Reviewerly/exHarmony huggingface dataset:
A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem

The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain.
Comparing minds and machines: implications for financial stability

Abstract Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterize possibilities and challenges in designing partnerships that combine the strengths of both minds and machines. Leveraging on those insights, we explain how the differences in human and artificial intelligence have driven the usage of new techniques in financial markets, regulation, supervision, and policy-making, and discuss their potential impact on financial stability. Finally, we describe how effective mind–machine partnerships might be able to reduce systemic risks.
PreDet: Large-scale weakly supervised pre-training for detection

State-of-the-art object detection approaches typically rely on pre-trained classification models to achieve better performance and faster convergence. We hypothesize that classification pre-training strives to achieve translation invariance, and consequently ignores the localization aspect of the problem. We propose a new large-scale pre-training strategy for detection, where noisy class labels are available for all images, but not bounding-boxes. In this setting, we augment standard classification pre-training with a new detection-specific pretext task. Motivated by the noise-contrastive learning based self-supervised approaches, we design a task that forces bounding boxes with high-overlap to have similar representations in different views of an image, compared to non-overlapping boxes. We redesign Faster R-CNN modules to perform this task efficiently. Our experimental results show significant improvements over existing weakly-supervised and self-supervised pre-training approaches in both detection accuracy as well as fine-tuning speed.
Path Planning for Mobile Robot Based on RGB-D SLAM and Pedestrian Trajectory Prediction

This paper implements the construction of three-dimensional point cloud map based on RGB-D SLAM, and takes the three-dimensional point cloud information output by SLAM system as the input of Octomap, generates octree map and performs three-dimensional projection transformation, which converts point cloud map into two-dimensional raster map for path planning research. In order to solve the problem of secondary obstacle avoidance and the problem of emergency stop caused by no local optimal solution in local path planning, a new path planning algorithm for mobile robot based on dynamic object trajectory prediction is presented, and the best path is selected by combining the reinforcement learning algorithm, Sarsa, to avoid dynamic obstacles effectively. On the basis of using RGB-D camera to locate pedestrians in real time, Kalman filter algorithm is used to predict the global coordinates of pedestrians in the next moment. Then a new reward and punishment mechanism is designed to realize the dynamic obstacle avoidance based on the improved Sarsa algorithm, so that the mobile robot can leave the radiation circle of pedestrian prediction coordinates as soon as possible and avoid the pedestrian walking path.
Robust Scale-Aware Stereo Matching Network

Recently, deep convolutional neural networks (CNNs) have emerged as powerful tools for the correspondence problem in stereo matching task. However, the existence of multiscale objects and inevitable ill-conditioned regions, such as textureless regions, in real-world scene images continue to challenge current CNN architectures. In this article, we present a robust scale-aware stereo matching network, which aims to predict multiscale disparity maps and fuse them to achieve a more accurate disparity map. To this end, powerful feature representations are extracted from stereo images and are concatenated into a 4-D feature volume. The feature volume is then fed into a series of connected encoder–decoder cost aggregation structures for the construction of multiscale cost volumes. Following this, we regress multiscale disparity maps from the multiscale cost volumes and feed them into a fusion module to predict final disparity map. However, uncertainty estimations at each scale and complex disparity relationships among neighboring pixels pose a challenge on the disparity fusion. To overcome this challenge, we design a robust learning-based scale-aware disparity map fusion model, which seeks to map multiscale disparity maps onto the ground truth disparity map by leveraging their complementary strengths. Experimental results show that the proposed network is more robust and outperforms recent methods on standard stereo evaluation benchmarks.
A Deep Learning Framework for News Readers’ Emotion Prediction Based on Features From News Article and Pseudo Comments

