Reviewerly/exHarmony huggingface dataset:
LFETT2021: A Large-scale Fine-grained Encrypted Tunnel Traffic Dataset

With the widespread use of tunnel technology, the volume of encrypted tunnel traffic rises sharply, which brings a new challenge to traditional encrypted traffic identification. A number of real-world application scenarios, including Quality of Service and intrusion detection, have put forward new re-quirements for identifying numerous tunnels, applications, and fine-grained behavior. However, previous studies and datasets on encrypted tunnel traffic identification fail to meet these requirements due to their low dataset coverage and coarse label granularity. These weaknesses further affect the extracted features based on these datasets, making them unable to adequately characterize encrypted tunnel traffic. In this paper, we refine the previous tunnel traffic identification granularity from prevalent application identification to behavior identification, and propose LFETT2021, a large-scale fine-grained encrypted tunnel traffic dataset. Our dataset expands the coverage to two operating system platforms, five tunnels, 23 applications, and 76 behaviors. Furthermore, we propose a set of Time-Packet-Related features to better characterize encrypted tunnel traffic. Our comprehensive experiments on LFETT2021 and Time-Packet-Related features show the best average precision of 85% and recall of 88% in 3 different granularity identification scenarios.
Chor-Rivest Knapsack Cryptosystem in a Post-quantum World

The threat posed to “classical” cryptography by the developing field of quantum computers and quantum algorithms running on them have set on fire researchers on their quest for new quantum-resistant problems that could come to the rescue, thus giving rise to the new field of post-quantum cryptography. In this work we present the state of the art of current post-quantum proposals, closely following the NIST Call for Candidates, and suggest a knapsack-problem-based one, not considered so far, whose key lengths and security could benefit from the development of quantum technology.
Data structures

Data and its information is an essential part, and various implementations are being made to store in different ways. Data are just a collection of facts and figures that are stored in a particular format. A data item refers to a single set of values, which are categorized into subitems or the group of items. In computer terms, a data structure is a specific way to store and organize data in a computer's memory so that the data can be used efficiently and effectively. Data may be arranged in many different ways, such as the logical or mathematical model for a particular organization of data is termed as a data structure. First, the data must be loaded in structure to show the actual relationships of the data with the real-world object. Second, the formation should be simple so that anyone can efficiently process the data. In this chapter, we represent the different forms of data structure to ease access of the data.
Enhance the LZW Compression Ratio Through the Use of Image Preprocessing Techniques for Gray Scale Images

Compression ratios of encoding algorithms degrade due to signal distortion, additive noise, and hacker manipulation. Large file size costs too much disk space, difficult to analyze, and high bandwidth to transmit over the internet. In this case, compression is mandatory. LZW is a general dictionary-based lossless compression algorithm. It is fast, simple, and efficient when it includes lots of repetitive data or monochrome images. Images with little data repetition and too much-blurred signal, the compression ratio of the LZW algorithm downgraded. Besides this, the execution time of the LZW compression algorithm increases dramatically. To preprocess and analyze the image information the researcher uses LZW encoding algorithm, bit plane slicing technique, Adaptive Median Filter, and MATLAB image processing toolbox. The MATLAB public grayscale image, salt &amp; pepper, Gaussian locavore blurred, and Bayern pattern image data sets are used. Those images dataset is used to test the normal LZW encoding algorithm and the proposed encoding algorithm compression ratio step by step. The noised dataset, the filtered datasets, and bit plane dataset images are processed and recorded quality and compression ratio parameters. The enhanced encoding algorithm average compression ratio is better by far from the normal LZW encoding algorithm by 160%. Not only has the compression ratio, but demising also improved the algorithms execution time. And the image quality metrics measurement of mean square error, peak signal to noise ratio, and structural similarity index measurement are 0, 99, and 1 respectively. This implies the enhanced encoding algorithm could decompress fully without scarifying image quality. The LZW encoding algorithm developmental environment specifies to select tiff and gif image formats. In addition, the LZW encoding algorithm functions are not available in the MATLAB image processing toolbox. The researcher challenged to write a MATLAB script for each personal function. Still, there is room to extend the compression ratio of the LZW encoding algorithm using the image masking technique.
Introduction

This chapter introduces artificial intelligence and covers techniques that constitute AI and maps AI to classical machine learning (ML) techniques. Next, it introduces techniques to process agricultural datasets and walks through field-collected sensor data processing. The chapter also introduces processing of simple classification and regression techniques. Finally, it introduces AI readiness of the countries and calls for a framework to net present value of AI project.
Clothes Identification Using Inception ResNet V2 and MobileNet V2

