Reviewerly/exHarmony huggingface dataset:
Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

In recent times, dimension size has posed more challenges as compared to data size.The serious concern of high dimensional data is the curse of dimensionality and has ultimately caught the attention of data miners.Anomaly detection based on local neighborhood like local outlier factor has been admitted as state of art approach but fails when operated on the high number of dimensions for the reason mentioned above.In this paper, we determine the effects of different distance functions on an unlabeled dataset while digging outliers through the density-based approach.Further, we also explore findings regarding runtime and outlier score when dimension size and number of nearest neighbor points (min_pts) are varied.This analytic research is also very appropriate and applicable in the domain of big data and data science as well.
Modeling the spatial distribution of Global Solar Radiation (GSR) over Ghana using the ÅngströmPrescott sunshine duration model

Solar radiation is an important geological and meteorological parameter. In most developing countries, data is readily unavailable owing to lack of instrumentation and skilled personnel. In this study, Global solar radiation (GSR) over Ghana has been quantified using the Angstrom–Prescott sunshine model with sunshine duration data from 22 synoptic stations distributed across the country’s ecological zones. The simulated data was gridded at 10 km by 10 km, establishing the spatial distribution of solar radiation over the country. Comparison with satellite data showed good results with root mean square error (RMSE) values of 1-5 MJm-2day-1 and correlation coefficient of 60-66%. Meanwhile, the estimated total GSR over the country was found to be 412.82 MJm-2day-1. The savanna zone had the maximum estimated total monthly mean GSR for the year, with the highest value of 20.76 MJm-2day-1 in Navrongo. The forest zone had the minimum estimated total annual mean GSR, with the lowest radiation level in Oda (17.11 MJm-2day-1). A maximum and minimum mean clearness index of 0.59 and 0.48 respectively are estimated, implying that about 53 % of solar radiation at the top of the atmosphere reaches the study area after attenuation. The satellite data has a total monthly mean horizontal Global Solar irradiance of 366.62MJm-2day-1. The study shows that the region is a potential field to harness and optimize solar energy for the operation of photovoltaic systems and solar collectors for industrial and domestic applications.
Low Power Mixed Signal with CVNS Based Neuron Chip

The usage of a CVNS neuron of blended flag of low power and configuration is introduced. The straight guess by parts in the simple area is utilized for the actuation work. The usage of the simple neural system has a constrained accuracy, so this neuron is proposed. Less simple digits of the nonstop esteem numerical framework (CVNS) are supplanted by general advanced yields of a sigmoid neuron, while in the meantime the most extreme methodology blunder continues as before as computerized structures. The execution of ASIC and the learning of chips for fitting neuronal chips is the aftereffect of this proposed CVNS neuron. Utilizing circuits of current mode, the VLSI usage of the neuron will be completed. The consequences of the usage contrast positively and the structures recently created regarding zone, postponement and vitality utilization.
Impact of the Angioplasty Procedure in Cohort of the Patients Older than 80 Years with Anemia: An Observational Study

Introduction: Cardiovascular diseases are the leading causes of death in the population. According to data from the World Health Organization of 2017, of the 21.7 million deaths from these diseases, more than ten million occur due to atherosclerotic coronary disease. In this context, anemia is a frequent problem among patients with acute myocardial infarction and ST elevation. Although there are advantages with this procedure, restenosis continues to be one of the main limiters of therapeutic success, especially in patients older than 80 years and with anemia.Objective: Therefore, the present study aimed to conduct a retrospective longitudinal and observational study on the impact of angioplasty procedure in patients older than 80 years and with anemia.Methods: A total of 185 participants were submitted to eligibility analysis, followed by STROBE (Strengthening the Reporting of Observational Studies in Epidemiology), The present study followed a retrospective longitudinal and observational model on the analysis of the profile of patients who underwent angioplasty at Brazil. The predictors were anemia and older patients.Results: The multivariate analysis showed that anemic patients older than 80 years who underwent angioplasty died, with a significance level of 33.97% in the female group and 34.40% in the male group, with a total of 68.40 % of deaths in statistical terms. The Graph matrix-Plot model showing the results of the regression analysis between continuous predictors and response, with p<0.05 as significant. All the results showed p<0.05 because the presence of the predictor “anemia” negatively influenced the angioplasty procedure in the patients.Conclusion: The present study showed that patients with anemia showed a significant increase in life risk with death occurring after an angioplasty procedure.
Stego-key-based image steganography scheme using edge detector and modulus function

