Reviewerly/exHarmony huggingface dataset:
Discriminatory Expressions to Improve Model Comprehensibility in Short Documents

Microblogging sites are being used as analysis avenues due to their peculiarities (promptness, short texts...). Lately, researchers have focused mainly in classification performance rather than interpretability. When the problem requires transparency, it is necessary to build interpretable pipelines, and even though, resulting models are too complex to be considered comprehensible, making it impossible for humans to understand the actual decisions. This paper presents a feature selection mechanism that is able to improve comprehensibility by using less but more meaningful features. Results show that our proposal is better and the most stable one in terms of accuracy, generalisation and comprehensibility in microblogging context.
A Reliable Patient Indoor Monitoring System Based on IoT in Ambient-Assisted Living

The health monitoring system (HMS) has become one of the most important technological systems in ambient-assisted living (AAL). AAL involves the use of technologies (like sensors) to ensure elderly people a healthy and independent life at home. The purpose of AAL is to aid the elderly patients that are at risk of dying unexpectedly, either as a result of their refusal to attend health services or as a result of a lack of medical treatment provided to the patients at the appropriate time. The primary goal of this paper is to present a reliable Internet of things (IoT)-based indoor HMS that continuously monitors patient’s health parameters at home such as pulse, heartbeat and temperature. By sending sensor data to an IoT analytic platform service called “Thinkspeak” over Wi-Fi, Arduino automatically monitors and stores data from the sensors in a Google Sheet. Thinkspeak gathers sensor data and sends it to the cloud in a secure manner so that it can be analysed and visualized in real time. In case of emergencies, this HMS can also send alerts to an email address through the “Webhooks” API on IFTTT service platform.
Introduction

This chapter provides a brief introduction of the textbook. The introduction chapter is in two sections. The first section explains what flow noise is and why it is important. The second section gives an overview of what is contained in each chapter of the book.
Hybrid Algorithm with Probability Density Function over 1D to 2D Electromagnetic

Summary Inversion of geophysical data has always been important due to nonlinear, ill pose and multidimensionality problem. In addition, improper sampling and noise in data lead the conventional inversion techniques to improper solution. This problem increases with complexity of problem from 1D to 2D and with the afraid to converge in local solution. Thus the study is focus in using global optimization techniques as it takes wide search ranges. As well as its aim to use a hybrid of particle swarm optimization (PSO) with Grey Wolf optimizer (GWO) due to their individual properties of balancing the exploration and exploitation characteristic. The result of proposed technique fulfils the motive of hybridizing algorithm to balance the exploration and exploitation properties. This hybrid algorithm with a rough priori information as well as with a small group of search agents/ particles can give faster convergence to optimum. And the use of parallel pooling has increase the performance at faster rate. Therefore, the present work aims at imposing the hybrid algorithm over the 2D electromagnetic data with the application of parallel pooling.
Fisher Task Distance and its Application in Neural Architecture Search

We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices, called Fisher task distance. This distance represents the complexity of transferring the knowledge from one task to another. We provide a proof of consistency for our distance through theorems and experiments on various classification tasks from MNIST, CIFAR-10, CIFAR-100, ImageNet, and Taskonomy datasets. Next, we construct an online neural architecture search framework using the Fisher task distance, in which we have access to the past learned tasks. By using the Fisher task distance, we can identify the closest learned tasks to the target task, and utilize the knowledge learned from these related tasks for the target task. Here, we show how the proposed distance between a target task and a set of learned tasks can be used to reduce the neural architecture search space for the target task. The complexity reduction in search space for task-specific architectures is achieved by building on the optimized architectures for similar tasks instead of doing a full search and without using this side information. Experimental results for tasks in MNIST, CIFAR-10, CIFAR-100, ImageNet datasets demonstrate the efficacy of the proposed approach and its improvements, in terms of the performance and the number of parameters, over other gradient-based search methods, such as ENAS, DARTS, PC-DARTS.
That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory

The evolution of language follows the rule of gradual change.Grammar, vocabulary, and lexical semantic shifts take place over time, resulting in a diachronic linguistic gap.As such, a considerable amount of texts are written in languages of different eras, which creates obstacles for natural language processing tasks, such as word segmentation and machine translation.Although the Chinese language has a long history, previous Chinese natural language processing research has primarily focused on tasks within a specific era.Therefore, we propose a cross-era learning framework for Chinese word segmentation (CWS), CROSSWISE, which uses the Switch-memory (SM) module to incorporate era-specific linguistic knowledge.Experiments on four corpora from different eras show that the performance of each corpus significantly improves.Further analyses also demonstrate that the SM can effectively integrate the knowledge of the eras into the neural network.
Further Understanding Towards Sparsity Adversarial Attacks

