Reviewerly/exHarmony huggingface dataset:
Mitigating data leakage by protecting memory-resident sensitive data

Gaining reliable arbitrary code execution through the exploitation of memory corruption vulnerabilities is becoming increasingly more difficult in the face of modern exploit mitigations. Facing this challenge, adversaries have started shifting their attention to data leakage attacks, which can lead to equally damaging outcomes, such as the disclosure of private keys or other sensitive data.
A Robust Matching Network for Gradually Estimating Geometric Transformation on Remote Sensing Imagery

In this paper, we propose a matching network for gradually estimating the geometric transformation parameters between two aerial images taken in the same area but in different environments. To precisely matching two aerial images, there are important factors to consider such as different time, a variation of viewpoint, size, and rotation. The conventional methods for matching aerial image pairs with the large variations are extremely time-consuming process and have the limitations finding correct correspondences, because the image gradient and grayscale intensity for generating the feature descriptors are not robust to the variations. We design the network architecture as an end-to-end trainable deep neural network to reflect the characteristics of aerial images. The hierarchical structures that orderly estimate the rotation and the affine transformations make it possible to reduce the range of predictions and minimize errors caused by misalignment, resulting in more precise matching performance. Furthermore, we apply transfer learning to make the feature extraction networks more robust and suitable for the aerial image domain with the large variations. For the experiment, we apply the remote sensing image datasets from Google Earth and International Society for Photogrammetry and Remote Sensing (ISPRS). To evaluate our method quantitatively, we measure the probability of correct keypoints (PCK) metrics for objectively comparing the degree of matching. In terms of qualitative and quantitative assessment, our method demonstrates the state-of-the-art performances compared to the existing methods.
QFactory: Classically-Instructed Remote Secret Qubits Preparation

The functionality of classically-instructed remotely prepared random secret qubits was introduced in (Cojocaru et al. 2018) as a way to enable classical parties to participate in secure quantum computation and communications protocols. The idea is that a classical party (client) instructs a quantum party (server) to generate a qubit to the server's side that is random, unknown to the server but known to the client. Such task is only possible under computational assumptions. In this contribution we define a simpler (basic) primitive consisting of only BB84 states, and give a protocol that realizes this primitive and that is secure against the strongest possible adversary (an arbitrarily deviating malicious server). The specific functions used, were constructed based on known trapdoor one-way functions, resulting to the security of our basic primitive being reduced to the hardness of the Learning With Errors problem. We then give a number of extensions, building on this basic module: extension to larger set of states (that includes non-Clifford states); proper consideration of the abort case; and verifiablity on the module level. The latter is based on "blind self-testing", a notion we introduced, proved in a limited setting and conjectured its validity for the most general case.
BLAST: Blockchain-based Trust Management in Smart Cities and Connected Vehicles Setup

The following topics are dealt with: graph theory; parallel processing; graphics processing units; learning (artificial intelligence); neural nets; field programmable gate arrays; message passing; application program interfaces; optimisation; multiprocessing systems.
Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning

We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in SGD. No estimate of the Hessian is maintained. We first validate our method, called CURVEBALL, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly-generated architectures.
Automatic processing of Historical Arabic Documents: A comprehensive Survey

Nowadays, there is a huge amount of Historical Arabic Documents (HAD) in the national libraries and archives around the world. Analyzing this type of data manually is a difficult and costly task. Thus, an automatic process is required to exploit these documents more rapidly. Processing historical documents is a recent research subject that has seen a remarkable growth in the last years. Processing Historical Arabic Documents is a particularly challenging problem. First, due to complicated nature of Arabic script compared to other scripts and second because the documents are ancient. This paper focuses on this difficult problem and provides a comprehensive survey of existing research work. First, we describe in detail the challenges making the automatic processing of Historical Arabic Documents a difficult task. Second, we classify this task into four applications of automatic processing of HAD: i) Analyze the document to extract the main text ii) Identify the writer of the document iii) Recognize some words or parts of the document in a reference dataset andiv) Retrieve and extract specific data from the document. For each application, existing approaches are surveyed and qualitatively described. Finally, we focus on available datasets and describe how they can be used in each application.
Fuzzy Ontology Based Activity Recognition for Assistive Health Care Using Smart Home

