Reviewerly/exHarmony huggingface dataset:
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search

Efficient video architecture is the key to deploying video recognition systems on devices with limited computing resources. Unfortunately, existing video architectures are often computationally intensive and not suitable for such applications. The recent X3D work presents a new family of efficient video models by expanding a hand-crafted image architecture along multiple axes, such as space, time, width, and depth. Although operating in a conceptually large space, X3D searches one axis at a time, and merely explored a small set of 30 architectures in total, which does not sufficiently explore the space. This paper bypasses existing 2D architectures, and directly searched for 3D architectures in a fine-grained space, where block type, filter number, expansion ratio and attention block are jointly searched. A probabilistic neural architecture search method is adopted to efficiently search in such a large space. Evaluations on Kinetics and Something-Something-V2 benchmarks confirm our AutoX3D models outperform existing ones in accuracy up to 1.3% under similar FLOPs, and reduce the computational cost up to ×1.74 when reaching similar performance.
Convolutional Codes With a Maximum Distance Profile Based on Skew Polynomials

We construct a family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n,k)$ </tex-math></inline-formula> convolutional codes with degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta \in \{k,n-k\}$ </tex-math></inline-formula> that have a maximum distance profile. The field size required for our construction is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (n^{2\delta })$ </tex-math></inline-formula> , which improves upon the known constructions of convolutional codes with a maximum distance profile. Our construction is based on the theory of skew polynomials.
A Hybrid Pooling Based Deep Learning Framework For Automated ICD Coding

ICD coding is the practice of allocating diagnostic and procedure codes to the clinical records following the International Classification of Diseases. The manual allocation of ICD codes to clinical notes is a very tedious job which has become costly, time-consuming and error-prone. Up to now, various methods for automated ICD coding have been devised, ranging from machine learning to deep learning methodologies. Earlier cutting-edge models relied on CNN’s with one or several fixed window widths. However, the length and dependency of text fragments linked to ICD labels in clinical literature differ considerably, posing a difficulty in determining the optimal window size. Apart from that, in prior models that utilized CNN architecture, the average features of the clinical notes have been ignored, resulting in a lack of preparation of rich features for the classifier. In this research, we present a Deep Recurrent Convolutional Neural Network with Hybrid Pooling (DRCNN-HP), which addresses all of the above mentioned issues. DRCNN-HP takes into account the different lengths as well as the dependency of the ICD code-related text chunks. Furthermore, we applied a powerful hybrid pooling layer in DRCNN-HP to capture the rich feature representation (i.e., by concatenating maximum and average features of text) for the classifier, which resulted in giving state-of-the-art results on the MIMIC-III top 50 dataset as compared to the prior competitive models.
On Paul Cilliers’ Approach to Complexity:Post-structuralism versus Model Exclusivity

Paul Cilliers has developed a novel post-structural approach to complexity that has influenced several writers contributing to the current complexity literature.Concomitantly however, Cilliers advocates for modelling complex systems using connectionist neural networks (rather than analytic, rule-based models).In this article, I argue that it is dilemmic to simultaneously hold these two positions.Cilliers' post-structural interpretation of complexity states that models of complex systems are always contextual and provisional; there is no exclusive model of complex systems.This sentiment however appears at odds with Cilliers' promotion of connectionist neural networks as the best way to model complex systems.The lesson is that those who currently follow Cilliers' post-structural approach to complexity cannot also develop a preferred model of complex systems, and those who currently advocate for some preferred model of complex systems cannot adopt the post-structural approach to complexity without giving up the purported objectivity and/or superiority of their preferred model.
Alternatives in Counterfactuals: What Is<i>Right</i>and What Is<i>Not</i>