The emergence of adversarial attacks validated the vulnerability of neural networks. Recently, highly sparse adversarial examples gradually attracted the attention of researchers for their ability in explaining what the neural networks have learned from datasets. For further understanding the sparsity adversarial attacks, we propose two different white-box techniques, the BPs, and binary fitting. BPs generates adversarial examples by validating the existence of adversarial samples in a top-down way, that is, decreasing the upper border of tempered pixel positions step by step. Additionally, the binary fitting method approximates the $$\mathcal {L}_0$$ distance to search for the optimal adversarial example considering the 0-norm function is non-convex. Experimental results illustrate that the proposed methods exhibit superior or competitive performance to the state-of-the-art attacks.
Quantum Fuzzy Least Squares Algorithm for Uncertain Environment

With the development of information technology, modern society has entered the era of big data. Big data means a huge amount of data, as well as the complexity and uncertainty of data. In order to find valuable data efficiently and accurately from the big data environment, researchers use machine learning algorithms to establish relevant data models to predict and classify new data. In the process of establishing the linear system model, the commonly used method is the least square method, which can easily obtain the optimal parameters of the linear system and make the established linear model more accurate. In order to deal with the fuzziness of the data itself and reduce the influence of abnormal data on the linear model, researchers put forward the fuzzy linear model. When the amount of data is increasing, the time required for traditional computers to process data and establish models is increasing. In order to reduce the time of establishing the model, a quantum fuzzy least square method is proposed on the basis of predecessors. The algorithm uses the advantages of quantum computing in data processing to improve the efficiency of fuzzy linear model establishment and reduce the model establishment time. Relevant research and analysis prove the efficiency of the algorithm.
User Experience of Social Web-based Virtual Reality for the Hybrid and Blended Learning Classroom

The use of social webVR for a classroom can offer a collaborative real-time environment that bridges the gap between virtual video conferences and gaming platforms. This paper examines how to use social webVR in a virtual classroom. It addresses some of the unique UX challenges of designing for a social VR classroom space. Finally, it will address access to the virtual environment through multiple devices, including an Oculus HMD, laptop, tablet, and smartphone. Social web-based VR offers promising potential. Designing a human-centered virtual environment and considering all participants' total user experience is critical to a successful learning tool. Future virtual environments could replace the physical classroom in some cases. This paper is not about replacing the physical classroom experience but how social VR can add to the experience in a hybrid or blended learning environment where students are not always in a physical classroom 100% of the time.
Robust Multi-task Least Squares Twin Support Vector Machines for Classification

Lately, there has been a shift to multi-task learning. Multi-task learning performs better than the classical single task learning by learning from the training signals inherent in all the tasks. Inspired by multi-task least squares twin support vector machine, we propose a robust multi-task least squares twin support vector machine. In the proposed work, we introduce an error factor which successfully handles the noise. The proposed model is easy to implement and fast. This allows the model to be of direct application to larger and real-world data sets. In addition, the model deals with nonlinear data patterns by using kernel trick.
Implementation of Traffic Sign Recognition System on Raspberry Pi

In this paper we intent to design and develop an intelligent traffic sign recognition system on Raspberry Pi. In the real situation, we could find the fact that drivers are often drive the car tiredly and cause dangerous situation. In order to avoid the dangerous accidents, automatic drive applications are appeared to assist drivers. However, the automatic driving car does not recognize the traffic sign correctly in real-time and depend on non-real time traffic sign information so that the dangerous accidents are appeared. So, in this paper we will implement a system that have recognition mechanism based on deep learning to distinguish the traffic signs. With the developing application, we would not depend on the map updating frequently to know the traffic sign situation. This mechanism also helps drivers to have secure driving behavior. On the other hand, we also design a prototype of automatic car based on Raspberry pi development board to evaluate our proposed method.
Reliable Collaboration Mechanism of Side End Resources for Discrete Services of Virtual Power Plants

With the substantial increase in the number of participants in virtual power plants and the in-depth popularization of power Internet of Things, a large number of IoT terminals will be deployed on the internal and external networks of the system. Service terminals are connected to the network in different ways, and transmitted to the service system and cloud platform via the backbone network. In the process of data transmission and processing, the communication, computing, and storage resources required by the service terminal are highly heterogeneous and different. The bearing relationship between services and resources, and the connection relationship between resources in different network segments are complex, and it is impossible to Realize the integrated management of end-to-end resources. In response to the above problems, this paper proposes a MEC server collaborative computing model, which combines the computing capabilities of the service terminal itself and edge nodes to coordinate the offload rate between different devices to minimize the system's delay and energy consumption, and proposes A load balancing model that transfers tasks between overloaded and lightly loaded edge nodes to maximize system operating efficiency. Through simulation experiments, the method proposed in this paper effectively reduces the service delay and system energy consumption, and improves the reliability of the system.
End-to-End Speech Synthesis Method for Lhasa-Tibetan Multi-speaker