Assistive health care system is a viable solution for elderly care to offer independent living. Such health care systems are feasible through smart homes, which are intended to enhance the living quality of the occupant. Activities of daily living (ADL) are considered in the design of a smart home and are extended to abnormality detection in the case of health care. Abnormality in occupant behavior is the deviation of ongoing activity with that of the built activity model. Generally, supervised machine learning strategies or knowledge engineering strategies are employed in the process of activity modeling. Supervised machine learning approaches incur overheads in annotating the dataset, while the knowledge modeling approaches incur overhead by being dependent on the domain expert for occupant specific knowledge. The proposed approach on the other hand, employs an unsupervised machine learning strategy to readily extract knowledge from unlabelled data using contextual pattern clustering and subsequently represents it as ontology activity model. Ontology offers enhanced activity recognition through its semantically clear representation and reasoning, it has restriction in handling temporal data. Hence, this article in addition to unsupervised modeling focuses at enabling temporal reasoning within ontology using fuzzy logic. The proposed fuzzy ontology activity recognition (FOAR) framework represents an activity model as a fuzzy temporal ontology. Fuzzy SWRL rules modeled within ontology aid activity recognition and abnormality detection for health care. The experimental results show that the proposed FOAR has better performance in abnormality detection than that of the existing systems.
User-defined interaction for smart homes

Smart home appliances and smart homes, in general, are on the verge of ubiquity. Research and industry proposed a range of modalities, including speech, mid-air gestures, and touch displays, to control smart homes. While previous work designed for the individual modalities, it is unclear how they compare from a user-centered perspective. Therefore, we conducted an elicitation study that asked participants to propose commands using speech, mid-air gestures, and a touch display. Also, we asked participants to rate their suggestions and the modalities. The results show that using voice commands or a touch display is clearly preferred compared to the use of mid-air gestures. As we found high agreement scores for voice commands, our results also highlight the potential of elicitation studies for voice interfaces.
Adaptive Sensor Fault Detection and Isolation using Unscented Kalman Filter for Vehicle Positioning

There is an increasing demand for sub-meter vehicle localization for advanced safety and autonomous systems. Fault detection and isolation (FDI) for sensor systems, such as camera, LIDAR, GNSS, and V2X has been a challenge because their performances are significantly affected by weather, geographical changes, and even spoofing. In this paper, a sensor FDI using Student's t-distribution based adaptive unscented Kalman filter is presented. The proposed filter evaluate each sensor by Hotelling's T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> test utilizing the predicted sensor output and its covariance. This method can assess the correlation between data that is generated within the same sensor, for accurate fault detection. In addition, measurement noise is adaptively updated by identifying both the covariance and the degree of freedom of the outlier robust Student's t-distribution. The robustness and accuracy of the localization and measurement noise estimation is confirmed through simulation and an experiment on a highway scenario. Furthermore, the result also shows that the precise FDI can be achieved without any prior information regarding sensor measurement noise. The proposed algorithm enhances the reliability of future position based systems such as autonomous control or V2V safety brake.
A Comparison of ACO, GA and SA for Solving the TSP Problem

The ACO algorithm is an optimization algorithm, recognized for being very efficient in problems of finding routes and planning paths in roads. In terms of the problem of the traveling salesman, ACO algorithm has been able to find optimal solutions to the problem, we want to make a comparison with the algorithms GA and SA, to determine which of these obtains better results.
Optimal Multi-View Video Transmission in Multiuser Wireless Networks by Exploiting Natural and View Synthesis-Enabled Multicast Opportunities

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission.
Measuring with Exogenous Data (MED), and Government Economic Monitoring (GEM)

