Reviewerly/exHarmony huggingface dataset:
De-biasing Neural Networks with Estimated Offset for Class Imbalanced Learning

The imbalanced distribution of the training data makes the networks biased to the frequent classes. Existing methods to resolve the problem involve re-sampling, reweighting, or cost-sensitive learning. Most of them anticipate that emphasizing the minority classes during the training would help the network to learn better representations. In this paper, we propose a method for reparameterizing softmax classifiers' offsets so that training is less sensitive to class imbalance. We first observe that the trained offset of the baseline linear classifier is biased toward the majority classes due to the imbalance. Instead of the trained offset, we define the estimated offset, and constrain it to be uniform over the classes. In experiments with long-tailed benchmarks, our method exhibits the best performance. These experiments verify that our proposed method effectively encourages the networks to learn better representations for minority classes while preserving the performance for the majority classes.
Analysis the Image Classification Problem Based on Transfer Learning

To solve the main problem of recognition accuracy, many image classification models have been implemented. A lot of attention was paid to Machine Learning. In this work, we will examine the problem of image classification related on transmission training to study whether it will work better in point of accuracy and efficiency with new sets of image data through Transfer Learning. Transfer Learning is a method of using the knowledge of a pre-trained model in another task. In this article, we will compare the image classification results of Logistic Regression (LR), Linear SVM and Random Forest Classifiers (RFC) using the pre – trained VGG-16 model. Image classification problem is implemented using Caltech - 101 and Flowers - 17 datasets.
A Depth Multiple Hierarchical StereoNet by Using Thermodynamic Color Guidance

Abstract Thermodynamics is devoted to the study of energy or materials, and few researchers have combined the laws of thermodynamics with stereoscopic vision. Thus, we propose Depth Multiple Hierarchical StereoNet (DMH-Net) by using thermodynamic color guidance, which present end-to-end deep architecture for real-time stereo matching, realize sub-pixel precision and produce high-quality refinement disparity on dataset benchmarks. At the same time, we use color thermodynamics to guide the image output of an accurate Hill disparity image. Our DMH-Net consists of four modules: the first module uses a deep spatial pooling method to extract multiple features that work on the initial left and right images. The second module use color guided to produce high-quality cost volume. We use the ahead of two modules to calculate depth disparity precision, which can segment coarse to fine to obtain background and edge of objects information. Thirdly module integrates the information about 3D cost volume and color channel to frame a 4D hierarchical cost volume. The fourth module is designed by depth hierarchical refinement to regress the final disparity. Finally, we achieve sub-pixel precision and real-time performance on benchmarks, which can produce high-quality thermodynamic disparity regression maps.
Hybrid Cloud Scheduling Using A Dynamic CostEffective Fuzzy Particle Swarm Optimization Algorithms

In cloud computing to attain scalability and security objective, the task scheduling mechanism isadopted in hybrid clouds. The cost effectiveness of cloud computing is affected when service providers offers thescheduling of the cloud services such as servers, storage space, intelligence and analytics to the customers. Toachieve minimization in cost and time an organized scheduling is necessary. Tasks in hybrid clouds usuallyallocated to the private cloud or to a public cloud on the basis pay and utilize. In this paper a Dynamic CostEffectiveFuzzy Particle Swarm Optimization (DCEFPSO) is utilized based on cloud scheduling. The DCEFPSOwhich is a Stochastic optimization technique that is being used to solve complex optimization problems.Comparison is performed between the proposed DCEFPSO and few traditional algorithms employing schedulingand the evaluation results proves that the Dynamic Cost-Effective Fuzzy Particle Swarm Optimization(DCEFPSO) is cost and time effective
More Constructions of 3-Weight Linear Codes

Linear codes with few weights have become an interesting research topic and important applications of cryptography and coding theory. In this paper, we apply some ternary near-bent and 2-plateaued functions or <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>r</mi> </math> -ary functions to construct more 3-weight linear codes, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>r</mi> </math> is a prime. Moreover, we determine the weight distributions of the resulted linear codes by means of some exponential sums.
Color fractal texture image complexity estimation using a convolutional neural network

In this paper we analyze the capacity of a convolutional neural network (CNN) to understand and model color texture. More specifically, we ask the question if a CNN is able to predict the complexity of a color texture image, in particular of a color fractal image. We used color entropy and color fractal dimension to compute the complexity of both natural color texture images from the VisTex image data base and synthetic color fractal images. We modified the last layer of a ResNet-18 CNN so that the network outputs a real number representing the input image complexity expressed either as color entropy or color fractal dimension. We trained the modified ResNet CNN in two scenarios: when fractal images were not part of the training set and when they were part of the training set. We report on our experimental results and draw the conclusions.
COMPETENCE APPROACH – PROJECTIONS IN EDUCATION

