Reviewerly/exHarmony huggingface dataset:
Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms (Extended Abstract)

The existence of an anomaly detection method that is optimal for all domains is a myth. Thus, there exists a plethora of anomaly detection methods which increases every year for a wide variety of domains. But a strength can also be a weakness; given this massive library of methods, how can one select the best method for their application? Current literature is focused on creating new anomaly detection methods or large frameworks for experimenting with multiple methods at the same time. However, and especially as the literature continues to expand, an extensive evaluation of every anomaly detection method is simply not feasible. To reduce this evaluation burden, we present guidelines to intelligently choose the optimal anomaly detection methods based on the characteristics the time series displays such as seasonality, trend, level change concept drift, and missing time steps. We provide a comprehensive experimental validation and survey of twelve anomaly detection methods over different time series characteristics to form guidelines based on several metrics: the AUC (Area Under the Curve), windowed F-score, and Numenta Anomaly Benchmark (NAB) scoring model. Applying our methodologies can save time and effort by surfacing the most promising anomaly detection methods instead of experimenting extensively with a rapidly expanding library of anomaly detection methods, especially in an online setting.
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract)

Current approaches for optimizing parameters in unrolled computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides the computation graph into a series of truncated unrolls, and performs an evolution strategies-based update step after each unroll. PES eliminates bias from these truncations by accumulating correction terms over the entire sequence of unrolls. PES allows for rapid parameter updates, has low memory usage, is unbiased, and has reasonable variance.
Nonsense videos teach AI physics

Algorithms that understand how objects obey physical laws could be more efficient
Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks

In this work, we propose an interactive platform to perform grammar-guided symbolic regression using a reinforcement learning approach from human-preference feedback. To do so, a reinforcement learning algorithm iteratively generates symbolic expressions, modeled as trajectories constrained by grammatical rules, from which a user shall elicit preferences. The interface gives the user three distinct ways of stating its preferences between multiple sampled symbolic expressions: categorizing samples, comparing pairs, and suggesting improvements to a sampled symbolic expression. Learning from preferences enables users to guide the exploration in the symbolic space toward regions that are more relevant to them. We provide a web-based interface testable on symbolic regression benchmark functions and power system data.
Document Clustering based on Phrase and Single Term Similarity using Neo4j

Document similarity generally rely on single term similarity such as cosine similarity. To achieve better document similarity, along with single term phrase- more informative feature can be used. To find out shared phrases across the corpus the Document Index graph (DIG) representation model is used. Document representation - DIG model incrementally construct the graph and simultaneously finds the shared phrase between current document and previously inserted documents from the graph. The similarity between documents is mainly depends on the number of shared phrases and single term similarity – known as hybrid similarity. The hybrid similarities are used with well- known density based clustering technique DBSCAN to assess their effect on quality of the clusters. Experimental results shows that hybrid similarity gives more accurate degree of document similarity and performs better cohesive clustering.
PENINGKATAN KEMAMPUAN GURU MADRASAH DALAM MEMBUAT APLIKASI ANDROID MELALUI PELATIHAN SELF-AWARENESS

This study aims to answer the challenges of teachers in increasing their competence in being creative during online learning. Because the Covid-19 pandemic condition requires an online learning process. One of the efforts made is to improve the competence of teachers in making android-based learning applications. The application used in this research is the one that refers to thunkable, which is one of the service providers for making android applications. This research was conducted through a one-shot case study approach with descriptive analysis. The results showed that the competence of madrasa teachers in making android-based applications increased after self-awareness-based training. The teacher's ability to increase both knowledge and skills is quite significant. This is indicated by a T-count of 0.072 with a level of trustworthiness of 95% which can be considered to have positive results on the implementation of training with a self-awareness approach. This ability indirectly has an impact on student motivation in online learning using teacher-made android-based applicationsKeywords: aplication based android, teacher ability, self-awareness
VMAgent: A Practical Virtual Machine Scheduling Platform

