Reviewerly/exHarmony huggingface dataset:
A Framework for Enhancing Supplier Selection Process by Using SOEKS and Decisional DNA

Supplier selection process is one of the significant stages in supply chain management for industrial manufactured products. It plays an integral role in the success of any manufacturing organization and is an important part starting right from selecting raw material to dispatch of finished products. This paper contributes to enhance the supplier selection process by proposing a multi-criteria decision making framework for industrial manufactured products. The proposed framework is based on smart knowledge management technique called Set of experience knowledge structure (SOEKS) and Decisional DNA, which makes the proposed approach dynamic in nature as it updates itself every time a decision is taken.
Speaker Accent Recognition Using Machine Learning Algorithms

Speaker recognition is a system that recognizes the speaker from the recorded voice signal. Speech and speaker recognition are important for many areas like online banking, telephone shopping, and security applications. In order to analyze and verify speech and speaker, Machine Learning (ML) algorithms can be used. With enough data it is possible to train a program to identify speech and speaker identity. In this paper, several ML algorithms used to identify speaker accents. Data set includes 329 speakers with 6 different accents and English and these words have been converted to metric representation using Mel-Frequency Cepstral Coefficients (MFCCs). MFCC is the most used technique in speech recognition because of the high performance of feature extraction performance and this data set utilizes MFCC to convert speech to data. In this study, 7 classification type ML algorithms used, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Tree (DT), Radial Basis Function (RBF), k-Nearest Neighbor (k-NN), Naive Bayes (NB) and Logistic Model Tree (LMT) methods used for Speaker Accent Recognition data set on UCI ML Repository. Performance metrics, compared using accuracy, Kappa Statistics, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), have been acquired and compared for each algorithm.
Popularity-based transcoding workload allocation for improving video quality in live streaming systems

Transcoding is essential for live video streaming systems, but video streams in many channels may not be transcoded due to the processing capacity limitations of transcoding servers with multiple CPU nodes. To address this, we propose an algorithm that determines the transcodeable bitrate versions for each channel and allocates the transcoding tasks, by taking into account video quality, popularity, and workload balancing. Simulation results show that it can improve popularity-weighted video quality by up to 10.7% over other popularity-based alternatives.
Fine-Grained PM2.5 Detection Method based on Crowdsensing

In this paper, we propose a fine-grained PM2.5 detection method based on crowdsensing technology. Firstly, we perform dark channel processing on sky images which have been collected by mobile users through APP. Secondly, we adopt a train model based on Tensorflow and Keras architecture, and utilize neural network to implement PM2.5 feature extraction and detection. Finally, a series of experiments have been carried out based on a large-scale real dataset to verify the performance of our proposed detection method.
SAN: Sampling Adversarial Networks for Zero-Shot Learning

In this paper, we propose a Sampling Adversarial Networks (SAN) framework to improve Zero-Shot Learning (ZSL) by mitigating the hubness and semantic gap problem. The SAN framework incorporates a sampling model and a discriminating model, and corresponds them to the minimax two-player game. Specifically, given the semantic embedding, the sampling model samples the visual features from the training set to approach the discriminator's decision boundary. Then, the discriminator distinguishes the matching visual-semantic pairs from the sampled data. On the one hand, by the measurement of the matching degree of visual-semantic pairs and the adversarial training way, the visual-semantic embedding built by the proposed SAN decreases the intra-class distance and increases the inter-class separation. Then, the reduction of universal neighbours in the visual-semantic embedding subspace alleviates the hubness problem. On the other, the sampled rather than directly generated visual features maintain the same manifold as the real data, mitigating the semantic gap problem. Experiments show that the sampler and discriminator of the SAN framework outperform state-of-the-art methods both in conventional and generalized ZSL settings.
The Novel Improving Algorithms on DRA Audio Entropy Coding

DRA (Digital Rise Audio) is referred to as specification for multi-channel digital audio coding technology which was issued as a Chinese national standard in 2008. There novel algorithms which are the differential Huffman, context-based Huffman and context-based arithmetic entropy coding, respectively, are presented to improve the compression efficiency of DRA entropy coding with 3%, 11.11% and 13.52% at the expense of the different implementing complexities and the intra-frame and/or inter-frame error propagation in live transmission.
An Anti-Counterfeiting Method of High Security and Reliability Based on Unique Internal Fiber Pattern of Paper