The current generation is facing a number of challenges related to their future. The education system is looking for adequate ways to change and adaptively focus the learning process. The application of a competency approach in education has a positive effect. The article attempts to explain the essence of the competency approach and its projections in the preparation, education and upbringing of children and students. Basic elements that are leading in organizing the learning process are outlined. Concepts necessary for subsequent theoretical research and methodological formulations are defined.
A NEW DESIGN OF TANGENT HYPERBOLIC FUNCTION GENERATOR WITH APPLICATION TO THE NEURAL NETWORK IMPLEMENTATIONS

A CMOS hyperbolic tangent function generator circuit suitable for the implementation of analog neural networks is presented.In order to obtain an accurate yet simple circuit realization, a judiciously chosen symmetrical Padé approximation of the hyperbolic tangent function is proposed.As an illustrative application, we set up an application in which the proposed circuit is used as the nonlinear block of a two-layer neural network.Simulation results using Spectre Simulation tool in Cadence design environment with 0.18µm CMOS process verify proper operation of the proposed circuit as well as the neural network built around.These results demonstrate the validity of the theoretical analysis and the feasibility of the proposed circuit.
Imbalanced Question Classification Using Generative Prototypes

A practical question-answering (QA) system typically categorizes a new question into the frequently asked questions (FAQs) and returns the corresponding answer. Having imbalanced FAQs data is actually prevalent in general. This paper proposes a meta-learning method for imbalanced question classification. The basic idea is to generate virtual training data for zero-shot questions and then construct question prototypes for training a question classifier, thereby relieving the problem of data imbalance and improving performance of question classifier. Experiments show that the proposed method improves the overall classification performance both for English and Chinese QA tasks. Especially, the classification performance of zero annotated questions increased significantly, and the generative prototypes has minute impact on the performance of large annotated question test set.
Assessing Dialectical Relevance Using Argument Distance

In this paper some lessons are learned regarding how to extend and deepen the theory of Macagno (Assessing relevance. Lingua 210–211:42–64, 2018) on assessing dialectical relevance by using the notion of argument distance. An argument is defined as dialectically relevant if it is an appropriate move in a multiagent dialogue exchange. Three examples are studied where a criticism of relevance is made against an argument, and the problem posed is how a response to this type of criticism should be judged to be justified or not, based on the evidence. Based on these examples, an algorithm is presented that helps the user to determine argument distance in a given case as a means of helping to judge whether a criticism that an argument is not relevant is justified or not.
Average Margin Regularization for Classifiers

Adversarial robustness has become an important research topic given empirical demonstrations on the lack of robustness of deep neural networks. Unfortunately, recent theoretical results suggest that adversarial training induces a strict tradeoff between classification accuracy and adversarial robustness. In this paper, we propose and then study a new regularization for any margin classifier or deep neural network. We motivate this regularization by a novel generalization bound that shows a tradeoff in classifier accuracy between maximizing its margin and average margin. We thus call our approach an average margin (AM) regularization, and it consists of a linear term added to the objective. We theoretically show that for certain distributions AM regularization can both improve classifier accuracy and robustness to adversarial attacks. We conclude by using both synthetic and real data to empirically show that AM regularization can strictly improve both accuracy and robustness for support vector machine's (SVM's), relative to unregularized classifiers and adversarially trained classifiers.
Lightweight face detection network improved based on YOLO target detection algorithm

Aiming at the problems of poor real-time capability and high hardware requirements of existing target detection networks, a lightweight face detection method based on YOLO target detection algorithm (You Only Look Once) is proposed. By modifying the basic network structure and internal parameters, different face detection network structures are obtained. Experiments with a face database show that this method greatly improves the time and efficiency of detection at the cost of reducing a certain accuracy.
Progressive challenge selection in the context of virtual education space

