Reviewerly/exHarmony huggingface dataset:
End-to-end spoofing speech detection and knowledge distillation under noisy conditions

State-of-the-art spoofing speech detection methods highly depend on hand-crafted features like LFCC, MFCC, CQCC, FFT spectrum, etc. But almost for all the features, during extracting procedure, some information will be lost permanently, which may be of vital importance to spoofing detection task. Most speeches on the web, are often accompanied by music or noise. Using monaural speech separation to get human voice and then doing spoofing detection later may be a natural idea, but it has two shortcomings: two models must be trained, and during first separation step, some voice will be filtered out while noise will be kept. In this paper, we propose an end-to-end anti-spoofing model which fully consists of one-dimensional convolutional neural networks for spoofing detection under noisy conditions. The proposed system achieved an EER of 1.50% on the ASVspoof2019 logical access evaluation set. Under noisy conditions, we proposed a novel approach based on knowledge distillation, and our proposed method achieved great performance improvements compared to multi-condition training method.
Global Skew Detection and Correction of Document Image Based on Least Square Method and Extensive Connected Component Analysis

Document images can have numerous noises that should be filtered to eliminate those unutilized objects. Skewness is liable not to understand the texts clearly in the document image. A morphological technique is being conducted to preprocess the image. Connected component analysis along with the bounding box approach is introduced to figure out a line. A number of longest connected components are extracted from numerous illustrations. In order to detect a precise skewed angle, the least square method is applied to the logically selected regions to acquire the best fitting line. The skewed angle is discovered by averaging angles of best-fitting lines. The proposed system is already applied on the printed document image and got tremendous accuracy during execution.
Quality of LAPAN website as a medium for digital information of aeronautics and space dissemination in the era of industrial revolution 4.0

Industrial revolution 4.0 has given an impact on the change of human life, which in this era brings disruption to the various fields of human activities, one of them is in government institutions. This research aims to analyst and evaluates the quality of the LAPAN website using the WebQual method and Importance-Performance Analysis (IPA) approach. The population is the whole employee of LAPAN with the number of samples is 193 respondents using purposive sampling and proportional sampling. The result of the research instrument validity and reliability test is valid and reliable because it has met the requirements. The research results show that (1) the performance gap score is less than the importance gap score resulting the LAPAN website performance has not yet fulfilled the user expectations; (2) there are 4 (four) website indicators in the quadrant I that need to be improved, namely (a) US2 (The website interaction is clear and understandable), the solution for improvement is to add the input facilities and web page navigation feature (site map), to monitor and evaluate the website periodically, and to optimize the search engine on the LAPAN website; (b) US4 (The website has an attractive appearance), the solution for improvement is to upgrade the LAPAN website appearance to be more responsive and to prepare the human resource with the UI/UX website design competencies through the functional technical training; (c) US5 (The website design is appropriate to the type of website), the solution for improvement is to adjust the website design as the government institutions website through benchmarking to the several institutions that has the similar characteristics with LAPAN and to prepare the human resource with the LAPAN website development competencies through the technical training about the recent trend on the website design; (d) IQ5 (The website provides updated information), the solution for improvement is to integrate the data and information of LAPAN and to determine the producer of the data and information according to the job and responsibility of each work unit to update the data and information on LAPAN website periodically and to prepare the human resource as a content creator for LAPAN website through the technical training on the website content creation and structure.
A New Schnorr Multi-Signatures to Support Both Multiple Messages Signing and Key Aggregation

A digital signature is essential for verifying people's reliability and data integrity over networks and is used in web server certificates, authentication, and blockchain technologies. Specifically, to solve the bitcoin scalability problem, Multi-Signature (MS) schemes have recently attracted attention because the MS's aggregate algorithm can reduce the amount of signature data in transactions. While such schemes support only a single message signing, Interactive Aggregate Signatures (IAS) and Aggregate Multi-Signature Protocol (AMSP) support signing of multiple messages. However, there are some issues with these schemes, for example, key aggregation is unavailable. In this paper, we propose a key aggregatable IAS scheme called KAIAS that can sign multiple messages with key aggregation. In terms of cases using Multi-Signature, previous studies have mainly discussed the benefits of reducing the size of signatures. On the other hand, we also propose a practical application of KAIAS that leverages its benefits in aggregating both signatures and public keys with a low computing cost for verification.
Revisiting Loss Functions for Person Re-identification