In this paper, an image steganography scheme is proposed with the intention to enhance the security along with the payload capacity of the cover image. To improve the embedding capacity of the cover image, the hiding process of secret message bits is furnished with the help of cover image characteristic, where more number of secret message bits are embedded into the edge-region instead of the smooth region of the cover image. The secret message bits are concealed into the cover image using the modulus function-based embedding process. For improving the security of the content, we have embedded the secret message bits into the cover-image with reference of keys, i.e., known as a stego-key. The scheme is implemented on some standard greyscale images and satisfactory results are achieved in terms of visual quality along with the higher payload.
Adaptive clustering based on element-wised distance for distributed estimation over multi-task networks

In multitask networks, neighboring agents that belong to different clusters pursue different goals, and therefore arbitrary cooperation will lead to a degradation in estimation performance. In this paper, an adaptive clustering method is proposed for distributed estimation that enables agents to distinguish between subneighbors that belong to the same cluster and those that belong to a different cluster. This creates an appropriate degree of cooperation to improve parameter estimation accuracy, especially for the case where the prior information of a cluster is unknown. In contrast to the static and quantitative threshold that is imposed in traditional clustering methods, we devise a method for real-time clustering hypothesis detection, which is constructed through the use of a reliable adaptive clustering threshold as reference and the averaged element-wise distance between tasks as real-time clustering detection statistic. Meanwhile, we relax the clustering conditions to maintain maximum cooperation without sacrificing accuracy. Simulations are presented to compare the proposed algorithm and some traditional clustering strategies in both stationary and nonstationary environments. The effects of task difference on performance are also obtained to demonstrate the superiority of our proposed clustering strategy in terms of accuracy, robustness, and suitability.
Learning Efficient Detector with Semi-supervised Adaptive Distillation.

Knowledge Distillation (KD) has been used in image classification for model compression. However, rare studies apply this technology on single-stage object detectors. Focal loss shows that the accumulated errors of easily-classified samples dominate the overall loss in the training process. This problem is also encountered when applying KD in the detection task. For KD, the teacher-defined hard samples are far more important than any others. We propose ADL to address this issue by adaptively mimicking the teacher's logits, with more attention paid on two types of hard samples: hard-to-learn samples predicted by teacher with low certainty and hard-to-mimic samples with a large gap between the teacher's and the student's prediction. ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical. Besides, ADL is effective in both the supervised setting and the semi-supervised setting, even when the labeled data and unlabeled data are from different distributions. For distillation on unlabeled data, ADL achieves better performance than existing data distillation which simply utilizes hard targets, making the student detector surpass its teacher. On the COCO database, semi-supervised adaptive distillation (SAD) makes a student detector with a backbone of ResNet-50 surpasses its teacher with a backbone of ResNet-101, while the student has half of the teacher's computation complexity. The code is avaiable at this https URL
Adaptive Data-hiding in Electrocardiogram Based on Integer Wavelet Transform Domain and Incremental Approach

Based on integer wavelet transform domain, an incremental bit-embedding and bit-extraction technique was employed in the proposed method. According to the predetermined criterion, data bits can be adaptively hidden into the host bundles of in an electrocardiogram (ECG) signal. Simulations revealed that the proposed method provides robustness performance better than existing techniques, while our bit-embedding/extraction procedures are much simpler than theirs. In addition, the average signal-to-noise ratio (SNR) and payload of the proposed method are competitive to that of existing techniques. Namely, our method has the merits of high hiding capacity and robust against the attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise-addition attacks.
Design of QoS and Energy Efficient VM Consolidation Framework for Cloud Data Centers