With the rapid development of the Internet, readers tend to share their views and emotions about news events. Predicting these emotions provides a vital role in social media applications (e.g., sentiment retrieval, opinion summary, and election prediction). However, news articles usually consist of objective texts that lack emotion words, making emotion prediction challenging. From prior studies, we know that comments that come directly from readers are full of emotions. Therefore, in this article, we propose a deep learning framework that first merges article and comment information to predict readers' emotions. At the same time, in the prediction process, we design a pseudo comment representation for unpublished news articles by the comments of published news. In addition, a better model is required to encode articles that contain implicit emotions. To solve this problem, we propose a block emotion attention network (BEAN) to encode news articles better. It includes an emotion attention mechanism and a hierarchical structure to capture emotion words and generate structural information during encoding. Experiments performed on three public datasets show that BEAN achieves the state-of-the-art average Pearson (AP) and accuracy (Acc@1). Moreover, results on four self-collected datasets show that both the introduction of emotional comments and BEAN in our framework improve the ability to predict readers' emotions.
On the effects of continuous delivery on code quality: A case study in industry

Continuous delivery has been adopted by organizations to make software available to their users at any time. The transition from traditional software delivery methodologies to continuous delivery can impact on the results generated by organizations, e.g., the quality of source code and products. Although widely adopted, little is known about its effects. To account for this, this article reports a case study on the effects of continuous delivery on the quality of source code and products produced. Our case study was carried out for 12 months within a software development company in Brazil. Our findings indicate that the adoption of continuous delivery practices improved the quality of delivered products, mainly considering the number of defects reported by customers, the number of demands delivered per month, and user satisfaction. However, the adoption of continuous delivery did not favor the quality of source code, including the number of bugs, security vulnerabilities, code smells, duplicated code, and code complexity. Researchers and practitioners may benefit from our findings typically when delivering software products, designing and seeking to improve deployment pipeline practices. Finally, our study draws up some implications and shows the potential of adopting continuous delivery for developing enterprise applications that are constantly evolving.
A Lightweight Deep Convolutional Neural Network with Squeeze-and-Excitation Modules for Efficient Human Activity Recognition using Smartphone Sensors

Human activity recognition (HAR) relying on wearable sensors has become a new challenging area of research in pervasive and ubiquitous computing due to the rapid advancements in sensor technology. Since several deep learning (DL) networks have been introduced to handle the problem of feature extraction in machine learning, these techniques have recently garnered considerable attention. Nevertheless, most recent DL networks utilize sensory data by automatically extracting spatial properties without addressing cross-channel data at the same level. This information from each sensor channel was independently conveyed in a hierarchical method from shallow levels to deeper levels. This paper introduces SE-DeepConvNet, a lightweight deep convolutional neural network with squeeze-and-excitation modules for recognizing human activity from smartphone sensor data. The recommended SE-DeepConvNet was developed and assessed on the UCI-HAR dataset, a public benchmark HAR dataset. According to the results obtained, the SE-DeepConvNet outperforms other baseline DL networks with a maximum accuracy of 99.27%.
Binarization of Degraded Photographed Document Images- A Variational Denoising Auto Encoder

Document image enhancement is one of the prime researches in the area of optical character recognition and computer vision. Preprocessing procedure for a document depends on document layout, aging and document material type. This paper proposes a preprocessing technique for the enhancement of ancient and degraded document images. Initially the degraded patches from the document image is collected and used for learning through a variational de-noising autoencoder followed by document image enhancement. Ground truth images of the degraded patches are trained with the help of an adamax optimizer. A deep learning architecture comprised of five levels of convolution is devised for encoding and decoding process. Down sampling is initially performed in the encoding stage after each level of convolution. Further up sampling is conducted in the decoding stage. Experimentations are conducted on DIBCO (2016, 2013, 2012, 2011, 2010 and 2009) datasets and the results of enhancement are found to be promising with an average RMSE of 0.106 for batch size 1 and 24 epochs.
The Dark Web Worldwide 2020: Anonymous vs Safety

In the pandemic year 2020, the Internet became the key trend and brought new emerging threats to most of humanity. This year, about 300,000 people used Dark Web resources every day. This article reviews the biggest hacks and data breaches of 2020. The intensity of access to Anonymous internet resources in wordwide 2020 is analyzed. Daily Top users per 100k internet users 2020 by most countries is calculated. There has been made an attempt to assess the predominance of light or gray in the Dark Web and to investigate whether the daily number of Anonymous internet users depends on the number of individuals using the Internet and whether it affects the level of cybersecurity for most countries.
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-Trained Models

