Reviewerly/exHarmony huggingface dataset:
Improved Reduction Between SIS Problems Over Structured Lattices

Many lattice-based cryptographic schemes are constructed based on hard problems on an algebraic structured lattice, such as the short integer solution (SIS) problems. These problems are called ring-SIS (R-SIS) and its generalized version, module-SIS (M-SIS). Generally, it has been considered that problems defined on the module lattice are more difficult than the problems defined on the ideal lattice. However, Koo, No, and Kim showed that R-SIS is more difficult than M-SIS under some norm constraints of R-SIS. However, this reduction has problems in that the rank of the module is limited to about half of the instances of R-SIS, and the comparison is not performed through the same modulus of R-SIS and M-SIS. In this paper, we propose that R-SIS is more difficult than M-SIS with the same modulus and ring dimension under some constraint of R-SIS. Also, we show that R-SIS with the modulus prime q is more difficult than M-SIS with the composite modulus c, such that c is divided by q. In particular, it shows that through the reduction from M-SIS to R-SIS with the same modulus, the rank of the module is extended as much as the number of instances of R-SIS from half of the number of instances of R-SIS. Finally, this paper shows that R-SIS is more difficult than M-SIS under some constraints, which is tighter than the M-SIS in the previous work.
The Image Saliency Problem and its Applications

In recent years, the research area of saliency analysis and Salient Object Detection (SOD) has been intensively investigated by the computer vision community. This paper explores the main directions of research in SOD by categorizing the important published works with regard to the proposed saliency extraction mechanism. Another aim of this paper is to provide details regarding specific application areas. An investigation into a highly active and prioritized medical problem, namely melanoma segmentation in skin cancer images using saliency is also analyzed. To advance the research scope of this paper, existing databases, benchmarks and metrics used in the performance evaluation are presented and discussed.
A Feature Based Fitness Cautionary System for Heavy Internet Users by using Deep Learning

Theoretical - Machine learning is the subset of manmade thinking that goes under data science. Without explicitly redid, getting PCs to learn is a science known as Machine Learning. The proposition systems present in the market are accepted to be working in mainstream applications like YouTube online media applications like Facebook, Instagram or thing based applications like Flipkart. Basically, these systems help to zero in on information that is concerned or important for a particular customer. One region where such structures can be particularly useful is contamination forewarning framework. Considering an ailment the customer commitments to the structure, that he thinks they are slanted to or they are encountering they will be proposed top 5 or top 3 afflictions they are for the most part slanted to reliant upon the similarity between the contamination customer inputted and the sickness customer is being recommended for the present circumstance being forewarned. As of now, everything is open on the web, every contamination and its information around there. Experts are there but simultaneously the count of ailments, number of patients for a disease are extending. An individual has one disorder then there are chances they will get another. Sickness incorporate among youths in this age bundle is growing at a gigantic rate. There is the fix of afflictions or conceivably not anyway shouldn't something be said about notice. If we alert someone before they are truly encountering a contamination. It will make him/her considerably more careful than beforehand. This paper examines existing recommender systems and besides includes the burdens of such structures. Drawbacks can be adaptability, cold start and sparsity. The proposed system partakes in its advantages anyway isn't yet available watching out. Assessment has been done on how this disease alerted system using content-based idea under AI is eliminating features from dataset and how this structure presents features like customer self-rule, straightforwardness and no infection start.
Evaluating the Morphosyntactic Well-formedness of Generated Texts

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L’AMBRE – a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.
I Feel it in Your Fingers: Inference of Self-Assessed Personality Traits from Keystroke Dynamics in Dyadic Interactive Chats

The question at the core of this work is whether it is possible to infer self-assessed personality traits from keystroke dynamics (the way people type on a keyboard). The experiments were performed over a corpus of 30 dyadic chats, 60 participants in total, collected through a text-based chat interface similar to those available in popular products (e.g., Skype). The results show that keystroke dynamics (typing speed, frequency of deletions, etc.) allow one to infer whether someone is below median or not along the Big Five personality traits. In particular, it was possible to achieve F1 Scores up to 72% depending on the trait. To the best of our knowledge, this is the first work aimed at recognizing personality traits through analysis of keystroke dynamics.
An Outdoor GPS Navigation Optimization Method Based On Naïve Bayes Method