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Agile Uncertainty Assessment for Benefit Points and Size Points

Abstract Agile methodology purports to deal with uncertainty through continuous monitoring and learning. To do so, we need to see how productivity is faring against our plans, as in the previous chapter. But we also need to communicate what our uncertainty is realistically. This is regularly done for cost, but must also be done for benefit to obtain a complete picture. In this chapter, we show how both benefit points and size points can be instantiated with values reflecting different levels of uncertainty.
Convolutional One-Dimensional Variational Autoencoder Based Intrusion detection

Intrusion detection technology is an important part of network security. It detects various intrusions by collecting and analyzing various information on the network, which is the focus of maintaining network security. With the popularization of the network and the increase of the network rate, the network attack behavior is increasing and the attack methods are constantly updated. The traditional detection technology can not meet the demand of the new network environment for network security. In view of the defects of the existing intrusion detection algorithm model based on deep learning, such as long training time, more hyperparameters and high data demand, it is feasible to detect malicious traffic by learning the characteristics of normal traffic. In this paper, a variational self-encoder (COD-VAE) intrusion detection model based on one-dimensional convolutional neural network is proposed, which can identify malicious traffic more accurately. The intrusion detection algorithm is used to detect the intrusion of the intrusion detection system. Experimental results show that all the indexes of the algorithm are higher than the comparison algorithm, which verifies the effectiveness of the algorithm.
Face Mask Detection Using Deep Learning

Coronavirus disease has had a major impact on the world this year. In public places, one of the most important safety precautions for humans is to wear masks. Many public provider carriers only allow customers to utilise the service if they correctly wear masks. However, there are only a few research studies that use photo analysis to detect face masks. This article proposes the Mobilenet Mask detection technique, which is a multi-model face mask detection technique based on deep learning. There are two face mask data sets that have been used to instruct and assess whether a face is covered in mask or not from photos, live camera, videos, and other sources.This experiment reveals that the validation of over 770 samples has a 93 percent accuracy, while 276 validation samples have a 100 percent accuracy. Our proposed strategy's accuracy was demonstrated by the penalties.
Attack versus Attack: Toward Adversarial Example Defend Website Fingerprinting Attack

Website Fingerprinting (WF) attack is a side channel attack against encrypted tunnels which infers network activities of encrypted tunnels users. WF attack has been successfully applied to the Tor network, which poses a huge threat to the privacy of Tor visitors. A lot of countermeasures are therefore proposed to defend against such attacks. However, the newest attack successfully undermined the existing defense leveraging deep learning technique. In this paper, we propose an defense named Attack to Attack (A2A) that leverages adversarial example to attack the attacker's classifier. A2A treats website fingerprinting model as a black box. In order to find effective adversarial examples for the attacker's model, A2A manipulates traffic iteratively according to the output of a substitute model which is an elaborate model intentionally learning a similar classification boundary with the attacker's model. We evaluate the effectiveness of A2A on a public tor traffic dataset and the newest WF attack. The experimental results show that the proposed method provides effective defense with a bandwidth overhead of 2.2%, which significantly outperforms the manually designed defense (typically has a bandwidth overhead of 31%).
Making Sense of It All: Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous Sensing

Situational awareness methods aim to identify and map what is happening in an operational environment in terms of operational terms that define certain decision-making contexts. The underlying assumption here is that an appropriate decision-making context is either known or can be identified a priori for accurately mapping incoming evidence. However, in many complex and unstructured operational environments where situational awareness systems are most useful (e.g., asymmetric battlegrounds, urban reconnaissance), the decision-making context is neither known a priori nor it is easy to determine by, say subject matter experts. This paper presents a data-driven approach for gaining insights on the decision-making context via judicious processing of ubiquitous soft (i.e., human-based) and hard (e.g., physics-based) data streams generated by voluntarily participating mobile sensors that are traversing the operational environment. In particular, by using spectral clustering in tandem with variable length sequence decoding methods, ubiquitous data stream are clustered and then processed for early identification of specific scenarios of interest (that may have generated the sensor measurements). This will enable a decision-maker to understand emerging situations in the operational environment to set the correct decision-making context and proactively identify what information will be most relevant to reducing uncertainty associated with them. Our approach is illustrated via a simulated example that provides insights into its behavior, performance and sensitivity to parameters.
A HEVC Video Steganography Algorithm Based on DCT/DST Coefficients with Improved VRCNN

