Reviewerly/exHarmony huggingface dataset:
Incrementally updating high utility quantitative itemsets mining algorithm

High utility quantitative itemsets (HUQI) mining is a new research topic in the field of data mining. It not only provides high utility itemset (HUI), but also provides quantitative information of individual item in the itemset. HUQI can provide decision makers with information about items and their purchase quantities. However, the currently proposed HUQI mining algorithms assume that the datasets are static. In order to solve this problem, an incremental quantitative utility list (IQUL) data structure is proposed to store item information, including item name, item number, transaction weight utility of item, each entry in the list stores the transaction identifier, the utility of the original data, the remaining utility, the utility of the incremental data, the remaining utility, and the sum of the utility and the remaining utility. When data is inserted, the item information will be updated. Based on IQUL, an incrementally updating HUQI (IHUQI) mining algorithm is proposed to mine HUQI on incremental update data. A large number of experiments on real datasets show that the IHUQI algorithm can effectively mine HUQI Experimental results show better performance on sparse datasets.
An incremental flow-based task scheduler for simulation tasks in cloud environments

Deploying distributed simulations on the cloud can obtain many benefits, including lower cost, higher efficiency, easier access and so on. However, due to the virtualization technology adopted by cloud computing, improper assignments of tightly coupled simulation tasks may degrade the overall user experience. At the same time, considering the frequent changes of the status of tasks and hosts, the scheduler should be able to give a good enough solution in time. In this paper, we mainly focus on the efficient scheduling of simulation tasks in the cloud, which has been recognized as an NP-hard combinational optimization problem. Besides mapping such a task-host matching problem as a min-cost max-flow problem, we also design an incremental flow-based task scheduler to deal with the dynamic changes of tasks and hosts. Simulation experiments on Alibaba cluster trace show that our design is adequate to this scenario.
FFDR: Design and implementation framework for face detection based on raspberry pi

In today’s world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. This paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language.
MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation

Image segmentation is one of the key components in systems performing computer vision recognition tasks. Various algorithms for image segmentation have been developed in the literature. Among them, more recently, deep learning algorithms have been remarkably successful in performing this task. A downside with deep neural networks for segmentation is that they require a large amount of labeled dataset for training. This prerequisite is one of the main reasons that led researchers to adopt data augmentation approaches in order to minimize manual labeling efforts while maintaining highly accurate results. This paper uses classical non-deep learning methods for background extraction to increase the size of the dataset used to train deep learning attention segmentation algorithms when images are presented as time-series to the model. The method presented adopts the Gaussian mixture-based (MOG2) foreground-background segmentation followed by dilation and erosion to create masks necessary to train the deep learning models. It is applied in the context of planktonic images captured in situ as time series. Various evaluation metrics and visual inspection are used to compare the performance of the deep learning algorithms. Experimental results show higher accuracy achieved by the deep learning algorithms for time-series image attention segmentation when the proposed data augmentation methodology is utilized to increase the training dataset.
Leveraging speaker-aware structure and factual knowledge for faithful dialogue summarization

Currently, sequence/graph-to-sequence models for abstractive dialogue summarization are being studied extensively. However, previous methods strive to integrate complex events spanning multiple utterances, and the generated summaries are often filled with incorrect facts. In this study, we first utilize the speaker-aware structure to model the information interaction process in the dialogue, which shows an excellent ability to settle the cross-sentence dependency. Then, we incorporate the factual representations via a dual-copy decoder to obtain summaries conditioned on both the tokens from source sequences and the factual knowledge from our designed fact graph, which enhances the factual consistency for dialogue summarization. We also propose some fact-level factual consistency metrics. Adequate experimental results demonstrate that our model outperforms the state-of-the-art baselines by a significant margin on the SAMSum and DialSumm datasets. A comprehensive analysis also proves the effectiveness of our model. Furthermore, human judges confirm that the outputs of our model contain more informative and faithful information.
Automatic classification of MRI contrasts using a deep siamese network and one-shot learning

