Reviewerly/exHarmony huggingface dataset:
Texture-based Feature using Multi-blocks Gray Level Co-occurrence Matrix for Ethnicity Identification

Abstract Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, k- Nearest Neighbour ( k -NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm.
Multilayer analysis of population diversity in grammatical evolution for symbolic regression

Abstract In this paper, we analyze the population diversity of grammatical evolution (GE) on multiple levels of genetic information: chromosome diversity, expression diversity, and output diversity. Thereby, we use a tree-similarity metric from tree-based GP literature to determine similarity of expression trees generated in GE. The similarity of outputs is determined via their correlation. We track the pairwise similarities for all individuals within a generation on all three levels and track the distribution of similarity values over generations. We demonstrate the analysis method using four symbolic regression problem instances and find that the visualization highlights some issues that can occur when using GE such as: large groups of individuals with highly similar outputs, a high fraction of trees with constant outputs, or short and highly similar trees in the early stages of the GE run. Especially in the early phases of GE, we see that a large subset of the population represents equivalent expressions. In early stages, rather short expressions are produced leaving large parts of the chromosome unexpressed. More complex expressions can be derived only after GE has successfully evolved well-working beginnings of chromosomes.
An Evaluation System and Its Model for Educational Competitiveness of Universities

There are several defects with the current evaluation strategy for educational competitiveness of universities, namely, the lack of a well-established evaluation system and the complexity of evaluation factors. To overcome these defects, this paper analyzes the key factors affecting the educational competitiveness of universities, and establishes an evaluation system covering such three aspects as basic input, progress and output. On this basis, an evaluation model was created to assess the educational competitiveness of universities, in the light of multiple factors on varied levels. Overall, our evaluation system and its model can effectively achieve the multi-factor evaluation of the educational competitiveness of universities, providing a good solution to complex system decision-making problems.
PV output forecasting by deep Boltzmann machines with SS‐PPBSO

Abstract This paper proposes an efficient method for photovoltaic (PV) system output forecasting by deep Boltzmann machines (DBM) with scatter search‐predator‐prey brain storm optimization (SS‐PPBSO). DBM plays a key role to extract features of input variables while SS‐PPBSO is a new evolutionary computation that combines PPBSO with scatter search. In recent years, as renewable energy, PV systems are positively introduced into power network in Japan so that power system operation becomes complicated due to the uncertainty. To overcome this challenge, it is required to forecast PV outputs that are influenced by weather conditions significantly. This paper proposes a new efficient PV output forecasting method with DBM that makes use of SS‐PPBSO in learning. The effectiveness of the proposed method is demonstrated for real data of a PV system.
Identifying Image Schemas: Towards Automatic Image Schema Extraction1

One of the missing pieces before image schemas can be used in conceptual blending and artificial intelligence is a method to automatically identify image schemas in natural language. In order to investigate this problem, the PATHfollowing family introduced in Chapter 3 will be empirically investigated by using a natural language corpus to detect existing members of the family and detect possible additional candidates. The experiment relies on a method of syntactic pattern matching using words strongly associated with movement and processes. The experiment includes four different languages to strengthen the idea that PATH-following in abstract domains (here finance) is not only found in one language but universal as assumed through their embodied manifestation. The experiment found that approximately 1/3 of extracted words could be image-schematic and could not only provide linguistic support for the members of the PATH family but also provide additional candidates.
Adaptive Region-Based Active Learning

We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active learning baselines.
Moving Object Detection Using Sparse Approximation and Sparse Coding Migration

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace.First, low-rank sparse decomposition is used to reduce the dimension of the data.Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets.And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time.According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas.Finally, experiments are carried out on VDAO datasets containing 59 sets of videos.The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.
Phase-tunable quantum router

