Reviewerly/exHarmony huggingface dataset:
An Investigation of Edge Bead Removal Width Variability, Effects and Process Control in Photolithographic Manufacturing

We present a low-cost, vision-based method to study the effects of photo resist edge bead removal (EBR) width variability in a manufacturing environment. In micro- and nanofabrication manufacturing wafer edge regions are frequently out of sight and less studied due to their non-yielding attributes. However, they might significantly influence product quality (delamination) and equipment health (contamination). The proposed method utilizes partial wafer edge inspection and open-source image processing to measure the EBR widths without requiring prior image model training. The method is based on the computation of the offset between two local maxima (peaks) in the Hough parametric space equivalent of the Euclidean distance between wafer edge and EBR line. The proposed method is robust to imperfect removal line definitions and highly integrable in automation workflows for equipment health monitoring and statistical process control (SPC). An extensive 180-day survey of EBR width variability revealed low frequency but high severity events.
Agentless Insurance Model Based on Modern Artificial Intelligence

Since past couple of years, Agents have been a crucial part of the financial sector, primarily focusing on the Auto Insurance sector, whose key responsibilities are centered around finding new prospective customers and maintaining a relationship with existing customers. But with every other company streamlining their business processes with the latest Technology, Insurance Industry is not too far behind. Currently, Insurance Industry has dived and started exploring the online space. Prospective customers can now get online insurance quotes, chat with an online robot and even purchase an Insurance policy online. Digitalization, Automation, and Streamlining are key buzzwords in every type of business sector. Given the above trends, Insurance Agents seem to be an unnecessary expense. In this paper, we propose an Artificial-Intelligence driven approach that eliminates the need for a human Insurance Agent that will ultimately reduce the overall cost for the end customer. As part of our contribution to the above problem statement, we have proposed a Software Application where four Statistical Models are deployed. These Models are tasked with determining prospective customers who will likely buy an Insurance Policy, identifying customers who are likely to cancel a policy so that we can provide them with something better, identifying customers submitting fraudulent insurance claims and finally a Recommendation System Model to recommend updates to current policy to existing policy of Customers. In our Experimentation Results, we identified a cluster of customers who were most likely to buy a product using an Unsupervised Statistical Machine Learning model.
Design and Implementation of Web Online Education Platform Based on User Collaborative Filtering Algorithm

research-article Share on Design and Implementation of Web Online Education Platform Based on User Collaborative Filtering Algorithm Author: Huaben Wang Anhui Xinhua University, China Anhui Xinhua University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2911–2918https://doi.org/10.1145/3482632.3487539Online:22 November 2021Publication History 0citation32DownloadsMetricsTotal Citations0Total Downloads32Last 12 Months32Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Prioritized Experience Replay Method Based on Experience Reward

In recent years, artificial intelligence has been widely used in modern construction, and reinforcement learning methods have played an important role in it. The experience replay method is an important means to enable the reinforcement learning method to be widely used in real tasks. In order to improve the efficiency of the experience replay method, this article improves the traditional experience replay method and combines the past experience reward value with the timing difference error (TD error) to form a R- T experience prioritized parameter. Based on the data priority, we use R- T experience prioritized parameter as a standard to update and storage the memory pool data method. The improvement is used to form an improved experience replay method based on experience data priority. This paper applies the improved experience replay method to DDPG (Deep Deterministic Policy Gradient) algorithm and conducts simulation experiments. As a result, the improved experience replay method verified by experiments has a good effect on improving the efficiency of algorithm training and the stability of the algorithm training process.
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning

Contrastive representation learning has proven to be an effective self-supervised learning method. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise. However, several instances in a dataset are drawn from the same distribution and share underlying semantic information. A good data representation should contain relations, or semantic similarity, between the instances. Contrastive learning implicitly learns relations but considering all negatives as noise harms the quality of the learned relations. To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE). Our training objective is a soft contrastive learning one. Instead of hard classifying positives and negatives, we estimate from one view of a batch a continuous distribution to push or pull instances based on their semantic similarities. This target similarity distribution is sharpened to eliminate noisy relations. The model predicts for each instance, from another view, the target distribution while contrasting its positive with negatives. Experimental results show that SCE is Top-1 on the ImageNet linear evaluation protocol at 100 pretraining epochs with 72.1% accuracy and is competitive with state-of-the-art algorithms by reaching 75.4% for 200 epochs with multi-crop. We also show that SCE is able to generalize to several tasks. Source code is available here: https://github.com/CEA-LIST/SCE.
Optimal Maximal Leakage-Distortion Tradeoff

