Reviewerly/exHarmony huggingface dataset:
A Distributed Autonomous Organization for Internet Address Management

The current system to manage the global pool of IP addresses is centralized in five transnational organizations, the Regional Internet Registries (RIRs).Each of them manages the address pool for many countries.In this paper we present InBlock, a Distributed Autonomous Organization that provides decentralized management of IP addresses.InBlock also fulfills the same objectives as the current IP address allocation organizations, i.e., uniqueness, fairness, conservation, aggregation, registration and minimized overhead.InBlock is implemented as a set of blockchain's smart contracts in Ethereum and it implements all the functions needed for the management of a global pool of addresses without any human intervention.Any entity may request an allocation of addresses to the InBlock registry by performing a (crypto)currency transfer to the InBlock.The fee required, along with the annual renewal fee, serves as a mechanism to deter stockpiling and other wasteful practices.As with any novel technology, there are many open questions about the usage of blockchains to build an IP address registry.For this reason, we believe that practical experimentation is in order to have hands-on experiences, so we propose to conduct an experiment on InBlock as a starting point to inform future directions in this space. I. INTRODUCTIONIP addresses are a cornerstone of the Internet.Every device must configure an IP address in order to be able to send and receive packets through the Internet.As such, the management of the global pool of IP address is of uttermost importance for the correct functioning of the Internet.IP addresses are globally administered by the Internet Corporation for Assigned Names and Numbers (ICANN), a private nonprofit organization.The global pool of IP addresses is managed through a hierarchical structure rooted in ICANN.ICANN delegates ranges of IP addresses to five Regional Internet Registries (RIRs), the second tier of the hierarchy.RIRs allocate address blocks to Local Internet Registries, (LIRs, usually Internet
On optimal stopping strategies for text recognition in a video stream as an application of a monotone sequential decision model

The paper describes the problem of stopping the text field recognition process in a video stream, which is a novel problem, particularly relevant to real-time mobile document recognition systems. A decision-theoretic framework for this problem is provided, and similarities with existing stopping rule problems are explored. Following the theoretical works on monotone stopping rule problems, a strategy is proposed based on thresholding the estimation of the expected difference between consequent recognition results. The efficiency of this strategy is evaluated on an openly accessible dataset. The results show that this method outperforms the previously published methods based on identical results cluster size thresholding. Notes on future work include incorporation of recognition result confidence estimations in the proposed model and more precise evaluation of the observation cost.
An Approach for Combining Multiple Weighting Schemes and Ranking Methods in Graph-Based Multi-Document Summarization

Automatic text summarization aims to reduce the document text size by building a brief and voluble summary that has the most important ideas in that document. Through the years, many approaches were proposed to improve the automatic text summarization results; the graph-based method for sentence ranking is considered one of the most important approaches in this field. However, most of these approaches rely on only one weighting scheme and one ranking method, which may cause some limitations in their systems. In this paper, we focus on combining multiple graph-based approaches to improve the results of generic, extractive, and multi-document summarization. This improvement results in more accurate summaries, which could be used as a significant part of some natural language applications. We develop and experiment with two graph-based approaches that combine four weighting schemes and two ranking methods in one graph framework. To combine these methods, we propose taking the average of their results using the arithmetic mean and the harmonic mean. We evaluate our proposed approaches using DUC 2003 & DUC 2004 dataset and measure the performance using ROUGE evaluation toolkit. Our experiments demonstrate that using the harmonic mean in combining weighting schemes outperform the arithmetic mean and show a good improvement over the baselines and many state-of-the-art systems.
Restricted Combinatory Unification

First-order theorem provers are commonly utilised as backends to proof assistants. In order to improve efficiency, it is desirable that such provers can carry out some higher-order reasoning. In his 1991 paper, Dougherty proposed a combinatory unification algorithm for higher-order logic. The algorithm removes the need to deal with $$\lambda $$ -binders and $$\alpha $$ -renaming, making it attractive to implement in first-order provers. However, since publication it has garnered little interest due to a number of characteristics that make it unsuitable for a practical implementation. It fails to terminate on many trivial instances and requires polymorphism. We present a restricted version of Dougherty’s algorithm that is incomplete, terminating and does not require polymorphism. Further, we describe its implementation in the Vampire theorem prover, including a novel use of a substitution tree as a filtering index for higher-order unification. Finally, we analyse the performance of the algorithm on two benchmark sets and show that it is competitive.
A Collaborative Multiagent Reinforcement Learning Method Based on Policy Gradient Potential