Virtual machine (VM) scheduling is one of the critical tasks in cloud computing. Many works have attempted to incorporate machine learning, especially reinforcement learning, to empower VM scheduling procedures. Although improved results are shown in several demo simulators, the performances in real-world scenarios are still underexploited. In this paper, we design a practical VM scheduling platform, i.e., VMAgent, to assist researchers in developing their methods on the VM scheduling problem. VMAgent consists of three components: simulator, scheduler, and visualizer. The simulator abstracts three general realistic scheduling scenarios (fading, recovering, and expansion) based on Huawei Cloud’s scheduling data, which is the core of our platform. Flexible configurations are further provided to make the simulator compatible with practical cloud computing architecture (i.e., Multi Non-Uniform Memory Access) and scenarios. Researchers then need to instantiate the scheduler to interact with the simulator, which is also pre-built in various types (e.g., heuristic, machine learning, and operations research) of scheduling algorithms to speed up the algorithm design. The visualizer, as an auxiliary component of the simulator and scheduler, facilitates researchers to conduct an in-depth analysis of the scheduling procedure and comprehensively compare different scheduling algorithms. We believe that VMAgent would shed light on the AI for the VM scheduling community, and the demo video is presented in https://bit.ly/vmagent-demo-video.
Research on Innovation strategies and best practices Model of Higher Education of biotechnology based on Hierarchical Ordered Probity

Aiming at the problem of low confidence in the traditional model of higher education innovation and reform, this paper designs a model of higher education of innovation and Reform Based on hierarchical ordered probity in biotechnology. By mining the data of higher education innovation and reform, deleting the redundant features of higher education innovation and reform data, filling in the data null and noise points, the data preprocessing of higher education innovation and reform is completed. Based on the hierarchical probity, the model parameters are estimated by calculating the core indicators of the model, and the functional relationship between the independent variables and the dependent variables in the model parameters is determined. The experimental results show that the confidence of the model based on hierarchical ordered probity is significantly higher than that of the control group, which can solve the problem of low confidence of the traditional higher education innovation model. The rate of return on schooling is steadily falling as social competitiveness heats up. As a result, the positive impact of education on well-being is lessened. This study used a bivariate ordered Probity model under the educational field theory to examine the effect of higher education innovation on social class and residents' satisfaction. Higher education reform and innovation will have an influence on people' satisfaction, according to these findings. People's subjective well-being may be considerably improved by increasing their education level, and there are major disparities between urban and rural areas. This demonstrates the need of educational reform and innovation strategies with best practices.
Evaluation of the Design of “Shape” and “Meaning” of Book Binding from the Perspective of Deep Learning

Book binding is the procedure of manually accumulating a book in codex format from a well-ordered pile of paper sheets, which are folded together into sections or occasionally left as a stack of individual sheets. The books undergo binding into different shapes and sizes. Numerous kinds of book bindings are available, each of which comes with its own merits and demerits. Some of them are highly durable, some of them are light-weight, and some of them are attractive. Therefore, it is needed to effectively identify and classify the shape and type of book bindings. With this motivation, this paper develops a butterfly optimization algorithm with a deep learning-enabled book binding classification (BOADL-BBC) model. The major intention of the BOADL-BBC technique is to identify and categorise three different types of book bindings from the input images, namely, hard binding, soft binding, and long-stitch binding. The proposed BOADL-BBC technique initially employs a DL-based Inception v3 model to derive useful feature vectors from the images. For effective classification of book bindings, the BOA with wavelet kernel extreme learning machine (WKELM) model can be applied. The weight and bias values involved in the WKELM model can be effectively adjusted by the use of BOA for book binding classification shows the novelty of the work. To ensure the enhanced performance of the BOADL-BBC technique, a series of simulations were carried out using a set of images that people collected on their own. The experimental results stated that the BOADL-BBC technique has obtained a maximum book binding classification accuracy of 95.56%.
Integration and Development of Enterprise Internal Audit and Big Data Based on Data Mining Technology