In order to solve the problem of low execution efficiency when the mobile robot encounters obstacles in outdoor navigation, Na&#x00EF;ve Bayes prediction is added to the general path planning algorithm in this paper. And the new algorithm makes the robot reduce the probability of encountering obstacles. When calculating a single path, risk weight which is obtained by Bayesian prediction is added and it is superimposed with the path length weight to obtain a new comprehensive weight. The Dijkstra algorithm with risk weight is used to obtain the shortest path for the robot to travel to the mission point. The dynamic programming algorithm is used to realize the path planning of robot outdoor inspection. Experiments show that after adding the path blockage risk prediction, the robot&#x0027;s completion time in patrol tasks and the probability of failure are reduced, which verifies the effectiveness of the algorithm.
Application of Distributed Robotic Systems in Earthquakes: Search, Planning and Control Abstract

In order to search and rescue injured during earthquake, we proposed a method for multi-robots motion planning and distributed control in this paper. At first, we have created two probabilistic search models to considering the search area and the characteristics of sensors, which we used to search the injured targets. And after finding the targets, they are assigned to the mobile robots on the land to afford emergency rescue. In order to reach to the targets, a path planning method based on map matching is proposed. There are three parts here. Firstly, to obtain the global and local map: continuous ground images are first collected using the UAV’s vision system, and subsequently, a global map of the ground environment is created by processing the collected images. The local map of the ground environment is obtained using the 2D laser radar sensor of the leader (UGA). Established the coordinate conversion relationship between UAV and UGV, unknown values during map matching are determined via the least square method. Secondly, our robots moved by group (leader-follower). The leader’s path was planned globally and locally. The other multi-robots moved along the path planned by the leader. Thirdly, in order to plan and coordinate the robots in the group, the finite state machine is used to describe the logical level of control system for each robot in the group. After that, at the tactical level of the control system, the movement control law of formation maintaining mode and formation switching mode is designed.
A Distributed Biased Boundary Attack Method in Black-Box Attack

The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios. Research on black-box attack methods and the generation of adversarial samples is helpful to discover the defects of machine learning models. It can strengthen the robustness of machine learning algorithms models. Such methods require queries frequently, which are less efficient. This paper has made improvements in the initial generation and the search for the most effective adversarial examples. Besides, it is found that some indicators can be used to detect attacks, which is a new foundation compared with our previous studies. Firstly, the paper proposed an algorithm to generate initial adversarial samples with a smaller L2 norm; secondly, a combination between particle swarm optimization (PSO) and biased boundary adversarial attack (BBA) is proposed. It is the PSO-BBA. Experiments are conducted on the ImageNet. The PSO-BBA is compared with the baseline method. Experimental comparison results certificate that: (1) A distributed framework for adversarial attack methods is proposed; (2) The proposed initial point selection method can reduces query numbers effectively; (3) Compared to the original BBA, the proposed PSO-BBA algorithm accelerates the convergence speed and improves the accuracy of attack accuracy; (4) The improved PSO-BBA algorithm has preferable performance on targeted and non-targeted attacks; (5) The mean structural similarity (MSSIM) can be used as the indicators of adversarial attack.
Search model for similar documents in the semantic library

The problem of finding the most relevant documents as a result of an extended and refined query is considered. For this, a search model and a text preprocessing mechanism are proposed, as well as the joint use of a search engine and a neural network model built on the basis of an index using word2vec algorithms to generate an extended query with synonyms and refine search results based on a selection of similar documents in a digital semantic library. The paper investigates the construction of a vector representation of documents based on paragraphs in relation to the data array of the digital semantic library LibMeta. Each piece of text is labeled. Both the whole document and its separate parts can be marked. The problem of enriching user queries with synonyms was solved, then when building a search model together with word2vec algorithms, an approach of "indexing first, then training" was used to cover more information and give more accurate search results.
A hybrid metaheuristic and machine learning algorithm for optimal task scheduling in cloud computing