Abstract We propose and analyze an efficient scheme for realizing the high transfer rate for quantum router composed of two coupled-resonator waveguides (CRWs) channels coupled with N sequential cavities with embedded N four-level atoms. In this paper, we focus on the effect of the phase difference between different coupling constants which has been ignored in the previous works. In this scheme, we demonstrate that the phase difference between the coupling constants has a very large impact on the band spectra of single-photon propagating from one of the CRW into the other. Besides, as the number of atoms embedded in the two CRWs increases, the efficiency of a single photon being routed into other CRW can reach 100%. More importantly, we also find that if the phase difference of the coupling strength exists, even if there is atomic dissipation in the system, the transfer rate of a single photon not only it not decrease but it increases.
Using Deep Time Delay Neural Network for Slot Filling in Spoken Language Understanding

Modeling the context of a target word is of fundamental importance in predicting the semantic label for slot filling task in Spoken Language Understanding (SLU). Although Recurrent Neural Network (RNN) has shown to successfully achieve the state-of-the-art results for SLU, and Bidirectional RNN is capable of obtaining further improvement by modeling information not only from the past, but also from the future, they only consider limited contextual information of the target word. In order to make the network deeper and hence obtain longer contextual information, we propose to use a multi-layer Time Delay Neural Network (TDNN), which is prevalent in current large vocabulary continuous speech recognition tasks. In particular, we use a TDNN with symmetric time delay offset. To make the stacked TDNN easily trained, residual structures and skip concatenation are adopted. In addition, we further improve the model by introducing ResTDNN-BiLSTM, which combines the advantages of both the residual TDNN and BiLSTM. Experiments on slot filling tasks on the Air Travel Information System (ATIS) and Snips benchmark datasets show the proposed SC-TDNN-C achieves state-of-the-art results without any additional knowledge and data resources. Finally, we review and compare slot filling results by using a variety of existing models and methods.
Two systems for thinking with a community

Evans and Over (1996) made a seminal contribution to the cognitive sciences by describing two different routes humans take to reason toward their goals, one associated with intuition, the other with deliberation. We show how knowledge provided by our communities influences both routes. Many methods of outsourcing cognitive effort – taking advantage of information that one does not know but assumes that someone else can supply – show the hallmarks of intuitive reasoning. Effective outsourcing requires fast and efficient ways of identifying what must be outsourced, and which individual or group of people is most likely to have the relevant expertise. Research has identified several fallible heuristics like the degree of entrenchment of a term in a community of use that help people figure out what needs to be outsourced (content heuristics), and a different group of heuristics that allow us to find experts, for instance, through associations with certain environments and disciplines (expertise heuristics). In contrast, deliberation is primarily concerned with facilitating intentional collaboration with others toward joint goals, often using natural language. This division of labor between two interacting but distinct systems allows humans to leverage the representational and computational capacities of their communities to achieve ever more sophisticated goals.
Generative adversarial network for bidirectional mappings between synthetic and real facial image

Currently, image-to-image translation tasks have been extensively investigated by Generative Adversarial Network (GAN) <sup>1</sup> and Neural Style<sup>2</sup> . However, the model of the GAN can easily lead to network collapse, making the generation process free and uncontrollable. Neural Style is more dependent on the selection of features layers and loss weights. Therefore, these may cause the network model to fail to produce deterministic results we wanted, and applications are very limited. To resolve these problems, we propose an improved transfer model based on CycleGAN and an image preprocessing method for the particularity of training datasets. In this paper, we convert the synthetic face images generated by simulator into more realistic face images. Besides, this process is reversible. Depending on your tasks and goals, you can choose from synthetic images to real images or from real images to synthetic images. This is valuable and challenging in computer vision and computer graphics. In the results, we compare our model with the most recent GAN models from qualitatively and quantitatively. We demonstrate the state-of-the-art effectiveness of the proposed method for generating high-quality images. You can see more results and discussion of our model on this website https://github.com/qiqi7788/dataset-and-result.
EFEKTIFITAS PELATIHAN KECERDASAN EMOSIONAL UNTUK MENINGKATKAN KESEJAHTERAAN PSIKOLOGIS REMAJA PANTI ASUHAN

