Reviewerly/exHarmony huggingface dataset:
Applications of dynamic parameter control in evolutionary computation

tutorial Open AccessApplications of dynamic parameter control in evolutionary computation Author: Gregor Papa Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 1064–1088https://doi.org/10.1145/3449726.3461435Published:08 July 2021Publication History 0citation170DownloadsMetricsTotal Citations0Total Downloads170Last 12 Months69Last 6 weeks14 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 SiteeReaderPDF
An investigation of solutions for handling incomplete online review datasets with missing values

Online review helpfulness prediction is an important research issue in electronic commerce and data mining. However, the collected datasets used for the analysis and prediction of the helpfulness of online reviews often contain some missing attribute values, such as reviewer background and rating information. In related literatures, many studies have either used the case deletion approach to remove the data containing missing values or considered the imputation of missing values by the mean/mode method. However, none of them consider the direct handling approach without missing value imputation for online review datasets by decision tree-related techniques. Therefore, in this paper, we investigate the suitability of different types of approaches to solve the incomplete dataset problem of online reviews. Specifically, for missing value imputation, several supervised learning techniques including MICE, KNN, SVM, and CART are examined. Moreover, for the direct handling approach without missing value imputation, CART is also performed for this task. The experimental results based on the TripAdvisor dataset for review helpfulness prediction show that the approach where incomplete online review datasets are handled directly without imputation by CART significantly outperforms the other approaches, including case deletion and missing value imputation approaches.
Efficient Implementations of Sieving and Enumeration Algorithms for Lattice-Based Cryptography

The security of lattice-based cryptosystems is based on solving hard lattice problems such as the shortest vector problem (SVP) and the closest vector problem (CVP). Various cryptanalysis algorithms such as (Pro)GaussSieve, HashSieve, ENUM, and BKZ have been proposed to solve these hard problems. Several implementations of these algorithms have been developed. On the other hand, the implementations of these algorithms are expected to be efficient in terms of run time and memory space. In this paper, a modular software package/library containing efficient implementations of GaussSieve, ProGaussSieve, HashSieve, and BKZ algorithms is developed. These implementations are considered efficient in terms of run time. While constructing this software library, some modifications to the algorithms are made to increase the performance. Then, the run times of these implementations are compared with the others. According to the experimental results, the proposed GaussSieve, ProGaussSieve, and HashSieve implementations are at least 70%, 75%, and 49% more efficient than previous ones, respectively.
Treatment Recommendations for COVID-19 Patients along with Robust Explanations

The global response to the pandemic introduced by COVID-19 is unprecedented.Scientists develop methods, which analyze data to identify an effective treatment that uncovers possible responses to the SARS-COV-2 virus.However, our global response should be based on knowledge exchange and collaboration among countries.In this paper, we present a recommender system for treatment recommendations, which exploits similar patterns among patients of different clinical studies, and recommends them health interventions (such as to provide oxygen therapy) and drugs (e.g., Remdesivir) based on their symptoms' or diseases' similarity with patients of other similar clinical studies.Our approach can also provide explanations along with recommended treatments to assist doctors in understanding the reasons behind a suggested drug or health intervention.We also perform experiments to identify the effectiveness of our system in terms of recommendation accuracy.Our results demonstrate that our system is able to minimize the false positive and false negative prediction rates.Finally, we provide web links to download both (i) our program's setup file and (ii) our Neo4j database file.
Reinforcement learning for dynamic optimization problems

Many real-world applications require optimization in dynamic environments where the challenge is to find optima of a time-dependent objective function while tracking them over time. Many evolutionary approaches have been developed to solve Dynamic Optimization Problems (DOPs). However, there is still a need for more efficient methods. Recently, a new interesting trend in dealing with optimization in dynamic environments has emerged toward developing new Reinforcement Learning (RL) algorithms that are expected to give a new breath in DOPs community. In this paper, a new Q-learning RL algorithm is developed to deal with DOPs based on new deifined states and actions that are mainly inspired from Evolutionary Dynamic Optimization (EDO) aiming appropriate exploitation of the strengths of both RL and EDO techniques to handle DOPs. The proposed RL model has been assessed using modified Moving Peaks Benchmark (mMPB) problem. Very competitive results have been obtained and good performance has been achieved compared with other dynamic optimization algorithm.
Feature Selection in Mobile Activity Recognition: A Comparative Study