The virtualization and Virtual Machine (VM) Consolidation are the effective solutions for energy consumption reduction. The VM consolidation algorithms designed recently shows the efficiency in terms of energy consumption, Service Level Agreement Violations (SLAV) etc. The methods still suffered from the challenges to achieve the trade-off between QoS and Energy efficiency at VM consolidation. The design of VM consolidation should be efficient in terms of VM’s allocation, migration, and workload management. In this paper, we proposed the design of novel VM consolidation approach in cloud data center to address the challenges of recent methods. First the Multi Resources based (CPU, RAM, network Traffic, bandwidth etc.) dynamic Overload Decision Algorithm (MR-ODA). For best VM selection, we used the policies of CloudSim framework. After selection of VMs to migrate, the efficient VM placement algorithm proposed based on optimization method called Particle Swarm Optimization (PSO). The algorithms designed to achieve the minimize energy consumption, QoS, reliability with minimum SLAV. The outcome of this paper is the new framework for VM consolidation to optimize the CloudSim tool.
Bayesian Online Meta-Learning with Laplace Approximation.

Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this problem for large-scale supervised classification, little has been done to overcome catastrophic forgetting for few-shot classification problems. We demonstrate that the popular gradient-based few-shot meta-learning algorithm Model-Agnostic Meta-Learning (MAML) indeed suffers from catastrophic forgetting and introduce a Bayesian online meta-learning framework that tackles this problem. Our framework incorporates MAML into a Bayesian online learning algorithm with Laplace approximation. This framework enables few-shot classification on a range of sequentially arriving datasets with a single meta-learned model. The experimental evaluations demonstrate that our framework can effectively prevent forgetting in various few-shot classification settings compared to applying MAML sequentially.
Multi-Document Text Summarization Based on Semantic Clustering and Selection of Representative Sentences Using Latent Dirichlet Allocation

Information in the form of online news texts has become one of the most important in this information age.There is a lot of online news that is produced every day, but this news often provides the same contextual content but with a different narrative.This makes it difficult for readers to get complete information.Therefore, we need a solution that can retrieve information in several online news texts to be more effective and efficient in the form of summarizing multi-document texts.In this research, extraction summarization is used, which is to arrange the sentences in the source document to a shorter form.The methods used are Latent Semantic Indexing (LSI) and Similarity-Based Histogram Clustering (SHC) to create semantic sentence clusters and the Latent Dirichlet Allocation (LDA) method to select representative sentences from the formed clusters.Recall-Oriented Understanding Testing for Gisting Evaluation (ROUGE) is used to measure test results.The proposed methods can reach a ROUGE-1 multi-F-measure value of 0.481.
Cryptanalysis of RNZ, an identity-based fully anonymous broadcast encryption

An identity-based fully anonymous broadcast encryption (IB-FAnoBE) is a novel concept for transmitting confidential data to a group of users in which subscribed users’ outsider and insider anonymity are at primary concern. In this paper, we review one of the most efficient IB-FAnoBE, proposed by Ren, Niu and Zhang (RNZ), and show that their construction does not provide the main message indistinguishability security requirement of broadcast encryption. For this purpose, we exhibit that an unauthorized user can decrypt the encrypted content without having legitimate decryption privilege. Furthermore, we suggest some modifications to improve this scheme and construct a secure IB-FAnoBE.
Improving English-Arabic statistical machine translation with morpho-syntactic and semantic word class

In this paper, we present a new method for the extraction and integrating of morpho-syntactic and semantic word classes in a statistical machine translation (SMT) context to improve the quality of English-Arabic translation. It can be applied across different statistical machine translations and with languages that have complicated morphological paradigms. In our method, we first identify morpho-syntactic word classes to build up our statistical language model. Then, we apply a semantic word clustering algorithm for English. The obtained semantic word classes are projected from the English side to the featured Arabic side. This projection is based on available word alignment provided by the alignment step using GIZA++ tool. Finally, we apply a new process to incorporate semantic classes in order to improve the SMT quality. We show its efficacy on small and larger English to Arabic translation tasks. The experimental results show that introducing morpho-syntactic and semantic word classes achieves 7.7% of relative improvement on the BLEU score.
ProSelfLC: Progressive Self Label Correction for Target Revising in Label Noise.

