Reviewerly/exHarmony huggingface dataset:
On modification of Boyer-Moore-horspool's algorithm for tree pattern matching in linearised trees

Tree pattern matching on ordered trees is an important problem in Computer Science. Ordered trees can be represented as strings with additional properties via various linearisations. We present a backward tree pattern matching algorithm for ordered trees for various linear representations of trees and tree patterns. The algorithm adaptations find all occurrences of a single given tree pattern which match an input tree regardless of the chosen linearisation. The algorithms preserve the properties and advantages of standard backward string pattern matching using Boyer-Moore-Horspool's bad character shift heuristics. The number of symbol comparisons in the backward tree pattern matching can be sublinear in the size of the input tree. As in the case of the string version of Boyer-Moore-Horspool's matching algorithm, the size of the bad character shift table used by the algorithm is linear in the size of the alphabet. We compare the algorithm adaptations with the algorithm using originally chosen linear representation and with the best performing previously existing algorithms based on (non-linearised) tree pattern matching using finite tree automata or stringpath matchers. We show that the presented backward tree pattern matching algorithms outperform the non-linearising ones for single pattern matching and they perform among themselves comparably.
An effective maximum entropy exploration approach for deceptive game in reinforcement learning

Deceptive games are games that utilize the reward structure to keep the agent away from the global optimization and have been grown up to become a huge challenge in the field of deep reinforcement learning intelligent exploration. Most of the cutting-edge exploration approaches, such as count-based and curiosity-driven, even with intrinsic motivation, which achieves better performance in the sparse reward game, still easily fall into local optimal traps in the deceptive game. To address this shortfall, we introduce a further exploration approach called Maximum Entropy Explore (MEE). Based on entropy rewards and the off-policy actor-critic reinforcement learning algorithm, we divided the agent exploration policy into two independent parts, namely, the target policy and the explorer policy. The explorer policy, taking the maximum entropy of the target policy as the optimization goal, is used to interact with the environment and generated trajectories for the target policy. The target policy regards the maximization of external reward as the optimization goal to achieve the global solution. To alleviate the catastrophic forgetting problem which leads to the training of the agent not stabilized during the off-policy exploration phrase, the optimal experience replay is applied. An on-policy mode switch trick is used to validly prevent the unstable and diverge which caused by the deadly triad. We conduct experiments comparing our approach with state-of-the-art deep reinforcement learning algorithm and exploration methods in the grid world and StarCraft II environments with deceptive reward. The experiment indicates that the MME approach sets out to be in the present paper effectively avoids the deceptive reward trap and learns the global optimal strategy.
Characterizing Genetic Programming Error Through Extended Bias and Variance Decomposition

An error function can be used to select between candidate models but it does not provide a thorough understanding of the behavior of a model. A greater understanding of an algorithm can be obtained by performing a bias-variance decomposition. Splitting the error into bias and variance is effective for understanding a deterministic algorithm such as k-nearest neighbor, which provides the same predictions when performed multiple times using the same data. However, simply splitting the error into bias and variance is not sufficient for nondeterministic algorithms, such as genetic programming (GP), which potentially produces a different model each time it is run, even when using the same data. This article presents an extended bias-variance decomposition that decomposes error into bias, external variance (error attributable to limited sampling of the problem), and internal variance (error due to random actions performed in the algorithm itself). This decomposition is applied to GP to expose the three components of error, providing a unique insight into the role of maximum tree depth, number of generations, size/complexity of function set, and data standardization in influencing predictive performance. The proposed tool can be used to inform targeted improvements for reducing specific components of model error.
A Comparative Study on HSV-based and Deep Learning-based Object Detection Algorithms for Pedestrian Traffic Light Signal Recognition

Small object detection has been a challenge in many image analysis applications. One such application is the ability to detect the status of Pedestrian Traffic Light (PTL) signal to allow decisions to be made by an intelligent system. The challenge is becoming more complex due to the increased complexity of urban environment, where objects of close similarity would confuse the detection mechanism. In this research, a study is carried out to compare two methods for the detection of small objects within large-sized images. The first method is a classical color-based segmentation approach while the second uses an intricate Deep Learning (DL) object detection algorithm. In the classical approach, objects within the selected range of Hue, Saturation and Value (HSV) composition are identified and extracted from the large-sized images. For DL approach, a Mask R-CNN was used where traffic light-like objects are identified by object instance segmentation process. From this research, it is shown that a two-tier approach, a hybrid HSV -DL model can detect the PTL signal directly and accurately from large-sized images in real-time on smart devices at an accuracy of 92.75%.
Studying Political Bias via Word Embeddings