Mobile sensor-based activity recognition is a growing research field with important applications areas, such as healthcare and well-being. Data collected from multiple sensors, including smartphone sensors that are now ubiquitous, can be exploited to build predictive models capable of recognizing actions and activities performed by humans in their daily life. This involves several processing steps, from the cleansing of raw data to the extraction of suitable features and the induction of proper classifiers. Dimensionality reduction techniques may also be important for the efficiency and the exploitability of the induced models, especially when dealing with multi-sensor data leading to high-dimensional feature vectors. In such a scenario, feature selection algorithms can be very useful to identify and retain only the most informative and predictive features. However, little research has so far investigated which selection approaches may be most appropriate in sensor-based activity recognition tasks. To give a contribution in this direction, our paper compares the performance of different feature selection methods, both univariate and multivariate, on a public domain benchmark containing smartphone sensor data. We performed a comprehensive evaluation considering the extent to which each method effectively identifies the most predictive features and the overall stability of the selection process, i.e., its robustness to changes in the input data. Our results give interesting insight on which methods may be most suited in this domain, showing that it is possible to significantly reduce the data dimensionality without compromising the activity recognition performance.
AI vs AI (Augmenting [Human] Intellect vs Artificial Intelligence) : Plenary Talk

Almost six decades ago, when proposing the scientific programme of Stanford Research Institute (SRI), Engelbart (1962) stated: “...By augmenting human intellect we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems.” More than twenty-five years, later Engelbart and Lehtman (1988) noticed that “In the optimum design [of a CSCW system], either a tool system or a human system is dependent on the match it must make with the other. The high degree of dependence implies that balanced co-evolution of both is necessary.” This paper aims at presenting how the usage of AI (Artificial Intelligence) has evolved towards a trustworthy discipline and set of tools and facilitated the augmenting the human intellect in order to enable the human to approach and solve complex problems of the day.
Research on Improved Intelligent Generative Dialogue Algorithm Based on Knowledge Graph

Abstract The rise of a new generation of artificial intelligence technology, represented by deep learning, has promoted the vigorous development of natural language processing technology. As a typical application of natural language processing technology, human-machine intelligent dialogue system, coupled with its commercial value in the fields of voice assistants and chat robots, has become a hot topic in the current academic and industrial circles. In the past, the responses generated by intelligent dialogue robots is single and universal, and even the content is inappropriate. Therefore, this study proposes to use the knowledge graph as the background knowledge when the dialogue model generates the responses[1]. In order to meet the needs of users more closely, it also proposes to introduce the user information participating in the dialogue and the dialogue scene into the model. The model is trained and evaluated on the DuRecDial public dataset, and the optimized model is compared with the original model. The experimental results show that the model with these two modules has better effect than the original model, especially in the generation index, the F1, BLEU2 and DIST-2 indexes have been improved by 0.91%, 0.5% and 0.9% respectively.
Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery

In this article, we consider a version of the challenging problem of learning from datasets whose size is too limited to allow generalisation beyond the training set. To address the challenge, we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects. In this study, the objects were smartphone photographs of near-complete Roman terra sigillata pottery vessels from the collection of the Museum of London. Taking the replicated features from published profile drawings of pottery forms allowed the integration of expert knowledge into the process through our synthetic data generator. After this first initial training the model was fine-tuned with data from photographs of real vessels. We show, through exhaustive experiments across several popular deep learning architectures, different test priors, and considering the impact of the photograph viewpoint and excessive damage to the vessels, that the proposed hybrid approach enables the creation of classifiers with appropriate generalisation performance. This performance is significantly better than that of classifiers trained exclusively on the original data, which shows the promise of the approach to alleviate the fundamental issue of learning from small datasets.
FaceRecGlasses: A Wearable System for Recognizing Self Facial Expressions Using Compact Wearable Cameras

Facial expression images are useful for life-logging because they represent one’s emotions and mental state. However, using a wearable system to capture facial expressions from the front of the user obstructs the field of view. In this paper, we propose FaceRecGlasses, a compact wearable system that constantly records the user’s face and surroundings as images and indexes them with emotions without obstructing the user’s daily life. Our eyeglass-shaped device comprises two compact cameras and three mirrors, which enable the user’s facial expressions and surroundings to be captured constantly. The proposed system outputs a real-time pseudo-face-image by combining the captured facial components with a stored base-face-image. We investigated suitable facial areas to be captured by the proposed system and evaluated the recognition accuracy of the facial expressions captured. The results confirmed accuracies of 87.5%, 66.7%, and 71.4% for neutral emotion, happiness, and surprise, respectively, indicating that this method reproduces these three facial expressions to a high degree and can be applied to indexing of facial images.
Aesthetic Evaluation of Card Game Interface Based on Analytical Hierarchy Process

