Reviewerly/exHarmony huggingface dataset:
Influence Maximization Based Active Learning in Noisy Setting

In recent years, active learning has attracted great attention. However, there are still many challenges. On the one hand, the features of instances are usually affected by noise in many real-world applications. It is difficult to select the real important instances by sampling strategy purely based on the original features of instances. On the other hand, most of the existing sampling strategies in active learning are based on either informativeness sampling or representativeness sampling through the original features of samples. It is hard to choose the most important instance based on a certain sampling criterion or a rough combination of multiple sampling criteria. To solve these issues, a novel active learning algorithm with a low-rank representation-based influence maximization sampling strategy (IMLRR) was proposed in this paper. First, low-rank representation is adopted to construct an true affinity matrix for noisy instances. Then, in such a network, the sampling strategy based on influence maximization is used to select the most informative and representative instances at the same time from unlabeled data set. Finally, our empirical results demonstrate the effectiveness of our approach on different degree of noisy instances.
Conditional Link Prediction of Category-Implicit Keypoint Detection

Keypoints of objects reflect their concise abstractions, while the corresponding connection links (CL) build the skeleton by detecting the intrinsic relations between keypoints. Existing approaches are typically computationally-intensive, inapplicable for instances belonging to multiple classes, and/or infeasible to simultaneously encode connection information. To address the aforementioned issues, we propose an end-to-end category-implicit Keypoint and Link Prediction Network (KLPNet), which is the first approach for simultaneous semantic keypoint detection (for multi-class instances) and CL rejuvenation. In our KLPNet, a novel Conditional Link Prediction Graph is proposed for link prediction among keypoints that are contingent on a predefined category. Furthermore, a Cross-stage Keypoint Localization Module (CKLM) is introduced to explore feature aggregation for coarse-to-fine keypoint localization. Comprehensive experiments conducted on three publicly available benchmarks demonstrate that our KLPNet consistently outperforms all other state-of-the-art approaches. Furthermore, the experimental results of CL prediction also show the effectiveness of our KLPNet with respect to occlusion problems.
A super-resolution method of combined color image with depth map based on deep learning

The task of a qualitative increase in image resolution is one of the most current issues of digital image processing. Super-resolution (SR) methods are the group of signal-processing algorithms, which allow producing a high-resolution (HR) image from single or multiple low-resolution (LR) images of the same scene. Convolutional neural network (CNN) has been widely applied to color image and depth map super-resolution problem, where a high-resolution depth map can be restored from a LR depth map with the guidance of an additional HR or LR color image of the same scene. Proposed method is based on the algorithm, that HR depth map is reconstructed by joint LR depth map and corresponding LR intensity image. The Joint double branch network (JDBNet) is formed with a multi-scale upsampling conception for solving image super-resolution problems. Such approach can considerably enhance the condition of the recovered HR depth images. Low-resolution intensity image and low-resolution depth map of the same scene are input data for training networks. The output data of the system is a high-resolution depth map. The performance of represented methods was evaluated by Root Mean Square Error (RMSE).
Learning Common and Transferable Feature Representations for Multi-Modal Data

LiDAR sensors are crucial in automotive perception for accurate object detection. However, LiDAR data is hard to interpret for humans and consequently time-consuming to label. Whereas camera data is easy interpretable and thus, comparably simpler to label. Within this work we present a transductive transfer learning approach to transfer the knowledge for the object detection task from images to point cloud data. We propose a multi-modal adversarial Auto Encoder architecture which disentangles uni-modal features into two groups: common (transferable) features, and complementary (modality-specific) features. This disentanglement is based on the hypothesis that a set of common features exist. An important point of our framework is that the disentanglement is learned in an unsupervised manner. Furthermore, the results show that only a small amount of multi-modal data is needed to learn the disentanglement, and thus to transfer the knowledge between modalities. As a result we our experiments show that training with 75% less data of the KITTI objects, the classification accuracy achieved is of 71.75%, only 3.12% less than when using the full data set. The implications of these findings can have great impact in perception pipelines based on LIDAR data.
A Study on the Implementation of GRU Autoencoder Model for Detecting Insider Anomaly Behavior

With the development of high-tech technologies, the release of confidential information by industrial spy has also increased every year, causing damage to companies. If released, it could have a crucial impact on the nation and the national economy, and the outflow of core technologies continues increasing every year. This paper proposes machine learning to detect insider aberrations when there is a significant shortage of data about users in the company, such as the early part of the company, the new employees or the new security tool addition, to prevent the release of industrial data.
Time to Learn: Temporal Accelerators as an Embedded Deep Neural Network Platform