To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealing as it exploits its own knowledge and requires no extra models. However, how to automatically decide the trust degree of a learner as training goes is not well answered in the literature? (2) Some methods penalise while the others reward low-entropy predictions, prompting us to ask which one is better? To resolve the first issue, taking two well-accepted propositions--deep neural networks learn meaningful patterns before fitting noise (Arpit et al., 2017) and minimum entropy regularisation principle (Grandvalet & Bengio, 2006)--we propose a novel end-to-end method named ProSelfLC, which is designed according to learning time and entropy. Specifically, given a data point, we progressively increase trust in its predicted label distribution versus its annotated one if a model has been trained for enough time and the prediction is of low entropy (high confidence). For the second issue, according to ProSelfLC, we empirically prove that it is better to redefine a meaningful low-entropy status and optimise the learner toward it. This serves as a defence of entropy minimisation. We demonstrate the effectiveness of ProSelfLC through extensive experiments in both clean and noisy settings. Keywords: label correction, entropy minimisation, maximum entropy, confidence penalty, knowledge distillation, regularization, label noise
Abstraction and Association: Cross-Modal Retrieval Based on Consistency between Semantic Structures

Cross-modal retrieval aims to find relevant data of different modalities, such as images and text. In order to bridge the modality gap, most existing methods require a lot of coupled sample pairs as training data. To reduce the demands for training data, we propose a cross-modal retrieval framework that utilizes both coupled and uncoupled samples. The framework consists of two parts: Abstraction that aims to provide high-level single-modal representations with uncoupled samples; then, Association links different modalities through a few coupled training samples. Moreover, under this framework, we implement a cross-modal retrieval method based on the consistency between the semantic structure of multiple modalities. First, both images and text are represented with the semantic structure-based representation, which represents each sample as its similarity from the reference points that are generated from single-modal clustering. Then, the reference points of different modalities are aligned through an active learning strategy. Finally, the cross-modal similarity can be measured with the consistency between the semantic structures. The experiment results demonstrate that given proper abstraction of single-modal data, the relationship between different modalities can be simplified, and even limited coupled cross-modal training data are sufficient for satisfactory retrieval accuracy.
Pengaruh Pelatihan Literasi Digital terhadap Kemampuan Literature Searching Mahasiswa Baru Program Pasca Sarjana Universitas ‘Aisyiyah Yogyakarta

The development of the internet provides a lot of information that is easily accessible, but many new students feel confusion in using it, including the information provided by the library (library anxiety). The ability to identify, access, evaluate and ethically use information is very important, so information literacy skills are needed to realize the ability of literature searching. Digital literacy is part of information literacy to overcome library anxiety. This study aims to determine the implementation of digital literacy training and literature searching abilities of new students of ‘Aisyiyah Yogyakarta University Postgraduate Program, as well as the influence of both. The type of this research is correlational using a quantitative approach. Data collection techniques using observation, documentation, interviews, and questionnaires. Data analysis is done by testing the validity, reliability, normality, linearity, and hypothesis. ADDIE (Analysis, Design &amp; Development, Implementation, Evaluation) models are used to assess the implementation of training. The results showed that the implementation of digital literacy training and the ability of literature searching of students with a good grades. Hypothesis testing shows that there is an effect of digital literacy training on the ability of literature searching. The recommended things are upgrading the material, preparing supporting facilities for training, and the suggestion that the training also be given to other students in order to realize ability of literature searching for all students in the University of ‘Aisyiyah Yogyakarta. Keywords: training, digital literacy, capabilities, literature searching, library axtiety, University of ‘Aisyiyah Yogyakarta.
Dynamic Graph Map Animation

Recent methods for visualizing graphs have used a map metaphor: vertices are represented as regions in the plane, and proximity between regions represents edges between vertices.In many real world applications, the data changes over time, resulting in a dynamic map. This paper introduces new methods for representing dynamic graphs with map animation. More specifically, we present three different animation methods: MDSV (Multidimensional scaling - Voronoi), TV (Tutte - Voronoi) and TD (Tutte - dual). These methods support operations such as addition and deletion of vertices and edges. Each of our methods uses a kind of matrix interpolation.
Advance and Prospect of Discrete Tchebichef Transform and Its Application

The discrete Tchebichef transform (DTT) is a transform method based on discrete orthogonal Tchebichef polynomials, which have applications found in image processing and pattern recognition. This paper first introduces the positive and inverse transform of DTT, so that readers can have a certain understanding of the mathematical background of DTT. Then it introduces and summarizes the application and research advance of DTT. Finally, some ideas about the prospect of DTT application are put forward. This paper is a comprehensive summary of the existing development of DTT and can be used as a reference for DTT research.