To explore the card game interface layout's aesthetic, some of the most popular card games worldwide were selected as examples. The six aesthetic indexes were selected and quantified, including balance, symmetry, order, cohesion, simplicity, and unity. Then AHP (analytic hierarchy process) was introduced to determine the weight of each indicator. Finally, a comprehensive aesthetic evaluation method of interface layout was obtained and proved to be effective. This method can provide designers with guidance and suggestions for the selection and improvement of interface design schemes.
USING THE POTENTIAL OF INFORMATION TECHNOLOGIES IN A PERSONALITY'S CIVIC EDUCATION

In the modern world digitalization increasingly covers various spheres of society. The importance of digitalization has increased due to the pandemic and the quarantine measures introduced in this regard. These circumstances have emphasized the need for digitalization of education. But, besides teaching, there is educational activity, as well. The article shows the ways and means of using the educational potential of information technologies in the educational process. Using the potential of information technology in civic education of students have been emphasized. Therefore, at present, there is a need for research on civic education in the context of digitalization, as well as optimization of organizational and substantive issues in this direction. The research problem is to reveal the actual aspects of using the potential of information technology in a personality's civic education. The purpose of the research is to reveal the topical issues of using the educational potential of information technologies in the formation of individual citizenship in modern conditions. The research methods used are analysis, synthesis, generalization. The research results are the following: digital technologies (EIOS, online courses, etc.) used in civic education and contributing to the transformation of the educational process have been described; the possibilities of using digital technologies in the development of a personality's general culture have been disclosed; the digital transformation of the educational process and the principles of civic education in educational institutions in the context of digitalization have been described; examples of the use of information technology in civic education have been presented (virtual museum, video podcasts, civic education portal, the use of mobile phones to raise students' awareness).
Augmented Foot: A Comprehensive Survey of Augmented Foot Interfaces

Augmented foot interfaces have been studied since the beginning of wearable computers. The worlds’ first wearable computer was an instrumented shoe that consisted of a toe operated switch with a wireless module. Since then, academic research and commercial products on augmented foot interfaces are booming with novel interfaces every year. This paper surveys the body of work on augmented foot interfaces and shows the current trends and guidelines for future augmented foot interfaces. We contribute a classification of over 100 academic papers and commercially-available products. We discuss the integration of augmented foot interfaces, interaction schemes and application domains. Finally, we contribute a set of design considerations to scaffold future research of augmented foot interfaces based on the classification and inspired by the surveyed work.
A multimethod approach to multimodal function optimization

Multimodal functions play a central role in artificial intelligence. In this paper we attempt to address limitations in existing research on multimodal function optimization by developing a novel multi-method memetic algorithm (MMA). We empirically test MMA on synthetic and natural combinatorial optimization problems, including feature selection. Our initial experiments suggest that MMA preserves diversity well and consistently finds good solutions.
Action Recognition based on Human Pose Estimation

In computer vision, human pose estimation and the following action recognition are active research topics, which have a wide application in human computer interaction, smart homes, athlete assistance training, etc. Driven by these applications, numerous algorithms and models are proposed in the recent years, among which deep learning becomes dominant. In this study, we summarize the recent progress of deep learning in human pose estimation and action recognition. We also point out some research challenges and future research directions.
Using Context Clues in Determining Contextual Meaning of Arabic Words

Language context plays an important role in determining the meaning of a word. To understand its meaning, it is imperative that context be considered. Today, many students have difficulty understanding the meaning of Arabic words as they are unable to understand the context. However, words, phrases or sentences in a text can serve as a guide or clue to understand the meaning of unknown words. This is termed as context clues. This study aims to identify how context clues help students determine the contextual meaning of an Arabic word. Their understanding process is analysed from the critical discourse analysis perspective using a three-dimensional model introduced by Fairclough (1989). This study was conducted using a questionnaire and interviews with twenty undergraduates studying Arabic language from five Malaysian universities. Five Arabic words were selected from five news texts quoted from the Arab news portal al-Ra’i (http://alrai.com/); covering sports, economics, politics, social and technology. The words selected have many meanings, depending on the context of the sentence. Students must determine the meaning contextually by stating the context clues in the text that guided them. The findings show that context clues can help students understand the meaning of Arabic words and enhance their understanding based on its sentence context. References
Architecture &amp; System Design of TERRA+: The wine production use case