Embedded Field-Programmable Gate Arrays (FPGAs) provide an efficient and flexible hardware platform to deploy highly optimised Deep Neural Network (DNN) accelerators. However, the limited area of embedded FPGAs restricts the degree of complexity of a DNN accelerator that can be deployed on them. Commonly an accelerator’s complexity is reduced to fit smaller FPGAs, often at the cost of significant redesign overhead. In this paper we present an alternative to this, which we call Temporal Accelerators. The main idea is to split an accelerator into smaller components, which are then executed by an FPGA sequentially. To do so, the FPGA is reconfigured multiple times during the execution of the accelerator. With this idea, we increase the available area of the FPGA ‘over time’. We show that modern FPGAs can reconfigure efficiently enough to achieve equally fast and energy efficient accelerators while using more cost efficient FPGAs. We develop and evaluate a Temporal Accelerator implementing an 1D Convolution Neural Network for detecting anomalies in ECG heart data. Our accelerator is deployed on a Xilinx Spartan 7 XC7S15. We compare it to a conventional implementation on the larger Xilinx Spartan 7 XC7S25. Our solution requires 9.06% less time to execute and uses 12.81% less energy while using an FPGA that is 35% cheaper.
Unsupervised Pathogenic Social Media Accounts Detection Without Content or Network Structure

This chapter introduces an unsupervised causality-based framework built upon the causal inference presented in Chap. 2 using label propagation. The merit of this approach is that it identifies PSM users without using network structure, cascade path information, content and user’s information which are usually hard to obtain. Results on the ISIS-A dataset discussed in the previous chapter, show that the proposed approach obtains higher precision (0.75) in identifying PSM accounts compared with the random (precision of 0.11) and existing bot detection (precision of 0.16) methods.
Blockchains for the Governance of Common Goods

This paper analyses the use of blockchain technology to support the governance of commons-pool resources, as studied by Elinor Ostrom. It argues that the technological guarantees of blockchain technology—in terms of ex-ante automation and ex-post verification—can replace the traditional requirements of monitoring and sanctioning. Despite its own limitations and challenges, this novel approach to governance could provide new opportunities for experimentation in the context of commons-pool resources.
EQLRI: Enhance the Quality of Low Resolution Image using Neural Network

This paper deals with the image resolution method. The excellence of the image plays a significant part in many applications such as pathology, satellite imaging, photoshop, and many more. The significant improvement is found in the excellence of the image through deep learning technology. A number of deep learning uses are discussed in this paper. The offered method works only on a simple three-layer neural architecture. The design of the neural network is lightweight. The proposed system gets analyzed by a change of parameters in convolutional neural network architecture. The performance of image quality is evaluated by peak signal to noise ratio (PSNR). The proposed method works better, as noticed in accuracy and visual improvements.
Improving the Performance of Target Tracker by Deepening Neural Network

Abstract Visual target tracking is an important topic in the field of vision research. Its main task is to automatically process and analyze the input video information by computer, so as to obtain the position and key motion information of the target in the input video sequence, and to facilitate the subsequent research and analysis of the track and behavior of the tracking target. At present, researchers in the field of visual target tracking have proposed a series of excellent algorithms and frameworks. However, in view of the problems of deformation, occlusion, illumination change and random motion in the process of target tracking, it is still a great challenge to ensure the real-time, accuracy and robustness of tracking algorithms. In recent years, with the excellent performance of depth neural network in many tasks, the fusion of depth features based on relevant filtering algorithms to achieve target tracking tasks has become a hot research topic. In this paper, an improved target tracking algorithm is proposed based on the framework of classical target tracking methods: (1) Integrating deeper ResNet-101 deep convolution neural network model to detect target features more accurately. (2) Improve the model training method to save the network training cost. Finally, the performance of the proposed tracking algorithm is tested on the OTB target tracking standard dataset, and compared with other mainstream algorithms. The results show that the improved method proposed in this paper can effectively improve the tracking effectiveness and robustness.
Adaptive Background Subtraction Models for Shot Detection

The paper projected a unique prospective on background subtraction for object identification which is in motion as a structural block for many multimedia application beaning the primary applicable stage for successive detection, classification and analysis of actively of task. Since color information is not sufficient for addressing issue like sadden changes in elimination or visibilities in foreground object and color conflict. We have projected this work in which subtraction of background for detection of shot boundary in video is based on adaptive technique. This method depends on detecting the difference of mean gray value of current frame and previous frame is incorporated the result as arranging. We have calculated the result for five video inputs and evaluated outcomes interims of precision, recall and F1 measure.
Pedestrian and Face Detection with Low Resolution Based on Improved MTCNN