Abstract Classical semantics for counterfactuals is based on a notion of minimal change: If ${\textsf {A}}$, would ${\textsf {C}}$ says that the worlds that make ${\textsf {A}}$ true and that are otherwise minimally different from the actual world are ${\textsf {C}}$-worlds. This semantics suffers from a well-known difficulty with disjunctive antecedents (see e.g. Alonso-Ovalle, 2009; Willer, 2018; Santorio, 2018, a.o.). In a recent study, Ciardelli, Zhang, and Champollion (Ciardelli et al., 2018b; henceforth, CZC) present new, related difficulties for the classical approach having to do with unpredicted differences between counterfactuals with De Morgan-equivalent antecedents, and related pattern of inferences. They propose a new semantics for counterfactuals, which builds on inquisitive semantics (see Ciardelli et al., 2018a) and gives up on minimal change. Building on this debate, we report on a series of experiments that investigate the role of overt negation in this data. Our results replicate CZC’s main effects, but they also indicate that those effects are linked to the presence of overt negation. We propose a novel account, based on three key assumptions: (i) the semantics for counterfactuals does involve a notion of minimal change, after all; (ii) the meanings of disjunction and negation are associated with alternatives, which interact with the meaning of counterfactuals; (iii) the alternatives generated by negation are partially determined by the question under discussion (QUD). We compare our account with other existing accounts, including CZC’s own proposal, as well as Schulz (2019) and Bar-Lev &amp; Fox (2020) ones.
Distributed Control for Multiple UAV Transport of Slung Loads

We propose a distributed control strategy for slung load transportation using multiple UAVs, with the goal to keep the load on the desired trajectory while maintaining formation. The load is equipped with an IMU and an optical sensor, and use inputs from these sensors to enable the multi-UAV system to generate appropriate control commands in order to maintain the formation. The proposed strategy takes advantage of the coupling between translational dynamics of UAVs and rotational dynamics of slung load to control the formation along vertical direction. Feedback from the optical sensor ensures formation in the rest of the two directions. The proposed controller compares the load’s current state with reference trajectory to generate control inputs, which then acts equally on all UAVs in the formation. Simulation results are presented to support the efficacy of the proposed control law. Implementation in aROS/Gazebo platform is also demonstrated.
SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.
Object Recognition System for Visually Impaired People

One of the biggest problems that visually Impaired (VI) individuals face in their daily lives is object detection and recognition. A model is created for an object detector that can detect items for VI persons and other important uses by recognizing them at a specific distance. Existing object detection algorithms necessitate a huge amount of training data, which takes longer time, more complicated, and it is a difficult process. As a result, a computer vision notion for converting an object to text was developed using the Caffemodel framework by importing a pretrained dataset model. The Mobilenet SSD method is then used to translate the texts into speech. On a single screen, this system can detect many objects. It aids visually challenged people in detecting objects in real time. This technology can also be put into any portable gadget to assist visually impaired people to recognize items at a certain distance.
Graph-based relational reasoning in a latent space for skeleton-based action recognition

Motivated by the powerful capability of deep neural networks in feature learning, a new graph-based neural network is proposed to learn local and global relational information on skeleton sequences represented as spatio-temporal graphs (STGs). The pipeline of our network architecture consists of three main stages. As the first stage, spatial–temporal sub-graphs (sub-STGs) are projected into a latent space in which every point is represented as a linear subspace. The second stage is based on message passing to acquire the localized correlated features of the nodes in the latent space. The third stage relies on graph convolutional networks (GCNs) to reason the long-range spatio-temporal dependencies through a graph representation of the latent space. Finally, the average pooling layer and the softmax classifier are then employed to predict the action categories based on the extracted local and global correlations. We validate our model in terms of action recognition using three challenging datasets: the NTU RGB+D, Kinetics Motion, and SBU Kinect Interaction datasets. The experimental results demonstrate the effectiveness of our approach and show that our proposed model outperforms the state-of-the-art methods.
Toward Design Assurance of Machine-Learning Airborne Systems