Auditing based on big data is the trend in the future audit development. First, the technical environment provides a technical support platform for continuous auditing. Through the development of information technology to promote the merger between financial services, the company’s business operations have been digitized, and the original paper audit is also facing changes. This article aims to study the integration and development of enterprise internal audit and big data based on data mining technology. To this end, this article proposes a big data audit system, improves and optimizes the clustering algorithm (key algorithm) of data mining, and designs experiments and analysis to explore its related effects and improved performance, so that it can be more suitable for the research topic. The experimental results of this article show that the improved big data audit system improves the resource perfection of internal audit by 17.4%. The improved algorithm’s accuracy rate has increased by 31.4%, and the best clustering ability has also been improved by 20.7%, which can be well applied to the company’s internal audit.
Open science practices in general and internal medicine journals, an observational study

Background As part of the Open Science movement, this study aims to analyze the current state of open access and open data policies concerning the availability of articles and raw data of the journals belonging to the category “Medicine, General &amp; Internal” of the Science Citation Index Expanded. Methods Journal data sharing policies were evaluated through the following variables: possibility of manuscript storage in repositories; reuse policy; publication on a website; and statement regarding complementary material. Subsequently, an analysis of the supplementary material associated with each article was performed through the PubMed Central repository. The study reported was assessed following the STROBE guidelines for observational studies. Results This study shows that only one-third of the journals included in the category “Medicine, General &amp; Internal” allow the depositing of their documents in repositories and its reuse, while approximately half of the journals agree to publish the document on a website as well as to deposit supplementary material along with the publication. However, the reality about this last variable is that only 9.5% of the articles analyzed contained supplementary material being the main journals involved, BMJ Open , JAMA Network Open , New England Journal of Medicine , Lancet and Plos Medicine . Conclusions The analysis of the opening policies of the journals concerning data availability in medical research reveals the unequal positioning of publishers towards the sharing of open data, the ambiguity regarding government policies about the obligation to deposit data and the need for ethical and standardization requirements in the typology/format of the data deposited without forgetting the important role that the researcher plays. Further studies based on journals indexed in medical databases other than Science Citation Index Expanded are needed.
MINING SOCIAL MEDIA CROWD TRENDS FROM THAI TEXT POSTS AND COMMENTS

Text mining from social media stream has attracted wide interests from both businesses and academics. Very large numbers of self-posts from crowd sources contains hidden trends, which can be valuable to a business enterprise. Crowd trend mining methods, together with an easily understood visual presentation, are thus in great demand. We present an approach to mining crowd trends from Thai text posts. A Thai language preprocessing module was necessary to transform continuous text into series of words. Our method could then mine general unforeseen crowd trends by using an automatic context extraction technique, tf-idf score and an aggregated opinion score calculated from automatically classified sentiments for each post or comment. The best sentiment classifier was chosen based on extensive experiments on the same data source. These scores were combined into one unified term popularity which was visualized as a word cloud on a web application. A case study used a popular Thai discussion website - Pantip.com - and achieved three interwoven desired goals: (1) extraction of general and unforeseen crowd trends from a Thai discussion website, (2) assigning unified popularity scores to each candidate term and (3) presenting those terms to end users in an easily comprehended form.
Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

In order to improve the anomaly detection ability of portable multidimensional control software test data, a software test data anomaly detection method based on K-means clustering is proposed. The abnormal data distribution structure model of portable multidimensional control software testing is constructed. The fuzzy semantic feature reconstruction method is adopted to identify the fuzzy parameters of portable multidimensional control software and extract the feature quantity of associated information. According to the evolution distribution of associated features, the joint combination feature analysis method is adopted to realize the fuzzy clustering center detection of abnormal data of portable multidimensional control software test, and the fusion of abnormal feature distribution is carried out to complete the joint multidimensional feature detection. The K-means clustering method is used for effective data combination control in portable multidimensional control software to extract and detect abnormal features of test data in portable multidimensional control software. Experimental results show that the accuracy of anomaly detection of software test data by the proposed method is always greater than 0.8. Conclusion. Using this method to detect abnormal data of portable multidimensional control software test has higher accuracy and better detection performance.
Quantum strategy of population initialization in genetic algorithm

