Reviewerly/exHarmony huggingface dataset:
Preacher: Network Policy Checker for Adversarial Environments

Private networks are typically assumed to be trusted as security mechanisms are usually deployed on hosts and the data plane is managed in-house. The increasing number of attacks on network devices, and recent reports on backdoors, forces us to revisit existing security assumptions and demands new approaches to detect malicious activity. This paper presents Preacher, a runtime network policy checker, which leverages a secure, redundant and adaptive sample distribution scheme that allows us to provably detect adversarial switches or routers trying to reroute, mirror, drop, inject, or modify packets (i.e., header and/or payload) even under collusion. Additionally, the analysis performed by Preacher is highly parallelizable. We show that emerging programmable networks provide an ideal vehicle to detect suspicious network activity. Furthermore, we analytically and empirically evaluate the effectiveness of our approach in different adversarial settings, report on a proof-of-concept implementation using ONOS, and provide insights into the resource and performance overheads of Preacher.
Novel Databases for Arabic Online Handwriting Recognition System

Inthis paper, novel databases for Arabic handwriting recognition, we call them Aswan online Arabic handwritten (AOLAH) databases, are presented. They are designed to help researchers in the field of online Arabic handwriting recognition. The first proposed database consists of 2,520 files, representing 28 Arabic characters written by 90 different writers. Those writers are volunteers from Aswan faculty of Engineering. The other database is extracted from the main database by taking only the strokes from characters. This second database contains 1,530 files for 17 strokes. The uniqueness of the recommended algorithm for data collection is the dealing with every stroke in the character separately. Stroke capturing is done by collecting data points along the path of an input device (stylus pen or mouse) same time those characters are drawn.
Optimal Protocols for Remote Entanglement Distribution

Entanglement swapping enables entanglement distribution between distant nodes in quantum networks. This paper aims at designing remote entanglement distribution (RED) protocols to schedule entanglement swapping for maximizing the entanglement distribution rate (EDR). The concept of Enodes is introduced to represent the entangled quantum bit pairs in the network. With such a concept, we can transform the design of the optimal RED protocols into solving certain linear programming problems. In addition, we study RED in the homogeneous repeater chain, which is a basic and critical quantum network. Specifically, we determine the closed-form expression for the maximum EDR in such a setting. Our results will enable the distribution of long-distance entanglement with noisy intermediate-scale quantum (NISQ) technologies.
Online/Offline Revocable Multi-Authority Attribute-Based Encryption for Edge Computing

Multi-authority attribute-based encryption (MA-ABE) is a promising technique to achieve fine-grained access control over encrypted data in cross domain applications. However, the dynamic change of users' access privilege brings security problems, and the heavy encryption computational cost is issue for resource-constrained users in IoT. Moreover, the invalid or illegal ciphertext will waste system resources. We propose a large universe MA-CP-ABE scheme with revocation and online/offline encryption. In our scheme, an efficient revocation mechanism is designed to change users' access privilege timely. Most of the encryption operations have been executed in the user's initialization phase by adding reusable ciphertext pool besides splitting the encryption algorithm to online encryption and offline encryption. Moreover, the scheme supports ciphertext verification and only valid ciphertext can be stored and transmitted. The proposed scheme is proven statically secure under the q-DPBDHE2 assumption. The performance analysis results indicate that the proposed scheme is efficient and suitable for resource constrained users in edge computing for IoT.
Security and Privacy Issues for Intelligent Cloud-Based Health Systems

Technology is already affecting every aspect of life, and our health is no exception. Artificial intelligence (AI) has become one of the most emerging technologies over the last few years in almost every environment. New technological advances such as cloud computing provide benefits and have changed the way we store, access and exchange information. Especially, in the Healthcare IT sector, cloud-based systems offer great potential, from many perspectives, including improved medical diagnosis, accurate and faster prediction and cost-effective management treatment. In an attempt to assist cloud providers and healthcare organizations to secure their cloud-based environment and to adopt the appropriate measures for data protection, we present an overview of the security and privacy requirements of cloud-based healthcare systems. Specifically, this chapter starts with the presentation of the reported threats in cloud-based health systems, continues with the identified objectives and assets and concludes with measures for the mitigation of the identified threats. Due to the fact, migration into cloud-based healthcare systems, in most cases, implies that data subjects lose control of their data, many scientists have raised their worries about this. It is therefore needed to re-consider security, privacy and trust requirements, in the context of cloud computing. This chapter makes concrete recommendations for improving the protection level of cloud-based health organizations, cloud providers, hospitals and patients.
On Fairness Issue of ABR and TCP Algorithms in Video Streaming