Machine Learning systems learn bias in addition to other patterns from input data on which they are trained. Bolukbasi et al. pioneered a method for quantifying gender bias learned from a corpus of text. Specifically, they compute a gender subspace into which words, represented as word vectors, can be placed and compared with one another. In this paper, we apply a similar methodology to a different type of bias, political bias. Unlike with gender bias, it is not obvious how to choose a set of definitional word pairs to compute a political bias subspace. We propose a methodology for doing so that could be used for modeling other types of bias as well. We collect and examine a 26 GB corpus of tweets from Republican and Democratic politicians in the United States (presidential candidates and members of Congress). With our definition of a political bias subspace, we observe several interesting and intuitive trends including that tweets from presidential candidates, both Republican and Democratic, show more political bias than tweets from other politicians of the same party. This work models political bias as a binary choice along one axis, as Bolukbasi et al. did for gender. However, most kinds of bias - political, racial and even gender bias itself - are much more complicated than two binary extremes along one axis. In this paper, we also discuss what might be required to model bias along multiple axes (e.g. liberal/conservative and authoritarian/libertarian for political bias) or as a range of points along a single axis (e.g. a gender spectrum).
King louie

Cloud resources have become a preferred operational model distributed Database Management Systems (DBMS) by offering the elasticity and virtually unlimited scalability, but increase the risk of failures with increasing cluster sizes. While distributed DBMS provide high-availability mechanisms, it is currently an open research question to what extent they are able to provide availability and performance guarantees in case of cloud resource failures. Especially as existing DBMS benchmarks do not consider availability. We present a comprehensive methodology for evaluating the availability of distributed DBMS in case of cloud resource failures. Based on this methodology, we introduce a novel framework that automates the full evaluation process, including the failure injection, and emphasizes reproducibility. The framework is validated by 16 diverse availability evaluations. The results show that distributed DBMS are not necessary available even if sufficient replicas are available and clients can experience significant downtimes.
Opinion Summarization of Bangla Texts using Cosine Simillarity Based Graph Ranking and Relevance Based Approach

The main idea of the automatic extractive text or opinion summarization is to find most important representative small subset of the original document without any loss of important information. There are many existing methods available for text summarization of English, Turkish, Arabic and other languages. But very few attempts has been done for Bangla language because of its having rich morphology and multifaceted structure. In this paper, we propose a joint cosine simillarity based graph ranking and Relevance based scoring and ranking approach for the summarization of bangla text. We developed a stemming algorithm based on Parts of Speech(POS) tagging consisting of around two lakhs POS tags for Bangla texts. A redundancy removal algorithm is also proposed to remove redundancy so that each sentences in the summary represents exactly the most important information in the document. The performance of the proposed approach is evaluated by measuring the recall, precision and f-score based on Rouge metric and it is also showed that proposed approach outperforms to other existing summarization methods for Bangla texts.
A FPGA-based Control-Flow Integrity Solution for Securing Bare-Metal Embedded Systems

Memory corruption vulnerabilities, mainly present in C and C++ applications, may enable attackers to maliciously take control over the program running on a target machine by forcing it to execute an unintended sequence of instructions present in memory. This is the principle of modern Code-Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI-based solutions have been proposed, resorting to both hardware and software implementations. However, many of these solutions are hardly applicable to microcontroller systems, often very resource-limited. The paper presents a generic, portable, and lightweight CFI solution for bare-metal embedded systems, i.e., systems that execute firmware directly from their Flash memory, without any Operating System. The proposed defense mixes software and hardware instrumentation and is based on monitoring the Control-Flow Graph (CFG) with an FPGA connected to the CPU. The solution, applicable in principle to any architecture which disposes of an FPGA, forces all control-flow transfers to be compliant with the CFG, and preserves the execution context from possible corruption when entering unpredictable code such as Interrupt Services Routines (ISR).
A Parallel Quad Itoh-Tsujii Multiplicative Inversion Algorithm for FPGA Platforms

Modular inversion in GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) is one of the computationally intensive tasks in cryptographic applications like Elliptic Curve Cryptography (ECC). For hardware implementation over binary extended field, Itoh- Tsujii inversion Algorithm (ITA) using sequential multiplication and squaring is considered as the most efficient algorithm. In this paper, we propose a new parallel Quad ITA(QITA) over the National Institute of Standards and Technology (NIST) recommended trinomials to efficiently compute inverse operation on Field-Programmable Gate-Array (FPGA) platforms. Due to the implementation of novel short length addition chain and parallel Quadblock, area-time efficiency has been enhanced in this architecture. This modification allows the computation of inversion with reduced clock cycles comparatively. The experimental results reveal that the proposed parallel QITA algorithm improves the area-time performance as compared to other existing works.
Higher-Order Label Homogeneity and Spreading in Graphs