Most methods for publishing data with privacy guarantees introduce randomness into datasets which reduces the utility of the published data. In this paper, we study the privacy-utility tradeoff by taking maximal leakage as the privacy measure and the expected Hamming distortion as the utility measure. We study three different but related problems. First, we assume that the data-generating distribution (i.e., the prior) is known, and we find the optimal privacy mechanism that achieves the smallest distortion subject to a constraint on maximal leakage. Then, we assume that the prior belongs to some set of distributions, and we formulate a min-max problem for finding the smallest distortion achievable for the worst-case prior in the set, subject to a maximal leakage constraint. Lastly, we define a partial order on privacy mechanisms based on the largest distortion they generate. Our results show that when the prior distribution is known, the optimal privacy mechanism fully discloses symbols with the largest prior probabilities, and suppresses symbols with the smallest prior probabilities. Furthermore, we show that sets of priors that contain more uniform distributions lead to larger distortion, while privacy mechanisms that distribute the privacy budget more uniformly over the symbols create smaller worst-case distortion. A full version of this paper is accessible at: https://arxiv.org/pdf/2105.01033.pdf
Spherical Amphibian Robot Design with Novel Driving Principle

The advantage of spherical robots is utilized in the development of amphibian robots. It leverages the spherical advantage in terms of mobility and concealment to operate underwater. The main issue when developing the robot is that the robot is not conceal properly. Mainly when travel underwater. Robot that applied propeller as the actuator capable to conceal the equipment but different actuator needed for terrestrial locomotion. Therefore, a suitable combination of both terrestrial and underwater actuators that capable to perform multiple motion axis with minimal energy consumption is crucially needed. The objective of this paper is to represent a novel amphibian spherical robot design with its driving principle in terrestrial and underwater motion. The proposed spherical robot consists of 4 motor and 1 water pump. Terrestrial motion applied servo motor to roll the sphere in surge while yawing motion was control by a pendulum that rotates around the x-axis controlled by another servo motor. When travel underwater, surge motion was achieved by using the propeller and ballast will maintain the depth. To change the thrust direction, servo motor will rotate the sphere body to a certain degree which makes it possible to perform heave and surge motion at the same time. Diving and floating motion utilized the variable ballast and the propeller to optimize the robot energy usage and performance. The flow test shows that the resistance is higher when the size is bigger. The hydrodynamic forces act to the robot is the same in all directions. Therefore, the proposed robot should experience the same magnitude of noise in all directions when traveling underwater.
Acceptable set topic modeling

Topic modeling is a significant branch of natural language processing and machine learning focused on inferring the generative process of text. Traditionally, algorithms for estimating topic models have relied on Bayesian inference and Gibbs sampling. This paper proposes a novel “acceptable set” framework for formulating topic modeling problems inspired by ideas from discrete component analysis and data-driven robust optimization. Our approach not only simplifies the design and inference of topic models, but also allows for extensions and generalizations that are challenging to integrate into traditional approaches. Different restrictions (e.g., sparsity) and assumptions (e.g., alternative generative processes) can be easily incorporated into our formulations through additional or modified constraints. Our formulations also naturally control a widely used metric of solution quality, perplexity. We adapt state-of-the-art stochastic gradient methods to find good local optima for the optimization formulations. The algorithms are efficient, scaling to realistic problem sizes with runtimes comparable to existing methods. Through extensive computational experiments, we show that our methods have improved solution quality compared to baseline methods and reconstruct more reliably the underlying generative models. Our framework overcomes known vulnerabilities of traditional topic modeling algorithms: our methods are effective in low-data settings, register good out-of-sample performance, and perform well for a variety of initial assumptions on input parameter values.
A Study of Piano Timbre Teaching in the Context of Artificial Intelligence Interaction