Anti-counterfeiting technique is a research hotspot in information security domain. Common techniques are usually based on artificial physical cloneable feature which is forgeable. In this paper, we proposed an anti-counterfeit method based on microscopic translucent texture of paper (MTTP). MTTP is image of internal fiber pattern of paper (IFPP) captured by microscope under translucent light source. The IFPP has property of uniqueness and randomness, and the fiber scale of IFPP is at sub-micron or micron level. Then the IFPP is unable to be forged. Furthermore, fiber texture in IFPP is stable under disturbances (including illumination intensity change and surface damage), which guarantees the stability of the MTTP. Experiment results show that the authentication accuracy achieve 100% in our dataset (totally 13,4550 pairs of MTTPs) and the false reject rate achieves the order of magnitude of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-136</sup> in ideal condition and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-59</sup> even in condition of illumination change or surface damage. Above results indicate that our method has rather high security and reliability.
Scabies Classification in Animal Using Uniform Local Binary Patterns

Scabies is a disease caused by the Sarcoptes Scabiei mite and can affect humans and animals. In animals, this disease is recognized by crusted skin on the ears, nose, and feet, but if the disease is not treated immediately, the mites will spread throughout the animal's body and can even cause death.Image processing has been done a lot to classify diseases in humans and plants, but there are still a few that involve scabies in animals, so in this research the authors build a system that can classify animal skin images into two classes, namely animals with scabies disease, and animals with other skin diseases. The Uniform Local Binary Pattern feature extraction method has been proven to optimize the feature extraction results and minimize the processing time for the feature extraction process, so the system is built by processing the dataset using the Uniform Local Binary Pattern method and the Random Forest classification method so that the system performance reaches 52%.
A Novel Cloud-Edge Cooperative Structure Model For Power System Stability Operation Control

In this paper, a cloud-edge cooperative structure model for power system stable operation control is proposed. Based on this model, load modeling and evaluation algorithm are theoretically made.Side VPP theory from the distribution network load model and the corresponding algorithm of reasoning results obtained in this paper the cloud side structure model to some extent can effectively solve the power system operation control level of high real-time performance, short cycle, tasks, complicated data problem, reduce the pressure of edge nodes communicate with cloud center, improve the quality of business services at the same time, guarantee the edge node of data privacy.
Design and Implementation of Video on Demand System Based on MPEG DASH

Video is one of the digital contents that is consumed by most of internet's user. Developer needs to build Video on Demand applications with ease and focus with the content. The increase in internet traffic caused by video is another reason for the development of video on demand based on MPEG DASH. In this research, MPEG DASH is a streaming method which allows to adjust the bit rate into multiple resolution. Video on Demand system consists of video transcoding, streamer, and video player. This research work has designed and implemented Video on Demand based on MPEG DASH that can be embedded with web application. The video player worked well and managed to carry out all its functions.
Digital security reference model: a survey and proposal

This paper presents a survey and proposal of a digital security reference model. The proposed reference model consists of three layers: technology and engineering layer, management layer, and legal layer. The first layer focuses on technologies and engineering processes to build a secure digital system. Then, the second layer is all about management or organizational procedures. Finally, the last layer consists of compliance with existing laws and regulations. The correlation between each layer is, first, focusing on the technology and engineering layer to get anything to build an organization's secure digital system. Then, create policies for all parties of the organizations. Last, make sure all of the technology used and procedures applied complied with the existing law. The last layer also has a role as if security breach success penetrates the two previous layers. The next is digital forensics to reveal the cyber incident and punish the criminals by the existing law. In general, this proposed reference model aims to guide organizations, companies, and governments in developing a secure digital system with a comprehensive perspective. In particular, the reference model has two use case actors, engineers, and researchers. For the engineer, this reference model guides to starting and developing a digital security system. There are five steps to creating a secure digital system using this reference model: preparation, specifications, system design, implementation, and evaluation. For the researcher, this reference model gives a comprehensive understanding. So the researcher gets a big picture of the secure digital system's research field before starting the research. After it, the researcher determines which topic they want.
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data

Complex activity recognition using multiple on-body sensors is challenging due to missing samples, misaligned data times tamps across sensors, and variations in sampling rates. In this paper, we introduce a robust training pipeline that handles sampling rate variability, missing data, and misaligned data time stamps using intelligent data augmentation techniques. Specifically, we use controlled jitter in window length and add artificial misalignments in data timestamps between sensors, along with masking representations of missing data. We evaluate our pipeline on the Cooking Activity Dataset with Macro and Micro Activities, benchmarking the performance of deep convolutional bidirectional long short-term memory (DCBL) classifier. In our evaluations, DCBL achieves test accuracies of 88% and 72%, respectively, for macro- and micro-activity classifications, exceeding performance over state-of-the-art vanilla activity classifiers.
End-to-End Deep Residual Network for Semantic Segmentation

