Reviewerly/exHarmony huggingface dataset:
Multi-exposure photomontage with hand-held cameras

The paper studies the image fusion from multiple images taken by hand-held cameras with different exposures. Existing methods often generate unsatisfactory results, such as blurring/ghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriately fusion of local regions (e.g., over or under exposed). In addition, they often require a high-quality image registration, which is hard to achieve in scenarios with large depth variations and dynamic textures, and is also time-consuming. In this paper, we propose to enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, the method first uses a Markov Random Field (MRF) energy for the labeling of all pixels, which assigns different labels to different aligned input images. During the labeling, it chooses well-exposed regions and skips moving objects at the same time. Then, the proposed method combines a Laplacian image according to the labels and constructs the fusion result by solving the Poisson equation. Furthermore, it adds some internal constraints when solving the Poisson equation for balancing and improving fusion results. We present various challenging examples, including static/dynamic, indoor/outdoor and daytime/nighttime scenes, to demonstrate the effectiveness and practicability of the proposed method.
Multiple Fault-Tolerance Mechanisms in Cloud Systems: A Systematic Review

Cloud systems are progressively taking over today's software market. These typically require constant operations with a minimum of failure. Multiple fault-tolerance mechanisms have been developed to maximize the availability of cloud systems. In this paper, we propose a systematic review of the literature on fault-tolerance mechanisms. We discussed the current trends, and we found that migration, checkpointing and replication are the most widely used based on the number of references. In contrast, some mechanism like task resubmission and n-version programming seems to incur less research.
OBBC: A Blockchain-Based Data Sharing Scheme for Open Banking

The concept of open banking has been a powerful trigger for the revolution in the financial services industry. When financial institutions disclose application programming interfaces (APIs) to third-party providers (TPPs), the biggest system risks concern issues such as malicious attack, data leakage and tampering, privacy disclosure and more. API is a new communication path for information systems, but it could be misused and tampered. To address this, we conceptualize a blockchain-based data sharing scheme for open banking named OBBC, in which the API’s information can be saved in a blockchain, that no one can dominate it. We propose an API consensus mechanism aims to ensures that the open API can’t be maliciously tampered. Moreover, zero knowledge proof and Merkel tree structure are used to realize that users’ privacy protection. In particular, we give the framework of our scheme and compare with existing data sharing schemes. We further implement a software prototype on fabric framework with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed open banking system.
Experience Matters

Information systems security is a major organizational concern. This study examines the role of vicarious experience on an individual's behavioral intent to perform a secure recommended response. The protection motivation theory model is expanded to include vicarious experience, which was examined through the separate constructs of vicarious threat experience and vicarious response experience. This study closes a gap in the literature by including vicarious experience in the PMT model and confirming its role as a significant direct influence on the PMT threat and coping constructs, and thus on the PMT model's ability to explain the variance of an individual's intent to perform secure behaviors. Additionally, vicarious experience measures were multi-item reflective scales rather than the single item measures that are more typically used to measure experience. Implications for theory and practice are discussed.
Towards Association Rule-Based Complex Ontology Alignment

Ontology alignment has been studied for over a decade, and over that time many alignment systems have been developed by researchers in order to find simple 1-to-1 equivalence alignments between ontologies. However, finding complex alignments, i.e., alignments that are not simple class or property equivalences, is a topic largely unexplored but with growing significance. Currently, establishing a complex alignment requires domain experts to work together to manually generate the alignment, which is extremely time-consuming and labor-intensive. In this paper, we propose an automated method based on association rule mining to detect not only simple alignments, but also more complex alignments between ontologies. Our algorithm can also be used in a semi-automated fashion to effectively assist users in finding potential complex alignments which they can then validate or edit. In addition, we evaluate the performance of our algorithm on the complex alignment benchmark of the Ontology Alignment Evaluation Initiative (OAEI).
SplitStreams: A Visual Metaphor for Evolving Hierarchies

The visualization of hierarchically structured data over time is an ongoing challenge and several approaches exist trying to solve it. Techniques such as animated or juxtaposed tree visualizations are not capable of providing a good overview of the time series and lack expressiveness in conveying changes over time. Nested streamgraphs provide a better understanding of the data evolution, but lack the clear outline of hierarchical structures at a given timestep. Furthermore, these approaches are often limited to static hierarchies or exclude complex hierarchical changes in the data, limiting their use cases. We propose a novel visual metaphor capable of providing a static overview of all hierarchical changes over time, as well as clearly outlining the hierarchical structure at each individual time step. Our method allows for smooth transitions between treemaps and nested streamgraphs, enabling the exploration of the trade-off between dynamic behavior and hierarchical structure. As our technique handles topological changes of all types, it is suitable for a wide range of applications. We demonstrate the utility of our method on several use cases, evaluate it with a user study, and provide its full source code.
Learning to Switch Between Machines and Humans.

Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions. In this work, our goal is to develop algorithms that, by learning to switch control between machine and human agents, allow existing reinforcement learning agents to operate under different automation levels. To this end, we first formally define the problem of learning to switch control among agents in a team via a 2-layer Markov decision process. Then, we develop an online learning algorithm that uses upper confidence bounds on the agents' policies and the environment's transition probabilities to find a sequence of switching policies. We prove that the total regret of our algorithm with respect to the optimal switching policy is sublinear in the number of learning steps. Moreover, we also show that our algorithm can be used to find multiple sequences of switching policies across several independent teams of agents operating in similar environments, where it greatly benefits from maintaining shared confidence bounds for the environments' transition probabilities. Simulation experiments in obstacle avoidance in a semi-autonomous driving scenario illustrate our theoretical findings and demonstrate that, by exploiting the specific structure of the problem, our proposed algorithm is superior to problem-agnostic algorithms.
Improving Data Availability in HDFS through Replica Balancing

Over time, the data distribution across an HDFS cluster may become unbalanced. The HDFS Balancer is a tool provided by Apache Hadoop that redistributes blocks by moving them from nodes with higher utilization to nodes with lower utilization. However, during block rearrangement, the HDFS Balancer does not aim to increase the availability of the data. This work presents a strategy that gives priority to block movements which increase the overall availability of the data stored in the HDFS. Thereby, increasing the fault tolerance as placing blocks in a higher number of racks tends to reduce the chances of data loss. In order to evaluate the implementation, an experimental investigation has been conducted to measure the system performance after balancing the cluster with the proposed solution.
Overcoming the Blind Spot in CNN-based Gate Detection for Autonomous Drone Racing

In recent years Autonomous Drone Racing has become a significant challenge due to the problems involved in developing an algorithm for autonomous navigation. One of the major problems is the estimation of the camera pose; several approaches can be founded to achieve the estimation of the camera pose. In particular, it is possible to estimates the position based on specific object detection. However, object detection at the same time of navigation entails the problem of a blind spot area when the camera is closest to the object. We propose a methodology that overcomes the blind spot in autonomous navigation based on CNN gate detection to perform pose estimation with a stochastic algorithm for distance estimation. We achieve over 95 % in gate detection and a mean error of around 35 cm in 1D pose estimation into the blind spot zone.
Lower Bounds for Parallel and Randomized Convex Optimization.

We study the question of whether parallelization in the exploration of the feasible set can be used to speed up convex optimization, in the local oracle model of computation. We show that the answer is negative for both deterministic and randomized algorithms applied to essentially any of the interesting geometries and nonsmooth, weakly-smooth, or smooth objective functions. In particular, we show that it is not possible to obtain a polylogarithmic (in the sequential complexity of the problem) number of parallel rounds with a polynomial (in the dimension) number of queries per round. In the majority of these settings and when the dimension of the space is polynomial in the inverse target accuracy, our lower bounds match the oracle complexity of sequential convex optimization, up to at most a logarithmic factor in the dimension, which makes them (nearly) tight. Prior to our work, lower bounds for parallel convex optimization algorithms were only known in a small fraction of the settings considered in this paper, mainly applying to Euclidean ($\ell_2$) and $\ell_\infty$ spaces. Our work provides a more general approach for proving lower bounds in the setting of parallel convex optimization.
Integration of Blockchain and Internet of Things