This paper provides an in-depth analysis and research on piano timbre teaching in the context of artificial intelligence interaction, a bold vision of piano teaching, proposes a feasible solution in terms of teaching modules in intelligent piano teaching for senior teachers, and proposes an implementation path for the integration of intelligent piano and piano teaching from the four main blocks of piano teaching. Based on the multiplicative harmonic model of monophonic signal, combined with the variability of timbre characteristics, an audio synthesis model with editable timbre is proposed, and the experimental results show that editing the timbre parameters in the model can realize timbre modification, and the synthesized timbre conforms to the piano timbre characteristics. Based on the timbre analysis and the timbre synthesis model, a piano timbre library generation system is designed. The detailed design of the software modules such as audio file reading and writing, audio information analysis, timbre parameter acquisition, timbre synthesis, and simulated performance is given. The system can generate piano timbre libraries of different qualities flexibly and meet the requirements of timbre realism. The teaching experiment designed for teaching practice from solo teaching, and the practice target is first-year undergraduate students in the university, and the practice period is six weeks, and finally, the feasibility of intelligent piano teaching application is analysed by combining the experimental results. Through the teaching objectives, teaching content, and teaching methods, teaching environment reflects intelligent piano teaching to make up for the limitations of traditional piano teaching. Analyse the development trend of intelligent piano teaching in the context of artificial intelligence interaction, and explore the value of intelligent piano teaching.
A restaurant process with cocktail bar and relations to the three-parameter Mittag–Leffler distribution

Abstract In addition to the features of the two-parameter Chinese restaurant process (CRP), the restaurant under consideration has a cocktail bar and hence allows for a wider range of (bar and table) occupancy mechanisms. The model depends on three real parameters, $\alpha$ , $\theta_1$ , and $\theta_2$ , fulfilling certain conditions. Results known for the two-parameter CRP are carried over to this model. We study the number of customers at the cocktail bar, the number of customers at each table, and the number of occupied tables after n customers have entered the restaurant. For $\alpha&gt;0$ the number of occupied tables, properly scaled, is asymptotically three-parameter Mittag–Leffler distributed as n tends to infinity. We provide representations for the two- and three-parameter Mittag–Leffler distribution leading to efficient random number generators for these distributions. The proofs draw heavily from methods known for exchangeable random partitions, martingale methods known for generalized Pólya urns, and results known for the two-parameter CRP.
Center Extraction Algorithm of Linear Structured Light Stripe Based on Improved Gray Barycenter Method

In the online structured light 3D vision measurement, the most important step is the extraction of the laser stripe center. Due to the roughness of the detected object and the light source and other factors affect the precision of the linear structured light stripe center, an improved algorithm which can detection of laser fringe center based on gray barycentric method is proposed. The algorithm uses adaptive threshold segmentation method to extract strip targets, extract the skeleton of the target stripe by thinning method, use template method to detect the normal direction of laser stripes. Finally, using the gray barycentric method to accurately extract the laser center. It has been verified that the algorithm has the characteristics of high extraction accuracy, short running time and good stability.
Reinforcement learning for robotic manipulation using simulated locomotion demonstrations

Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrated that using sparse rewards, i.e. rewarding the agent only when the task has been successfully completed, can lead to better policies. However, state-action space exploration is more difficult in this case. Recent RL approaches to learning with sparse rewards have leveraged high-quality human demonstrations for the task, but these can be costly, time consuming or even impossible to obtain. In this paper, we propose a novel and effective approach that does not require human demonstrations. We observe that every robotic manipulation task could be seen as involving a locomotion task from the perspective of the object being manipulated, i.e. the object could learn how to reach a target state on its own. In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator. This policy is then used to generate auxiliary rewards, called simulated locomotion demonstration rewards (SLDRs), which enable us to learn the robot manipulation policy. The proposed approach has been evaluated on 13 tasks of increasing complexity, and can achieve higher success rate and faster learning rates compared to alternative algorithms. SLDRs are especially beneficial for tasks like multi-object stacking and non-rigid object manipulation.
Cross-language Code Coupling Detection: A Preliminary Study on Android Applications

Framework-based multi-lingual software is increasingly prevalent, but it also brings negative effects and extra burden on software maintenance and evolution, because of the introduced cross-language code coupling, which are usually mixed with framework-specific conventions. Researchers have proposed various approaches to code coupling detection, but there is still a lack of necessary support for cross-language coupling detection in framework-based software development. In this paper, we present a preliminary study about cross-language coupling detection in software development based on the Android application framework. We investigate the characteristics of multi-lingual changes in the top-100 starred open-source Android repositories on GitHub, and find that multi-lingual commits are non-trivial: their code changes are more scattered, and more inclined to introduce bugs than other commits. To mitigate the side-effect of multi-lingual development, we propose Grace, a Graph-based cross-language co-change suggestion approach for Android application development. Grace (a) designs a language-agnostic graph to represent code elements from different languages, and (b) employs an entity-based collaborative filtering algorithm to detect and rank candidates of cross-language code couplings, from the graph representation of the latest version as well as the historical multi-lingual commits of a repository. To evaluate the effectiveness of Grace, we apply it to the two tasks of cross-language co-change suggestion and inconsistency checking. Results show that Grace (a) can effectively suggest cross-language co-changed files and types, and (b) can also find existing and potential bugs or code smells caused by inconsistent co-changes.
Some aspects of critical information infrastructure risk management