ABSTRACT The emergence of enormous disparate data sources, available in a multitude of alternate formats, and on very different devices, is leading to a business process revolution. This paper argues for a new approach in which these technologies are used to replace outdated blanket government intervention programs. Highly targeted interventions for social programs identified and addressed by Big Data-based methodologies would replace outdated government programs. This approach advocates for original detective methodologies, direct government action, and outcome monitoring all of which utilize Big Data-based frequent examination. The government would be in a privileged situation where it could obtain data not available to most other parties due to privacy laws. The data structures of the government would be protected by encryption and blockchain privacy-protection methodologies that would only reveal targeted outcomes to the direct-action agent. These same methodologies are applicable for business measurement (accounting) and assurance (audit).
Performance Analysis and Functionality Comparison of FHRP Protocols

In this era, every field requires high availability of network with the least probability of data loss. Therefore, redundancy should be involved as much as possible for network design. However, the high availability of network necessitates higher managerial and operational cost. Redundancy protocols substantially help to solve the problem. First Hop Redundancy Protocols (FHRPs) are implemented to overcome traffic loss from source to destination in network communications. The FHRP consists of different types of protocols. Each protocol has its own purpose and has its own advantages and disadvantages. These protocols assist specific association to successfully transmit the traffic from source to goal without loss of numerous packet. The FHRP includes different kind of protocols but contains three main protocols. in this paper evaluates three particular protocols of FHRPs, namely the Hot Standby Router Protocol (HSRP), Virtual Router Redundancy Protocol (VRRP), and Gateway Load Balancing (GLBP). In this paper, we evaluate the three FHRPs by using GNS3 tool. The performances of the three FHRP protocols are analyzed and their functionalities are compared.
Pose Guided Dynamic Image Network for Human Action Recognition in Person Centric Videos

The most emerging concerns in computer vision are size of data to process and privacy preserving of the end user. Camera sensors are all around us these days, recording and analysing our day-to-day activities. In this scenario the privacy perseverance becomes a question of concern especially in case of devices working on the basis of human action recognition (HAR). Another important concern in computer vision is the size of data. The surveillance requires continues transfer of huge amount of data through the network. The processing time required to transfer the video to central server and analyses the video directly depends on the resolution of the video. The research in computer vision is exploring the possibility of working on different aspects of videos such as using only pose information or representing whole video using a single frame for the purpose of HAR. Here, an attempt is made to explore the concept of pose estimation and video representation using dynamic image to solve the dual purpose of privacy preserving and decreasing the load on network for transfer of videos over the network for analysis. In this paper, a new Pose Guided Dynamic Image (PDI) network is proposed for HAR which is capable of providing a summarized single frame for the person's activity in any given video. Unlike dynamic image network, this approach considers only the person's motion and discards the background motion. Therefore, PDI provides more specific information required for HAR as compared to the dynamic image. Also, by summarizing the video, the identity of the person remains masked. The proposed method is able to provide better result on both of the benchmark datasets used namely JHMDB and UCF-sports for the experimentation.
Monocular Depth Estimation Using Synthetic Images With Shadow Removal

Learning based monocular depth estimation has become popular in recent years. However, training of reliable depth estimators requires large volumes of ground truth depth data, which is expensive to obtain. To overcome this challenge, we propose a novel monocular depth estimation system that adopts pixel-perfect synthetic image training. The model is trained with synthetic data but inferred with realistic images by applying image domain adaptation. Considering realistic constraints, such as shadow regions, which cause the performance drops in depth estimation, the system adopts a dedicated module to remove such ambient shadows from images to guarantee premier performance in the task. Experimental results, with both synthetic and realistic benchmarks, indicate the efficacy and the advantages of the system compared to existing state-of-the-art technologies.
Deep reinforcement hashing with redundancy elimination for effective image retrieval