This study aims to improve psychological well-beimg of adolescents Harapan orphanage through emotional intelligence training. Emotional Intelligence aspects in this study are perceiving emotion, facilitating thought with emotion, understanding emotion, and managing emotion. The concept of this training is experiencing learning. Material of training taught and delivered through lecturing, games, discussion, writing task, reflection, role play, presentation, and self record. Participants are 14 teenage boys who lived in HA Orphanage. The design chosen for this study was one-group pretest-posttest design. The result of quantitative analysis was significant differences before and after training. It showed that emotional intelligence training was effective to improve psychological well-being of teenage boys Harapan’s orphanage.
Calibration, Entropy Rates, and Memory in Language Models

Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing. Towards this end, we present a calibration-based approach to measure long-term discrepancies between a generative sequence model and the true distribution, and use these discrepancies to improve the model. Empirically, we show that state-of-the-art language models, including LSTMs and Transformers, are \emph{miscalibrated}: the entropy rates of their generations drift dramatically upward over time. We then provide provable methods to mitigate this phenomenon. Furthermore, we show how this calibration-based approach can also be used to measure the amount of memory that language models use for prediction.
Analyzing An Audit Population Via Either Excel Pivot Tables and/or R Language Cluster Analysis

SUMMARY This case provides instructors the opportunity to have students analyze an audit population via either Excel Pivot Tables and/or cluster analysis via the R programming language and RStudio free software environment. The analysis takes place in the context of audit risk analysis and planning. The case was developed with a background description based on a real company and instructor-generated synthetic data containing seeded misstatements. Analysis via Excel Pivot Tables is fairly straightforward and allows students to easily get into the underlying audit planning questions. Use of cluster analysis via R is more difficult but provides students with an opportunity to think about cluster analysis when there is no underlying basis for discrete population splits or as a population stratification methodology. Student feedback from use of the R language approach was very positive with students indicating they had a better understanding of how to apply cluster analysis in an audit context
Machine Learning for Personalized Medicine: Clinical Outcome Prediction and Diagnosis : Plenary Talk

Machine learning and computational intelligence have been applied to a wide range of medical problems to assist medical professionals in decision-making. Especially artificial neural networks, fuzzy systems and powerful hybrid neuro-fuzzy approaches have already proven their strong potentials in medicine. This is especially important and interesting in emerging field of personalized medicine, which is often described as providing ”the right patient with the right drug at the right dose at the right time” and represents tailoring of medical treatment to the individual patient characteristics, needs and preferences. Machine learning focuses on the development of computer programs that can access data and use it to learn by themselves. Regarding medical applications concerning prediction of patient's clinical outcome, machine learning can be considered as a data-driven analytic approach that specializes in the integration of multiple risk factors into a predictive tool. In this talk hypothesis has been considered that machine learning based models may help to improve prediction of clinical outcome in various medical fields, in comparison to traditional statistical and scoring approaches. This hypothesis has been tested with our own results from several studies. Our experiences have been considered regarding artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance, adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability, machine learning leukemia clinical outcome prediction, neural prediction of mortality in spontaneous intracerebral hemorrhage based on initial clinical parameters and others. Finally, to demonstrated potentials of machine learning in medical diagnosis, our results concerning cervical cancer detection by improving standard screening tests have been considered.
Data mining in telemedicine

To date, the field of telemedicine is at a critical standpoint and faces a wide variety of challenges. Voluminous data are generated through the interaction among the telemedicine stakeholders, which are ever increasing. It is well conjectured that the successful implementation of telemedicine largely depends on the effective and efficient knowledge extraction from this available data cloud. However, due to lack of proper integration of the data mining techniques, the stakeholders are not getting the full-fledged benefit from this promising platform. Considering the aforementioned fact, this book chapter provides a contrivance to integrate data mining techniques into telemedicine connecting all the stakeholders into a single podium using data engine. It illustrates the prospects of different data mining techniques and their integration for telemedicine. These techniques combine all the basic classification and clustering method including the state-of-the-art artificial neural network (ANN) and deep learning procedure for disease prediction. Two case studies, heart diseases, and breast cancer prediction have been demonstrated applications of the integrated data mining engine.
Research on fault diagnosis of aeroengine endoscopic detection based on CBR and RBR

