigate the effects of distractors on pointing operations and how increasing the interval size changes user performance. We find that the movement time is significantly affected by the clickable width and intervals whereas it is only slightly affected by the visual width. Based on the results, we construct a time prediction model for considering the differences between clickable and visual widths and the intervals between a target and distractors. Our model shows a good fit for not only the data of our two experiments but also for those of three previous studies.
PointFPN: A Frustum-based Feature Pyramid Network for 3D Object Detection

3D object detection is crucial to ensure the reliability and stability of autonomous driving systems. In recent years, researchers have made great progress in 3D object detection by combining features of images and point clouds. However, there is still much room for detection accuracy improvement, especially for small object detection. In this paper, we propose a frustum-based 3D object detection model named PointFPN. The core idea of PointFPN is learning expressive semantic and contextual information for small objects, e.g., pedestrians and cyclists in an urban street scene. To detect 3D objects, our model uses frustums to bridge the gap between images and point clouds and thus generating proposals. Then, a feature pyramid structure is designed to extract and fuse multi-level features of target objects represented by point clouds. Meanwhile, we develop a multilevel regression network to calculate different parameters of 3D bounding boxes at different feature levels. Through elaborate structure designed above, our model can learn discriminative features which are highly relevant to bounding box parameters at different feature levels. Experimental study shows that our model is effective in detecting small objects and has a strong robustness to sparse point clouds. Our model demonstrates state-of-the-art performance on small object detection on KITTI benchmark.
Development of a Low-Latency and Real-Time Automatic Speech Recognition System

In this study, a real-time automatic speech recognition (ASR) system based on the Kaldi ASR toolkit, with low-latency and customizable models, without any internet connection, was developed. The proposed ASR system includes a voice activity detection (VAD) module and an audio transmitter as a front-end speech processing and a decoder for the received audio signals. The ASR system was evaluated in terms of ASR accuracy and speech processing speed. Consequently, the ASR system achieved high ASR accuracy on the CSJ (Corpus of Spontaneous Japanese) test set with super low-latency.
Integrating Transformer into Global and Residual Image Feature Extractor in Visual Question Answering for Blind People

Visual Question Answering (VQA), the novel task among the intersection between Computer Vision (CV) and Natural Language Processing (NLP), extracts answers from features of both questions and images. The current approaches in VQA rely on the combination between convolution and recurrent networks, which leads to the huge number of parameters for learning phase. With the success of employing pre-trained models, we integrate BERT [1] for embedding text and two models: ResNets [2] and VGG [3] for embedding image. In addition, we also propose to take advantages of fine-tuning techniques and stacked attention mechanism to combine textual and visual features in a novel learning phase considered its ability to reduce the size of models. To demonstrate our model's performance, we conduct experiments in the VizWiz VQA Challenge 2020. According to the experimental results, the proposed approach outperforms existing methods for Yes-No questions on VizWiz VQA dataset.
Enhanced soft attention mechanism with an inception-like module for image captioning

Visual soft attention has been widely adopted in image captioning models. Traditional Soft Attention Mechanism (TSAM) assigns a weight to a certain region by using a multilayer perceptron with input from its own features. As image classification networks extract regional features based on spatial locations, TSAM fails to adequately consider the spatial contexts of regions, which leads to unreasonable weight distribution. In this paper, we introduce a flexible and universal attention framework with an inception-like module, named Enhanced Soft Attention Mechanism (ESAM), which can balance the attention levels of adjacent regions and alleviate the problem caused by local features with weak representational ability. Furthermore, we add an LSTM to the attention module so that it can take into account the previous attention distribution while generating the current word. Experimental results show that our ESAM significantly surpasses the TSAM by 4.1% on BLEU-4 and 2.7% on CIDEr, and achieves better results when verifying universality under the same experimental setups.
Adversarial momentum-contrastive pre-training

Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which will bring heavy computational overhead on platforms with limited resources. In order to help the network learn more powerful feature representations in smaller batches and fewer epochs, this paper proposes a novel adversarial momentum contrastive learning method, which introduces two memory banks corresponding to clean samples and adversarial samples, respectively. These memory banks can be dynamically incorporated into the training process to track invariant features among historical mini-batches. Compared with the previous adversarial pre-training model, our method achieves superior performance with smaller batch size and less training epochs. In addition, the model outperforms some state-of-the-art supervised defensive methods on multiple benchmark datasets after being fine-tuned on downstream classification tasks.
Adaptive Audio Mixing for Enhancing Immersion in Augmented Reality Audio Games

