检索结果-内蒙古大学图书馆

Turtle Score - Similarity Based Developer Analyzer

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Varshini, Sanjjushri Ponshriharini, V. Kannan, Santhosh Suresh, Snekha Ramesh, Harshavardhan Mahadevan, Rohith Raman, Raja C.S.P.

In day-to-day life, a highly demanding task for IT companies is to find the right candidates who fit the companies' culture. This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits right in the company. Data is examined and collected for each employee who works in the IT domain focusing on their performance measure. This is done based on various different categories which bring versatility and a wide view of focus. To this data, learner analysis is done using machine learning algorithms to obtain learner similarity and developer similarity in order to recruit people with identical working patterns. It's been proven that the efficiency and capability of a particular worker go higher when working with a person of a similar personality. Therefore this will serve as a useful tool for recruiters who aim to recruit people with high productivity. This is to say that the model designed will render the best outcome possible with high accuracy and an immaculate recommendation score. © 2022, CC BY.

关键词： learning algorithms

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Ahmed, Sharif Ahmed, Arif Eisty, Nasir U. Dept. of Computer Science Boise State University Boise United States

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches to UML generation, such as restrictions on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research. Copyright © 2022, The Authors. All rights reserved.

关键词： learning algorithms

Adversarially Robust PAC Learnability of Real-Valued Functions

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Attias, Idan Hanneke, Steve Department of Computer Science Ben-Gurion University Department of Computer Science Purdue University United States

We study robustness to test-time adversarial attacks in the regression setting with p losses and arbitrary perturbation sets. We address the question of which function classes are PAC learnable in this setting. We show that classes of finite fat-shattering dimension are learnable in both realizable and agnostic settings. Moreover, for convex function classes, they are even properly learnable. In contrast, some non-convex function classes provably require improper learning algorithms. Our main technique is based on a construction of an adversarially robust sample compression scheme of a size determined by the fat-shattering dimension. Along the way, we introduce a novel agnostic sample compression scheme for real-valued functions, which may be of independent interest. Copyright © 2022, The Authors. All rights reserved.

关键词： learning algorithms

Can we achieve robustness from data alone?

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tsilivis, Nikolaos Su, Jingtong Kempe, Julia Center for Data Science New York University United States

We introduce a meta-learning algorithm for adversarially robust classification. The proposed method tries to be as model agnostic as possible and optimizes a dataset prior to its deployment in a machine learning system, aiming to effectively erase its non-robust features. Once the dataset has been created, in principle no specialized algorithm (besides standard gradient descent) is needed to train a robust model. We formulate the data optimization procedure as a bi-level optimization problem on kernel regression, with a class of kernels that describe infinitely wide neural nets (Neural Tangent Kernels). We present extensive experiments on standard computer vision benchmarks using a variety of different models, demonstrating the effectiveness of our method, while also pointing out its current shortcomings. In parallel, we revisit prior work that also focused on the problem of data optimization for robust classification (Ilyas et al., 2019), and show that being robust to adversarial attacks after standard (gradient descent) training on a suitable dataset is more challenging than previously thought. © 2022, CC BY.

关键词： learning algorithms

THE THEORY OF ARTIFICIAL IMMUTABILITY: PROTECTING ALGORITHMIC GROUPS UNDER ANTI-DISCRIMINATION LAW

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wachter, Sandra Oxford Internet Institute University of Oxford 1 St. Giles OxfordOX1 3JS United Kingdom Governance of Emerging Technologies Oxford Internet Institute United Kingdom

Artificial Intelligence (AI) and machine learning algorithms are increasingly used to make important decisions about people. Decisions taken on the basis of socially defined groups can have harmful consequences, creating unequal, discriminatory, and unfair outcomes on the basis of irrelevant or unacceptable differences. Equality and anti-discrimination laws aim to protect against these types of harms. While issues of AI bias and proxy discrimination are well explored, less focus has been paid to the harms created by profiling based on groups that do not map to or correlate with legally protected groups such as sex or ethnicity. Groups like dog owners, sad teens, video gamers, single parents, gamblers, or the poor are routinely used to allocate resources and make decisions such as which advertisement to show, price to offer, or public service to fund. AI also creates seemingly incomprehensible groups defined by parameters that defy human understanding such as pixels in a picture, clicking behavior, electronic signals, or web traffic. These algorithmic groups feed into important automated decisions, such as loan or job applications, that significantly impact people’s lives. A technology that groups people in unprecedented ways and makes decisions about them naturally raises a question: are our existing equality laws, at their core, fit for purpose to protect against emergent, AI-driven inequality? This paper examines the legal status of algorithmic groups in North American and European anti-discrimination doctrine, law, and jurisprudence. I propose a new theory of harm to close the gap between legal doctrine and emergent forms of algorithmic discrimination. Algorithmic groups do not currently enjoy legal protection unless they can be mapped onto an existing protected group. Such linkage is rare in practice. In response, this paper examines three possible pathways to expand the scope of anti-discrimination law to include algorithmic groups. The first possibility is to