In recent years, Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology, have demonstrated impressive progress and provides historic opportunities for the aviation industry. However, several key aspects of ML technology are not compatible with existing design assurance standards and make certification problematic. In this paper, we present a case study of a visual system with a Deep Neural Network (DNN) intended to detect and identify airport runway signs. Different use cases and variants of this system exhibit different levels of criticality ranging from design assurance level (DAL) D to B. We use the case study to illustrate the challenges of certification according to the current standards, such as DO-178C. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D variant of the system. We also discuss gaps and potential approaches for the higher design assurance levels.
Perfect state transfer on Cayley graphs over the dicyclic group

Perfect state transfer (PST) has great importance due to its applications in quantum information processing, quantum communication networks and cryptography. In the present work, we establish a characterization of Cayley graphs over dicyclic groups T4n, having PST.
Design and Implementation of OpenCL-Based FPGA Accelerator for YOLOv2

Convolutional neural networks (CNNs) are widely used in practical scenarios such as license plate recognition, face recognition and radar image detection, where the main accelerators used are GPU platforms with high power output. However, FPGA is more suitable to be used in some application scenarios because of its own flexibility and a large number of computing resources, and it has lower energy consumption than GPU. In deploying large neural network models, pure hardware development requires a long time cycle. Our work chooses to use a High Level Synthesis tool based on OpenCL for development, which significantly improves the development efficiency and enables fast implementation of the model. The winograd algorithm is also used in the convolutional kernel module to accelerate the convolutional operation. The final verification is completed on the FPGA development board DE5a-Net, where we process a 544*544 format image in 149ms, as well as achieving a peak performance of 248.7 GOP/S.
Building a Toxic Comments Classification Model

A large portion of online comments are typically beneficial, but a significant portion are harmful in nature. Harmful or toxic remarks are impolite, oppressive, or absurd online remarks that typically cause other clients to leave a conversation. The risk of web-based tormenting and badgering influences the free progression of thoughts by confining individuals’ contradictory assessments. Locales struggle to advance conversations in a viable manner, prompting many networks to limit or shut down client comments entirely. This paper will deliberately analyze the degree of online badgering and characterize the substance into marks to look at the harmfulness as effectively as it could be expected. Here, this research study utilizes six machine learning calculations and apply them to our information to tackle the issue of text classification and distinguish the best machine learning calculation that becomes dependent on our assessment measurements for performing harmful remarks classification. This study targets on analyzing the harmfulness with high accuracy to restrict its unfavorable impacts, which will be a motivation for associations to make fundamental strides.
HybridQ: A Hybrid Simulator for Quantum Circuits

Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
Novelty Generation Framework for AI Agents in Angry Birds Style Physics Games

Handling novel situations is a critical capability of Artificial Intelligence (AI) agents when working in open-world physical environments. To develop and evaluate these agents, we need realistic and meaningful novelties, that is, novelties that are detectable and learnable. However, there is a lack of research in the area of creating novelties for AI agents in physical environments. Physics-based video games are popular among AI researchers due to the ability to create realistic and controllable physical environments. In this paper, we present a systematic novelty generation framework for physics-based video games. This framework allows the user to define a specific objective when generating novel content that ensures detectability. We instantiate the proposed framework for the video game Angry Birds and conduct experiments to show that the generated novel content is consistent with the user-defined objectives. Furthermore, we use a reinforcement learning agent to experiment with the learnability of the generated novel content.
Ada: Adversarial learning based data augmentation for malicious users detection