The paper presents the architectural design and the technical details of the TERRA+ platform, an Ethereum based Blockchain infrastructure aiming to address the lack of confidentiality among consumers and producers in the agrifood sector. TERRA+ first application on the wine supply chain is presented; aiming to facilitate winemakers to build their brand's integrity via employing blockchain technology as the driving force of the proposed solution. Particularly, the presented approach, empowered by blockchain technology, is expected to (i) increase food confidence by leveraging blockchain technology and IoT, (ii) support brand integrity by providing all involved parties with data insights towards unique characteristics acquired from the vineyard origin and the producers and (iii) digitize food business models by allowing consumers and/or 3rd party services to capture robust data collections about each product.
Workload-Aware Placement Strategies to Leverage Disaggregated Resources in the Datacenter

Disaggregation of resources is a datacenter strategy that aims to decouple the physical location of resources from the place where they are accessed, as opposed to physically attached devices connected to the Peripheral Component Interconnect Express (PCIe) bus. By attaching and detaching resources through a fast interconnection network, it is possible to increase the flexibility to manage datacenter infrastructures while keeping the performance of the pooled and disaggregated devices. This article introduces workload scheduling and placement policies for environments with disaggregated memories. These policies are driven by accurate prebuilt performance degradation models. We focus on the use of nonvolatile memory to store data and/or to provide memory extensions. Following a software-defined approach, persistent memories are combined to provide higher capacity and/or bandwidth devices, or used by multiple workloads to increase the number of running workloads. Different combinations of workloads and associated soft deadlines are used to evaluate the placement policies using a system simulator. When using the first-fit policy, results show that a disaggregated system can reduce missed deadlines up to 49% when compared to a physically attached system. When our proposed policy with workload awareness is enabled in a disaggregated system, missed deadlines can be reduced up to 100% (no deadlines missed).
IFP-ADAC: A Two-stage Interpretable Fault Prediction Model for Multivariate Time Series

Fault prediction is critically important for many mobile equipments such as vehicles, ships and spacecrafts. Sensors deployed on these equipments continuously collect the status data, which are usually multivariate time series data. It is challenging to accurately predict the failure of the equipments based on the generated multivairate time series due to the complex correlations among the variables and the dynamic operation conditions. Though many methods have been proposed, they are not effective to provide an interpretable and accurate fault prediction result. This paper proposes a two-stage Interpretable Fault Prediction method based on Anomaly Detection and Anomaly Accumulation, called IFP-ADAC. Specially, we first design an anomaly detection module based on Generative Adversarial Nets due to the lack of samples. The generator captures the correlations among multiple variables and the temporal dependency within each variable jointly. Second, we design an anomaly accumulation model based on LSTM to capture the anomaly growth pattern, and the attention mechanism has been introduced to consider the severity of the detected anomalies. Compared with the end- to-end methods, our two-stage fault prediction method based on anomaly detection and accumulation has better interpretability. Extensive experiments conducted on two real-world datasets show the superior performance of our method.
Imbalanced Toxicity Prediction Using Multi-Task Learning and Over-Sampling

Chemical compound toxicity prediction is a challenge learning problem that the number of active chemicals obtained for toxicity assays are far smaller than the inactive chemicals, i.e. imbalanced data. Neural Networks learned from these tasks with imbalanced data tend to misclassify the minority samples into majority samples. In this paper, we propose a novel learning method that combine multi-task deep neural networks learning with over-sampling method to handle imbalanced data and lack of training data problems of toxicity prediction. Over-sampling is a kind of re-sampling method that tackle the class imbalance problem by replicating the minority class samples. For each toxicity prediction task, we apply over-sampling method on training set to generate synthetic samples of the minority class to balance the training data. Then, we train the multi-task deep neural network on the tasks with balanced training set. Multi-task learning can share common information among tasks and the balanced data set have larger number of training data that benefit the multi-task deep neural networks learning.Experiment results on tox21 toxicity prediction data set shows that our method significantly relieve imbalanced data problem of multi-task deep neural networks learning and outperforms multi-task deep neural network method that without over-sampling and many other computational approaches like support vector machine and random forests.