Fully automatic classification of magnetic resonance (MR) brain images into different contrasts is desirable for facilitating image processing pipelines, as well as for indexing and retrieving from medical image archives. In this paper, we present an approach based on a Siamese neural network to learn a discriminative feature representation for MR contrast classification. The proposed method is shown to outperform a traditional deep convolutional neural network method and a template matching method in identifying five different MR contrasts of input brain volumes with a variety of pathologies, achieving 98.59% accuracy. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. We demonstrate accurate one-shot learning performance on a sixth MR contrast that was not included in the original training.
Research on Network Architecture Design Based on Artificial Intelligence Application Technology

Abstract: With the continuous development of AI technology, the training of massive data and the emergence of large-scale models have made stand-alone model training increasingly unable to meet the needs of AI applications. Distributed machine learning technologies (such as data parallelism and model parallelism) have appeared at historic moments and will have extreme large-scale application scenarios. At present, the training speed of distributed machine learning models is slow, and the scale of model parameters is still the main problem in this field. From the perspective of model parallelism, this article aims to design the optimal division method for different models under model parallelism by analyzing the structure of the existing AI application model. According to the framework structure of artificial intelligence application model, design the model optimization partition strategy and model based on parallelism. A network architecture suitable for accelerating AI application training, focusing on solving technical problems, such as network architecture design based on AI applications and model optimization and partitioning under model parallelization.
Lambda the Ultimate SSA: Optimizing Functional Programs in SSA

Static Single Assignment (SSA) is the workhorse of modern optimizing compilers for imperative programming languages. However, functional languages have been slow to adopt SSA and prefer to use intermediate representations based on minimal lambda calculi due to SSA’s inability to express higher-order constructs. We exploit a new SSA construct — regions — in order to express functional optimizations via classical SSA-based reasoning. Region optimization currently relies on ad-hoc analyses and transformations on imperative programs. These ad-hoc transformations are sufficient for imperative languages as regions are used in a limited fashion. In contrast, we use regions pervasively to model sub-expressions in our functional IR. This motivates us to systematize region optimizations. We extend classical SSA reasoning to regions for functional-style analyses and transformations. We implement a new SSA + regions based backend for LEAN4, a theorem prover that implements a purely functional, dependently typed programming language. Our backend is feature-complete and handles all constructs of LEAN4’s functional intermediate representation $\lambda _{\mathbf{rc}}$ within the SSA framework. We evaluate our proposed region optimizations by optimizing $\lambda_{\mathbf{rc}}$ within an SSA + regions based framework implemented in MLIR and demonstrating performance parity with the current LEAN4 backend. We believe our work will pave the way for a unified optimization framework capable of representing, analyzing, and optimizing both functional and imperative languages.
A Study And Analysis Of Decentralized Cloud Based Platform

Cloud computing and cloud storage has opened the doors for many services that are used by the users for many different purposes but the centralized architecture and issues like malicious attacks on the cloud hinder the process of maintaining the integrity, accountability, privacy, access control, authentication and authorization which are the key goals for data protection. Decentralization of cloud can be done to ensure that the data is secured. There are many opportunities that blockchain technologies provide for maintaining the confidentiality over cloud but the research challenges which require extensive efforts need to be addressed. In this paper we will closely look at the major challenges of cloud storage and compare various decentralized platforms which can be used to store the data. A blockchain based framework has been proposed and an encryption algorithm which is quantum safe is implemented to maximize the security that can be offered using the cloud platforms.
Solving Maximal Boolean Polynomial Based on Improved Heuristic Algorithm by Using Forward Error Correction Coding