Not all students can progress equally, not every single educational using can be grasped with equal dept and speed. Usually, the level of understanding varies between students/classes or even an entire school. The common approach to this problem is to average the learning material based on the audience and to ensure that the concepts are understandable enough for the students in general. This approach is generally speaking a workable solution but does not fit the needs of every single student.The E-learning platform can provide a plethora of tools for managing the level of confides of the different users regarding the learning material. The main goal of this kind of education is not only to provide a useful interface between the student and the supervisor but to make the learning process more suitable for the individual needs of the student. The ability to ignore the learning paste of the other students and to configure your activities is one of the selling points of the electronic education, so it is necessary to approach this problem with an appropriate methodology and tools for providing the proper experience for every single student that take part of online education programIn this article, we are going to describe a concept for dynamically and progressively tailoring education program based on the student achievement in the context of the online learning platform UniPlayground used for training and analyzing the student's behaviour at Plovdiv University.
A privacy-preserving and correctness audit method in multi-party data sharing

In general, deep learning can perform better if much training data is introduced. Recently, as a technology that uses large amounts of data, federated learning is more and more popular, where multiple parties cooperate in training a common model. However, data sharing in cooperative training raises the risk of privacy leakage. Besides, the encryption applied in federated learning may cause the participants to behave maliciously. To solve this problem, we design a practical framework based on blockchain to protect privacy and prevent malicious behavior. If anyone tries to cheat in cooperative learning, his behavior is discovered in the audit process. Our framework is composed of several message transmission processes. The transmitted content contains the evaluation and correctness verification of participants and so on. We conduct experiments on a dataset of messages and verify that our scheme works.
Instrumenting Compiler Pipeline to Synthesise Traceable Runtime Memory Layouts in Mixed-critical Applications

Ensuring traceability between software code and its runtime memory is a required design measure in a number of application fields to achieve functional safety targets. For mixed-critical systems, where a code with different levels of criticality may coexist, this aspect is of particular importance. In the course of safety audits for example, this information may serve to build an evidence that safety-critical code/data is sufficiently isolated from non-critical parts. Unfortunately, addressing the evidence for every byte in memory is not supported by modern compilers. In this paper, we introduce a method where the compiler pipeline is instrumented to recover traceability links between the code and runtime memory. We qualify our proposal on a real-world industrial use case in which the C/C++ code is synthesised for ARM Cortex-M3 controllers. Our experimental results suggest that such an accurate traceability support may serve as a solid basis when analysing memories for mixed-critical applications.
Bifacial Solar Sensitivity to Project Capacity Size

Bifacial solar photovoltaics projects have recently seen a significant increase in installation in the US and across the world. As the solar industry gets introduced to this new technology, developers, financiers and lenders alike are trying to get comfortable with the long-term energy sustainability of bifacial modules. One of the critical areas is accurate modeling of bifacial parameters to reflect long-term P50 energy estimates. DNV GL's SolarFarmer software has the capability to model this complex physics and will be used to test distributed generation and utility scale generation outputs. There have been many studies which attempt to model bifacial systems at either a distributed generation scale (<; 5 MW) or a utility scale (>5 MW) [1], [2]. This study will attempt to compare the scalability of the bifacial model to understand if the sensitivities at different sizes of the system remain valid. It will recommend the critical parameters (height above ground, albedo, and ground coverage ratio (GCR) that developers, financers and lenders can use to optimize the energy output from their bifacial projects.
To Measure or not to Measure? Adaptive Repetition Management in Parameter Tuning

Variance is an inherent part of manifold real-world empirical studies. It can appear due to non-deterministic behavior, measurement or systematic errors, information bias, etc. The former two causes are especially important for parameter tuning, as they can lead to an inaccurate search space estimation and a subsequent decrease in quality of the best found solution. Under parameter tuning we understand an optimization problem of determining the best configuration setting, given a system under investigation and a set of its configurations. Repetitive configuration evaluation is a standard approach to tackle the problem of variance. However, a high number of repetitions can overwhelmingly bloat an effort spent on the search, motivating the development of repetition management strategies. Nevertheless, numerous parameter tuning approaches are either neglecting this problem or suggesting repetition management strategies that are based on a predefined number of repetitions, which can vary for different problem instances. In this paper, we present a repetition management strategy that is inspired by the notion of acceptable measurement error to adaptively determine the number of repetitions for each configuration. Moreover, we investigate the influence of the knowledge on already performed measurements as the additional feedback for the presented repetition management strategy. We evaluate the effectiveness of our approaches with a set of energy optimization problems. The presented strategies prove to be more scalable and robust to changes, while the utilization of experiment-related knowledge can even further increase the quality of the found solution.
Chinese Word Segmentation Based on Bi-GRU Integrating Dictionary Information

