Reviewerly/exHarmony huggingface dataset:
Evolutionary Computation and Genetic Programming

This chapter introduces the basic concepts of evolutionary computation. It briefly introduces representative evolutionary computation techniques, then describes the basics of genetic programming, including representation, functions, terminals, population initialisation, genetic operators, and strongly typed genetic programming, in detail. Finally, it reviews typical works on genetic programming for feature learning.
PENGARUH KEBIASAAN BELAJAR TERHADAP HASIL BELAJAR MATEMATIKA

THE EFFECT OF HABIT LEARNING TOWARD MATHEMATICS LEARNING OUTCOMES This research is a quantitative research with a survey approach conducted on fourth grade students of Sukasari State Elementary School, Bogor City, 2020/2021 school years. The populations are 93 student and sample 48 student. The data in this study were obtained throungh an instrument in the for learning habits variables and a test instrument in the form multiple choice questions for variable mathematics learning outcomes. This is based on statistical analysis which produces path coefficients (r_xy) in the amount of 0,335 and the pattern Y = 43,85 + 0,236x. From the level of influence is low and obtained t_count 2,713 greater than t_table1,990 the real level 0,05 significant. Based on the results of the study, it can be concluded that there is a positive influence between the learning habits on the mathematics learning outcomes of fourth grade student.
Simple and Effective Approaches for Uncertainty Prediction in Facial Action Unit Intensity Regression

Knowing how much to trust a prediction is important for many critical applications. We describe two simple approaches to estimate uncertainty in regression prediction tasks and compare their performance and complexity against popular approaches. We operationalize uncertainty in regression as the absolute error between a model's prediction and the ground truth. Our two proposed approaches use a secondary model to predict the uncertainty of a primary predictive model. Our first approach leverages the assumption that similar observations are likely to have similar uncertainty and predicts uncertainty with a non-parametric method. Our second approach trains a secondary model to directly predict the uncertainty of the primary predictive model. Both approaches outperform other established uncertainty estimation approaches on the MNIST, DISFA, and BP4D+datasets. Furthermore, we observe that approaches that directly predict the uncertainty generally perform better than approaches that indirectly estimate uncertainty.
A Multifunctional Unit For Reverse Conversion and Sign Detection Based on The 5-Moduli Set

The high dynamic range residue number system (RNS) five-moduli { 2 2n , 2 n + 1, 2 n − 1, 2 n + 3, 2 n − 3 } has been recently introduced as an arithmetically balanced five-moduli set for computation-intensive applications on wide operands such as asymmetric cryptography algorithms. The previous dedicated design of RNS components for this moduli set is just an unsigned reverse converter. In order to utilize of the moduli set { 2 2n , 2 n + 1, 2 n − 1, 2 n + 3, 2 n − 3 } in applications handling with signed numbers, two important components are needed: Sign Detector and Signed Reverse Converter. However, having both of these components results in high hardware requirements which makes RNS impractical. This paper overcomes to this problem by designing a unified unit which can perform both signed reverse conversion as well as sign detection through the reuse of hardware. To the authors knowledge, this is the first attempt to design sign detector for a moduli set including 2n±3 moduli. In order to achieve a hardware-amenable design, we first improved the performance of the previous unsigned reverse converter for the moduli set { 2 2n , 2 n + 1, 2 n − 1, 2 n + 3, 2 n − 3 }. Then, we extract a sign detection method from the structure of the reverse converter. Finally, we make the unsigned reverse converter to sign converter through the use of the extracted sign signal from the reverse converter. The experimental results shown that the proposed multifunctional unit has relatively the same performance in terms of area, delay and power-consumption than the previous unsigned reverse converter for the set { 2 2n , 2 n + 1, 2 n − 1, 2 n + 3, 2 n − 3 } while it can perform two complex signed operations.
Web document classification using topic modeling based document ranking

In this paper, we propose a web document ranking method using topic modeling for effective information collection and classification. The proposed method is applied to the document ranking technique to avoid duplicated crawling when crawling at high speed. Through the proposed document ranking technique, it is feasible to remove redundant documents, classify the documents efficiently, and confirm that the crawler service is running. The proposed method enables rapid collection of many web documents; the user can search the web pages with constant data update efficiently. In addition, the efficiency of data retrieval can be improved because new information can be automatically classified and transmitted. By expanding the scope of the method to big data based web pages and improving it for application to various websites, it is expected that more effective information retrieval will be possible.
A Hybrid Neural Collaborative Filtering Model for Drug Repositioning