Nowadays, the steganography of HEVC(High Efficiency Video Coding) videos has been widely researched. The existing steganography algorithms mainly focused on eliminating inter-block distortion and only embedded data in small-sized blocks, which did not take intra-block distortion into consideration and wasted large embedding capacity. In this paper, a HEVC steganography algorithm based on DCT/DST coefficients is proposed. It uses the intra-frame error propagation-free method to embed data in all blocks for eliminating inter-block distortion and especially, introduces CNN(Convolutional Neural Network) to reduce intra-block distortion. Experimental results show that the proposed algorithm improves the embedding capacity greatly and performs well on visual quality, bitrate increasing and anti-steganalysis.
A Decentralized Framework for Semantic Web Services Discovery Using Mobile Agent

Web services discovery provided by the UDDI registries is relatively primitive. It does not take into account the continuous growth in the number of services on the Web. The UDDI standard has been proposed and used for Web service publication and discovery. However, it does not allow users to choose the best provider. It does not offer a mechanism to choose a Web service based on its quality. The standard also lacks of sufficient semantic description in the content of Web services, this lack makes it difficult to find and compose suitable Web services during analysis, search, and matching processes. In addition, a central UDDI suffers from one centralized point problem and the high cost of maintenance. To get around these problems, the authors propose in this paper a novel framework based on mobile agent and metadata catalogue for Web services discovery. Their approach is based on user profile in order to discover appropriate Web services, meeting customer requirements, in less time and taking into account the QoS properties.
Mulu caves, Malaysia

The limestone mountains of the Gunung Mulu National Park and the adjacent Gunung Buda National Park, in Sarawak on Borneo, contain around 500 km of known cave passages. Many of these caves, both the active river passages and the abandoned high-levels, are very large and they include the world's largest cave passage and the world's largest cave chamber. Sediment dating has demonstrated that the caves’ major trunk passages evolved as a sequence over more than two million years in response to falling base levels as land surfaces were lowering adjacent to the limestone mountains.
The Effect of Land use Changes on the Paya Tumpi Flash Flood, Central Aceh District

Central Aceh Regency is a highland area with little risk of flooding due to its elevation, allowing water to drain quickly. Floods began to occur more frequently in the Central Aceh District in 2015s, with overflowing water in Urban Drainage in various locations. The land use of the Paya Tumpi Waterway Network has shifted from forest to agricultural. As evidenced by the high flow coefficient, which can lead to flooding in the Paya Tumpi watershed area, insufficient agricultural land processing can wreak havoc on the hydrological function of the water flow network. A low topography characterizes the site. Due to an overflown of the water flow network in Paya Tumpi village, Central Aceh Regency experienced a Flash Flood on May 13, 2020. The main issue addressed in this research is the impact of land changes on the hydrological function of the water flow network against Banjir Bandang. Using hydrological analysis and land change, we determined the flood discharge that occurred. In deciding the flood discharge, hydrological analysis and land change analysis using GIS software were used. The findings revealed that the flood influenced changes in land use, causing the water network's capacity to become ineffective.
Approaches to Cloud Computing in the Public Sector

Cloud computing refers to a scalable network infrastructure where consumers receive IT services such as software and data storage through the Internet on a subscription basis. Potential benefits include cost savings, simpler IT and reduced energy consumption. The UK government and local authorities, like commercial organisations, are considering cloud-based services. Concerns have been raised, however, over issues such as security, access, data protection and ownership. This study attempts to investigate the likely impact of cloud computing on local government based on a conceptual framework and case studies of four London borough councils. It reveals that the concept of cloud computing is new and not clearly understood. Local authorities, who face further cuts in government funding, welcome a cloud-based IT infrastructure which may lead to considerable savings. Yet local government is conservative, so with their risk-adverse attitude local authorities are more likely to adopt a hybrid approach to implementation.
Simulation of the Passive Protection Device in the Acoustoelectric Leakage Channel

One of the most frequently encountered auxiliary technical devices installed in a protected room is a telephone that created acoustoelectric leakage channels of the acoustic speech information. In the NI Multisim simulation environment, typical models of passive telephone protection devices have been designed. The device protects a telephone set of the speech information leakage arising from the acoustoelectric effect and ≪high-frequency injection≫. The study was carried out of the main characteristics of the developed protection device and an assessment of the performance when connected to a telephone network using the virtual laboratory bench.
The Performance of Selfish Mining in GHOST