Objects and things that are used in the everyday life is connected to the internet to form Internet of Things (IoT). The objects communicate and interact through the internet; they can also be controlled or monitored remotely. The collection of large set of data is to yield useful information by which we can make the object/device smart. IoT reduces lots of human work, time, money, and tracking becomes much easier. But as there are enormous amount of data from the everyday device which are transferred through the internet, data security and privacy is much needed. Blockchain is a distributed tamper resistant ledger, it consist of data blocks called records; each transaction block is recorded and maintained with a timestamp. The centralized body which needs to store data can use the blockchain technology. The blockchain has the capacity to maintain the data security in the network of IoT, since blockchain is a decentralized network with the properties of anonymity and integrity. When giving security to the data in the IoT, a lot of challenges arise in the blockchain, such as large set of device management, usage of computational power, network structure, and bandwidth for communication. So, existing blockchain technologies are to be analyzed with importance to the IoT. The blockchain technology that suits the IoT applications are adapted and enhanced. The future work will be for integrating the blockchain technology into the network of IoT in an effective manner.
Active Learning to Improve Static Analysis

Static analysis tools are programs that run on source code prior to their compilation to binary executables and attempt to find flaws or defects in the code during the early stages of development. If left unresolved, these flaws could pose security risks. While numerous static analysis tools exist, there is no single tool that is optimal. Therefore, many static analysis tools are often used to analyze code. Further, some of the alerts generated by the static analysis tools are low-priority or false alarms. Machine learning algorithms have been developed to distinguish between true alerts and false alarms, however significant man hours need to be dedicated to labeling data sets for training. This study investigates the use of active learning to reduce the number of labeled alerts needed to adequately train a classifier. The numerical experiments demonstrate that a query by committee active learning algorithm can be utilized to significantly reduce the number of labeled alerts needed to achieve similar performance as a classifier trained on a data set of nearly 60,000 labeled alerts.
Improved Differential Attacks on GIFT-64

GIFT is a new lightweight PRESENT-like block cipher, proposed by Banik et al. at CHES 2017. There are two versions, i.e., GIFT-64 and GIFT-128, with block size 64 and 128 respectively. Both versions have a 128-bit key. The Sbox and the linear layer of GIFT are chosen carefully to avoid single difference bit or linear mask bit path in 2 consecutive rounds. This improves the security of GIFT against differential, linear and linear hull attacks. In this paper, we implement a new automatic search algorithm of differential characteristics on GIFT-64. Considering the situations that some characteristics have the same input and output difference, we find a few of improved differentials with longer rounds or higher probabilities. Among them, the best probability for 12-round differential is $$2^{-56.5737}$$ , while that for 13-round differential is $$2^{-61.3135}$$ . In addition, we find 52 13-round differentials with the same output differences. Based on them, we mount a multiple differential attack on 20-round GIFT-64 with $$2^{62}$$ chosen plaintexts, which attacks one more round than the best previous result. Also, we can attack 21-round GIFT-64 with the full codebook, using one differential with probability $$2^{-62.0634}$$ . This is the longest attack as far as we know.
A New Concept of Multiple Neural Networks Structure Using Convex Combination

In this article, a new concept of convex-combined multiple neural networks (NNs) structure is proposed. This new approach uses the collective information from multiple NNs to train the model. Based on both theoretical and experimental analyses, the new approach is shown to achieve faster training convergence with a similar or even better test accuracy than a conventional NN structure. Two experiments are conducted to demonstrate the performance of our new structure: the first one is a semantic frame parsing task for spoken language understanding (SLU) on the Airline Travel Information System (ATIS) data set and the other is a handwritten digit recognition task on the Mixed National Institute of Standards and Technology (MNIST) data set. We test this new structure using both the recurrent NN and convolutional NNs through these two tasks. The results of both experiments demonstrate a 4×-8× faster training speed with better or similar performance by using this new concept.
Generating Smooth Trajectories in Local Path Planning for Automated Guided Vehicles in Production

When operating automated guided vehicles (AGVs) in a logistics or production environment, they are likely to face unknown obstacles, e.g. logistics containers or human personnel. In order to ensure a high productivity of the AGVs, instead of stopping, they are supposed to circumvent such obstacles if possible. At the same time, the jerk in the circumventing movement must be kept small to reduce wear on the drive system and to ensure that the AGV’s load does not become disordered or damaged. Circumventing obstacles is done using local path planning. Local path planning aims to find a short-term path planning solution in an unknown environment. However, common algorithms for local path planning, like vector field histogram or dynamic window, generate a steering command for the next interval without respect to smoothness of the path. If the generated paths are smoothed, this happens by statistically reducing jitter in the results. Such a smoothing method does not take the jerk into account. This paper shows that paths of G2 differentiability are needed to reduce jerk on a path planning level. A path planning concept for the vector field histogram and for dynamic window and an evaluation method for this concept are shown.
Input Strictly Local Tree Transducers