Traditional drug development is a time-consuming, high-cost and high-risk process. Computational drug repositioning has become an important strategy to discover new indication for existing drugs due to its remarkable reduction in time, cost and risk. Related studies have indicated that data integration could be helpful to discover novel indications of drugs. However, how to adopt effective system framework to represent and integrate data from different data sources remains a challenging problem. In this study, we propose a computational approach named BioHNCFR, which employs a hybrid neural collaborative filtering framework combined with data integration to predict the potential drug indications. The experimental results on benchmark data set reveal that the performance of our model is improved in predict drug-diseases associations compared to the recent state-of-the-art approaches when evaluated by 10-fold cross validation. The AUC, AUPR on 10-fold cross validation, 93.24%, 39.89%. The HitRate@l reach to 45.54%. More comprehensive experiments show that our model can predict indications for new drugs.
Improved JPEG image watermarking in data compression domain using block selection strategy

Nowadays, the joint photographic experts group (JPEG) is still the most dominant format among various digital image formats used in our daily life.Therefore, watermarking JPEG images is important and useful for many applications such as archive management and image authentication.Along with the embedding capacity and visual quality that must be considered for uncompressed images, the storage size of the watermarked JPEG image should be also considered.In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we propose a new JPEG image watermarking in the data compression domain, in which only coefficients with values 1 and -1 are expanded to carry message bits, coefficients with values 2 and -2 are shifted, and all the others coefficients remain unchanged.Moreover, a block selection strategy based on the number of 1 and -1 coefficients in each 8×8 block is proposed, which can be utilized to adaptively choose DCT coefficients for data embedding.Experimental results demonstrate that by using the proposed method, we can easily realize high embedding capacity and good visual quality.Furthermore, the storage size increase for the watermarked JPEG image is well lessened.
Evolutionary Multilabel Classification Algorithm Based on Cultural Algorithm

As one of the common methods to construct classifiers, naïve Bayes has become one of the most popular classification methods because of its solid theoretical basis, strong prior knowledge learning characteristics, unique knowledge expression forms, and high classification accuracy. This classification method has a symmetry phenomenon in the process of data classification. Although the naïve Bayes classifier has high classification performance in single-label classification problems, it is worth studying whether the multilabel classification problem is still valid. In this paper, with the naïve Bayes classifier as the basic research object, in view of the naïve Bayes classification algorithm’s shortage of conditional independence assumptions and label class selection strategies, the characteristics of weighted naïve Bayes is given a better label classifier algorithm framework; the introduction of cultural algorithms to search for and determine the optimal weights is proposed as the weighted naïve Bayes multilabel classification algorithm. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in classification performance.
Counter-measures to spoofing and jamming of drone signals

Technological revolution has done wonders in Aeronautics. With the evolution Unmanned Aerial vehicle, not only the military sector but also the commercial and industrial sectors use drones for various purposes. The global Unmanned Aerial Vehicle (UAV) market size is projected to reach $133.5 billion by 2026, from $25.9 billion in 2019, at a compound annual growth rate of 26.4% during 2021-2026. As the technology usage increases, its security plays a vital role in research. The idea behind this paper is to discuss definite and efficient countermeasures to spoofing and jamming of drone signals. The detection and mitigation of spoofing and jamming using new cyber security techniques including blockchain, deep learning using multiclass SVM, Co-operative localisation, Hardware sandboxing and Visual Odometry are explored. It also presents a deep analysis of all techniques along with the pros and cons of each method.
Similarity-based health risk prediction using Domain Fusion and electronic health records data

Electronic Health Record (EHR) data represents a valuable resource for individualized prospective prediction of health conditions. Statistical methods have been developed to measure patient similarity using EHR data, mostly using clinical attributes. Only a handful of recent methods have combined clinical analytics with other forms of similarity analytics, and no unified framework exists yet to measure comprehensive patient similarity. Here, we developed a generic framework named Patient similarity based on Domain Fusion (PsDF). PsDF performs patient similarity assessment on each available domain data separately, and then integrate the affinity information over various domains into a comprehensive similarity metric. We used the integrated patient similarity to support outcome prediction by assigning a risk score to each patient. With extensive simulations, we demonstrated that PsDF outperformed existing risk prediction methods including a random forest classifier, a regression-based model, and a naïve similarity method, especially when heterogeneous signals exist across different domains. Using PsDF and EHR data extracted from the data warehouse of Columbia University Irving Medical Center, we developed two different clinical prediction tools for two different clinical outcomes: incident cases of end stage kidney disease (ESKD) and severe aortic stenosis (AS) requiring valve replacement. We demonstrated that our new prediction method is scalable to large datasets, robust to random missingness, and generalizable to diverse clinical outcomes.
WITHDRAWN: Gesture recognition smart glove for speech impaired people

In this world, compared with ordinary people numerous speech impaired people were facing a lot of problems in communication. Likewise, it is difficult for normal people also to interact with muted people. Various sensor-based techniques were developed earlier but it did not provide widespread solution for communication between them. Here method of using hand gesture to communicate with them without sensors are introduced. Images of hand gesture will be captured by using web cam and it should be displayed in the screen. It helps in some ways like switch off the electric items such as lights, fans and it helps to on the light only if there is presence of person. It includes actuators for appliance, sensors to follow controller, user interface used here is raspberry pi microcontroller which helps for relay communication. Hence proposed system helps to provide output of speech and text from hand gesture. It will be displayed on the screen, email and SMS will be sent to linked persons.
Hybrid Framework for Named Entity Recognition in Turkish Social Media

