Reviewerly/exHarmony huggingface dataset:
Decoy Allocation Games on Graphs with Temporal Logic Objectives

We study a class of games, in which the adversary (attacker) is to satisfy a complex mission specified in linear temporal logic, and the defender is to prevent the adversary from achieving its goal. A deceptive defender can allocate decoys, in addition to defense actions, to create disinformation for the attacker. Thus, we focus on the problem of jointly synthesizing a decoy placement strategy and a deceptive defense strategy that maximally exploits the incomplete information the attacker about the decoy locations. We introduce a model of hypergames on graphs with temporal logic objectives to capture such adversarial interactions with asymmetric information. Using the hypergame model, we analyze the effectiveness of a given decoy placement, quantified by the set of deceptive winning states where the defender can prevent the attacker from satisfying the attack objective given its incomplete information about decoy locations. Then, we investigate how to place decoys to maximize the defender’s deceptive winning region. Considering the large search space for all possible decoy allocation strategies, we incorporate the idea of compositional synthesis from formal methods and show that the objective function in the class of decoy allocation problem is monotone and non-decreasing. We derive the sufficient conditions under which the objective function for the decoy allocation problem is submodular, or supermodular, respectively. We show a sub-optimal allocation can be efficiently computed by iteratively composing the solutions of hypergames with a subset of decoys and the solution of a hypergame given a single decoy. We use a running example to illustrate the proposed method.
A Deep Reinforcement Learning Based Approach for AGVs Path Planning

Autonomous path planning of automated guided vehicles (AGVs) is an important part of the intelligent logistics systems (ILS), which can greatly improve the abilities of intelligence and automation. Traditional AGVs navigation, electromagnetic navigation, and tape navigation, only navigate on metal belts or magnetic tapes, and the freedom of navigation is greatly reduced. This paper models the AGVs path planning problem as a reinforcement learning model to improve the capabilities of autonomous path planning and freedom of navigation. The path planning method based on the Dueling Double Deep Q Network with Prioritized Experience Reply (Dueling DDQN-PER) is used to learn the AGVs control in a simulation environment of an ILS. Using multi-modal sensory environment information, the AGV's ability to approach the target location and avoid obstacles has been significantly improved. Experimental results show that the AGVs can autonomously plan the path in the environment with obstacles, and has a high success rate and obstacle avoidance ability.
GridKG: Knowledge Graph Representation of Distribution Grid Data

Distribution grid systems are complex networks containing multiple pieces of equipment. All of them interconnected, and all of them described a variety of pieces of information. A knowledge graph provides an interesting data format that allows us to represent information in a form of graphs, i.e., nodes and edges - relations between them. In this paper, we describe an application of a knowledge graph to represent information about a power grid. We show the main components of such a graph - called GridKG, a simple process of identifying electrical paths, and a few examples of grid analysis related to primary switches.
Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design

As the demand for the image processing increases, the image features become increasingly complicated. Although the Convolutional Neural Network (CNN) have been widely adopted for the imaging processing tasks, it has been found easily misled due to the massive usage of pooling operations. A novel neural network structure called Capsule Networks (CapsNet) is proposed to address the CNN challenge and essentially enhance the learning ability for the image segmentation and object detection. Since the CapsNet contains the high volume of the matrix execution, it has been generally accelerated on modern GPU platforms with the highly optimized deep-learning library. However, the routing procedure of CapsNet introduces the special program and execution features,including massive unshareable intermediate variables and intensive synchronizations, causing inefficient CapsNet execution on modern GPU. To address these challenges, we propose the software-hardware co-designed optimizations, SH-CapsNet, which includes the software-level optimizations named <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-CapsNet</i> and a hybrid computing architecture design named <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PIM-CapsNet</i> . In software-level, S-CapsNet reduces the computation and memory accesses by exploiting the computational redundancy and data similarity of the routing procedure. In hardware-level, the PIM-CapsNet leverages the processing-in-memory capability of today's 3D stacked memory to conduct the off-chip in-memory acceleration solution for the routing procedure, while pipelining with the GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet. Evaluation results demonstrate that either our software or hardware optimizations can significantly improve the CapsNet execution efficiency. Together, our co-design can achieve greatly improvement on both performance (3.41 x) and energy savings (68.72 percent) for CapsNet inference, with negligible accuracy loss.
Intelligent Scene Recognition Based on Deep Learning