User perceived performance of MPEG DASH video streaming deeply depends on interaction of higher layer (application layer) behavior of ABR algorithm and lower layer (transport layer) behavior of TCP algorithm. The lower layer behavior of MPEG DASH is determined by a TCP version, e.g. CUBIC and Compound TCP. The higher layer behavior of MPEG DASH depends on an ABR algorithm for bitrate selection. In this work-in-progress paper, we would like to show a potential approach of fairness improvement for carefully selected combination of the ABR algorithm and the TCP version, which is obtained from our on-going evaluation results.
Discussion on water saving technology of building water supply and drainage

Abstract According to the present situation of building water supply and drainage, water saving technology of building water supply and drainage is analyzed, and the effective utilization of water resources is realized. Water saving technology mainly includes the reasonable limit water pressure point, to avoid pressure flow; improve water supply system and reduce the invalid water; using water-saving water distribution equipment, sanitary ware; promote the use of high quality pipeline and valve; improve the status of the fire pool construction and development and promote the regeneration of water and rainwater utilization technology.
Improved Handwritten Digit Recognition method using Deep Learning Algorithm

One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).
Comparison of Privacy-Preserving Models Based on a Third-Party Auditor in Cloud Computing

Cloud computing is emerging as a significant utility service with a focus on outsourcing the data for individual consumers as well as for organizations. It has drastically transformed the way consumers store and organize their private and confidential data. Despite significant advancements in the cloud computing technology, concerns about security and other cloud adoption issues, are holding consumers from fully trusting this promising technology. We discuss the involvement of a TPA, the security requirements, as well as the vulnerabilities that lead to security threats. We extensively analyze and classify the TPA's privacy preserving models into different categories with a focus on their dynamicity. Furthermore, we discuss the characteristics of each TPA model from the privacy-preserving perspective. Finally, we compared our proposed LAPP (Light-weight Accountable Privacy Preserving) with existing known privacy preserving model.
CONVERSATIONS: Conversation with a prominent propagator

research-article Free Access Share on CONVERSATIONS: Conversation with a prominent propagator: Beth Quinn and Stephanie Weber, EngageCSEdu Authors: David P. Bunde Knox College Knox CollegeView Profile , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderView Profile , Cynthia Taylor Oberlin College Oberlin CollegeView Profile Authors Info & Claims ACM InroadsVolume 12Issue 4December 2021 pp 6–9https://doi.org/10.1145/3490178Published:15 November 2021Publication History 1citation179DownloadsMetricsTotal Citations1Total Downloads179Last 12 Months179Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
An Efficient Scheduling based cloud computing technique using virtual Machine Resource Allocation for efficient resource utilization of Servers

Cloud computing gives greatest supply and boosts to the income, main purpose of cloud computing using visualization increases sinking work rate and power use and also increases complete throughput and minimum feedback time. Cloud climate is adopting minimum overhead with capable usage of available resources and allocating maximum resources to the processing unit. The scheduling of virtual machine uses the technique for allocating different time varying slots to cloud climate and data center. Cloud climate provides the facility for many clients to use the services of cloud based data environment therefore virtual machine is widely used for scheduling of task on multi core systems as scheduling enhances the performance of system and reducing the cost and provide efficient resource allocation. In this research paper various scheduling techniques are consider for virtual machine and different resource allocating techniques are presented. In this paper a technique is proposed that provides flawless task execution and availability of data sets connected over cloud in virtual machine that consider various resource management units. Various workloads integrate with this scheme and efficiently extend the server utilization.
Building Executable Secure Design Models for Smart Contracts with Formal Methods

Smart contracts are appealing because they are self-executing business agreements between parties with the predefined and immutable obligations and rights. However, as with all software, smart contracts may contain vulnerabilities because of design flaws, which may be exploited by one of the parties to defraud the others. In this paper, we demonstrate a systematic approach to building secure design models for smart contracts using formal methods. To build the secure models, we first model the behaviors of participating parties as state machines, and then, we model the predefined obligations and rights of contracts, which specify the interactions among state machines for achieving the business goal. After that, we illustrate executable secure model design patterns in TLA+ (Temporal Logic of Actions) to against well-known smart contract vulnerabilities in terms of state machines and obligations and rights at the design level. These vulnerabilities are found in Ethereum contracts, including Call to the unknown, Gasless send, Reentrancy, Lost in the transfer, and Unpredictable state. The resultant TLA+ specifications are called secure models. We illustrate our approach to detect the vulnerabilities using a real-estate contract example at the design level.
Approximate Aggregate Queries Under Additive Inequalities