Malicious user detection in the recommender systems has attracted much attention in the last two decades because malicious users can seriously affect the recommendation results and user experience. The up-to-date detection models usually concentrate on distinguishing users according to their latent features represented in user embeddings. These models can improve detection performance; however, they are usually not fully up to expectations, especially in the scenarios with unbalanced use samples. From these models that concentrate on user embedding representations, we can summarize the following difficulties: (1) the cost of manual labeling malicious causes the lack of labeled malicious users in training data, which leads to imprecise representations of users; (2) current augmentation methods that aim at mitigating the lack of labeled malicious users are hard to simulate the distribution of malicious users. In this paper, we propose a detection model, using adversarial learning based data augmentation (a.k.a. Ada) to alleviate these problems. Concretely, to get precise representations of users, the model integrates potential user relations and structural similarities into user embeddings. After obtaining precise user representation, it presents a novel data augmentation based on the deep convolutional generative adversarial networks (DCGAN) to simulate the distribution of malicious user embeddings and generate additional fake user embeddings. Experiments on public datasets show our model outperforms state-of-the-art detection models with sparse labeled malicious users, and the ablation study confirms the importance and effectiveness of each component of the model.
A comparison of obfuscation methods used for privacy protection: Exploring the challenges of polygon data in agricultural research

Abstract A major challenge of sharing spatially explicit agricultural and agri‐environmental data is to identify the trade‐off between field parcel confidentiality and spatial pattern preservation. In this work, 27 point‐based obfuscation and evaluation methods were applied on agricultural data, collected by the Irish Nutrient Management Planning Online (NMP Online) platform, which is a high‐density polygon dataset developed to inform precision agriculture through nutrient management based on soil fertility and agronomic targets. Broad categorizations of methods—including N*Rand, Donut, Density, Pinwheel, AHilb, and k ‐anonymity—were developed, combined, and modified to achieve the best trade‐off between security and accuracy. To improve geoprivacy and spatial pattern preservation of existing Donut and Density methods, qualitative approaches, including Donut‐ k and Density‐ k methods, were introduced which identify the optimal values of radii based on a combination of the Donut method and k ‐anonymity satisfaction, and subsequently optimal k ‐anonymity determination. Modified AHilb and Donut‐AHilb methods were also developed to generate smaller and arbitrary obfuscation areas to improve location security. The Donut‐AHilb method was found to be the best at spatial pattern preservation and satisfying larger k ‐anonymity, but the risk of false identification and non‐unique obfuscation was high when considering the polygon nature of agricultural data such as field parcels. Further, we introduce the term “non‐unique obfuscated points,” which is important when obfuscating static objects as two or more points might have the same obfuscated location, which has relevance to the wider GIScience community.
Iraqi Traffic Signs Detection Based On Yolov5

Traffic signs object detection has gained high interest in recent years, as one of the most significant object detector applications. The development of deep learning technologies gives support to traffic signs detector which it offers several advantages, including the benefit of high detection precision and the timely response to condition changes of traffic signs. Therefore, this paper shows an efficient method for detecting traffic signs. Hence, it implements a new Iraqi Traffic Sign Detection Benchmark (IQTSDB) dataset based on You Only Look Once version 5 (YOLOv5) algorithm. The experimental results show that the implementation of the IQTSDB dataset with YOLOv5 has high efficiency in different conditions such as sunny, cloudy, weak light, and rainy conditions. Besides, real images has been captured for the traffic signs in Baghdad. In addition, the results show that the YOLOv5 has high efficiency in detecting traffic signs of different sizes (small, medium, large), and mean Average Precision (mAP) compared to yolov2, and yolov3.
Crooked Indifferentiability of Enveloped XOR Revisited

In CRYPTO 2018, Russell, Tang, Yung and Zhou (RTYZ) introduced the notion of crooked indifferentiability to analyze the security of a hash function when the underlying primitive is subverted. They showed that the n-bit to n-bit function implemented using enveloped XOR construction (EXor) with \(3n+1\) many n-bit functions and \(3n^2\)-bit random initial vectors can be proven secure asymptotically in the crooked indifferentiability setting. We identify several major issues and gaps in the proof by RTYZ, We argue that their proof can achieve security only in a restricted setting. We present a new proof of crooked indifferentiability where the adversary can evaluate queries related to multiple messages. Our technique can handle function-dependent subversion.