Gradient-based method has been extensively used in today's multiagent reinforcement learning (MARL). In a gradient-based MARL algorithm, each agent updates its parameterized strategy in the direction of the gradient of some performance index. However, studies on the convergence of the existing gradient-based MARL algorithms for identical interest games are quite few. In this article, we propose a policy gradient potential (PGP) algorithm that takes PGP as the source of information for guiding the strategy update, as opposed to the gradient itself, to learn the optimal joint strategy that has a maximal global reward. Since the payoff matrix and the joint strategy are often unavailable to the learning agents in reality, we consider the probability of obtaining the maximal reward as the performance index. Theoretical analysis of the PGP algorithm on the continuous model involving an identical interest repeated game shows that if the component action of every optimal joint action is unique, the critical points corresponding to all optimal joint actions are asymptotically stable. The PGP algorithm is experimentally studied and compared against other MARL algorithms on two commonly used collaborative tasks-the robots leaving a room task and the distributed sensor network task, as well as a real-world minefield navigation problem where only local state and local reward information are available. The results show that the PGP algorithm outperforms the other algorithms in terms of the cumulative reward and the number of time steps used in an episode.
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks

Wide adoption of artificial neural networks in various domains has led to an increasing interest in defending adversarial attacks against them. Preprocessing defense methods such as pixel discretization are particularly attractive in practice due to their simplicity, low computational overhead, and applicability to various systems. It is observed that such methods work well on simple datasets like MNIST, but break on more complicated ones like ImageNet under recently proposed strong white-box attacks. To understand the conditions for success and potentials for improvement, we study the pixel discretization defense method, including more sophisticated variants that take into account the properties of the dataset being discretized. Our results again show poor resistance against the strong attacks. We analyze our results in a theoretical framework and offer strong evidence that pixel discretization is unlikely to work on all but the simplest of the datasets. Furthermore, our arguments present insights why some other preprocessing defenses may be insecure.
Decentralized Attestation of Conceptual Models Using the Ethereum Blockchain

Decentralized attestation methods for blockchains are currently being discussed and standardized for use cases such as certification, identity and existence proofs. In a blockchain-based attestation, a claim made about the existence of information can be cryptographically verified publicly and transparently. In this paper we explore the attestation of models through globally unique identifiers as a first step towards decentralized applications based on models. As a proof-of-concept we describe a prototypical implementation of a software connector for the ADOxx metamodeling platform. The connector allows for (a.) the creation of claims bound to the identity of an Ethereum account and (b.) their verification on the blockchain by anyone at a later point in time. For evaluating the practical applicability, we demonstrate the application on the Ethereum network and measure and evaluate limiting factors related to transaction cost and confirmation times.
A reduced-reference quality assessment metric for super-resolution reconstructed images with information gain and texture similarity

Super-resolution (SR) image reconstruction has been extensively studied in recent years due to its broad uses in machine vision, medical imaging, remote sensing and monitoring systems. However, evaluating the performance of SR algorithms is still an ongoing problem. A number of image quality metrics have been reported in recent years, however, they are not specifically designed for SR reconstructed images, so they are usually limited when assessing SR images. Here, we propose a reduced-reference image quality assessment (IQA) metric for SR images. First, saliency detection is used on the high-resolution (HR) images, and low-resolution (LR) images are used to generate the corresponding saliency maps. Second, the information gain and texture similarity between the HR images and the LR images are calculated to quantify the image quality degradation. Finally, the information gain and the texture similarity are weighted to predict the quality of SR images. Extensive experiments illustrate that the proposed metric has better performance for SR images than the existing state-of-the-art IQA algorithms.
An adaptive template matching-based single object tracking algorithm with parallel acceleration

Existing template matching based visual object tracking algorithms usually require to manually update the template and have high execution cost on general embedded systems. To address these issues, an adaptive template matching-based single object tracking algorithm with parallel acceleration is proposed in this paper. In this algorithm, we propose an adaptive single object tracking algorithm framework to achieve template update online. Based on the Faster-RCNN model, we design a single object capture method to update the template. Meanwhile, we present a parallel strategy to accelerate the process of template matching. To evaluate the proposed algorithm, we use OTB benchmark to compare the performance with several state-of-the-art trackers on TX2 embedded platform. Experimental results show that the proposed method achieves a 5.9 times execution speed and 71.9% accuracy improvement over the comparison methods.
Constrained Relation Network for Character Detection in Scene Images