We generalize the class of input strictly local string functions (Chandlee et al. 2014) to tree functions. We show they are characterized by a subclass of frontier-to-root, deterministic, linear tree transducers. We motivate this class from the study of natural language as it provides a way to distinguish local syntactic processes from non-local ones. We give examples illustrating this kind of analysis.
BDmark: A Blockchain-Driven Approach to Big Data Watermarking

Big data, as a driving force to the business growth, creates a new paradigm that encourages large number of start-ups and less-known data brokers to adopt data monetization as their key role in the data marketplace. As a pitfall, such data-driven scenarios make big data prone to various threats, such as ownership claiming, illegal reselling, tampering, etc. Unfortunately, existing watermarking solutions are ill-suited to big data due to a number of challenging factors, such as V’s of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. This paper presents a novel approach BDmark that provides a transparent immutable audit trail for data movement in big data monetizing scenarios, by exploiting the power of both watermarking and blockchain technologies. We describe in detail how our approach overcomes the above-mentioned challenging factors. As a proof of concept, we present a prototype implementation of the proposed system using Java and Solidity on Ethereum platform and the experimental results on smart contracts show a linear growth of gas consumption w.r.t. input data size. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.
Neyman-Pearson classification: parametrics and sample size requirement

The Neyman-Pearson (NP) paradigm in binary classification seeks classifiers that achieve a minimal type II error while enforcing the prioritized type I error controlled under some user-specified level $\alpha$. This paradigm serves naturally in applications such as severe disease diagnosis and spam detection, where people have clear priorities among the two error types. Recently, Tong, Feng and Li (2018) proposed a nonparametric umbrella algorithm that adapts all scoring-type classification methods (e.g., logistic regression, support vector machines, random forest) to respect the given type I error upper bound $\alpha$ with high probability, without specific distributional assumptions on the features and the responses. Universal the umbrella algorithm is, it demands an explicit minimum sample size requirement on class $0$, which is often the more scarce class, such as in rare disease diagnosis applications. In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis. Leveraging both the existing nonparametric and the newly proposed parametric thresholding rules, we propose four LDA-based NP classifiers, for both low- and high-dimensional settings. On the theoretical front, we prove NP oracle inequalities for one proposed classifier, where the rate for excess type II error benefits from the explicit parametric model assumption. Furthermore, as NP classifiers involve a sample splitting step of class $0$ observations, we construct a new adaptive sample splitting scheme that can be applied universally to NP classifiers, and this adaptive strategy reduces the type II error of these classifiers.
The Current Main Distributed Denial of Service and Defence Methods

The popularization of Internet has significantly changed how we work and live. While it brings great convenience, it has also harbored many safety hazards. The distributed denial of service (DDOS) is a form of attack that can seriously harm the network availability. Currently denial of service has been listed as a major form of attack to threaten the normal operation of the network. It has become a primary task of each operation and maintenance unit how to eliminate the denial of service and guarantee the normal operation of the network. The author will introduce the causes for the formation of distributed denial of service attack and the preventive measures based on the frequent attack means of DDOS in the current operation and maintenance practice. Through a comparison of each method, the author will mainly introduce the widely adopted traffic cleaning method. Using an attack suffered by a user as an example, the author will introduce the mechanism of traffic cleaning method and the structural deployment of the operation and maintenance industry in actual applications.
Directvisor

Bare-metal cloud platforms allow customers to rent remote physical servers and install their preferred operating systems and software to make the best of servers' raw hardware capabilities. However, this quest for bare-metal performance compromises cloud manageability. To avoid overheads, cloud operators cannot install traditional hypervisors that provide common manageability functions such as live migration and introspection. We aim to bridge this gap between performance, isolation, and manageability for bare-metal clouds. Traditional hypervisors are designed to limit and emulate hardware access by virtual machines (VM). In contrast, we propose Directvisor - a hypervisor that maximizes a VM's ability to directly access hardware for near-native performance, yet retains hardware control and manageability. Directvisor goes beyond traditional direct-assigned (pass-through) I/O devices by allowing VMs to directly control and receive hardware timer interrupts and inter-processor interrupts (IPIs) besides eliminating most VM exits. At the same time, Directvisor supports seamless (low-downtime) live migration and introspection for such VMs having direct hardware access.