Appearance-based person re-identification is very challenging, i.a. due to changing illumination, image distortion, and differences in viewpoint. Therefore, it is crucial to learn an expressive feature embedding that compensates for changing environmental conditions. There are many loss functions available to achieve this goal. However, it is hard to judge which one is the best. In related work, the experiments are only performed on the same datasets, but the use of different setups and different training techniques compromises the comparability. Therefore, we compare the most widely used and most promising loss functions under identical conditions on three different setups. We provide insights into why some of the loss functions work better than others and what additional benefits they provide. We further propose sequential training as an additional training trick that improves the performance of most loss functions. In our conclusion, we provide guidance for future usage an d research regarding loss functions for appearance-based person re-identification. Source code is available (Source code: https://www.tu-ilmenau.de/neurob/data-sets-code/re-id-loss/).
Network Private Cloud Storage Solution for Teaching Resource Management

This paper introduces the classification and characteristics of cloud computing, as well as the implementation principles and details of the central components, automation process and application layer of private cloud computing system. Aiming at these problems, the Microsoft Private Cloud is integrated into each key technology of teaching resource platform design, for the architecture characteristics and problems of traditional private cloud. This paper also focuses on the design and implementation of NAS based private cloud storage platform. It adopts NAS protocol encryption technology, multi-channel access optimization and data compression to solve the problems existing in traditional private cloud. Finally, a cloud scheme for teaching resource management under cloud computing is constructed and implemented, and it is applied to the sample universities. The experiments show that the platform has a certain degree of improvement in resource utilization, and it has the advantages of high data storage security, good access point network under peak state, high data network transmission interaction rate and small delay.
A Method for Building the Quantitative and Qualitative Part of Bayesian Networks for Intelligent Tutoring Systems

Abstract The Bayesian network (BN) is an important technique to represent and infer knowledge in an Intelligent Tutoring System (ITS); however, ITSs are complex to build. Diverse authors have built BNs based on ontologies to accelerate the building process; nonetheless, they did not fully automate the process, and did not follow the ontologies standard Web Ontology Language, or simplified the final domain. This work proposes a method to build BNs based on ontologies and Wikipedia information to be employed on ITSs. The proposed method constructs the qualitative part of the BN through classes and relations of ontologies; the quantitative part is created based on frequencies, hops, and a measure of similarity between concepts of the ontology represented by Wikipedia articles. This study carried out an experiment to determine the correlation of our method against domain experts opinions; the method obtained a positive correlation of $0.647$ according to the Spearman test. The method constructs a BN to represent the knowledge in ITSs, in a similar way as experts would, supporting the automatic build of these systems.
Application of Neural Network in Oracle Bone Inscriptions

The recognition of Oracle Bone Inscriptions(OBIs) is of great significance to archaeology, history, and linguistics. To realize the fast and accurate retrieval of images for large-scale OBIs datasets and break through the limitations of current conventional retrieval methods, this paper proposes a convolutional neural network for OBIs recognition. The model is designed according to the characteristics of OBIs. The experimental results show that the improved network can better extract the features of OBIs characters, and the recognition rate reaches 84.45%, which is 13.74% higher than the network before the improvement.
A Random Forest with Minority Condensation and Decision Trees for Class Imbalanced Problems

Building an effective classifier that could classify a target or class of instances in a dataset from historical data has played an important role in machine learning for a decade. The standard classification algorithm has difficulty generating an appropriate classifier when faced with an imbalanced dataset. In 2019, the efficient splitting measure, minority condensation entropy (MCE) [1] is proposed that could build a decision tree to classify minority instances. The aim of this research is to extend the concept of a random forest to use both decision trees and minority condensation trees. The algorithm will build a minority condensation tree from a bootstrapped dataset maintaining all minorities while it will build a decision tree from a bootstrapped dataset of a balanced dataset. The experimental results on synthetic datasets apparent the results that confirm this proposed algorithm compared with the standard random forest are suitable for dealing with the binary-class imbalanced problem. Furthermore, the experiment on real-world datasets from the UCI repository shows that this proposed algorithm constructs a random forest that outperforms other existing random forest algorithms based on the recall, the precision, the F-measure, and the Geometric mean
Smart Attendance Monitoring System Using Local Binary Pattern Histogram