In recent years, the application of deep learning based on deep convolutional neural networks has gained great success in face detection. However, the large visual variations of pedestrians and faces, such as large pose variations and dark lightings, resulting in lower resolution targets, impose great challenges for these tasks in real-world applications. To solve this problem, we present a conceptually simple, end-to-end, and general framework for pedestrian and face detection. Our approach efficiently detects both pedestrian and face in an image. First, an efficient improved P-Net is developed to detect a pedestrian. Then an efficient improved R-Net1 is developed to filter pedestrian targets in the second level, and improved R-Net2 carries out the preliminary detection of face targets in the remaining pedestrian targets. In order to improve the face detection rate on a small scale, improved R-Net2 introduces a multi-level feature fusion mechanism. Last, an improved O-Net is proposed to identify pedestrian and face regions. Compared to state-of-the-art face detection methods such as Multiscale Cascade CNN、 Faceness、 Two-stage CNN、 MTCNN, the proposed method achieves promising performance on WIDER FACE benchmarks, our method also reaches promising results on the Caltech benchmarks.
Design of Protocol for Handwriting Recognition Using FPGA

The abstract should summarize the contents of the paper in short. An authentication of handwritten documents like signatures, written text, and optical character recognition are the major issues for identification of persons, recognition of addresses, post codes on envelops, document analysis, interpretation of amount on bank transactions, and scripts including English. In this peculiarity, the system architecture for real-time handwritten implementation for handwritten recognition demands a novel automated system to recognize the handwritten text, which is more effective with noise free and proposed work includes probabilistic patch based (PPB)-filter, Canny edge detection and classification. The additive Gaussian noise and multiplicative speckle noise are filtered by using PPB, the characters in the text are extracted by Lifting scheme transformation, the edges of text letters are detected by Canny edge detection and finally, the database is classified as matched or not matched. The complete work is designed in Software Development Kit (SDK), Embedded Development Kit (EDK) and these two are interfaced with Virtex-5 FPGA development board bearing part name XC5VLX50T.
Toward Continuous-Time Representations of Human Motion

For human motion understanding and generation, it is common to represent the motion sequence via a hidden state of a recurrent neural network, learned in an end-to-end fashion. While powerful, this representation is inflexible as these recurrent models are trained with a specific frame rate, and the hidden state is further hard to interpret. In this paper, we show that we can instead represent the continuous motion via latent parametric curves, leveraging techniques from computer graphics and signal processing. Our parametric representation is powerful enough to faithfully represent continuous motion with few parameters, easy to obtain, and is effective when used for downstream tasks. We validate the proposed method on AMASS and Human3.6M datasets through reconstruction and on a downstream task of point-to-point prediction, and show that our method is able to generate realistic motion. See our demo at www.github.com/WeiyuDu/motion-encode .
Extending Egocentric Vision into Vehicles: Malaysian Dash-Cam Dataset

Egocentric Vision (EV) has recently become one of the emerging areas in computer vision. It is a process of recording a person’s activity by using a wearable camera installed on the head of that person to gain an exclusive viewpoint. This field has gained more attention recently due to the wide availability of wearable cameras commercially and the recent success of Deep Learning (DL). Such attention has resulted in publishing more EV datasets. In this paper, we argue that the concept of egocentric vision is similar to the recording of road activities by a portable camera installed on the dashboard of a vehicle (commonly known as DashCam) to gain an exclusive viewpoint. We attempt to extend the EV concept into vehicles and show how the two viewpoints are similar. To support this argument, we collect and annotate the first Malaysian DashCam dataset, which consists of 228 video clips about road activities classified into ten categories. We use this dataset to train a DL model on top of VGG16 model -a DL model that is pre-trained on ImageNet-, to perform an initial experiment (activity classification), which reveals some limitations of this work such as small dataset size and less distribution of examples among categories. From this work, we learn that such a dataset may lead to the discovery of valuable statistical knowledge about most frequently occurring behaviors that cause accidents and that we may leverage DL to understand such behaviors to save human life.
Path Finding Using PSO Cooperated with Randomized Noise Functions

