Reviewerly/exHarmony huggingface dataset:
Automatic Text Summarization Using Deep Reinforcement Learning and Beyond

In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.
Fitness-Distance Balance with Functional Weights: A New Selection Method for Evolutionary Algorithms

In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.
The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation.

Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural language dialogs between a human Driver controlling a robot and a human Commander providing guidance towards navigation goals. In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move the robot to a sequence of target objects. We introduce a Localization from Dialog History (LDH) and a Navigation from Dialog History (NDH) task where a learned agent is given dialog and visual observations from the robot platform as input and must localize in the global map or navigate towards the next target object, respectively. RobotSlang is comprised of nearly 5k utterances and over 1k minutes of robot camera and control streams. We present an initial model for the NDH task, and show that an agent trained in simulation can follow the RobotSlang dialog-based navigation instructions for controlling a physical robot platform. Code and data are available at this https URL.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN.
ERP System in the Logistics Information Management System of Supply Chain Enterprises

In the era of rapid development of information, with the popularization of computers, the development of science and technology, and the continuous growth of IT technology and business, the enterprise resource planning (ERP) system has become a platform and guarantee for the completion of business management processes after long-term operations. Establishing a logistics management information system is an important means for Chinese companies to improve their logistics efficiency. This is the prerequisite for modern companies to gain a competitive advantage in the management of logistics companies. Therefore, organizations must adapt to different needs, fierce competition, and rapid strategic changes. This is a powerful supplement to the nature of logistics companies. The logistics management information system has continuously experienced the processes of MRP, MRP II, and ERP. This article aims to study how to use computers in logistics management to create an ERP system-based enterprise logistics information management system. This article puts forward the development significance of logistics information management system, uses ERP system to analyze complex logistics business in detail, and designs the overall framework, specific functions, and data model of the system in detail. The experimental results of this paper show that the ERP system has important application value in the logistics information management system of supply chain enterprises, and it can effectively increase the value of customer satisfaction by 86.7%.
Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models

Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.
Bayesian Deep Learning with Monte Carlo Dropout for Qualification of Semantic Segmentation

Despite the intense development of deep neural networks for computer vision, and especially semantic segmentation, their application to Earth Observation data remains usually below accuracy requirements brought by real-life scenarios. Even if well-known deep learning methods produce excellent results, they tend to be over-confident and cannot assess how relevant their predictions are. In this work, a Bayesian deep learning method, based on Monte Carlo Dropout, is proposed to tackle semantic segmentation of aerial and satellite images. Bayesian deep learning can provide both a semantic segmentation and uncertainty maps. Based on the popular U-Net architecture, our model achieves semantic segmentation with high accuracy, e.g. F1-score and overall accuracy respectively reaching 90.84% and 93.22% on a public standard dataset. Uncertainty maps, also derived from our model, show a strong interest in qualitative evaluation of the segmentation and in the improvement of the database.
EyeMU Interactions: Gaze + IMU Gestures on Mobile Devices

As smartphone screens have grown in size, single-handed use has become more cumbersome. Interactive targets that are easily seen can be hard to reach, particularly notifications and upper menu bar items. Users must either adjust their grip to reach distant targets, or use their other hand. In this research, we show how gaze estimation using a phone's user-facing camera can be paired with IMU-tracked motion gestures to enable a new, intuitive, and rapid interaction technique on handheld phones. We describe our proof-of-concept implementation and gesture set, built on state-of-the-art techniques and capable of self-contained execution on a smartphone. In our user study, we found a mean euclidean gaze error of 1.7 cm and a seven-class motion gesture classification accuracy of 97.3%.
Design of Flexible Hardware Accelerators for Image Convolutions and Transposed Convolutions