Hashing is one of the most promising techniques in approximate nearest neighbor search due to its time efficiency and low cost in memory. Recently, with the help of deep learning, deep supervised hashing can perform representation learning and compact hash code learning jointly in an end-to-end style, and obtains better retrieval accuracy compared to non-deep methods. However, most deep hashing methods are trained with a pair-wise loss or triplet loss in a mini-batch style, which makes them inefficient at data sampling and cannot preserve the global similarity information. Besides that, many existing methods generate hash codes with redundant or even harmful bits, which is a waste of space and may lower the retrieval accuracy. In this paper, we propose a novel deep reinforcement hashing model with redundancy elimination called Deep Reinforcement De-Redundancy Hashing (DRDH), which can fully exploit large-scale similarity information and eliminate redundant hash bits with deep reinforcement learning. DRDH conducts hash code inference in a block-wise style, and uses Deep Q Network (DQN) to eliminate redundant bits. Very promising results have been achieved on four public datasets, i.e., CIFAR-10, NUS-WIDE, MS-COCO, and Open-Images-V4, which demonstrate that our method can generate highly compact hash codes and yield better retrieval performance than those of state-of-the-art methods.
Usability Testing of a Virtual Reality Tutorial

Immersive simulation technology has transformed the training and learning environment. Virtual reality (VR) and augmented reality (AR) devices have been adopted by medical professionals, military forces, and marketing firms. Aviation training facilities are also integrating VR and AR technology into a variety of training. To ensure students begin training on equal footing, an engaging, guided tutorial for the virtual environment (VE) was created. A usability study was conducted to evaluate the tutorial’s learnability, effectiveness, and satisfaction for two user groups varying in VR experience. Results show users found the tutorial enjoyable with high usability and playability. Novice users reported the tutorial as more mentally effortful than expert users and were less comfortable with self-maneuvering. Users successfully completed most tasks on the first attempt after completing the tutorial. Those who noted difficulty in completing tasks in a post-assessment reported user error and corrected themselves without instruction. The tutorial demonstrated learnability, effectiveness, and satisfaction ensuring that users will be able to enter the VE with more confidence after engaging with the tutorial.
Blockchain-Enabled 5G Autonomous Vehicular Networks

Blockchain technology is getting more attention due to its inherent nature in resistance to data modification. Blockchain combined with IoT enables to improve the level of services for various domains with security guarantees. Numerous research has begun in order to link the blockchain along with autonomous vehicles system on 5G networks. Ultrafast connections, speedier data downloads, and the ability to handle millions of connections more than LTE networks are crucial to support a rapid autonomous system. Therefore, the system requires proper data storage management, high secure transaction, and non-interference network. The blockchain is suitable for the 5G vehicular system since it is immutable, tamper-proof, and secure by design. Although the decentralized 5G autonomous vehicular network provides countless benefits, yet it raises more than a few challenges. This paper provides an initial stage of the blockchain-enabled 5G vehicular networks, architecture, and technical aspects. Some remarks and challenges are also discussed.
Guidelines for Designing Online Courses for Mobile Devices

College students frequently use mobile devices (e.g., smartphones and tablets) to access online courses yet online course designers often do not design courses with mobile learning in mind. This research identified seven national and statewide online course design evaluation instruments and examined the criteria that guide course designers designing online courses for learning with mobile devices. Currently, minimal guidance on course design for mobile learning is offered in most of the national and statewide online course design instruments. Research-supported design tips that promote device compatibility, content readability, format optimization, and mobile-friendly navigation are suggested in this paper to guide future online course design for mobile delivery.
Segmentation of Overlapping Cervical Smear Cells Based on U-Net and Improved Level Set

Full convolution network (FCN) is widely used in medical image segmentation and its performance is better than other conventional techniques. This paper proposes a new fusion algorithm that combined the convolutional neural network U-net with a new modified level set method to segment overlapping cervical smear cells. U-net could provide more excellent segmentation results of nuclei and cytoplasm cluster. Then, a modified level set energy function with distance map and a new shape prior term is applied to extract the contour of cervical cells. Owing to this new level set energy function, the segmentation of every individual cell performed well, especially in overlapping area of cells. The evaluation of results also proves the improvement of our fusion algorithm.