Reviewerly/exHarmony huggingface dataset:
An Improved Method of Blockchain Consortium Chain Consensus Mechanism Based on Random Forest Model

As blockchain technology has attracted more and more attention from all walks of life, the industry application prospects of alliance chains are very broad. Consensus algorithms are very important in blockchain applications. Now the alliance chain mainly uses the PBFT consensus algorithm, but the main node selection step of the algorithm needs to be maintained by all nodes in the alliance chain, which has high consumption and high latency performance and low security. The problem. This paper uses the integrated learning random forest model of machine learning by adding a credit scoring mechanism, taking the characteristic data of some influencing factors of the alliance chain that affect the selection of the master node as input, the selection of the master node as the observation sample, and the training sample to obtain the prediction model. The predicted master node is used to replace the master consensus node selection step in the PBFT algorithm to complete the consensus, and RFBFT (Random Forest Byzantine Fault-Tolerant Algorithm) is proposed. The high accuracy of the experimental random forest prediction model ensures the accuracy and safety of the master node selection. Comparing the algorithms before and after the improvement, the consumption of RFBFT is reduced by 20% and the delay is reduced by 19%, which improves the operation of the alliance chain. Performance and safety.
Encryption of Images Using the Modified AES Algorithm and Its Comparison with the Original AES

In today’s world, optimization is what needs to be talked about. The explosive growth of the Internet and communication technologies has made the widespread use of images inevitable. The special characteristics of the image, such as high bit rate with limited bandwidth, redundancy, bulk capacity, and correlation between pixels, make standard algorithms unsuitable for image encryption. As cybercrime has reached an all-time high, pre-encryption of data is required for protection. From personal data traded on the dark web to application vulnerabilities, our data is constantly monitored, making encryption paramount. When it comes to encryption, AES (Advanced Encryption Standard) is the gold standard for security. It is a subset of the Rijndael block cipher, which uses a 128-bit data block and a 128-bit, 192-bit and 256-bit key size, giving three different versions of AES, namely AES128, AES192, and AES256. It is a symmetric encryption algorithm that uses the same key for encryption and decryption. The article proposes an algorithm based on a modified AES key extension, in which the encryption process is a bitwise exclusive or an operation of a set of image pixels together with 128-bit key that changes for each set of pixels.
Building Dataset and Deep Learning-Based Inception Model for the Character Classification of Tigalari Script

Image classification and optical character recognition are important research areas in computer vision. With advancement in machine learning and deep learning techniques, these fields are attracting lot of researchers to develop models with near human perfection. Many character recognition models are available for modern languages. But, it is still a challenging task to analyze the handwritten text in Indian scripts. It is further complex for the scripts with large alpha syllabary and complex nature. This paper proposes a technique for the recognition and classification of ancient Tigalari characters from the handwritten text. Tigalari is widely used in coastal Karnataka and Kerala for documenting Sanskrit, Tulu, and Malayalam languages. Method involves the creation of database, design of deep convolution neural network (DCNN)-based architecture to classify the text, training the model with the data and recognizing text using test set. Being an inception model for this script, proposed method classifies 46 basic Tigalari characters with an impressing accuracy of 98.55%.
Representation Learning in Sequence to Sequence Tasks: Multi-filter Gaussian Mixture Autoencoder

Heterogeneity of sentences exists in sequence to sequence tasks such as machine translation. Sentences with largely varied meanings or grammatical structures may increase the difficulty of convergence while training the network. In this paper, we introduce a model to resolve the heterogeneity in the sequence to sequence task. The Multi-filter Gaussian Mixture Autoencoder (MGMAE) utilizes an autoencoder to learn the representations of the inputs. The representations are the outputs from the encoder, lying in the latent space whose dimension is the hidden dimension of the encoder. The representations of training data in the latent space are used to train Gaussian mixtures. The latent space representations are divided into several mixtures of Gaussian distributions. A filter (decoder) is tuned to fit the data in one of the Gaussian distributions specifically. Each Gaussian is corresponding to one filter so that the filter is responsible for the heterogeneity within this Gaussian. Thus the heterogeneity of the training data can be resolved. Comparative experiments are conducted on the Geo-query dataset and English-French translation. Our experiments show that compares to the traditional encoder-decoder model, this network achieves better performance on sequence to sequence tasks such as machine translation and question answering.
Normal or tingly? A story about hands and feet