Nowadays, computer vision relies heavily on convolutional neural networks (CNNs) to perform complex and accurate tasks. Among them, super-resolution CNNs represent a meaningful example, due to the presence of both convolutional (CONV) and transposed convolutional (TCONV) layers. While the former exploit multiply-and-accumulate (MAC) operations to extract features of interest from incoming feature maps (fmaps), the latter perform MACs to tune the spatial resolution of the received fmaps properly. The ever-growing real-time and low-power requirements of modern computer vision applications represent a stimulus for the research community to investigate the deployment of CNNs on well-suited hardware platforms, such as field programmable gate arrays (FPGAs). FPGAs are widely recognized as valid candidates for trading off computational speed and power consumption, thanks to their flexibility and their capability to also deal with computationally intensive models. In order to reduce the number of operations to be performed, this paper presents a novel hardware-oriented algorithm able to efficiently accelerate both CONVs and TCONVs. The proposed strategy was validated by employing it within a reconfigurable hardware accelerator purposely designed to adapt itself to different operating modes set at run-time. When characterized using the Xilinx XC7K410T FPGA device, the proposed accelerator achieved a throughput of up to 2022.2 GOPS and, in comparison to state-of-the-art competitors, it reached an energy efficiency up to 2.3 times higher, without compromising the overall accuracy.
An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method

At present, fractional differential is the effective mathematical approach which deals with the factual problems. This projected technique employs the fractional derivatives definitions Riemann-Liouville (R-L), Grunwald-Letnikov (G-L) and the caputo technique for denoising medical image. The presented method based on fractional derivative which in turn improves the quality of image. The input image is processed on integer order method such as pre-processing operation, image conversion and noise image. The fractional differential mask method is to be applied with the help of Riemann Liouville, and Caputo algorithm. After denoising the medical image enhanced using Anisotropic diffusion process and the result is analyzed to finally get denoised and predicted image.
Programmable Polarities: Actuating Interactive Prototypes with Programmable Electromagnets

This demo introduces a framework that uses programmable electromagnets as a method to rapidly prototype interactive objects. Our approach allows users to to quickly and inexpensively embed actuation mechanisms into otherwise static prototypes in order to make them dynamic and interactive. Underpinning the technique is the insight of using electromagnets to interchangeably create attractive and repulsive forces between adjacent parts, and programmatically setting their polarities in a way that allows objects to translate rotationally and linearly, respond haptically, assemble, and locomote.
Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study

Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics.We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et al., 2019), and systematically analyze its reproducibility.Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context.We ask: To what extent is stance detection topicindependent and generalizable across topics?We compare the model's performance on various unseen topics, and find topic (e.g.abortion, cloning), class (e.g.pro, con), and their interaction affecting the model's performance.We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.
A minimum-time obstacle-avoidance path planning algorithm for unmanned aerial vehicles

Abstract In this article, we present a new strategy to determine an unmanned aerial vehicle trajectory that minimizes its flight time in presence of avoidance areas and obstacles. The method combines classical results from optimal control theory, i.e. the Euler-Lagrange Theorem and the Pontryagin Minimum Principle, with a continuation technique that dynamically adapts the solution curve to the presence of obstacles. We initially consider the two-dimensional path planning problem and then move to the three-dimensional one, and include numerical illustrations for both cases to show the efficiency of our approach.
An Energy-Friendly Scheduler for Edge Computing Systems

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.
Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks

Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly improve and accelerate training while the accuracy is frequently bottlenecked by the limited dataset size of the new target task. To solve the problem, some regularization methods, constraining the outer layer weights of the target network using the starting point as references (SPAR), have been studied. In this article, we propose a novel regularized transfer learning framework \operatorname{DELTA} , namely DE ep L earning T ransfer using Feature Map with A ttention . Instead of constraining the weights of neural network, \operatorname{DELTA} aims at preserving the outer layer outputs of the source network. Specifically, in addition to minimizing the empirical loss, \operatorname{DELTA} aligns the outer layer outputs of two networks, through constraining a subset of feature maps that are precisely selected by attention that has been learned in a supervised learning manner. We evaluate \operatorname{DELTA} with the state-of-the-art algorithms, including L^2 and \emph {L}^2\text{-}SP . The experiment results show that our method outperforms these baselines with higher accuracy for new tasks. Code has been made publicly available. 1
Quadrotor Autonomous Navigation in Semi-Known Environments Based on Deep Reinforcement Learning