Characters are the basic components of text. Accurate character detection plays an important role in text detection and recognition. Previous character detectors tackle characters as independent objects, without considering the meaningful context information among them. In this paper, we propose a new module named constrained relation module which utilizes both the geometric and contextual information to exploit the strong relationship between characters. With this module, we build a new network named constrained relation network for character detection and recognition. To the best of our knowledge it is the first work to utilize contextual information among texts for character detection in scene images. The module can improve the detection results by suppressing the confusing text-like regions and recalling the hard examples. Experiments on SynthText, ICDAR2013 and SCUT-FORU demonstrate the effectiveness of our method on both detection and recognition tasks.
Memory Forensics: Recovering Chat Messages and Encryption Master Key

In this pervasive digital world, we are witnessing an era where cybercriminals are improving their abilities in taking advantage of wide-spread digital devices to perform various malicious activities. By utilizing anti-forensic techniques, cybercriminals are able to erase or alter digital evidence that can otherwise be used against them in court. One of the most critical sources of digital evidence that forensic investigators examine is the physical memory of a digital device, i.e., Random Access Memory (RAM). RAM is a volatile memory containing data that might be of significant value to forensic investigation. RAM, which stores data about recent activities, stores data only when the device is powered on. Once the device powers off, all the data stored in the RAM is lost permanently. Forensic investigators find great value in RAM data and thus need to preserve such data without harming the integrity of the collected evidence. Many existing tools provide the ability to acquire and analyze images of the data stored in RAM. This paper tackles the fundamental topic of security, privacy, and digital forensics. Specifically, this paper examines memory dumps of 4GB Windows 7 computers with the objective of identifying an instant messaging tool and recovering its chat messages, and recovering master encryption keys of volumes encrypted by BitLocker and TrueCrypt. Throughout this paper, we utilize two widely-used tools, namely Volatility and WinHex, due to their various functionalities designed specifically for memory forensic investigation.
Game theory-based optimization of distributed idle computing resources in cloud environments

With the rapid cloud technological advancement and economic growth, more and more organizations have purchased cloud resources for daily business operations besides building their own IT infrastructures. Thus, it is very important to understand the economy of cloud computing. In this paper, we mainly focus on examining the private idle computing resources owned by various organizations who are willing to form a network of ad hoc cloud provider and sell the services to cloud users. In such a case, the organizations cannot only meet their own demands, but also sell their idle computing resources in the form of ad hoc cloud. Naturally, the organizations, as provider, aim at maximizing their own profit through adjusting business costs and sale prices. Due to the uncertainty of the amount of idle computing resources, dynamic pricing is challenging. We approach the problem from the perspective of game theory and formulate it as a non-cooperative game among multiple organizations, i.e., the game player. For each player, a utility function is used to represent its profits. The players choose request strategies and sales service strategies to maximize the utility function. This paper has proved that there exists Nash equilibrium for this game problem. We proposed an iterative proximal algorithm (IPA) for calculating the Nash equilibrium. After analyzing the convergence of the IPA, we found that the algorithm converges to the Nash equilibrium solution when reasonable conditions are satisfied and conforms to the theoretical proof. Experimental results show that our proposed algorithm can quickly converge to a stable state, and by calculating the appropriate service (resource) request strategies and selling service strategies for all organizations, organizations' profit are increased compared to without IPA algorithm.
Resolution of bipolar fuzzy relation equations with max-Łukasiewicz composition

The bipolar system of fuzzy relation equations with the max-Łukasiewicz composition is investigated in this work. A bipolar path approach is proposed for such a system. It is found that the complete solution set of the bipolar system is fully determined by its conservative bipolar paths, which are finite. Our proposed resolution approach is performed using the so-called path-based algorithm, step by step, and illustrated with numerical examples. Moreover, the global and local minimal (or maximal) solutions are discussed in this paper with a comparison to those of a classical unipolar system.
Your IoTs Are (Not) Mine: On the Remote Binding Between IoT Devices and Users

Nowadays, IoT clouds are increasingly deployed to facilitate users to manage and control their IoT devices. Unlike the traditional cloud services with communication between a client and a server, IoT cloud architectures involve three parties: the IoT device, the user, and the cloud. Before a user can remotely access her IoT device, remote communication between them is bootstrapped through the cloud. However, the security implications of such a unique process in IoT are less understood today. In this paper, we report the first step towards systematic analyses of IoT remote binding. To better understand the problem, we describe the life cycle of remote binding with a state-machine model which helps us demystify the complexity in various designs and systematically explore the attack surfaces. With the evaluation of 10 real-world remote binding solutions, our study brings to light questionable practices in the designs of authentication and authorization, including inappropriate use of device IDs, weak device authentication, and weak cloud-side access control, as well as the impact of the discovered problems, which could cause sensitive user data leak, persistent denial-of-service, connection disruption, and even stealthy device control.
A Human-Following Mobile Robot Providing Natural and Universal Interfaces for Control With Wireless Electronic Devices