Using sensor-rich smartphones to sense various contexts attracts much attention, such as transportation mode recognition. Local solutions make efforts to achieve trade-offs among detection accuracy, delay, and battery usage. We propose a real-time recognition model consisting of two long short-term memory classifiers with different sequence lengths. The shorter one is a binary classifier distinguishing elevator scene and the longer one implements a finer classification among bus, subway, high-speed railway, and others. Light-weighted sensors are employed with a much smaller sampling rate (10Hz) compared with previous works. A two-stage setting makes it robust to scenes with different duration and therefore reduces the latency of recognition greatly. Further, the real-time system refines the classification results and attains smoothed predictions. We present experiments on accuracy and resource usage and prove that our system realizes a latency-low and power-efficient scene recognition approach by trading off a reasonable performance loss (averaged recall of 92.22%).
A new video watermarking using redundant discrete wavelet in singular value decomposition domain with multi‐objective optimization

Abstract Day by day, the transmission or sharing of digital information online is increasing due to the fact that Internet usage has become somewhat of an addiction for the majority of people. This results in larger copyright violation problems. Nowadays, most of the data or information is being transmitted in the form of digital images or videos, which are rather simple for the violators of copyright to forge or fake and then share for profit. Thus, digital watermarking came into existence as a possible solution to address these violations of copyright. This article proposes a new blind video watermarking (BVW) using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain, which utilizes the advantages of both RDWT and SVD to embed and extract the watermark information into the cover video without degrading the quality of the watermark. In addition, an efficient meta‐heuristic optimization with multi‐objective optimization (MOO) is employed to further optimize the proposed RDWT‐SVD approach. Further, various attacks were applied to the watermarked frame to disclose the robustness of proposed BVW using RDWT‐SVD with MOO approach. Extensive simulations on several test videos with comparison to the conventional BVW methodologies exhibit the transcendency of the proposed BVW using RDWT‐SVD with MOO approach in terms of watermarking quality evaluation metrics such as peak signal‐to‐noise ratio (PSNR), structural similarity index, normalized correlation, and even that of root mean square error as well.
Improving Object Detection with Relation Mining Network

Due to the deteriorated quality of feature in the propagation process of the neural network, it may be hard for traditional detector to identify a small object by just utilizing information within one region proposal. To overcome the limitation of the traditional object detector, we proposed a graph based relation mining network, to capture the relation information from labels and images. The semantic relation network is proposed to mine the global semantic relation in labels, and the spatial relation network is proposed to capture the local spatial relation in images. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the relation mining network explores more advanced feature information. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that key relation information significantly improve the performance of object detection with better ability to detect small objects and reasonable bounding box. The results on COCO dataset demonstrate our method can detect objects robustly, increasing the detection performance of small objects from average precision and average recall by 4.7% and 7.6% respectively in performance relative to Faster R-CNN.
Multiview Ghost-Free Image Enhancement for In-the-Wild Images With Unknown Exposure and Geometry

The multiview low dynamic range images captured with sparse camera arrangement under ill-lighting conditions contain highlighted and shadow regions due to over-exposed and under-exposed regions.The processing of these images produces contrast distortion, and it is challenging to maintain relative brightness with color consistency.Moreover, the disparity map estimation faces the challenges of holes and artifacts due to a wide baseline and poor visibility, with a shared view of vision.In this article, we propose a multiview ghost-free image enhancement strategy for in-the-wild images with unknown exposure and geometry.We address the complex geometric alignment problem for a wide variational baseline among multiple sparsely arranged cameras.The features among multiple viewpoints are detected and matched for the image restoration.The restored image contains highlighted and shadow regions with a color imbalance problem.We synthesize virtual images following the intensity mapping function, which compensates for the relative brightness and color distortions.Finally, we fuse all the images to obtain high-quality images.The proposed method is more frequent and feasible for future multiview systems with varying baselines without relying on disparity maps.The experimental results demonstrate that the proposed method outperformed the state-of-the-art approaches.
Research on Face Recognition Algorithm Based on HOG Feature