Self-supervised pre-training is an effective approach to leveraging a large amount of unlabelled data to reduce word error rates (WERs) of automatic speech recognition (ASR) systems. Since it is impractical to use large pre-trained models for many real-world ASR applications, it is desirable to have a much smaller model while retaining the performance of the pre-trained model. In this paper, we propose a simple knowledge distillation (KD) loss function for neural transducers that focuses on the one-best path in the output probability lattice under both streaming and non-streaming setups, which allows a small student model to approach the performance of the large pre-trained teacher model. Experiments on the LibriSpeech dataset show that despite being 10 times smaller than the teacher model, the proposed loss results in relative WER reductions (WERRs) of 11.5% and 6.8% on the test-other set for non-streaming and streaming student models compared to the baseline transducers trained without KD using the labelled 100-hour clean data. With an additional 860 hours of unlabelled data for KD, the WERRs increase to 48.2% and 38.5% for non-streaming and streaming students. If language model shallow fusion is used for producing distillation targets, a further improvement in the student model is observed.
Moving beyond content‐specific computation in artificial neural networks

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two takes advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence.
Surveillance and Obstacle Avoiding Autonomous Robot

This paper presents an effective work and deployment of mobile automation that has lush options. Surveillance robots are now a days used everywhere for various purposes such as homes, offices, industries, power plants etc., they are very useful in which these robots are useful in monitoring the places or surroundings where human intervention is not allowed. Also, Obstacles avoiding, as in the name itself these robots are used for avoiding any obstacles in the desired position where the robot is roaming. This proposed Surveillance and Obstacle avoiding Robot uses the WEBOTS. This paper presents the design of an autonomous robot as a basic development of an intelligent miniature and simple user-friendly robot which has just made of 2 or 3 shapes. In this paper, we tend to propose a design for intelligent work automation that's able to avoid obstacles. These types of robots are used in the applications like patrolling robot where it is used in industries both for surveillance and obstacle avoidance.
Reactive Obstacle–Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence

Obstacle–Avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow for comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies a fuzzy inference system (FIS) to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on a multilayer perceptron (MLP) architecture, which is a class of feedforward artificial neural network (ANN), and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive Obstacle–Avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “experience” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.
On-Device Content Moderation

With the advent of internet, not safe for work (NSFW) content moderation is a major problem today. Since, smartphones are now part of daily life of billions of people, it becomes even more important to have a solution which could detect and suggest user about potential NSFW content present on their phone. In this paper we present a novel on-device solution for detecting NSFW images. In addition to conventional pornographic content moderation, we have also included semi-nude content moderation as it is still NSFW in a large demography. We have curated a dataset comprising of three major categories, namely nude, semi-nude and safe images. We have created an ensemble of object detector and classifier for filtering of nude and semi-nude contents. The solution provides unsafe body part annotations along with identification of semi-nude images. We extensively tested our proposed solution on several public dataset and also on our custom dataset. The model achieves F1 score of 0.91 with 95% precision and 88% recall on our custom NSFW_16k dataset and 0.92 MAP on NPDI dataset. Moreover it achieves average 0.002 false positive rate on a collection of safe image open datasets.
Transient Electromagnetic Inversion: An ICDE-Trained Kernel Principal Component OSELM Approach

The traditional extreme learning machine (ELM) inversion of transient electromagnetic method (TEM) based on random initial weights is known to be inept for its low-computational efficiency and poor generalization performance. To solve these problems, a kernel principal component online sequential extreme learning machine (KPCOSELM) trained by an improved chaotic differential evolution (ICDE) method is proposed. An additional kernel principal component (KPC) layer is used, which reduces the dimension of TEM data and enhances the computational efficiency of online sequential extreme learning machine (OSELM). Moreover, a novel ICDE algorithm is presented for improving the learning ability and generalization performance of OSELM inversion. In the proposed ICDE, the tent chaotic sequence is adopted to enhance the global exploitation ability, and a constraint factor is added to ensure better convergence. The feasibility and effectiveness of the proposed inversion method are evaluated via four groups of experiments. The inversion results of the synthetic and field examples show that the proposed approach outperformed other methods in terms of computational efficiency and prediction accuracy, and realized satisfactory performance in TEM inversion, which provides a new strategy for the application of neural networks in TEM inversion.
Hierarchical Reinforcement Learning-Based Policy Switching Towards Multi-Scenarios Autonomous Driving