The efficient scheduling of tasks in the cloud has been a challenging issue for years. Very high-performance computing can be achieved with optimal scheduling of tasks to available virtual machines in the cloud. Thus, in this paper, we propose a hybrid machine learning algorithm in which the input of the machine learning algorithm comes from the output of metaheuristics. A hybrid metaheuristic is used to overcome the drawback of one over the other. We have used hybrid Genetic Algorithm and Particle Swarm Optimization metaheuristic to overcome the local optima problem. This data is further fed to the Majority Voting Ensemble to train, so that in future any set of given tasks can be allocated to the given available virtual machines by the classifier in the most optimal manner. The experimental results reveal that the hybrid GA-PSO-MVE gives a better makespan, total execution time, and accuracy in comparison to many previously used approaches such as GA-ANN.
SPAN: Subgraph Prediction Attention Network for Dynamic Graphs

This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction. This proposed end-to-end model learns a mapping from the subgraph structures in the current snapshot to the subgraph structures in the next snapshot directly, i.e., edge existence among multiple nodes in the subgraph. A new mechanism named cross-attention with a twin-tower module is designed to integrate node attribute information and topology information collaboratively for learning subgraph evolution. We compare our model with several state-of-the-art methods for subgraph prediction and subgraph pattern prediction in multiple real-world homogeneous and heterogeneous dynamic graphs, respectively. Experimental results demonstrate that our model outperforms other models in these two tasks, with a gain increase from \(5.02\%\) to \(10.88\%\).
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers

Quantile (and, more generally, KL) regret bounds, such as those achieved by NormalHedge (Chaudhuri, Freund, and Hsu 2009) and its variants, relax the goal of competing against the best individual expert to only competing against a majority of experts on adversarial data. More recently, the semi-adversarial paradigm (Bilodeau, Negrea, and Roy 2020) provides an alternative relaxation of adversarial online learning by considering data that may be neither fully adversarial nor stochastic (i.i.d.). We achieve the minimax optimal regret in both paradigms using FTRL with separate, novel, root-logarithmic regularizers, both of which can be interpreted as yielding variants of NormalHedge. We extend existing KL regret upper bounds, which hold uniformly over target distributions, to possibly uncountable expert classes with arbitrary priors; provide the first full-information lower bounds for quantile regret on finite expert classes (which are tight); and provide an adaptively minimax optimal algorithm for the semi-adversarial paradigm that adapts to the true, unknown constraint faster, leading to uniformly improved regret bounds over existing methods.
Crowd Density Estimation Based on Multi-level Attention Maps

Focusing on the crowd counting task in crowded scene, a crowd counting model with U-Net architecture is designed based on multi-level attention maps, which is composed of encoder, decoder and branch network. In the encoder network, The VGG16 convolution block and channel attention module are used for extracting the image features. In the decoder network, 5 Decoder Modules are stacked to generate 5 crowd density maps with different scales, 5 attention maps with different scales are used to extract the key information of head position and overcome the interference of complex background. The branch network is designed for generating above mentioned attention maps. In optimizing the model, a multi-level supervised learning mechanism is introduced by using crowd density maps of 5 different scales and attention maps of 5 different scales. The model is trained and evaluated on the crowd counting dataset Shanghai Tech. A series of ablation experiments are carried out on the dataset Shanghai Tech PartA to validate the effectiveness of the key components of the model. In addition to Mean Absolute Error(MAE) and Mean Squared Error(MSE), Mean Relative Error(MRE) and Structural Similarity(SSIM) are also adopted in the model evaluation. The experimental results demonstrate that the proposed model can effectively improve the accuracy and robustness of crowd counting compared with some typical crowd counting models.
Development of Text-to-Sign Language Translator for Bengali Sign Language

The article focuses on the development of a text-to-sign language translator for Bengali sign language. The task includes problem statements, existing approaches, scopes for a better outcome in the future, proposed approaches, and recommendations for future work. The static and dynamic signs that have been used for advancing the translator are original and reviewed by the sign language experts for their accuracy. The existing open-source code aiming to create a text to sign language translator is being modified and improved to match the proposed methodology and further development. #COMESYSO1120.
Classification and data processing tasks: Optimization of number of features

The article proposes an algorithm for optimizing the number of features in classification problems. The algorithm combines the differential evolution method and the bat echolocation algorithm. The experiments used the k-nearest neighbors' classifier and the classifier based on the construction of a decision tree. The performance of the algorithm was evaluated on reference datasets from the UCI library. The performance of the algorithm was compared with competing algorithms using the Wilcoxon T-test and the Friedman test.
Algorithms for the Minimal Rational Fraction Representation of Sequences Revisited