In this work we present an adaptive audio mixing technique to be implemented in the design of Augmented Reality Audio (ARA) systems. The content of such systems is delivered entirely through the acoustic channel: the real acoustic environment is mixed with a virtual soundscape and returns to the listener as "pseudoacoustic" environment. We argue that the proposed adaptive mixing technique enhances user immersion in the augmented space in terms of the localization of sound objects. The need to optimise our ARA mixing engine emerged from our previous research, and more specifically from the analysis of the experimental results regarding the development of the Augmented Reality Audio Game (ARAG) "Audio Legends" that was tested on the field. The purpose of our new design was to aid sound localization, which is a crucial and demanding factor for delivering an immersive acoustic experience. We describe in depth the adaptive mixing along with the experimental test-bed. The results for the sound localization scenario indicate a substantial increase of 55 percent in accuracy compared to the legacy ARA mix model.
Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems

Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.
MDS linear codes with one-dimensional hull

The hull of a linear code C is the intersection of C with its dual C⊥, where the dual is often defined with respect to Euclidean or Hermitian inner product. The Euclidean hull with low dimensions gets much interest due to its crucial role in determining the complexity of algorithms for computing the automorphism group of a linear code and for checking permutation equivalence of two linear codes. Recently, both Euclidean and Hermitian hulls have found another application to quantum error correcting codes with entanglements. This paper aims to explore explicit constructions of families of MDS linear codes with one-dimensional hull for both cases. We use tools from algebraic function fields in one variable to study such codes. Sufficient conditions for an algebraic geometry code of genus zero to have one-dimensional hull are provided, and some construction methods are presented. We construct many families of MDS linear codes with one-dimensional hull for the Euclidean case and three families for the Hermitian case, respectively.
Assessment of Unknown Phase Shift for Speckle Interferometry Using Sample Pearson Correlation Coefficient

A sample Pearson correlation coefficient (SPCC) is proposed to calculate an unknown phase shift (UPS) between speckle fringe patterns (speckle interferograms (SIs)) of the same test object. The SIs sequences of an initial flat optically rough surface and the SIs sequences of a deformed surface were synthesized to estimate the systematic errors of the UPS extraction. The low level of these errors in the range from 0 to 180° and the significantly shorter time to calculate the UPS compared to the time required to calculate the UPS using a population Pearson correlation coefficient proves the effectiveness of using the SPCC to extract the UPS.
Multi-access Edge Computing Based User Experience Driven Multicast Video Conference Algorithm

Our research proposed a user experience driven multicast video conference algorithm based on MEC. This algorithm was designed to improve the utilization efficiency of network resources while ensuring user requirements and delay constraints. The algorithm is able to use the MEC server in the network as a forwarding node, and builds multicast trees for video transmission, and continually transcodes the video stream according to user requests. In this article, we modeled the problem of constructing a multicast tree capable of video transcoding as a nonlinear integer programming problem, and designed a heuristic algorithm. Amounts of simulation experiments proved that, our algorithm has better performance in terms of end-to-end delay and network resource utilization efciency compared with other algorithms.
Projecting Performance for PIUMA using Down-Scaled Simulation

Programmable Integrated Unified Memory Architecture (PIUMA) is Intel's novel graph analysis optimized processor architecture, targeted at efficiently executing graph algorithms on very large graphs. Simulation is used to project its performance for various algorithms before the system is built. However, simulators are limited in the number of cores and threads they can simulate, because of their low simulation speed, high resource usage and poor scalability. Therefore, it is practically impossible to simulate PIUMA at its full system scale. In this paper, we present downscaled simulation, a technique to project performance of a large scale system using a small scale simulation. We apply the technique to PIUMA, showing how to configure the downscaled system in order to accurately reflect the characteristics of the full system. We evaluate downscaled simulation on a set of graph applications, showing that it accurately tracks simulation results of small scale simulations, as well as the projections to large systems made by an analytical model.