Traditional heuristic algorithms have the disadvantages of being &#x201C;premature&#x201D; and always falling into local optimal solutions. Coding theory is a forward error correction technique, and when coding theory is used to solve the problem, the feature of the channel coding technique itself makes the problem be solved. Coding theory is used to solve these problems and improve algorithm performance. Take genetic algorithm as an example, when coding theory is applied to improve heuristic algorithms performance such as genetic algorithms, it can overcome the shortcomings of heuristic algorithms such as &#x201C;premature&#x201D;, localization, instability, and greatly increase the certainty and robustness of the algorithm. Improving the heuristic algorithm based on forward error correction coding to solve maximal Boolean polynomial equations not only obtains better results but also the algorithm complexity is low.
Evolution of Neural Collaborative Filtering for Recommender Systems

Recommender Systems are a highly active area in research and development that has taken advantage of the recent progress in artificial intelligence and deep learning algorithms. Collaborative Filtering approaches have utilized neural networks for modelling complex nonlinear relationships regarding the interactions of users and items, with numerous commercial platforms utilizing such systems for providing personalized recommendations to their users. In this work, we present the evolution of the field and the most influential approaches, from simpler neural network models expanding matrix factorization techniques, to increasingly more complex architectures. We report notable applications and highlight key differences between research and production settings. At the same time, we note that the evaluation approaches followed by the literature vary, and we underline the significance of testing models during production with methods such as A/B tests and the measurement of key performance indicators, aside from offline testing.
Critical Analysis of Existing Punjabi Grammar Checker and a Proposed Hybrid Framework Involving Machine Learning and Rule-Base Criteria

An important area of research involving Artificial Intelligence (AI) is Natural Language Processing (NLP). The objective of training a machine is to imitate and manipulate text and speech of humans. Progressive research is undertaken to find connections between humans and their usage of language commonly used being referred as Natural Language. Various tools for different languages have been developed for operating the natural languages widely used by public. NLP integrates various disciplines and works cohesively for processing text, Information Retrieval, AI and so on. One such tool used for checking the accuracy of a given sentence in any language is referred to as a Grammar Checker. So a Grammar checker of a particular language explores grammatical errors (if any) and provides remedial suggestions for correction of the same. Such feature is imbibed by virtue of Natural Language Processing using Computational Linguistics. We have justified the need of an emerging Machine Learning technique by critically evaluating the existing Punjabi Grammar checker that was developed earlier in light of certain real-time cases. This process is accomplished by critically evaluating the output of each phase and identifying the component accountable for generating maximum errors and false alarms. Based on this analysis, we have proposed a hybrid framework as an efficient way of analyzing correction in sentences. This is attainable through the said booming technique of Machine Learning explicitly using Deep Neural Networks in combination with the existing rule-based approach. It's a novel approach as no work using machine learning has been done earlier in Punjabi Grammar Checker.
Accelerating SQL with Complex Visual Querying

This dissertation includes the design, implementation, and evaluation of prototypes with different fidelity levels, through an iterative design process. The aim is to optimize the effectiveness and efficiency of the process where users communicate to the system regarding data they intend to extract from the database. Moreover, the readability and comprehension improvement of the query visual representation is intended, reducing the time and the effort required to understand what data will be gathered from the database. The final implemented interface is currently incorporated on the OutSystems Platform to accelerate the query formulation process without harming the leamability of the system. The users’ interviews and the results of the SQL queries evaluation have foreseen that the cause of the reduced acceptance of the visual approach, could be the existing usability problems on the interface. Furthermore, the interface is inadequate to build more complex queries, which involve more entities and conditions.
Persistent S/MIME signature in e-mails forwarding

The e-mail is nowadays one of the primary communication mean, that still lacks some simple security possibilities. Among them, the electronic signature is a powerful technology, but it can regrettably leads to misconceptions for the users in some case. In particular, considering the forwarding of an e-mail, the security attributes of the content set by the initial sender are not preserved. More precisely, when a signed e-mail is forwarded, the signature is lost after the forwarding. This is also misleading because, if the forwarder uses his own signature, the final receiver will see the message as signed, and will consider the signature as valid, whereas the signature is not the one of the authentic author of the e-mail. To address this problem, we propose the use of a header to indicate the forwarding of the e-mail without modifying its initial signature and content. With this simple header, the forwarding of an e-mail preserves its integrity and grants the receiver that the initial e-mail has not been modified. We also take into account the use of encrypted e-mails and propose a scheme for the proposed header to be operative. We also prove the proposed header is usable with the common e-mail providers and agents.
Multi-strategy improved parallel antlion algorithm and applied to feature selection

Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data.
SIAR: Signing in the Air Using Finger Tracking Technology for Authentication on Embedded System

In this paper, we propose a novel, convenient, fast and contactless identity authentication method. We use the camera to capture the image data when the user is signing in the air. In addition, the trajectory of the finger is captured by using deep learning model on the embedded system. The trajectory of the finger is the unique signature of the user&#x0027;s identity. For signature verification, the dynamic time warping algorithm is adopted to compare the similarity between the current signature and the registered signature. In order to evaluate the accuracy in the experiment, we collected the trajectories of the signatures in the air, including the registered, genuine and forged signatures. The experimental results show that the proposed method has significant effects. Add to this, this paper has the commercial values of low power consumption and low cost to meet the needs of the consumer market.
Stable learning laws design for long short-term memory identifier for uncertain discrete systems via control Lyapunov functions

This study introduces a method for designing stable learning laws of Long Short-Term Memory (LSTM) networks working as a non-parametric identifier of nonlinear systems with uncertain models. The strategy applies the concept of stability for discrete-time systems in the sense of Lyapunov to prove that origin is a practical stable equilibrium point for the identification error. The laws consider a general class of sigmoidal functions placed at the different gates of a LSTM structure (long and short memory). The design of the learning laws uses a matrix inequality framework to obtain the rate gains associated with the evolution of the weights. Numerical results show the designed learning laws for the non-parametric identifier based on a LSTM approximation tested on two classes of nonlinear systems: the first one describes the ozone-based degradation of organic contaminants, and the second one represents the dynamics of a Van Der Poll oscillator. The LSTM identifier is compared against a classical Lyapunov-based recurrent neural network. This comparison demonstrates how the proposed algorithm approximates the trajectories of both systems with a smaller mean squared error, which serves as an indicator of the benefits obtained with these new learning laws.
Expander-Based Cryptography Meets Natural Proofs

We introduce new forms of attack on expander-based cryptography, and in particular on Goldreich’s pseudorandom generator and one-way function. Our attacks exploit low circuit complexity of the underlying expander’s neighbor function and/or of the local predicate. Our two key conceptual contributions are: We prove two types of technical results. First, in the regime of quasipolynomial stretch (in which the output length of the PRG and the running time of the distinguisher are quasipolynomial in the seed length) we unconditionally break Goldreich’s PRG, when instantiated with a specific expander whose existence we prove, and for a class of predicates that match the parameters of the currently-best “hard” candidates. Secondly, conditioned on the existence of expanders whose neighbor functions have extremely low circuit complexity, we present attacks on Goldreich’s PRG in the regime of polynomial stretch. As one corollary, conditioned on the existence of the foregoing expanders, we show that either the parameters of natural properties for several constant-depth circuit classes cannot be improved, even mildly; or Goldreich’s PRG is insecure in the regime of a large polynomial stretch for some expander graphs, regardless of the predicate used.
Improve Generalization Ability of CNN by Data Augmentation and SE Block in Landmark Classification

In this paper, we investigate how to improve the generalization ability of simple convolutional neural networks. Previous work showed that techniques like regularization, transfer learning and data augmentation could improve the generalization ability. We first focused on data augmentation and showed that the generalization ability can be largely improved by augmenting the training samples. However, the increasing amount and difficulty of the training set will lower the speed of machine learning models. In order to speed up the model and keep relatively high performance, we applied Squeeze-and-Excitation block on to our convolutional neural network. Our results showed great improvement by applying data augmentation. The speed of our model is increased after applying the Squeeze-and-Excitation block. Our method improved 6-time convergence time and 37.65 percentage of accuracy, which can validate the effectiveness of the proposed method.