Spontaneous sensations (SPS) are sensations that are felt in the body in the absence of external stimulation. The literature on SPS has used explicit measures, such as questionnaires to explore SPS, while no studies to date have examined SPS on an implicit level. This study was conducted to collect representative stimuli that can be used to build such a task, for example, an Implicit Association Test.An online survey was completed by 18 participants to identify the most frequent words used to describe our limbs in the presence or absence of SPS.Individuals who perceive and those who do not perceive SPS in their limbs describe their limbs as normal, while the most frequently described SPS were itching and tingling.Thus, we use the same words/adjectives to describe how we perceive our limbs. However, the way we experience SPS varies as we experience more SPS in hands than feet.
Denoising of Computed Tomography Images for Improved Performance of Medical Devices in Biomedical Engineering

Image preprocessing is the most significant step for improved segmentation and classification in the Computer Aided Diagnosis (CAD) system. Certain noises such as Gaussian and Poisson destroy the performance of the CAD system. There are various types of medical images such as Computed tomography (CT), Magnetic resonance imaging (MRI), Positron emission tomography (PET) and Ultrasound. CT is the most commonly used imaging modality used in the detection of pancreatic cancer responsible for an increased rate of deaths globally. These images are attenuated during compression, transmission and acquisition which cannot be removed completely. The paper presents the comparative analysis of the filters used for denoising the CT images in order to improve the accuracy of the segmentation and classification for the improved performance of the CAD system. The Peak-signal-to-noise (PSNR), Mean square error (MSE) and Structural similarity index (SSIM) have been calculated for the images denoised using the anisotropic diffusion filter, wavelet filter, bilateral filter, Non-local mean filter (NLM), wiener filter, total variation filter (TV) BM3D filter, median and Gaussian filter. The wavelet filter outperformed the other filters with 28.43, 103.12 and 0.59 values of PSNR, MSE and SSIM respectively. The suggested approach can be embedded in CT scanners to filter out the noises present in images with inbuilt technology and improve the performance of diagnostic devices.
Iterative Cross-Scanner Registration for Whole Slide Images

The successful registration of digitized microscopic images is required for many applications in digital pathology. In particular, the registration of specimens scanned by different slide scanning systems may be beneficial to transfer expert annotations from one image domain to another and thereby reduce labeling effort. We present an iterative approach to register microscopic specimens digitized with multiple scanning systems, aiming to compute an optimal global transformation for the images at highest resolution. For this purpose, an initial registration based on a down-scaled version of the images is followed by a patch-based iterative update scheme. We make use of the hierarchical structure of digitized whole slide images to gradually approximate the optimal transformation. By using kernel density estimation to weight local transformation estimates, the influence of registration errors can be further mitigated. We validate our method on five histologic and five cytologic samples, each scanned with four different scanning systems. Furthermore, we perform first experiments on samples stained with different stain combinations. Our experiments demonstrate the potential of the proposed method for a variety of datasets and application fields.
Post-Crash Detection and Traffic Analysis

Road traffic accidents (RTAs) have arisen as a major public concern which requires a multidisciplinary strategy to address it. In highly populated countries like India, the rise in RTA injuries and deaths is alarming. The number of deadly and disabling road accidents is increasing every day, posing a serious public health issue for all authorities involved in their prevention. In the present paper, a post-crash detection is performed using CCTV cameras, so as to aid in saving life and providing quick response to an emergency after a crash is detected. There is also functionality of traffic analysis which does object detection through CCTV cameras and indicates about the types of vehicles moving in that area. It can also be used for security purposes in educational and residential campus areas. Furthermore, the intersections prone to accidents can be monitored and can notify the users or the authorities in case an accident occurs. Thus, the best path can be taken up by analyzing traffic in an area. The present work shall have a very beneficial impact on saving human lives.
Complementary Temporal Classification Activation Maps in Temporal Action Localization