Path planning is a crucial navigation technology for routing and shortest path problems. The paper discusses a modified approach to the collision-free searching problem using particle swarm optimization incorporated with noise functions like Gaussian and Perlin noise. Aiming at PSO’s shortcoming of quickly diving into local minima, the added random noise functions escape the local minima in the convergence process; hence, look for global convergence maintaining fast speed in the early phase. The random sampling improves the particle update procedure to look for broader search space, which is otherwise constraint to global best of population. This variation guarantees solution space exploitation. Particle position vector precision is improved by adding noise (by some predefined factor), and the PSO algorithm is run to get the best particle as a candidate solution. Particle swarm optimization is a low-overhead and easy to implement the technique. Obstacles are incorporated into the algorithm to improve effectiveness. Particles need to reach the destination without colliding with any of the obstacles. Finally, simulation scenarios demonstrate effectiveness considering multiple target positions.
Enhancing teleportation of a single-qubit state by the unitary transformation in arbitrary decoherence rate

Abstract The proposal of the quantum teleportation(QT) is to transfer an unknown quantum state from one place to another through local operations and classical communication. However, the efficiency of standard QT will be significantly reduced due to the influence of the inevitable noise in environments. In this work, we propose two schemes to improve the efficiency of the QT protocol when quantum channel is subjected to bit-flip or phase-flip noise. We find that the so-called more entanglement means low efficiency in the performance of the standard teleportation protocol, and the optimal fidelity is obtained only by using the appropriate unitary operation. Specially, we show that the optimal averaged fidelity to our schemes is always more than the best classically achievable fidelity 2/3. We also provide a physical explanation of the obtained conclusions and our results will be helpful for improving quantum communication with real implementation.
Time-limited search and optimization of the spatial positioning of agents in virtual environments

Simulation systems are becoming a widely used tool for planning and staff training in different applications. To support the execution of agents' deployment tasks in virtual simulation systems, this work evaluates the effectiveness of search and optimization algorithms with different nature, namely, the Simulated Annealing and the Tabu Search algorithms, in the resolution of a constraint-based and time-limited optimization problem. Particularly, the modeled simulated application problem regards the spatial positioning (or distribution) of agents in tactical positions of a virtual terrain scenario. The work addresses how to model this problem and to solve it during simulation runtime, where strict processing time limits are considered. Experimental results show that the proposed approach permits to efficiently distribute the agents according to the required formation pattern, both in execution time and agent distribution error, with a significant optimization rate.
A Time-Series Rank Ordering Control System Data-Driven Fault Detection Approach for HVAC Systems in Buildings

Abstract About 41% of total energy consumption in the United States of year 2014 is used for heating and air conditioning, which is about 40 quadrillion (40 × 1015) British thermal units (BTUs). Despite the fact that people have been working on fault detection and diagnosis (FDD) for heating, ventilation, and air conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. To address this challenge, we will propose an approach that focuses on control system data. Several machine learning algorithms are introduced for data exploration (e.g., DBSCAN, k-means) and analysis, rank-ordered weather data are used to define comparable days, a control system data focused model free approach is presented as well, and finally, fault detection is carried out by implementing anomaly algorithms (local outlier factor and isolation forest). Hidden faults were detected with a detection rate up to 90%+. The threshold parameter can be determined by selecting an acceptable true positive and false positive rate pair, which can be visualized using receiver operating characteristic (ROC) curves and is demonstrated in this article. A simulation model that is used to generate about 250 GB of data is used to evaluate the performance of various algorithms.
Intrusion Detection System Based on Secure Hashing Techniques

Today, sharing knowledge and maintaining your safety are a great challenge. The user within the knowledge exchange system transfers his file with the victimization code of the non-public code. This property is particularly vital for any large-scale knowledge exchange system, as any user loses key data, so it will be difficult for the information owner to maintain knowledge security. This paper offers a concrete and economic representation of the subject, demonstrates its safety, and provides the implementation of the associated qualification to indicate its usefulness. There is a unity of millions of challenges for the knowledge owner to share their knowledge on servers or in the cloud. There are different solutions to solve these problems. These techniques are extraordinarily crucial to manage the key shared by the owner of the information. This document may present secure authority to highlight users who have access to information in the cloud. The SHA algorithmic program is used by the secure authority to obtain the key, which can be shared with the user in addition to the owner. The secure authority module receives the AES algorithmic encoding program for the victimization of encrypted files from the owner of the information and calculates the algorithmic algorithm for victimizing the hash prices MD-5. Stores the key in its information that can be used in all dynamic operations and to solve the trap in the system. The secure authority sends the file to the cloud service provider's form for cloud storage. The resulting unit of key set area has been shown to possess a variety of fascinating properties that ensure the confidentiality of communication sessions against collusion attacks by different network nodes.