Abstract Recent work has made significant progress in improving for the pixelwise labeling with convolutional neural networks by using residual networks. In this paper, we explore the impact of residual network in semantic segmentation by residual encoder-decoder model. The residual block can improves the dimension of feature maps, and obtains more efficient feature maps, and then ensures the effective of deep network structure. Moreover, we select the activat ion function with zero-centered characteristics to speed up the model convergence. The SELU activation function is used to remove the BN function in the network due to normalized deploy ment, thereby reducing the amount of network calculations. The proposed residual encoder-dec oder model can still output high-precision prediction results without any post-processing, and t he convergence speed is significantly faster. Our approach has achieved 61.13% mIoU, 86.92% PA, 73.42% MPA on Camvid dataset.
Deep Learning for Analyzing Thyroid Nodule Malignancy Based on the Composition Characteristic of the Ultrasonography Images

There are ten characteristics of ultrasound thyroid nodules, and each one has its categories. One particular characteristic is composition characteristic, as the occurrence of its categories can be one of the lead to the incidence of malignancy. This study developed a method to help experts identifying the categories of the composition ultrasound characteristic using data collected from the Department of Radiology RSUP Dr. Sardjito. This dataset was already cropped by the experts, leaving only the thyroid nodule area as the region of interest. The dataset of ultrasound images was going to pre-processing first to remove the labels, markers, and unnecessary artifacts. To further remove any unnecessary artifacts, the pre-processed image was segmented. Afterward, the data augmentation begins using the synthetic minority over-sampling technique (SMOTE). The augmentation result was sent to LeNet to be classified into three categories those are cystic, solid, and complex. The testing result outperformed previous studies with 92% accuracy, 85.71% sensitivity, 92.50% specfficity, 93.5S% PPV, 95.09% NPV, 0.918 F Score, and 146. 4s testing time.
Mutual Restriction between Concurrence and Intrinsic Concurrence for Arbitrary Two-Qubit States

Concurrence is viewed as the most commonly approach for quantifying entanglement of two-qubit states, while intrinsic concurrence contains concurrence of four pure states consisting of a special pure state ensemble concerning an arbitrary two-qubit state. Thus, a natural question arises: Whether there is a specified relation between them. We firstly examine the relation between concurrence and intrinsic concurrence for the maximally nonlocal mixed state under a special unitary operation, which is not yet rigorously proved. In order to obtain a general result, we investigate the relation between concurrence and intrinsic concurrence using randomly generated two-qubit states, and derive an inequality relation between them. Finally, we take into account the relation between concurrence and intrinsic concurrence in open systems, and reveal the ratio of the two quantum resources, which is only correlated with the experiencing channels.
Solving Optimization Problems using Hybrid Metaheuristics: Genetic Algorithm and Black Hole Algorithm

During the last two decades, many optimization algorithms have been developed for solving optimization problems. These algorithms have inspired from an intelligent behaviour of a living species, or a natural phenomenon. Black Hole (BH) algorithm has been developed recently, it as a metaheuristic that is based on population imitates the black hole event in the universe, whereby circulating solution in the search space represents an individual star. Although the original BH has shown better performance on benchmark datasets, it does not possess exploration capabilities but performs a good local search. In this paper, a new hybrid metaheuristic based on the combination of BH algorithm and Genetic Algorithm (GA) is proposed. The type of the proposed hybrid algorithm is High level hybridization, when GA represent the initialization phase (Global Search), while BH represents the searching Phase (Local Search). The proposed GA-BH is examined based on several optimization problems. The results obtained showed that the proposed algorithm is better than the original BH and GA algorithm.
Combined Single and Multi-frame Image Super-resolution

Super-resolution (SR) is the process of obtaining a high-resolution (HR) image from one or more low-resolution (LR) images. Single-frame image super-resolution (SFISR) and multi-frame image super-resolution (MFISR) methods have been evolved independently for years. A neglected area in the field of SR is the combination of SFISR and MFISR. To fill this gap, we propose a novel optimization framework for using SFISR and MFISR simultaneously. Simulation results illustrate that our proposed method outperforms other benchmark SFISR and MFISR ones, both quantitatively and qualitatively.
People Tracking and Re-Identifying in Distributed Contexts: PoseTReID Framework and Dataset