Previous chapter Next chapter Full AccessProceedings Symposium on Algorithmic Principles of Computer Systems (APOCS)Approximate Aggregate Queries Under Additive InequalitiesMahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, and Alireza SamadianMahmoud Abo-Khamis, Sungjin Im, Benjamin Moseley, Kirk Pruhs, and Alireza Samadianpp.85 - 99Chapter DOI:https://doi.org/10.1137/1.9781611976489.7PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider the problem of evaluating certain types of functional aggregation queries on relational data subject to additive inequalities. Such aggregation queries, with a smallish number of additive inequalities, arise naturally/commonly in many applications, particularly in learning applications. We give a relatively complete categorization of the computational complexity of such problems. We first show that the problem is NP-hard, even in the case of one additive inequality. Thus we turn to approximating the query. Our main result is an efficient algorithm for approximating, with arbitrarily small relative error, many natural aggregation queries with one additive inequality. We give examples of natural queries that can be efficiently solved using this algorithm. In contrast, we show that the situation with two additive inequalities is quite different, by showing that it is NP-hard to evaluate simple aggregation queries, with two additive inequalities, with any bounded relative error. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-648-9 https://doi.org/10.1137/1.9781611976489Book Series Name:ProceedingsBook Code:APOSC21Book Pages:ii + 158
Arabic Text Segmentation using Contextual Exploration and Morphological Analysis

The specificities of the Arabic language (syntactic flexibility, agglutination phenomenon, semi-cursivity1, etc.), generate a large number of ambiguities in the Arabic language processing, such as: morpho-lexical and syntactic. These problems are mainly due to a bad segmentation (sentence, syntagm, graphic word, morpheme, etc.). The segmentation of texts is based both on the linguistic study, and computer modeling, these two (02) studies complement each other.In this paper, we present our work on the Arabic text segmentation, where our contribution consists in the introduction of two techniques: contextual exploration that divides the text into sentences and morphological segmentation to solve the agglutination phenomenon.
Text Classification with K-Nearest Neighbors Algorithm Using Gain Ratio

Content classification is the errand of naturally arranging a lot of records into classifications from a predefined set. This implies that it allocates predefined classifications to free-content archives. This paper introduces a unique two-phase determination strategy for content classification using data gain (CCDG) that will guide and examine the hereditary calculation of the given dataset. In the first phase of CCDG, each term inside the archive is positioned depending on its significance for grouping and data gain. In the second stage, hereditary calculation through GA and main segment investigation through PCA determines and highlights the relevant extraction of the trend of the given stream of bits in decreasing impact. In this manner, all the content that has lesser significance can be overlooked while only impactful content remains for providing details. Experiments show encouraging and better results for proposed CCDG as compared to conventional methods under all the dataset and test conditions.
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking

The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most instances we have to choose which property is most important to us. We used the opportunity of the TREC 2019 Deep Learning track to evaluate the effectiveness of a balanced neural re-ranking approach. We submitted results of the TK (Transformer-Kernel) model: a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of lightweight Transformer layers to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. Our best result for the passage ranking task is: 0.420 MAP, 0.671 nDCG, 0.598 P@10 (TUW19-p3 full). Our best result for the document ranking task is: 0.271 MAP, 0.465 nDCG, 0.730 P@10 (TUW19-d3 re-ranking).
SAED: A Self-Adaptive Encryption and Decryption Architecture

The Transport Layer Security (TLS) and Secure Socket Layer (SSL) are currently the predominant protocols to provide information security and data integrity between two communication endpoints. However, with dramatically increased network traffic, the cryptography algorithms used in SSL/TLS for encryption and decryption introduce substantial computation overhead due to the expensive CPU execution time. To accelerate the complex encryption computation, researchers have proposed novel architectures based hardware accelerators such as FPGAs, which can handle a massive amount of offloaded traffic with complex processing at very high throughput. In this paper, we study the real-world traffic workload and find that the performance of several crypto algorithms is heavily data-dependent: for small packets, software-based encryption and decryption solutions outperform hardware-based solutions, while hardwarebased solutions show higher processing speed for large packets. So there is no one-size-fits-all solution for packet processing. As a result, we propose a software-hardware co-design with quick assist technology. It combines both advantages of softwareand hardware-based approaches and be able to choose the optimal cryptography solution dynamically based on traffic status and system state. Our evaluation shows that our proposed architecture could achieve high throughput for encrypted traffic on a secured network compared to pure software or hardware solutions.
Speaker Recognition Based on Deep Learning