Do higher-order network structures aid graph semi-supervised learning? Given a graph and a few labeled vertices, labeling the remaining vertices is a high-impact problem with applications in several tasks, such as recommender systems, fraud detection and protein identification. However, traditional methods rely on edges for spreading labels, which is limited as all edges are not equal. Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks.
Unsupervised detail-preserving network for high quality monocular depth estimation

In this paper, we propose an unsupervised learning framework to address the problems of the inaccurate inference of depth details and the loss of spatial information for monocular depth estimation. First, as an unsupervised technique, the proposed framework takes easily collected stereo image pairs instead of ground truth depth data as inputs for training. Second, we design a rectangle convolution to capture global dependencies between neighboring pixels across entire rows or columns in an image, which can bring significant promotion on depth details inference. Third, we propose a learned depth refinement module including a color-guided refinement layer and a learned composite proximal operator to preserve depth discontinuities and obtain high quality depth map. The proposed network is fully differentiable and end-to-end trainable. Extensive experiments evaluated on KITTI, Cityscapes and Make3D dataset demonstrate our state-of-the-art performance and good cross-dataset generalization ability.
Decentralized Tourism Destinations Rating System Using 6AsTD Framework and Blockchain

A rating assessment of supporting elements in tourism destinations used to provide information for tourists before making a tour. Along with the development of communication technology, each traveler shares data about the ranking of travel destinations through their mobile devices. The commonly used data sharing architecture is the centralized model. However, this architecture still has some weaknesses, so it needs to be improved, for example, through decentralized architecture as developed in this research in a Tourism Destination Rating System (TDRS). This system is a combination of 6AsTD as a framework for assessing the success level of tourism destinations and blockchain as the data-sharing architecture between tourists. The 6AsTD frameworks use six variables as a reference for assessing tourism destinations. The rating results distributed to each traveler's mobile device connected to the blockchain network. Furthermore, the system built-in this study was tested on several tourists in Batu Tourism City. The TDRS can generate assessments that can be quickly submitted to other users using applications connected to the Blockchain network.
Microscopic characterisation of metallic nanoparticles in ore rocks, fault gouge and geogas from the Shanggong gold deposit, China

Geogas prospecting is a potential method for exploring concealed ore bodies. However, a knowledge gap still exists regarding the relationship between metal-bearing nanoparticles (NPs) found in geogas and concealed ore bodies. In this paper, transmission electron microscopy (TEM) and scanning electron microscopy (SEM) were applied to study the occurrence of metal-bearing NPs in ore, gouge and geogas samples collected from a fault zone and a background area of the concealed Shanggong gold deposit. SEM observations of ore rocks showed that gold was mainly concentrated in microfractures and its distribution was correlated with that of Mo, S, Pb, and Co. According to the results of SEM, gold can coexist with pyrites in the form of sub-microscopic particles, as NPs or dispersed in a lattice. TEM analysis and in situ SEM observations of ore rocks suggested that gold and other metallic particles can form during the process of mineralization. After mineralization, it is hypothesized that NPs can be generated by faulting and weathering. Meanwhile, morphological and chemical composition analyses indicate that aggregation and biological activity can change the status of NPs occurrence in fault gouges. TEM showed that there were more particles in the ascending geogas collected in the fault zone than in the background area. In terms of composition, particle samples collected from the background area were primarily non-metallic oxides and aluminium silicate. More metallic NPs containing Zn, Cu, Cr and Ni were observed in geogas collected in the fault zone. As for morphology, while the NPs in the fault zone geogas showed various morphologies, spherical dominated in geogas from the background area. These results suggest that NPs in ore rocks and gouge can be rapidly channelled to the surface through faults. The driving forces of NP transportation may include of temperature and pressure differences and the earth's magnetic field. This knowledge consolidates the view that NPs in geogas can provide information about the deep Earth. This study increases our knowledge of NPs in gold deposits, fault gouges and geogas, and enhances our understanding of NP generation and migration mechanism sin fault systems.
Design and Implementation of Constant-Round Dynamic Group Key Exchange from RLWE