Abstract As a common biometric recognition technology, face recognition is also an important research direction in the field of computer. Although compared with the initial research, the current research has made great progress, but there are still many difficulties in practical application. In this paper, by extracting HOG features, after introducing the detailed steps of PCA and LDA subspace feature extraction methods, dimensionality reduction feature extraction method combing PCA with LDA is applied to extract face features. This method first uses PCA to reduce the dimension of face features, and then uses LDA for linear discriminant analysis. Finally, the feature extraction methods based on PCA and LDA are tested and compared in FERET standard face database and CAS-PEAL database of Chinese Academy of Sciences
Improving Human Decision-making by Discovering Efficient Strategies for Hierarchical Planning

Abstract To make good decisions in the real world, people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. Our ability to compute those strategies used to be limited to very small and very simple planning tasks. Here, we introduce a cognitively inspired reinforcement learning method that can overcome this limitation by exploiting the hierarchical structure of human behavior. We leverage it to understand and improve human planning in large and complex sequential decision problems. Our method decomposes sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. Our method can discover optimal human planning strategies for larger and more complex tasks than was previously possible. The discovered strategies achieve a better tradeoff between decision quality and computational cost than both human planning and existing planning algorithms. We demonstrate that teaching people to use those strategies significantly increases their level of resource-rationality in tasks that require planning up to eight steps ahead. By contrast, none of the previous approaches was able to improve human performance on these problems. These findings suggest that our cognitively informed approach makes it possible to leverage reinforcement learning to improve human decision-making in complex sequential decision problems. Future work can leverage our method to develop decision support systems that improve human decision-making in the real world.
Poverty Level Prediction Based on E-Commerce Data Using K-Nearest Neighbor and Information-Theoretical-Based Feature Selection

The Central Statistics Agency (BPS) is a government agency that focuses on matters concerning household economic and social needs. Every two years BPS conducts Susenas (National Socio-Economic Survey) to measure poverty levels in Indonesia. Every year BPS is tasked with providing information about the community's welfare in terms of their socio-economic aspects. In this very rapid development, many methods can be used to determine the poverty level. One of them is with the use of the rapid development of E-commerce in Indonesia, which can determine the level of poverty in Indonesia. In this study, we proposed a method to predict the poverty level based on an e-commerce dataset using K-Nearest Neighbor and Information Theoretical Based Feature Selection. Our method is expected to be able to complement the BPS Census and Susenas in predicting poverty levels in an area. Our test results show that our method data can predict the poverty level although there are rooms for improvement in terms of accuracy.
JPEG Images Encryption Scheme Using Elliptic Curves and A New S-Box Generated by Chaos

This paper proposes a new symmetric encryption system based on an elliptical curve and chaos, where the encryption is done in a single block and runs for 14 rounds. Here, the 15 encryption keys have the same size as the image and are generated using a solution point of a strong elliptic curve. Using a string of random numbers obtained with a logistic map, a permutation and its inverse are generated, which improve the encryption level and add diffusion to the cryptosystem. Another important contribution to this research is the generation of a substitution box with a non-linearity of 100, which strengthens the cryptosystem against differential and linear attacks that add confusion to the scheme. Moreover, the cryptographic properties of the proposed S-Box are compared with those of the S-Box of the Advanced Encryption Standard (AES) to ascertain that it is a suitable alternative that it is resistant to differential power analysis (DPA) attacks. To verify the robustness of proposed cryptosystem against cryptanalysis and the quality of the cipher text obtained, this system is subjected to different tests, such as entropy, correlation coefficient, χ2, Changing Pixel Rate (NPCR), and Unified Averaged Changing Intensity (UACI). The results are shown and compared with recently proposed systems.
Analytical review and study on object detection techniques in the image

Object detection is the most fundamental but challenging issues in the field of computer vision. Object detection identifies the presence of various individual objects in an image. Great success is attained for object detection/recognition problems in the controlled environment, but still, the problem remains unsolved in the uncontrolled places, particularly, when the objects are placed in arbitrary poses in an occluded and cluttered environment. In the last few years, a lots of efforts are made by researchers to resolve this issue, because of its wide range of applications in computer vision tasks, like content-enabled image retrieval, event or activity recognition, scene understanding, and so on. This review provides a detailed survey of 50 research papers presenting the object detection techniques, like machine learning-based techniques, gradient-based techniques, Fast Region-based Convolutional Neural Network (Fast R-CNN) detector, and the foreground-based techniques. Here, the machine learning-based approaches are classified into deep learning-based approaches, random forest, Support Vector Machine (SVM), and so on. Moreover, the challenges faced by the existing techniques are explained in the gaps and issues section. The analysis based on the classification, toolset, datasets utilized, published year, and the performance metrics are discussed. The future dimension of the research is based on the gaps and issues identified from the existing research works.
Word Association Thematic Analysis: A Social Media Text Exploration Strategy

Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many s
The research of garbage classification and recognition based on surf and geometric hashing algorithm

The classified collection and treatment of municipal solid waste is an important way to solve the problems of the rapid increase of urban waste production and the low efficiency of garbage treatment. In order to improve the efficiency of front-end collection in the process of garbage classification, the problem of fast and efficient matching of garbage has always been a research hot spot [1]. The development of machine vision technology provides a method to solve this problem. For the environment of garbage classification is more complex, the detection target may have the influence of occlusion, rotation and other aspects, leading to the detection target and template matching problems, so we must explore the corresponding solution for the specific target characteristics. In order to adapt to the extraction of feature points and target matching in complex environment, we select the more adaptive Surf algorithm [2] detects feature points and geometric hash algorithm to achieve target matching, and the two algorithms are optimized and improved. The experimental results show that the method can adapt to the complex environment such as rotation and occlusion in garbage classification, effectively achieve target matching, and improve the accuracy and speed of target recognition.
Object Detection and Localization Using Sparse-FCM and Optimization-driven Deep Convolutional Neural Network

Abstract Object detection and localization attract the researchers to address the challenges associated with the computer vision. The literature presents numerous unsupervised methods to detect and localize the objects, but with inaccuracies and inconsistencies. The problem is tackled through proposing a novel model based on the optimization algorithm. The object in the image is detected using the Sparse Fuzzy C-Means (Sparse FCM) that is the enhanced Fuzzy C-Means algorithm used to manage the high-dimensional data. The detected objects are subjected to the object localization, which is performed using the proposed Cat Crow Optimization (CCO)-based Deep Convolutional Neural Network. The proposed CCO is the integration of Cat Swarm Optimization Algorithm and Crow Search Algorithm and inherits the advantages of both the optimization algorithms. The experimentation of the proposed method is performed using images obtained from the Visual Object Classes Challenge 2012 dataset. The analysis revealed that the proposed method acquired an average accuracy, precision, and recall of 0.8278, 0.8549, and 0.7911, respectively.
A Deep Learning Approach to Mobile Camera Image Signal Processing

The quality of the images obtained from mobile cameras has been an important feature for modern smartphones. The camera Image Signal Processing (ISP) is a significant procedure when generating high-quality images. However, the existing algorithms in the ISP pipeline need to be tuned according to the physical resources of the image capture, limiting the final image quality. This work aims at replacing the camera ISP pipeline with a deep learning model that can better generalize the procedure. A Deep Neural Network based on the UNet architecture was employed to process RAW images into RGB. Pre-processing stages were applied, and some resources for training were added incrementally. The results demonstrated that the test images were obtained efficiently, indicating that the replacement of traditional algorithms by deep models is indeed a promising path.
Human Action Recognition Using A Novel Deep Learning Approach

Abstract Behavior human analysis is always a significant aspect in societal communication. The human behavior analysis is developed based on few factors like human activity and action recognition. Human action recognition is an significant feature in different safety fields. The assessment of the action recognition algorithm depends on the appropriate extraction and the learning data. In the human action recognition, classification plays the major role so in order to this effectively Gated Recurrent Neural Network is used with an increased computation level. Feature extraction is one of the essential factor in human action recognition it will influence the performance and computation time of the algorithm. This paper presented an approach for human action recognition based on new mixture deep learning model. The proposed method is evaluated on the different data sets like UCF Sports, KTH and UCF101. On UCF Sports data set the proposed method has given an average of 96.8%.
Research on The Innovation of Higher Vocational English Teaching Mode based on OBE Concept under The Background of Big Data

Abstract This essay makes a series of explorations on Vocational school’s English teaching mode on the basis of OBE concept and on the age of big data, analyzes its research significance, research contents and main innovations, Therefore, it puts forward the innovation path of Higher Vocational English teaching mode based on OBE concept. The final conclusion is that the h igher vocational English teaching mode based on the OBE concept under the background of big data can promote college English teaching, and can promote vocational schools to be employment-oriented by using big data