Named Entity Recognition (NER) is a task of extracting entities such as person, location, and organization from texts. NER is more challenging in the social media texts compared to the formal texts due to the noisy language including grammatical errors and abbreviations. However, the problem of NER in the social media gained significant attention in the literature due to the amount of information flow in the social media. In this paper, we propose a comprehensive model for NER in Turkish texts of distinct social media domains, i.e. Twitter, Facebook, and Donanimhaber Forum. The model employs Conditional Random Fields followed by Bidirectional Long Short Term Memory. To overcome the challenges of social media texts, we incorporate word embeddings, character representations, morphology, domain information, pattern-matching, dictionary, part-of-speech, and casing based features to our model. We perform ablation studies to analyze the effect of these features. We demonstrate the success of our model for tagging Turkish social media texts through the largest Turkish NER database.
Weights Estimation in the Completeness Measurement of Bibliographic Metadata

The weighted completeness metrics for metadata use a weighting factor to indicate the importance of each field. In the case of bibliographic metadata, a common way of representing the importance of a field is its frequency of appearance in a given repository. The inaccuracy of this method is why we need to recalculate the weights as the volume of the repository grows. In this paper, we used the Particle Swarm Optimization (PSO) method in the estimation of the weights for the completeness metrics of bibliographic metadata. This method is independent of the metadata format, of the collection and the volume of the repository used. As part of this work, we defined the fitness function of the PSO method to reflect the importance levels of the fields. Finally, we presented a case study with the estimated weights and the calculated completeness of the bibliographic records described at the full cataloging level in MARC 21 format.
Performance Optimization of Cloud Data Centers with a Dynamic Energy-Efficient Resource Management Scheme

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.
Contrast Enhancement Technique using Discrete Wavelet Transform with Just Noticeable Difference Model for 3D Stereoscopic Degraded Video

The Video Technologies for Medical, cultural, and social activities prefer 3D visual data rendering and processing. So 3D videos are captured by any capturing devices, like the digital cameras are not acceptable all the time due to the lack of capturing devices or indecent illumination or due to poor weather surroundings like Low light, rain, fog, mist, etc. reduces the contrast, thus the videos get degraded. 3D video contrast enhancement technique is an essential process for upgrading the quality and information content in the videos. The proposed work employs a discrete wavelet transform based enhancement technique with Jut noticeable difference model to improve the video frames and it is simple and computationally inexpensive. The application of DWT results in the Low and High-frequency sub-bands. The low-frequency components that contain the greatest amount of the information are improved using weighted threshold histogram equalization(WTHE) with the JND model algorithm while the high-frequency sub-bands are distortions and highly affected by noise. The Gaussian high pass filter is applied to each high-frequency sub-bands to remove the noise. Besides, enhancement gain control and luminance preservation are used to acquire the enhanced output video. At the end check the quality of the degraded video frame, the presented work is implemented in MATLAB 2018a and evaluated using objective parameters. Experimental results show that the proposed method can generate better and agreeable results than 2D videos.
The Effects of Keyboard Layout and Size on Smartphone Typing Performance

Usability and typing performance on a smartphone with two unique QWERTY keyboard layouts (standard vs. curved) on two phone sizes (4.0-inch vs. 5.5-inch displays) was investigated in this study. The effect of hand posture was also investigated (one- vs. two-thumbs). Results show users typed the slowest when using one thumb with the curved keyboard on the small phone (15 WPM), and the fastest when using two thumbs with the standard keyboard on the large phone (24 WPM). Typing performance with the curved keyboard on the large phone size (19 WPM) did not differ between typing with one thumb using the standard keyboard on the large or small phone, or with two thumbs using the standard keyboard on the small phone. Error rates were higher when using the curved keyboard, regardless of phone size. Subjectively, the curved keyboard was rated inferior for both phone sizes in comparison to the standard layout.
Balancing the needs of consumers and producers for scientific data collections

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three data producer scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We posit that a new mechanism is also needed to enable citation of multiple datasets and credit to data producers.
A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.
Toward a “thoughtful lightness”: Education in viral times

The Covid-19 pandemic has changed our way of life temporarily and perhaps forever. As such, how educators respond to the contemporary situation is not without consequence. Inspired by the writings of Giorgio Agamben, this article argues that, while the way forward is not unambiguous, the Covid-19 situation offers educators an unanticipated opportunity to pause; to reconsider our aspirations; and, ultimately, to reclaim education as an ethico-political activity. To embrace this opportunity requires the interpretation of our current situation as a real state of exception in which the neoliberal order and its utopian-learning culture can be suspended. In a state of suspension, one can begin thinking afresh about the Covid-19 pandemic and what reactions to and conversations about the event reveal about (more desirable) ways of learning and living together in schools and society.