Group Key Exchange (GKE) is required for secure group communication with high confidentiality.In particular, a trusted authority can handle issues that happen by the malicious actions of group members, but it is expensive to deploy and not suitable in a dynamic setting where the network requires frequent membership status changes.To overcome these issues, we designed yet another quantum-resistant constant-round GKE based on lattice without a trusted authority based on Apon et al.'s protocol (PQCrypto 2019) by modifying their key computation phase.Then, we describe the novel dynamic authenticated GKE (called DRAG) with membership addition/deletion procedures in Ring Learning with Errors (RLWE) setting, while the former ones are built from Diffie-Hellman problem.Under the specific adversary who can leak the long-term secret key from the party, we suggest a rigorous proof of DRAG in the random oracle model based on the hardness assumption of RLWE problem and the property of Rényi divergence.As a proof of concept, implementation details are described to meet level 1 NIST security.Our implementation is reasonable for practical use since the total runtime to get a group secret key takes about 3 msec and it can be considered as a reference implementation of other quantum-resistant GKEs.INDEX TERMS Authenticated group key exchange, key establishment, lattice-based cryptography, post quantum cryptography, ring learning with errors (RLWE). I. INTRODUCTIONThese days, network topology is becoming more and more complicated such as group chatting in instant messaging applications, file sharing between multiple parties, etc.Hence, secure communication between multiple parties is required to keep the confidentiality of their messages.Key establishment is a pre-determined protocol where two or more parties make a shared secret for subsequent cryptographic use [1].This can be subdivided into key transport and key agreement protocols.Key transport protocol enables one party to create a secret value and securely transfer it to other parties but key agreement protocol derives the shared secret of two or more parties as a function of information contributed by each party, so that no party can estimate the result.
A Blockchain-Based Framework for Information Security in Intelligent Transportation Systems

Efficient utilization of the IoT generated data helps in the decision-making process as well as the realization of the concept of smart cities into a reality. The smart cities utilize Intelligent Transportation System (ITS) to facilitate smart vehicles to operate independently without much human intervention. The ITS system utilizes the data produced by the smart infrastructure to intelligently operate the smart vehicles and make highly precise decisions in the whole smart city ecosystem. Although ITS has been in its evolutionary phase, this is the right time to realize the vulnerabilities present in the ITS infrastructure that can be exploited by the adversaries to render devastating attacks. The ITS network involves highly sophisticated infrastructure where any anomaly provokes threat to human lives. The ITS data can be manipulated by the adversaries for attacks such as password theft, information spoofing, data manipulation, information loss, eavesdropping, and key fob attacks.
A Two-step Approach to Providing a Desired Visual Quality in Image Lossy Compression

It has been always a problem in lossy image compression to provide a desired visual quality quickly and accurately. Here we propose a two-step approach that is applicable not only for standard metric as peak signal-to-noise ratio (PSNR) but also for two other metrics PSNR-HVS and PSNR-HVS-M that take into account peculiarities of human visual system (HVS). Factors that influence accuracy of providing a desired metric are considered and limitations of the proposed approach are discussed. Our studies are carried out for a particular coder AGU based on discrete cosine transform (DCT) although the proposed approach seems applicable for other DCT-based image compression techniques.
Advances in Neuroevolution through Augmenting Topologies – A Case Study

Inspired by the evolution of biological nervous systems, Neuroevolution (NE) is an approach to Artificial Intelligence (AI) which uses evolutionary algorithms to evolve complex artificial neural networks capable of intelligent behavior. Kenneth O' Stanley et al. proposed an extension of this approach called Neuroevolution of Augmenting Topologies (NEAT) [1], which evolves both topology and parameters. It possesses key features such as complexification, avoiding competing conventions via historical markings, speciation and fitness sharing. Over the years, the performance of NEAT has been improved with better approaches such as HyperNEAT and CoDeepNEAT. Better training methods for NEAT have evolved over the years as well. In this paper, we deduce an analysis of the efficiency and performance of the various algorithms which have been proposed for Topology and Weight Evolving Artificial Neural Networks (TWEANNs). A survey on the existing approaches based on their applications and purpose, along with the various training methods and the challenges incurred has also been discussed. This work will provide learners with a better overview of the past and current research trends in the field of Neuroevolution.
An Integrated Approach to the Regression Problem in Forest Fires Detection

Machine learning procedures are used in almost humans' life areas: recommendations systems for online-services, self-driving cars, machine translation systems and so on. But machine learning tasks complexity and importance are grown up and solutions based on a single algorithm can have bad quality. The complex approach was developed for burned area estimating on the forest fires detection problem. The developed approach was tested on the data of Montensinho park. In this paper developed approach is compared with classic regression algorithms and its advantages are shown. The significantly better quality of the proposed solution is demonstrated.