Weakly-supervised temporal action localization aims to correctly predict the categories and temporal intervals of actions in an untrimmed video by using only video-level labels. Previous methods aggregate category scores through a classification network to generate temporal class activation map (T-CAM), and obtain the temporal regions of the object action by using a predetermined threshold on generated T-CAM. However, class-specific T-CAM pays too much attention to those regions that are more discriminative for classification tasks, which ultimately leads to fragmentation of localization results. In this paper, we propose a complementary learning strategy for weakly-supervised temporal action localization. It obtains the erasure feature by masking the high activation value position of the original temporal class activation map, and takes it as input to train an additional classification network to produce complementary temporal class activation map. Finally, the fragmentation problem is alleviated by merging two temporal class activation map. We have conduct sufficient experiments on the THUMOS'14 and ActivityNet1.2, and the experimental results show that the localization performance of the proposed method has been greatly improved compared with the existing methods.
Virtual Equipment System: Toward Peripersonal Equipment Slots with Machine Learning

Peripersonal Equipment Slots are locations to place equipment that are egocentric to the user but do not reside within the user’s personal space. Instead, these slots reside within the user’s arm reach, known as peripersonal space. In this paper, we present our initial approach and results from our attempt to realize peripersonal equipment slots through the use of machine learning.
Video Magnification Techniques: Medical Applications and Comparison of Methods

Abstract The unassisted visual system cannot note minute temporal variations in video and image sequences. In many applications, these differences and small signals are highly informative. A new technique used to expose video variations by measuring and amplifying video variations over time in a fixed position (pixel) was used to Eulerian video magnification (EVM). The objective of the study is to investigate and evaluate different processes for the creation and testing of EVM techniques and video quality parameters for each one of those methods. This research employed four new methods; EVM, Riesz pyramid for fast phase-based video magnification (FPBM), phase-based video magnification (PBM), and Enhanced Eulerian video magnification (E2VM). The experimental findings compared with their output for certain enlargement methods; time and quality parameters of image. A new magnification method is required based on the study of the exiting methods, which takes account of noise elimination, video quality and time reduction.
Exploiting Named Entity Recognition via Pre-trained Language Model and Adversarial Training

Named Entity Recognition (NER) is a typical sequence labeling problem as a foundation of text information processing, which has gradually played a key role in the technology of natural language processing (NLP). Also, it is widely applied to handle enormous sub-tasks of NLP. The current methods mainly use BERT and other powerful deep learning components to extract the semantic information of sentences from complex texts. Then, they perform sequence labeling to identify entity information. However, such methods usually encounter the problem of overfitting. Moreover, those methods always show poor performance in generalization ability and robustness. Compared with such methods, to extract effective entities, we propose a named entity recognition model based on adversarial training. For the encoding layer, we present Bidirectional Encoder Representations From Transformers (BERT) as the pre-trained language model to get the word vector to enrich the semantic representation. After combining the word vector obtained by BERT and the word vector obtained by the glove, we investigate a method of Bi-LSTM in the feature extraction layer for training to adapt to the disturbance and recognize the named entity. Our core innovation is introducing the Fast Gradient Method (FGM) to generate adversarial examples for the adversarial attack. The adversarial attack would add disturbance data to the encoding layer. In this way, we successfully strengthen the abilities of both generalization and robustness, thereby improving the model's performance. We conduct experiments on widely used NER datasets on Chinese Resume NER for the model we proposed. Additionally, the experimental results show that our model has achieved some comparable results on recognizing named entities. The Precision rate, Recall rate, and F1 score obtained are respectively 0.9541,0.9538,0.9536.
High-Quality Noise Detection for Knowledge Graph Embedding with Rule-Based Triple Confidence

Knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise.
Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features

Roles of nodes are defined as classes of equivalent nodes. Nodes that have similar local connective patterns may share the same role. As a complementary concept of community, role can also help to recognize real-world entities. For example, it can denote identity or function in social networks. Role has been studied over the past decades, and learning role-based network representations is crucial to many downstream tasks. The important step for role-based network embedding method is extracting features to measure structural similarity instead of proximity. Although some methods have been developed to capture role features to learn structural similarities between nodes, they all design these features of fixed types, such as the global, local, and higher-order features. These features can only represent a certain type of structure, and it is very difficult to model the complex relationship between different scale features in the field of role-based network embedding. Therefore, we propose a novel role-oriented network embedding framework based on adversarial learning between higher-order and local features (ARHOL) to generate powerful role-based node representations. The higher-order features are discrete so we leverage the Auto-Encoder on them to obtain continuous representations. Then we apply the GIN on its outputs to aggregate local information. Finally, we consider the GIN as the generator and design an adversarial game between local features and GIN outputs to integrate these two aspects of features, which can enhance each other and improve the robustness. The extensive experiments on real-world networks demonstrate the superiority and efficiency of our model, and prove the effectiveness of integrating higher-order and local features.
Radar Based Gestures of Deaf Sign Language Signatures