Chinese word segmentation (CWS) is an important and essential pre-processing step for Chinese language processing tasks. To date, various models based on deep neural networks have been extensively applied in CWS. Most of them learn from large scale labeled data. However, these models typically lack the capability of processing rare words and OOV words. In this paper, we use character embedding and bigram embedding as the inputs of Bi-GRU model and construct a feature vector to capture dictionary information of characters. we use the multi-head attention mechanism to get the weight of different features which is the inputs of a parallel Bi-GRU network. To evaluate the performance of the proposed model, we conducted experiments on PKU and MSR datasets. The experimental results of datasets show that our model achieves the-state-of-art performance compared to several baselines.
Resume Extraction and Validation from Public Information

With the development of information technologies, knowledge graphs have attracted much attention from more and more fields, due to their usage in information retrieval, recommendation, etc. However, the scale of many knowledge graphs is relatively small due to lack of huge data, it is difficult to perform text understanding and knowledge reasoning on them. Consequently, it is necessary to combine different knowledge graphs. Nevertheless, in some scenarios, an attribute may have multiple values in different knowledge graphs, and some values may be wrong. Knowledge validation is a core and difficult point in knowledge graph fusion. In this paper, we study resume extraction and validation from many public resources. Specifically, we extracted personnel information from four encyclopedia websites; we also propose a new multi-truth discovery and validation method, which evaluates the credibility of attribute values through Internet public information. The proposed method can not only be applied to the research of knowledge graph fusion, but also can be widely adopted in various fields such as rumor discovery, text understanding, knowledge reasoning.
A Deep Learning based Interlingua Representation for Malayalam Documents

Compact representation of sentences like feature vectors, offer better understanding of the sentence formation. Majority applications in natural language processing often requires the help of such meaningful representations. If an interlingua is constructed for the same, it will be useful for the applications like machine translation, text summarization etc. Here we are proposing a deep learning based Interlingua whereby creating a structured meaningful representation for a Malayalam document.
IndoAbbr: A New Benchmark Dataset for Indonesian Abbreviation Identification

Automatic abbreviations identification is an essential task for natural language processing on Indonesian, such as named entity recognition, information retrieval and question answering. One of the challenges is the publicly available and large-scale dataset that is relatively rare and difficult to construct. The problem is even worse for low-resource languages such as Indonesian. In order to bridge the gap of Indonesian abbreviations identification, we constructed a dataset for Indonesian abbreviation identification and compared different methods on Indonesian abbreviation identification. Besides, we extracted three external features, respectively capitalization, consonant and whether appears in the Indonesian dictionary to improve the performance of the models. The results demonstrate that the external features while using the Bi-LSTM-CRF model and CNN-Bi-LSTM-CRF model are effective. Base on the dataset for Indonesian abbreviation identification mentioned above, the BERT model shows the best performance, achieving the average accuracy of 98.28% on the dataset.
Ethno-informatics for anthroponymy classification in Sumedang District using data mining

Abstract Ethnoscience is used to look at culture from a scientific perspective, helping to understand how to develop different forms of knowledge and beliefs. One of the fields studied in Ethnoscience is Ethno-informatics, which is the application of informatics in culture. The methodology used is the process of knowledge discovery in databases in data mining, the process of automatically extracting knowledge from large databases, to get interesting patterns so that knowledge can be obtained. The application of anthroponymy by exploring the population database in Sumedang district, the results of the 2019 census from the Sumedang District Population Office. Then the anthroponymy application was developed in Sumedang district. The results of data processing show that the top ten names of people who are most used or most favorite in Sumedang district are: Muhammad, Muhamad, Asep, Siti, Agus, Dede, Ade, Ai, Tati, Dadang. Muhammad’s name has been a favorite since 2000 until now 2020. Meanwhile, the top ten favorite names that have just appeared in the last ten years (2010-2020) are sorted based on ranking including; Naura, Arsyla, Keyla, Raffa, Rafka, Aqila, Zahra, Keusha and Alesha. In general, the Sundanese name in Sumedang district is still widely used, but generally it is relatively down-to-earth, replaced by new names as an uptake of other cultural names. Changes in self-naming in Sumedang regency in particular, generally self-names in Sundanese culture in West Java, were caused by the rapid development of information and communication technology. This research is expected to provide input for the community, especially for the younger generation as a guide in their daily life. It takes a significant effort from the government to maintain it, so that local culture and wisdom, especially the naming of people in Sumedang district, generally Sundanese culture can be well preserved.