Reinforcement learning has gained a lot of attention and applications in the field of autonomous driving in recent years. However, the actual scenarios of automatic driving applications are often diverse, so the reinforcement learning algorithm using only a single driving strategy is difficult to meet the multiple requirements of efficiency and safety in the multi-scenarios autonomous driving task. To solve this challenge, we propose a hierarchical reinforcement learning structure to learn a unified top-level switching master policy between different driving styles policies. The whole framework uses a bottom-up training manner with diverse reward function designing. Through experimental comparison, our method exceeds the performance of single policy and rule-based switching strategy. Based on this framework, we won the first place in the DAI 2020 Autonomous Driving Workshop single-agent track competition.
A multiple surrounding point set approach using Theta* algorithm on eight-neighbor grid graphs

Path planning on grid graphs has been studied for decades in several applications such as robotics and computer games. However, the process for determining short paths can still be inefficient on large grid graphs. The SPS (Surrounding Point Set) method has made it efficient by considering only a few unblocked cells. Since eight-neighbor grid graphs do not provide sufficient connectivity for SPS, it found a path on a graph where two cells are connected with an edge within a given density bound. However, verifying whether the resulting edges are blocked is time-consuming; further, it is unclear which density bound to use and restrictive to allow only edges within the bound. To address these limitations, we propose a multi-SPS with Theta* that uses eight-neighbor grid graphs, allows distant connectivity by Theta*, and improves the SPS by considering more obstacles between the start and goal cells. Our experimental results demonstrate that compared with the existing path planning algorithms, multi-SPS with Theta* and variants of Theta* can run faster and obtain similar path lengths. The results also demonstrate the beneficial effect for Theta* in that the expensive runtime of Theta* can be reduced by the SPS approach.
Explainable Video Entailment with Grounded Visual Evidence

Video entailment aims at determining if a hypothesis textual statement is entailed or contradicted by a premise video. The main challenge of video entailment is that it requires fine-grained reasoning to understand the complex and long story-based videos. To this end, we propose to incorporate visual grounding to the entailment by explicitly linking the entities described in the statement to the evidence in the video. If the entities are grounded in the video, we enhance the entailment judgment by focusing on the frames where the entities occur. Besides, in the entailment dataset, the entailed/contradictory (also named as real/fake) statements are formed in pairs with subtle discrepancy, which allows an add-on explanation module to predict which words or phrases make the statement contradictory to the video and regularize the training of the entailment judgment. Experimental results demonstrate that our approach outperforms the state-of-the-art methods.
CAG-QIL: Context-Aware Actionness Grouping via Q Imitation Learning for Online Temporal Action Localization

Temporal action localization has been one of the most popular tasks in video understanding, due to the importance of detecting action instances in videos. However, not much progress has been made on extending it to work in an on-line fashion, although many video related tasks can benefit by going online with the growing video streaming services. To this end, we introduce a new task called Online Temporal Action Localization (On-TAL), in which the goal is to immediately detect action instances from an untrimmed streaming video. The online setting makes the new task very challenging as the actionness decision for every frame has to be made without access to future frames and also because post-processing methods cannot be used to modify past action proposals. We propose a novel framework, Context-Aware Actionness Grouping (CAG) as a solution for On-TAL and train it with the imitation learning algorithm, which allows us to avoid sophisticated reward engineering. Evaluation of our work on THUMOS14 and Activitynet1.3 shows significant improvement over non-naive baselines, demonstrating the effectiveness of our approach. As a by-product, our method can also be used for the Online Detection of Action Start (ODAS), in which our method also outperforms previous state-of-the-art models.