The combination of rule-based reasoning (RBR) and case-based reasoning (CBR) is used to build an expert system for the endoscopic detection of aero-engines. The aircraft maintenance manual is converted into knowledge rules, and a rule base is constructed. Typical damages and corresponding maintenance decisions adopted are taken as cases to construct a case base. The rule base and the case base together form the expert system knowledge base, which is stored in the database. At the same time, the corresponding learning mechanism is established to realize the maintenance of diagnostic rules and typical cases. When the expert system works, through the man-machine interface, the user inputs the image related information, extracts the image damage information, carries on the reasoning in the rule base, obtains the maintenance decision according to the matching rule, regarding is difficult to make the maintenance decision in the critical state damage, needs to carry on the case-based reasoning, retrieves the similar historical case from the case base, finally unifies RBR and CBR reasoning result to make the maintenance decision comprehensively.
A Real-Time Police Dog Action Recognition System Based on Vision and IMU Sensors

A new human-dog coordination system is in urgent need to enhance the working efficiency of both the police and the police dogs. In view of the problem of sensor data disorder caused by jitters in the state estimation of non rigid animals such as police dog, this demo paper proposes a real-time solution for action reconstruction and video transmission of police dogs based on vision and IMU sensors. These sensors are integrated in a camera helmet and a police dog vest, which have the characteristics of easy wearing, high accuracy and good real-time performance. With the proposed user-friendly GUI, the police are able to work remotely with police dogs with quick reaction and low security risks.
Efficient Intervention Design for Causal Discovery with Latents

We consider recovering a causal graph in presence of latent variables, where we seek to minimize the cost of interventions used in the recovery process. We consider two intervention cost models: (1) a linear cost model where the cost of an intervention on a subset of variables has a linear form, and (2) an identity cost model where the cost of an intervention is the same, regardless of what variables it is on, i.e., the goal is just to minimize the number of interventions. Under the linear cost model, we give an algorithm to identify the ancestral relations of the underlying causal graph, achieving within a $2$-factor of the optimal intervention cost. This approximation factor can be improved to $1+\epsilon$ for any $\epsilon > 0$ under some mild restrictions. Under the identity cost model, we bound the number of interventions needed to recover the entire causal graph, including the latent variables, using a parameterization of the causal graph through a special type of colliders. In particular, we introduce the notion of $p$-colliders, that are colliders between pair of nodes arising from a specific type of conditioning in the causal graph, and provide an upper bound on the number of interventions as a function of the maximum number of $p$-colliders between any two nodes in the causal graph.
Convenience of Voice Interaction Design in the 5G Era to Adapt to Agricultural Machinery

With the rapid development of science and technology, 5G era is coming. Under the guidance of OFDM technology, 5G has a profound impact on all walks of life in the future, providing a flexible, efficient and extensible strong support for the new network, and applying the voice interface design to the operation panel of agricultural machinery. This paper introduces the hidden Markov algorithm as the core, designs the isolated word speech recognition strategy and the noise pretreatment improvement strategy. On this basis, the construction of voice interactive service system technology can play a certain role in improving the operation convenience of agricultural machinery and promoting agricultural production.
Towards Hypervisor Support for Enhancing the Performance of Virtual Machine Introspection

Virtual machine introspection (VMI) is the process of external monitoring of virtual machines. Previous work has demonstrated that VMI can contribute to the security of cloud environments and distributed systems, as it enables, for example, stealthy intrusion detection. One of the biggest challenges for applying VMI in production environments is the performance overhead that certain tracing operations impose on the monitored virtual machine. In this paper, we show how this performance overhead can be significantly minimized by incorporating minor extensions for VMI operations into the hypervisor. In a proof-of-concept implementation, we demonstrate that the pre-processing of VMI events in the Xen hypervisor reduces the monitoring overhead for the use case of VMI-based process-bound monitoring by a factor of 18.