In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to realize autonomous navigation in semi-known environments. Specifically, the proposed framework utilizes the dueling double deep recurrent Q-learning, which can implement global path planning with the obstacle map as input. Moreover, the proposed framework combined with contrastive learning-based feature extraction can conduct real-time autonomous obstacle avoidance with monocular vision effectively. The experimental results demonstrate that our framework exhibits remarkable performance for both global path planning and autonomous obstacle avoidance.
Toward Automatically Labeling Situations in Soccer

We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task.
Dataset Diversity

Many popular visual datasets used to train deep neural networksfor computer vision applications, especially for facial analytics,are created by retrieving images from the internet. Search enginesare often used to perform this task. However, due to localisationand personalisation of search results by the search engines alongwith the image indexing method used by these search engines, theresultant images overrepresent the demographics of the region fromwhere they were queried from. As most of the visual datasets arecreated in western countries, they tend to have a western centricbias and when these datasets are used to train deep neural networks,they tend to inherit these biases. Researchers studying the issue ofbias in visual datasets have focused on the racial aspect of thesebiases. We approach this from a geographical perspective. In thispaper, we 1) study how linguistic variations in search queries andgeographical variations in the querying region affect the social andcultural aspects of retrieved images focusing on facial analytics, 2)explore how geographical bias in image search and retrieval cancause racial, cultural and stereotypical bias in visual datasets and3) propose methods to mitigate such biases.
Improved Camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman

Camshift algorithm tracking is susceptible to interference when a tracking object is occluded or when its hue is similar to the background. An improved Camshift object-tracking algorithm combining AKAZE (Accelerated-KAZE) feature matching and Kalman filtering is proposed. First, the video channel is converted for processing. Second, AKAZE is used to match the object feature points and Kalman filtering is used to predict the next position. Then different scenes are judged by the threshold and the Camshift and Kalman tracking algorithms are used for object tracking, respectively. Finally, the improved Camshift algorithm is used to test the moving object in a variety of situations and compared with the traditional Camshift algorithm and the Kalman filter improved Camshift algorithm. Experimental results show that the improved joint tracking algorithm can continue tracking under full occlusion. The effective frame rate of recognition is increased by about 20%, and the single-frame image processing time is less than 35 ms, which can meet the real-time tracking requirements.
The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model

The Quantum Approximate Optimization Algorithm (QAOA) finds approximate solutions to combinatorial optimization problems. Its performance monotonically improves with its depth $p$. We apply the QAOA to MaxCut on large-girth $D$-regular graphs. We give an iterative formula to evaluate performance for any $D$ at any depth $p$. Looking at random $D$-regular graphs, at optimal parameters and as $D$ goes to infinity, we find that the $p=11$ QAOA beats all classical algorithms (known to the authors) that are free of unproven conjectures. While the iterative formula for these $D$-regular graphs is derived by looking at a single tree subgraph, we prove that it also gives the ensemble-averaged performance of the QAOA on the Sherrington-Kirkpatrick (SK) model defined on the complete graph. We also generalize our formula to Max-$q$-XORSAT on large-girth regular hypergraphs. Our iteration is a compact procedure, but its computational complexity grows as $O(p^2 4^p)$. This iteration is more efficient than the previous procedure for analyzing QAOA performance on the SK model, and we are able to numerically go to $p=20$. Encouraged by our findings, we make the optimistic conjecture that the QAOA, as $p$ goes to infinity, will achieve the Parisi value. We analyze the performance of the quantum algorithm, but one needs to run it on a quantum computer to produce a string with the guaranteed performance.