This article presents the development of a mobile robot using a human-following algorithm based on human path prediction and provides a natural gesture interaction for an operator to control wireless electronic devices using a low-cost red-green-blue-depth (RGB-D) sensor. The overall experimental setup consists of a skid-steered mobile robot, Kinect sensor, laptop, red-green-blue (RGB) camera, and two lamps. OpenNI middleware is used to process the depth data from the Kinect sensor, and OpenCV is used to process data from the RGB camera. The human-following control system consists of two feedback control loops for linear and rotational motions, respectively. A lead-lag and proportional-integral derivative controllers are developed for the linear and rotational motion control loops, respectively. There are small delays (0.3 s for linear motion and 0.2 s for rotational motion) of the system's response. However, the delays are acceptable since they do not cause the tracking distance or angle out of our desirable range (±0.05 m and ±10° of the reference input). A new human-position prediction algorithm based on human orientation is proposed for human following. Experimental results show that the tracking algorithm reduces the distance and angular errors by 40% and 50%, respectively. There are four gestures designed for the operator to control the robot. Success rates of gesture recognition are more than 90% within the detectable range of the Kinect sensor.
Performance comparison of signed algorithms on JSON Web Token

Abstract The system survive in this digital era is a system that can work on multiple platforms, the use of web services is one of the solution. Exchange data using JSON format and for the security of authentication using JSON Web Token (JWT). The importance of token-based authentication using JWT on web services can solve interoperability problems. JWT is stateless and can include information in the token authorization. JWT has several options for using algorithms namely HMAC, RSA, and ECDSA. Unfortunately, it is unknown which algorithm has better performance. Here, we directly tested the signing algorithm in the three algorithms seen from several parameters. The experimental results showed the use of HMAC algorithm produces an average value of token-generating time is 21.3 s, token size 109 bytes and data transfer token speed 91.2 s. It was considered that the HMAC had an excellent performance.
A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias

Interaction is the cornerstone of how people perform tasks and gain insight in visual analytics. However, people's inherent cognitive biases impact their behavior and decision making during their interactive visual analytic process. Understanding how bias impacts the visual analytic process, how it can be measured, and how its negative effects can be mitigated is a complex problem space. Nonetheless, recent work has begun to approach this problem by proposing theoretical computational metrics that are applied to user interaction sequences to measure bias in real-time. In this paper, we implement and apply these computational metrics in the context of anchoring bias. We present the results of a formative study examining how the metrics can capture anchoring bias in real-time during a visual analytic task. We present lessons learned in the form of considerations for applying the metrics in a visual analytic tool. Our findings suggest that these computational metrics are a promising approach for characterizing bias in users' interactive behaviors.
Obligatory Irrelevance and the Computation of Ignorance Inferences

Abstract In recent work, Fox (2016) has argued, on the basis of both empirical and conceptual considerations, that relevance (the set of propositions relevant in an utterance context) is closed under speaker belief: if $\phi $ is relevant, then it’s also relevant whether the speaker believes $\phi $. We provide a formally explicit implementation of this idea and explore its theoretical consequences and empirical predictions. As Fox (2016) already observes, one consequence is that ignorance inferences (and scalar implicatures) can only be derived in grammar, via a covert belief operator of the sort proposed by Meyer (2013). We show, further, that the maxim of quantity no longer enriches the meaning of an utterance, per se, but rather acts as a filter on what can be relevant in an utterance context. In particular, certain alternatives (of certain utterances) are shown to be incapable of being relevant in any context where the maxim of quantity is active — a property we dub obligatory irrelevance. We show that the resulting system predicts a quite restricted range of interpretations for sentences with the scalar item some, as compared to both neo-Gricean (Geurts, 2010; Horn, 1972; Sauerland, 2004) and grammatical (Chierchia et al., 2012; Fox, 2007; Meyer, 2013) theories of scalar implicature, and we argue that these predictions seem largely on the right track.
Tinsel: A Manythread Overlay for FPGA Clusters

Commodity FPGA boards with advanced networking facilities have great potential in the construction of high-performance compute clusters that scale. However, low-level design tools and long synthesis times are major barriers to productivity for application developers. In this paper, we explore the potential of a distributed soft-processor overlay, programmed in software at a high-level of abstraction, to deliver a useful level of performance for FPGA clusters. In particular, we demonstrate the use of hardware multhreading to achieve a fast, space-efficient, high-throughput overlay, and compare a 12-FPGA instance of it (12,288 RISC-V threads) against a conventional Xeon cluster on the problem of distributed graph processing.