There are challenges facing by deaf people to communicate with people who do not understand their sign language. Identification of hand gestures of deaf sign language using radar may lead to the development of an automatic system that can ease deaf people communication. This paper describes the extractions of signatures of the radar-based hand gestures of deaf sign language. The gesture recording procedure was first designed before acquiring the gesture signal. Fast Fourier Transform Analysis, Power Spectral Density and Short-Time Fourier Transform were carried out on the five signs to study the characteristics of the gesture signals and extract their features. The spectrogram was then threshold to remove the background noise. It was found that there are unique patterns of the power spectrum and spectrogram for each gesture and there is a similarity in the features for all subjects. These features are very useful for recognising each gesture.
Computational Complexity of Jumping Block Puzzles

In combinatorial reconfiguration, the reconfiguration problems on a vertex subset (e.g., an independent set) are well investigated. In these problems, some tokens are placed on a subset of vertices of the graph, and there are three natural reconfiguration rules called “token sliding,” “token jumping,” and “token addition and removal”. In the context of computational complexity of puzzles, the sliding block puzzles play an important role. Depending on the rules and set of pieces, the sliding block puzzles characterize the computational complexity classes including P, NP, and PSPACE. The sliding block puzzles correspond to the token sliding model in the context of combinatorial reconfiguration. On the other hand, a relatively new notion of jumping block puzzles is proposed in puzzle society. This is the counterpart to the token jumping model of the combinatorial reconfiguration problems in the context of block puzzles. We investigate several variants of jumping block puzzles and determine their computational complexities.
Deciphering Feature Effects on Decision-Making in Ordinal Regression Problems: An Explainable Ordinal Factorization Model

Ordinal regression predicts the objects’ labels that exhibit a natural ordering, which is vital to decision-making problems such as credit scoring and clinical diagnosis. In these problems, the ability to explain how the individual features and their interactions affect the decisions is as critical as model performance. Unfortunately, the existing ordinal regression models in the machine learning community aim at improving prediction accuracy rather than explore explainability. To achieve high accuracy while explaining the relationships between the features and the predictions, we propose a new method for ordinal regression problems, namely the Explainable Ordinal Factorization Model (XOFM). XOFM uses piecewise linear functions to approximate the shape functions of individual features, and renders the pairwise features interaction effects as heat-maps. The proposed XOFM captures the nonlinearity in the main effects and ensures the interaction effects’ same flexibility. Therefore, the underlying model yields comparable performance while remaining explainable by explicitly describing the main and interaction effects. To address the potential sparsity problem caused by discretizing the whole feature scale into several sub-intervals, XOFM integrates the Factorization Machines (FMs) to factorize the model parameters. Comprehensive experiments with benchmark real-world and synthetic datasets demonstrate that the proposed XOFM leads to state-of-the-art prediction performance while preserving an easy-to-understand explainability.
On the Effectiveness of Interpretable Feedforward Neural Network

Deep learning models have achieved state-of-the-art performance in many classification tasks. However, most of them cannot provide an explanation for their classification results. Machine learning models that are interpretable are usually linear or piecewise linear and yield inferior performance. Non-linear models achieve much better classification performance, but it is usually hard to explain their classification results. As a counter-example, an interpretable feedforward neural network (IFFNN) is proposed to achieve both high classification performance and interpretability for malware detection. If the IFFNN can perform well in a more flexible and general form for other classification tasks while providing meaningful explanations, it may be of great interest to the applied machine learning community. In this paper, we propose a way to generalize the interpretable feedforward neural network to multi-class classification scenarios and any type of feedforward neural networks, and evaluate its classification performance and interpretability on interpretable datasets. We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful explanations. Thus, this kind of neural network architecture has great practical use.