关键词： learning algorithms

Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Khannouz, Martin Glatard, Tristan Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases. However, this assumption is unrealistic when data comes in the form of infinite data streams, or when learning algorithms are deployed on devices with reduced amounts of memory. Such memory constraints impact the model behavior and assumptions. In this paper, we show that under memory constraints, increasing the size of a tree-based ensemble classifier can worsen its performance. In particular, we experimentally show the existence of an optimal ensemble size for a memory-bounded Mondrian forest on data streams and we design an algorithm to guide the forest toward that optimal number by using an estimation of overfitting. We tested different variations for this algorithm on a variety of real and simulated datasets, and we conclude that our method can achieve up to 95% of the performance of an optimally-sized Mondrian forest for stable datasets, and can even outperform it for datasets with concept drifts. All our methods are implemented in the OrpailleCC open-source library and are ready to be used on embedded systems and connected objects. © 2022, CC BY.

关键词： learning algorithms

A Combined PCA-MLP Network for Early Breast Cancer Detection

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Arnob, Md Wahiduzzaman Khan Pooja, Arunima Dey Onim, Md Saif Hassan Dhaka1216 Bangladesh

Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women. Any improvisation of the existing diagnosis system for the detection of cancer can contribute to minimizing the death ratio. Moreover, cancer detection at an early stage has recently been a prime research area in the scientific community to enhance the survival rate. Proper choice of machine learning tools can ensure early-stage prognosis with high accuracy. In this paper, we have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Due to the implicit behavior of early-stage features, we have implemented a multilayer perception model with the integration of PCA and suggested it to be more viable than other detection algorithms. Our 4 layers MLP-PCA network has obtained the best accuracy of 100% with a mean of 90.48% accuracy on the BCCD dataset. © 2022, CC BY.

关键词： learning algorithms

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Marris, Luke Gemp, Ian Anthony, Thomas Tacchetti, Andrea Liu, Siqi Tuyls, Karl DeepMind UCL United Kingdom DeepMind United Kingdom

Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunately, solving a normal-form game could take prohibitive or non-deterministic time to converge, and could fail. We introduce the Neural Equilibrium Solver which utilizes a special equivariant neural network architecture to approximately solve the space of all games of fixed shape, buying speed and determinism. We define a flexible equilibrium selection framework, that is capable of uniquely selecting an equilibrium that minimizes relative entropy, or maximizes welfare. The network is trained without needing to generate any supervised training data. We show remarkable zero-shot generalization to larger games. We argue that such a network is a powerful component for many possible multiagent algorithms. © 2022, CC BY.

关键词： learning algorithms

Engineering Intelligent Systems

学校读者我要写书评

暂无评论

Engineering Intelligent Systems 2022年第3期30卷 233-242页

作者： Liu, Ying Pan, Hong College of Science North China University of Science and Technology Tangshan063000 China ChangJiang Nanjing Waterway Bureau ChangJiang Waterway Bureau Nanjing210011 China

Because the traditional similar sequential data search algorithm considers only one-dimensional data, its data search accuracy is low, and the search data is not comprehensive. Hence, a similar sequential data search algorithm based on the dimension-by-dimension strategy is proposed. The algorithm measures the similar time series data in order to fill in the missing data in a time series, searches similar sub-sequences in time series data based on the strategy of dimension-by-dimension according to the data measurement results, gives a similarity threshold, queries the dynamic time-bending distance between sequences and the starting position of sub-sequences and candidate sub-sequences according to the threshold, and obtains the data by dynamically adjusting the search target hierarchical matching and search tasks. The experimental results show that under the influence of different levels of interference data, compared with the traditional search algorithm, the search matching accuracy of the proposed search algorithm is maintained at a relatively high level, the data search can obtain 89% of the expected search amount. Moreover, the process takes less time, demonstrating that the performance of the algorithm is superior, and can meet the current search requirements of similar time series data. © 2022 CRL Publishing Ltd.

关键词： learning algorithms