Quantum Genetic Algorithm is a relatively new field of study to enhance the computational efficiency of the Darwinian optimization process in genetic algorithms with quantum speedup techniques. This paper introduces an application strategy of the quantum counting algorithm to genetic algorithms, particularly aimed to enhance the initial population setup at the beginning of optimization. More specifically, our goal is to exploit a quantum algorithm to count the number of marked items from an unstructured list quadratically faster than classical algorithms in order to detect the presence and amount of unsuitable individuals in a stochastically generated initial population, thereby starting optimization with a mark of potential to improve the performance in the later stage. The advantage of our method is examined via a conventional genetic algorithm to solve the 0-1 Knapsack problem with varying cases of the constraints, and a comparative analysis on the optimizing performance is made accordingly.
Adapting mutation and recombination operators to range-aware relations in real-world application data

Optimisation problems with higher-dimensional search spaces do usually not only come with equality or inequality constraints, but also with dependencies between the different variables. In real-world applications, especially in experimental data from material sciences, these relations as well as the constraints may not be true for the entire search space, but only for certain areas. Other constraints or relations may hold then for different areas of the search space. We build on correlation-aware mutation and recombination operators that are used in genetic algorithms and adjust them to be able to deal with area-specific constraints and relations. This can be configured by a domain expert using a domain-specific language. Our approach is evaluated with well-known benchmark functions, carefully designed distributions, and data description files and shows a better capability of generating feasible solutions in the population.
Quality-diversity optimisation

tutorial Share on Quality-diversity optimisation Authors: Antoine Cully Imperial College London, London, UK Imperial College London, London, UKView Profile , Jean-Baptiste Mouret Université de Lorraine, France Université de Lorraine, FranceView Profile , Stéphane Doncieux Sorbonne Université, Paris Sorbonne Université, ParisView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 864–889https://doi.org/10.1145/3520304.3533637Published:19 July 2022Publication History 0citation54DownloadsMetricsTotal Citations0Total Downloads54Last 12 Months54Last 6 weeks12 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A bi-level evolutionary approach for the multi-label detection of smelly classes

This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.
Object-Level Targeted Selection via Deep Template Matching

Retrieving images with objects that are semantically similar to objects of interest (OOI) in a query image has many practical use cases. A few examples include fixing failures like false negatives/positives of a learned model or mitigating class imbalance in a dataset. The targeted selection task requires finding the relevant data from a large-scale pool of unlabeled data. Manual mining at this scale is infeasible. Further, the OOI are often small and occupy less than 1% of image area, are occluded, and co-exist with many semantically different objects in cluttered scenes. Existing semantic image retrieval methods often focus on mining for larger sized geographical landmarks, and/or require extra labeled data, such as images/image-pairs with similar objects, for mining images with generic objects. We propose a fast and robust template matching algorithm in the DNN feature space, that retrieves semantically similar images at the object-level from a large unlabeled pool of data. We project the region(s) around the OOI in the query image to the DNN feature space for use as the template. This enables our method to focus on the semantics of the OOI without requiring extra labeled data. In the context of autonomous driving, we evaluate our system for targeted selection by using failure cases of object detectors as OOI. We demonstrate its efficacy on a large unlabeled dataset with 2.2M images and show high recall in mining for images with small-sized OOI. We compare our method against a well-known semantic image retrieval method, which also does not require extra labeled data. Lastly, we show that our method is flexible and retrieves images with one or more semantically different co-occurring OOI seamlessly.
A multi-objective evolutionary feature selection approach for the classification of multi-label data

There has been a growing need to resolve the Curse Of Dimensionality (COD) in multi-label data in recent years, which has attracted much attention for Feature Selection (FS). The multi-label FS method has gotten a lot of interest since it can considerably increase classification accuracy by selecting important features. Here, we have designed a binary version of multi-objective FS approach for Multi-Label Classification (MLC) based upon Whale Optimization Algorithm (WOA). Instead of a random search in WOA, we have applied the tournament search for the selection of a new whale. Tournament Selection (TS) entails holding a series of “tournaments” among a select group of people randomly from the general population. Here the multi-objective criteria consist of two objectives; where the first objective is to maximize the Jaccard similarity, and the another is to reduce the selected features. To check the robustness of the proposed method, we have used multi-label datasets from different areas. We added a comparative analysis of the proposed methodology with various traditional machine learning and multi-label classifiers. Empirical results on widely used multi-label datasets show that proposed FS achieves competitive performance, especially when labels are limited.