Attendance marking of the students in a classroom is one of the most important activity carried out by the teachers to maintain the physical presence of students as well as for records. The traditional process of taking attendance of students is time consuming, error prone, and student can proxy. To eliminate or reduce these disadvantages, a new scheme is proposed in this paper in which the attendance monitoring is done through face detection. This will eliminate the time required for the teacher to read out the names of students and also eliminate the chances of students making proxies. The system uses face recognition algorithm which captures the images of students present in the class using a camera and compares with the images stored in the database. The key challenges involved are the quality of images getting captured and the accuracy of the face recognition algorithm. Experiments prove that the proposed system achieves more than 90% accuracy when implemented with a database consisting of 493 images and tested against a class of 54 students. The accuracy can be improved by equipping better quality camera, better lighting conditions, and more accurate face recognition algorithm.
Generating Dance Videos Using Pose Transfer Generative Adversarial Network With Multiple Scale Region Extractor and Learnable Region Normalization

In this article, we propose a pose transfer framework that can deal with large body motion to generate dance videos. To solve the problem of body shape deformation from large movements, a multiple scale region extractor (MSRE) is proposed. The features of each body region can be extracted from multiple layers of the encoder according to the body key points and passed through shortcuts to the decoder to reduce the spatial information loss. We add a region style loss calculated by the style representations of the body regions to the loss function to improve the quality of the generated images. In addition, the concept of learnable region normalization is integrated into the proposed framework to prevent introducing undesired mean and variance shifts by the corrupted regions during normalization. The experiments have shown that the proposed system can significantly improve the pose generation results compared with existing methods, especially when there are large body movements in the dancing poses.
Overview of accurate coresets

Abstract A coreset of an input set is its small summarization, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models/classifiers/loss functions). Coresets have been suggested for many fundamental problems, for example, in machine/deep learning, computer vision, databases, and theoretical computer science. This introductory paper was written following requests regarding the many inconsistent coreset definitions, lack of source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply and develop coresets. The article provides folklore, classic, and simple results including step‐by‐step proofs and figures, for the simplest (accurate) coresets. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp current results that usually generalize these fundamental observations. Experts might appreciate the unified notation and comparison table for existing results. Open source code is provided for all presented algorithms, to demonstrate their usage, and to support the readers who are more familiar with programming than mathematics. This article is categorized under: Algorithmic Development &gt; Structure Discovery Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Machine Learning
White blood cell segmentation based on dual path network and channel attention

White blood cell (WBC, leukocyte) in blood smear image plays a crucial role in the diagnosis of many blood diseases. WBC segmentation is an important preprocessing step in blood disease diagnosis. However, due to some factors such as different dying technologies and different WBC appearances, WBC segmentation is still challenging. To improve accuracy of WBC segmentation, we propose an end-to-end WBC segmentation network. Specifically, we first introduce Dual Path Network (DPN) as a context-aware feature encoder to extract multi-scale image features. Then, we use a channel attention module to capture the interdependencies between channel maps, and further enhance feature representation capability of the proposed network. Finally, we reconstruct WBC feature maps by a feature decoder, and use skip connections to reduce image information loss. Experimental results on three datasets show that the proposed method obtains higher segmentation accuracy than the other methods.
Optimal Design of Linear Quadratic Regulator Controller Based on Adaptive Cuckoo Search Algorithm