Given a binary sequence with length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , determining its minimal rational fraction representation (MRFR) has important applications in the design and cryptanalysis of stream ciphers. There are many studies of this problem since Klapper and Goresky first introduced an adaptive rational approximation algorithm with a time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2}\log n\log \log n)$ </tex-math></inline-formula> . In this paper, we revisit this problem by considering both adaptive and non-adaptive efficient algorithms. Compared with the state-of-art methods, we make several contributions to the problem of finding MRFR. Firstly, we find a general and precise recursive relationship between the minimal bases for two adjacent lattices generated by successive truncation sequences. This enables us to improve the currently fastest adaptive algorithm proposed by Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> . Secondly, by optimizing a time-consuming step of the well-known Lagrange reduction algorithm for 2-dimensional lattices, we obtain a non-adaptive, and yet practically faster MRFR-solving algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global</i> Euclidean algorithm. Thirdly, we identify theoretical flaws on some non-adaptive methods in the literature by counter-examples and correct the problems by designing modified Euclidean algorithm named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partial</i> Euclidean algorithm. Meanwhile, we further reduce the time complexity of existing algorithm from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n\log ^{2}n\log \log n)$ </tex-math></inline-formula> by invoking the half-gcd algorithm. We also conduct a comprehensive experimental comparative analysis on the above algorithms to validate our theoretical analysis.
An Optimized Single Sign-On Schema for Reliable Multi -Level Security Management in Clouds

Because of its irreplaceable facilities, Cloud computing is a widely-accepted technology in both industry and academy. On-demand services, flexible provisioning, a broad range of functionalities are some of the advantages of this trend. Being able to use diverse services from distinct providers encourages organizations and individuals to follow this tendency. On the other hand, this flexibility brings security threats as well, especially in the area of authorization and authentication. In this paper, a flexible and secure access control method is proposed that supports security-level based authorization technique. While attribute mapping approach makes access control easy by categorizing a diverse range of attributes and authentication methods (e.g. PKI, FIDO U2F, LoA, etc.), utilized multi-factor authentication design helps to keep security consistent. Moreover, reliability and distinction of the proposed model are evaluated by security and competitive analysis.
Handwritten Digit Recognition Application Based on Fully Connected Neural Network

At present, the handwritten digit recognition the problem has received more attention because it has a large standard and easy-to-use mature data set such as the MNIST data set, simple 0–9 digit recognition has been regarded as an entry problem in the field of computer vision. The paper Introduce the characteristics and applications of handwritten digit recognition at first. Then, the traditional research methods and their shortcomings is pointed out; the concept of deep learning are introduced. Taking the convolutional neural network as an example, the key technical characteristics of the convolutional neural network in detail is introduced. Finally, an example explain the application of convolutional neural network in handwritten digit recognition.
Verifying multiple replica possession with public key segment under public cloud storage environment

Abstract Replication is widely used in cloud storage systems to provide availability and reliability for outsourced cloud data. However, untrusted providers would like to maintain fewer replicas than promise for economic profits or other benefits. Since providers hardly ever offer remote checking tools, it becomes a challenge to validate from user side whether Providers are faithfully keeping enough replicas or not. Existing solutions have several disadvantages, including low efficiency, high computation overload, and lack of supporting for colluding attack. In this article, a collector‐based multiple replica storage model is introduced. Based on that, a novel multiple replica possession proving scheme public key segment is proposed in detail. Particularly, a public key is divided into several private segments, which will be distributed to corresponding storage servers for evidence generation. Only if every replica node shows its correct possession evidence, can the challenge be concluded as success. Theoretical analysis and prototype experiments show the correctness, security, and efficiency of the scheme.
Application of Cluster Analysis in Bitcoin Deanonymization

Bitcoin is the world's first decentralized cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address.Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. Some criminal activities that use Bitcoin as an intermediary are becoming more rampant, and it is difficult for law enforcement agencies to identify and track them. In order to identify the identity behind the Bitcoin address and realize the supervision of the blockchain, this paper propose to give a review of the most used Bitcoin clustering algorithms. The research is divided into two categories: One is the Transaction-based heuristic method for static address information; The other is classic clustering algorithms for dynamic behavior patterns, so as to achieve the purpose of de-anonymization of Bitcoin.