The article proposes a descending functional framework for assessment and management risk of critical information infrastructures (CII), including defining the risk management scope, defining CII functions, analyzing the value chain of critical functions and interconnections, assessing the risk of critical functions, prioritizing and processing the risks of critical functions.
Generalized Structure for Adaptable Immersive Learning Environments

Immersive Learning Environments (ILEs) developed in Virtual and Augmented Reality (VR/AR) are a novel professional training platform. An ILE can facilitate an Adaptive Learning System (ALS), which has proven beneficial to the learning process. However, there is no existing AI-ready ILE that facilitates collecting multimedia multimodal data from the environment and users for training AI models, nor allows for the learning contents and complex learning process to be dynamically adapted by an ALS. This paper proposes a novel multimedia system in VR/AR to dynamically build ILEs for a wide range of use-cases, based on a description language for the generalizable ILE structure. It will detail users' paths and conditions for completing learning activities, and a content adaptation algorithm to update the ILE at runtime. Human and AI systems can customize the environment based on user learning metrics. Results show that this framework is efficient and low-overhead, suggesting a path to simplifying and democratizing the ILE development without introducing bloat.
A Precise Framework for Source-Level Control-Flow Analysis

This paper presents INTRACFG, a declarative and language-independent framework for constructing precise intraprocedural control-flow graphs (CFGs) based on the reference attribute grammar system JastAdd. Unlike most other frameworks, which build CFGs on an Intermediate Representation level, e.g., bytecode, our approach superimposes the CFGs on the Abstract Syntax Tree, enabling accurate client analysis. Moreover, INTRACFG overcomes expressivity limitations of an earlier RAG-based framework, allowing the construction of AST-Unrestricted CFGs: CFGs whose shape is not confined to the AST structure. We evaluate the expressivity of INTRACFG with INTRAJ, an application of INTRACFG to Java 7, by comparing two data flow analyses built on top of INTRAJ against tools from academia and from the industry. The results demonstrate that INTRAJ is effective at building precise and efficient CFGs and enables analyses with competitive performance.
Development of Dialogue Management System for Banking Services

Rapid increase in conversational AI and user chat data lead to intensive development of dialogue management systems (DMS) for various industries. Yet, for low-resource languages, such as Azerbaijani, very little research has been conducted. The main purpose of this work is to experiment with various DMS pipeline set-ups to decide on the most appropriate natural language understanding and dialogue manager settings. In our project, we designed and evaluated different DMS pipelines with respect to the conversational text data obtained from one of the leading retail banks in Azerbaijan. In the work, the main two components of DMS—Natural language Understanding (NLU) and Dialogue Manager—have been investigated. In the first step of NLU, we utilized a language identification (LI) component for language detection. We investigated both built-in LI methods such as fastText and custom machine learning (ML) models trained on the domain-based dataset. The second step of the work was a comparison of the classic ML classifiers (logistic regression, neural networks, and SVM) and Dual Intent and Entity Transformer (DIET) architecture for user intention detection. In these experiments we used different combinations of feature extractors such as CountVectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer, and word embeddings for both word and character n-gram based tokens. To extract important information from the text messages, Named Entity Extraction (NER) component was added to the pipeline. The best NER model was chosen among conditional random fields (CRF) tagger, deep neural networks (DNN), models and build in entity extraction component inside DIET architecture. Obtained entity tags fed to the Dialogue Management module as features. All NLU set-ups were followed by the Dialogue Management module that contains a Rule-based Policy to handle FAQs and chitchats as well as a Transformer Embedding Dialogue (TED) Policy to handle more complex and unexpected dialogue inputs. As a result, we suggest a DMS pipeline for a financial assistant, which is capable of identifying intentions, named entities, and a language of text followed by policies that allow generating a proper response (based on the designed dialogues) and suggesting the best next action.