In order to quickly and effectively determine the weighted matrix Q and R in the linear quadratic regulator (LQR) controller of active suspension, this paper presents adaptive cuckoo search algorithm (ACS) for LQR optimization design. ACS improves step size control and discovery probability in standard cuckoo search algorithm (CS) based on adaptive dynamic adjustment strategy. Compared with other test algorithms, for optimizing active suspension LQR controller, experimental results show that the convergence speed of ACS is faster, the solution accuracy is higher, the stability is stronger, and the performance indexes of active suspension are significantly improved.
Improving RNN-T ASR Performance with Date-Time and Location Awareness

In this paper, we explore the benefits of incorporating context into a Recurrent Neural Network (RNN-T) based Automatic Speech Recognition (ASR) model to improve the speech recognition for virtual assistants. Specifically, we use meta information extracted from the time at which the utterance is spoken and the approximate location information to make ASR context aware. We show that these contextual information, when used individually, improves overall performance by as much as 3.48% relative to the baseline and when the contexts are combined, the model learns complementary features and the recognition improves by 4.62%. On specific domains, these contextual signals show improvements as high as 11.5%, without any significant degradation on others. We ran experiments with models trained on data of sizes 30K hours and 10K hours. We show that the scale of improvement with the 10K hours dataset is much higher than the one obtained with 30K hours dataset. Our results indicate that with limited data to train the ASR model, contextual signals can improve the performance significantly.
Simulated Annealing Strategy in Chaotic Neural Network with Chebyshev Polynomials

By combining the Chebyshev polynomials and the Sigmoid function into a new non-monotonic excitation function, a new transient chaotic neural network model is constructed. The dynamic characteristics of the single neuron are analyzed by the time evolution graph and the inverted bifurcation graph of the largest Lyapunov exponent. Verify the rationality of the additional energy term of the constructed model. The effectiveness of the model is verified by nonlinear function optimization and traveling salesman problem. The simulation results show that the newly constructed model can effectively avoid the problem of converging to a local minimum in the optimization process, and can effectively solve the combinatorial optimization problem.
Performance Analysis of Named Entity Recognition Approaches on Code-Mixed Data

Online content is growing in multiple languages every day, and end-users share their opinions, reviews, blogs, tweets, etc., on Online Social Network (OSN) platforms. Such contents written by users having bi-lingual or multi-lingual abilities are harder to understand and analyze various aspects of user preferences and behaviors. In order to understand such code-mixed content, a family of tools such as language identification, named entity recognizer, parts-of-speech (POS) taggers, and other natural language tools have been developed. Named Entity Recognition (NER) is an essential task in text analytics and a prominent sub task of information extraction. This helps to identify, locate and classify entities into a fixed set of categories like name of a person or an organization or a location or time. It also helps to perform various NLP tasks such as role labeling, annotation and parse tree generation, etc. In this work, we present a study on the performance analysis of various approaches to the automatic NER of code-mixed data and propose an improved approach based on BiLSTM-CRF for NER. In this approach, we pass pre-trained word embeddings, trainable character embeddings and a few selected word structural features to BiLSTM-CRF network, which can make use of sentence level tag information along with context from both the directions in the sentence. We also compare various existing NER approaches for social media content, and present a detailed performance analysis of these approaches with the performance of our approach. It is observed that our proposed model gives comparable or outstanding results in the case of both the standard datasets [, ] used in our experiments.
An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.
The Metagraph Model for Complex Networks: Definition, Calculus, and Granulation Issues

This article attempts to look at complex graph models (specifically, the metagraph model) through the prism of information granulation. The basic provisions and definitions of information granulation are explored. The formal definitions of the metagraph data model and metagraph agent model are given. The examples of data metagraph and metagraph rule agents are discussed. The concept of an active metagraph, aimed for the data metagraph model and metagraph agent model combination, is proposed. It is shown that the metagraph vertex, the metagraph edge, attributes, the metagraph fragment, metagraph agents, and the active metagraph can be represented as a special kind of a metavertex. The metagraph calculus adopted for the information granulation is proposed. The metagraph calculus from the point of view of atomicity definition is described. The granularity of the metagraph model, including the atomicity property, the holonic organization, refinement and coarsening, partial ordering, similarity relationship, partitions and coverings, measures of granularity, the granularity of metagraph agents, are discussed.