Combination of deep learning and I-vector can significantly improve the performance of speaker recognition system, however, how to optimize the traditional feature parameters and how to model the speaker recognition system through deep learning are two most important research topics. This paper explores the system recognition performance from the types of input and neural network, and researches the optimal feature parameters and the most appropriate neural network structure of a speaker recognition system. Furthermore, the existing speaker recognition algorithms based on deep learning (Deep Neutral Network (DNN), Convolutional Neural Network (CNN)) have been analyzed and several improved models based on deep learning networks have been implemented and compared. Experiment shows that the network model after combination has a higher recognition rate in speaker recognition than the traditional system model.
A Side-Channel Attack Resistive ECDSA

Abstract Elliptic curve plays an important role in securing critical software applications as it is one of the most powerful and widely utilized encryption algorithms. It is used in many applications such as digital signature schemes. Elliptic curve algorithms are hard to break, however, attackers find another indirect and more efficient methods to estimate secret information behind any secured systems with the help of information leakages so that they can successfully reconstruct critical objects like private keys. These methods are called side- channel attacks. In this paper we propose a software implementation of ECDSA which can counteract cache memory side-channel attack using three techniques, namely, “Fisher Yates” algorithm, volatile memory objects, and thread locking.
SDE-KG: A Stochastic Dynamic Environment for Knowledge Graphs

State-of-the-art techniques that perform reasoning over large knowledge graphs incorporate a path-finder and a path-reasoner. The path-reasoner is a learning agent that performs the inference task by producing a relation. The path-finder is usually the knowledge graph environment that moves the agent to the next-hop entity. While the path-reasoner can work on a continuous state space, the knowledge graph environment on which it is being trained, operates on a discrete state space. This restricts the agent from deducing implicit paths. In this paper, a novel path-finder called Stochastic Dynamic Environment for Knowledge Graphs (SDE-KG) has been proposed. SDE-KG is a meta-framework that can be combined with many embedding methods to create a continuous function of the knowledge graph environment which may facilitate smarter multi-hop reasoning.
Combining Extreme Multi-label Classification and Principal Label Space Transformation for Cold Start Thread Recommendation

The recommendation system has been widely used in various areas, e.g., entertainment, education, and travel. However, this technique faces two main challenges which are Cold Start and High-Dimensionality problems. The cold start happens when the system does not have enough profile of new users; therefore, the system cannot recommend products to them. The second issue comes from the fact that there are a lot of distinct products or users to be recommended. Recently, Extreme Multi-label Classification (XMLC) has been applied to the recommendation system and addressed the Cold Start issue. However, the previous method still has a high-dimensionality issue. In this paper, we proposed a new approach, namely XMLC-PAO, which integrated label space reduction with XMLC. In more details, we transformed the recommendation problem to XMLC and applied Singular Value Decomposition (SVD) to generate reducing operator of label space (products' or users' label space). For the feature space, Deep Learning technique has been used to extract features from texts. From the experiments with Stackoverflow online forums dataset, we have found that the XMLC-PAO showed better performance in terms of RECALL@M and NDCG@M when the dimensions were reduced to 50% and 80% of the original size.
On the Efficient Design of Neural Networks in Communication Systems

Various types of neural networks (NNs) have shown promising performance in communication systems. However, the low-latency implementation of these tasks is currently impractical due to the high computational complexity and large model size of NNs. In this paper, we propose an iterative optimization framework with retraining process to adaptively find the quantization scheme for different NNs. Moreover, the efficient design of convolutional neural networks is presented to reduce the required parameters and computational complexity. Experiment results for modulation classification, channel decoder and equalizer are presented. Compared to the original full-precision models, the quantized NN models achieve comparable performance with only 4 to 5 weight bits and 8-bit activation. The size of optimized models is significantly compressed and the hardware complexity of the NN inference is also reduced.