We introduce a generic framework which is designed for effective real-time 2D multi-person tracking in distributed people interaction spaces like malls or amusement parks where long-term people's identities are important for other studies such as behavior analysis. The framework relies on multi-person pose detector for detecting bodies' parts and a recognizer for re-identifying people. We carefully selected existing sub-modules for our contexts, and the framework can efficiently track people and re-identify them using faces even later after tracking losses or reappearances of people. Along with this paper, since all existing datasets for people tracking barely have visible faces, we also introduce our people tracking dataset which is specifically designed for distributed people interaction spaces where people's faces are visible and recognizable. The results of the proposed PoseTReID framework are very interesting in all scenarios when compared on our dataset to a recent state-of-the-art tracking method. This efficient people tracking framework in distributed and interactive contexts, which is achieved here, is an important brick towards future works of people grouping and behavior analysis.
Hardware- and Memory-Efficient Architecture for Disparity Estimation of Large Label Counts

Belief propagation (BP)-based stereo matching has popular owing to its regularity and ability to yield promising results. Some commonly observed hardware-implementation challenges pertaining to the use of this algorithm are large memory requirements and trade-offs between speed and chip area, along with an increasing disparity range. The paper presents a hardware- and memory-efficient architecture for building a BP-based disparity estimation system capable of overcoming issues associated with large disparity ranges. The proposed architecture is memory-efficient owing to the regularity of its underlying algorithm. In addition, the improved hardware efficiency can be attributed to processing element modifications to demonstrate shareable characteristics. Results obtained in this study reveal a 67.8% reduction in required memory corresponding to a time-area term complexity of O(L(logL) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), where L denotes the disparity range. This result is in stark contrast to the O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> logL) and O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) complexities observed in extant studies. Compared to state-of-the-art implementations, the proposed architecture offers an 86.2% gate count reduction for message update units at a disparity range of 512. These results confirm the proposed architecture's suitability for use in large disparity scenarios.
Continuous Distributed Key Generation on Blockchain Based on BFT Consensus

VSS (Verifiable Secret Sharing) protocols are used in a number of block-chain systems, such as Dfinity and Ouroboros to generate unpredicted random number flow, they can be used to determine the proposer list and the voting powers of the voters at each height. To prevent random numbers from being predicted and attackers from corrupting a sufficient number of participants to violate the underlying trust assumptions, updatable VSS protocol in distributed protocols is important. The updatable VSS universal setup is also a hot topic in zkSNARKS protocols such as Sonic [19]. The way that we make it updatable is to execute the share exchange process repeatedly on chain, this process is challenging to be implemented in asynchronous network model, because it involves the wrong shares and the complaints, it requires the participant has the same view towards the qualified key generators, we take this process on chain and rely on BFT consensus mechanism to solve this. The group secret is thus updatable on chain. This is an enhancement to Dfinity. Therefore, even if all the coefficients of the random polynomials of epoch n are leaked, the attacker can use them only in epoch n+2. And the threshold group members of the DKG protocol can be updated along with the updates of the staked accounts and nodes.
Research on Automatic Dish Recognition Algorithm Based on Deep Learning

Deep learning can automatically extract and learn multi-layer feature representations hidden between data, and has been successfully applied in image recognition and segmentation, semantic analysis and other fields. In order to improve restaurant settlement efficiency, save time and cost, this paper proposes a method that uses Convolution Neural Network and Huff transform to joint recognition, conducts preliminary detection of images taken with multiple dishes, uses Huff transform to extract and label individual dishes according to the shape of the plate. Then, the article constructs a convolutional neural network in the classification algorithm. Under different network parameters, this paper experiments on 10 dishes images in the cleaned VireoFood172 dataset. Through continuous optimization of network parameters, the classification accuracy of dishes Top2 has reached 87.2%. Under the fixed environment, the plate can be effectively extracted. It is proved that the method has a certain improvement in performance compared with the traditional dish recognition method, and can provide effective help for dish recognition.
The Concept of Moral and Aesthetic Education in a Modern Foreign Language Classroom

The problem of increasing deviant behavior of adolescents is widespread and requires the development of new approaches to its solution. We considered the basics of moral and aesthetic education in school in a holistic manner. The purpose of our research was to define the concept of moral and aesthetic education in the modern foreign language classroom and develop the structure of moral and aesthetic education.