The blockchain technology is regarded as a significant trust-building technology and has attracted much attention from the public. The longest chain rule has been widely applied in blockchain systems to reach consensus on the distributed ledger. However, the longest chain rule cannot support a higher transaction throughput due to its lower security. As an alternative solution to the longest chain rule, GHOST is proposed as a safer consensus rule. Existing studies show that the longest chain rule can suffer from selfish mining attacks. However, it is unclear how selfish mining attacks perform on GHOST. In this paper, we explore the performance of selfish mining on GHOST. We first propose the original selfish mining (GHOST-SM) and stubborn mining (GHOST-StuM) for GHOST. We then evaluate these two selfish mining strategies on our blockchain simulation system. The experimental result shows that GHOST achieves better security than the longest chain rule. However, when the block generation rate increases, the security of GHOST is close to the longest chain rule. For example, the threshold for selfish mining attacks of GHOST is increased by 47.55% and 0.60% compared to the longest chain rule corresponding to the block generation interval of 1 second and 15 seconds.
BiEAF: An Bidirectional Enhanced Attention Flow Model for Question Answering Task

Question answering is an crucial module to construct an intelligent chatbot. Traditional question answering system utilizes various of deep learning models to answer the queries requested by users. While BiDAF modelled the interaction between context and query for the first time, which is a milestone attempt in this domain. However, BiDAF only takes the correlation across the sentences into account, but the association within the sentence is not considered. In this paper, based on BiDAF, we present a novel method to both utilize the inter-and-intra sentence interaction by deploying the proposed enhanced attention-flow layer. The experimental results on SQUAD dataset show that our method outperforms the baseline models in terms of both EM and F1 evaluation metrics, which proves our model effectiveness.
Communication-Aware Secret Share Placement in Hierarchical Edge Computing

Secret sharing (SS) and secure multiparty computation (MPC) are now widely considered for privacy-preserving data processing. However, related applications can suffer from large transmission overhead. In this article, we propose a communication-aware secret share placement strategy to optimize communication overhead by minimizing transmission hop counts in a hierarchical edge computing architecture. Meanwhile, relevant privacy constraints in SS can still be guaranteed. We show that the constructed optimization problem is NP-hard, and efficient heuristic algorithms can be applied to find suboptimal solutions. With this consideration, we first evaluate two traditional heuristics, i.e., the genetic algorithm (GA) and particle swarm optimization (PSO). Besides, we introduce two basic heuristics, i.e., top-down and bottom-up heuristic, which can outperform GA and PSO in certain cases. Finally, we propose an algorithm, called bottom-up top-down (BUTD) heuristic, which can outperform all of the above four heuristics when communication among different shares of the same secret is comparable to that among different secrets. Comprehensive experimental results demonstrate the advantage of the proposed algorithms.
Image-based 3D reconstruction precision using a camera mounted on a robot arm

Abstract In this paper, we study 3D object reconstruction based on a set of 2D images. In order to get the best camera path that increases accuracy we focus on this strategy to be used. Euclidean 3D image-based reconstruction is developed in three steps, which are primitive extraction, correspondence of these primitives and then triangulation. The extraction and triangulation are purely geometrical, whereas the matching step can have precision issues especially in the case of noisy images. An experimental study is carried out where a camera is attached to a robot arm and moved precisely relative to a scene containing a checkerboard calibration pattern. The reconstruction results are compared with values of motion given to the robot. A geometric and analytical study of the impact of the motion of the camera with respect to the scene on the error of a 3D image-based reconstructed point was also carried out. It has been demonstrated that the impact of a correspondence error on the reconstruction accuracy point varies drastically depending on the image capture strategy.
Toward an Ontology of the Interface: Identifying the Interface as a Mediation Entity

Abstract The interface is more than an assemblage of processes and effects; it is more than a pure relational instance. The interface can be identified as an entity in itself and in relation to other entities. In this article, the author presents an ontological work on the interface, defining what it is and how it exists, and discussing its relation to other entities. The interface is defined both abstractly as a mediation complex—by identifying characteristics present in its multiple instantiations—and in relation to other entities—by observing the case of the human-computer interface.
Attacks and Countermeasures

Information technology has benefitted the society enormously in all spheres of life. Medical sciences have not been left untouched, rather it is using information technology extensively for storing, retrieving, transmitting, and manipulating data. There are various simulators and software designed using virtual reality explicitly to train the medical students like computer-assisted learning (CAL). Biomedical science is a discipline that connects information science, computer science, and healthcare. Biomedical science is critically analyzing, understanding, and knowing the human body. Real-time monitoring can help studying and analyzing the chronic diseases and managing it before the adverse events. Information technology has been proven as a boon in all areas, but it has certain limitations, making it vulnerable to attacks. Information security is a matter of great concern, especially when the data is traveling through the internet, which is an insecure channel. This chapter focuses on varied attacks and their countermeasures.