Tibetan text-to-speech generally focuses on a single speaker or a single dialect, and there is a lack of research on Tibetan multi-speaker speech synthesis. This paper explores the speech synthesis methods based on an end-to-end model for Lhasa-Tibetan multi-speaker. We propose to convert Tibetan characters into Latin letters to improve the effect of model learning. We compare the end-to-end model using the speaker ID embedded into the spectrogram feature prediction network against using some WaveNet vocoders trained on specific speaker data. Referring to the results of objective and subjective experiments, our method has better speech quality than the model using some WaveNet vocoders trained on specific speaker data.
A Survey Paper on Sign Gesture Interpreter for Better Communication between a Normal and Deaf Person

Deaf and hearing-impaired people all around the globe choose to communicate through sign language. When utilised in a computer vision or other approach, recognition of sign language can have varied degrees of success. Sign language is claimed to contain a systematic collection of motions, each with its own mean- ing. Some studies have proven to be successful in identifying sign language, but commercialization is prohibitively expensive. Researchers are now paying greater emphasis to building commercially viable Sign Language Recognition systems. Researchers conduct their studies in a variety of methods. It all be- gins with the data collection methods. Because of the high cost of a decent device, the data collecting technique varies, but a low-cost approach is required for the Sign Language Recognition System to be commercialised. The method- ologies utilised to create Sign Language Recognition differ from one researcher to the next. In comparison to other approaches, each method has its own set of restrictions. The purpose of this work is to examine several ways to sign language recognition and determine the most effective method that has been adopted by researchers.
Analysis of video conferencing platforms for online teaching during the Covid-19 pandemic

The global emergency due to the COVID-19 disease has forced educational systems around the world to move from a face-to-face pedagogical model to an online model which is developed in virtual learning environments. In this sense, this research aims to perform an analysis of the main video conference platforms such as Microsoft Teams, Zoom, Google Meet and WhatsApp that are used for teaching in order to understand the characteristics and usefulness that can be given to them in the teaching-learning process as well as the review of previous studies that validate its application and effects in the educational process.
Byzantine Fault-Tolerant Consensus Algorithm Based on Dynamic Weight Sharding

As a consensus algorithm widely used in the alliance chain, PBFT achieves a good balance between security and fairness, but it can only exert good performance in a network with a small number of network nodes. With the continuous joining of nodes, the efficiency of consensus gradually declines, and it does not have horizontal expansion. In order to solve the horizontal expansion ability of PBFT in order to solve its performance problems in large-scale networks, this paper introduces the Byzantine fault-tolerant consensus algorithm DWS_BFT based on the dynamic weight sharding strategy. Through the sharding technology, the nodes in the alliance chain are divided into different heterogeneous areas, and each area has a parallel consensus to improve the consensus efficiency. And by reasonably adjusting the weight score of the node, and dynamically adjust the sharding members according to the node weight score, the problem of low security of a single shard brought by the sharding technology is solved. At the same time, transactions are divided into different processing between on-chip transactions and inter-chip transactions, to solve the problem of low performance of cross-shard transactions caused by sharding technology.
Simulating Strongly Coupled Quantum Field Theory with Quantum Algorithms

Nonperturbative quantum field theory problems can often be difficult to solve with classical algorithms. Researchers now develop quantum computing algorithms to understand such problems in the Hamiltonian formalism.
Bi-Objective Optimizing for Data-Intensive ‎Scientific Workflow Scheduling in Cloud ‎Computing

Cloud Computing is increasingly recognized as a new way to use on-demand, computing, ‎storage and network services in a transparent and efficient way. Cloud Computing environment ‎consists of large customers requesting for cloud resources. Nowadays, task scheduling problem ‎and data placement are the current research topic in cloud computing. In this work, a new ‎technique for workflow scheduling and data placement are proposed based on genetic ‎algorithm to fulfill a final bi-objective goal such as minimizing total workflow response time and ‎cost of their execution. the scheduling of scientific workflows is considered to be an NP-complete ‎problem, i.e. a problem not solvable within polynomial time with current resources. The ‎performance of this proposed algorithm has been evaluated using CloudSim toolkit, Simulation ‎results show the effectiveness of the proposed algorithm in comparison with well-known ‎algorithms such as genetic algorithm with Random data placement. ‎
Oscillation Measurement of the Magnetic Compass Needle Employing Deep Learning Technique

This paper presents a novel approach to measure the oscillation of the magnetic compass needle. The proposed approach uses a deep convolutional neural network to detect both pivot and the end of the needle. Based on the detected positions of these parts, the angle between the current and initial positions of the needle is estimated. The oscillation of the magnetic needle can be determined through the relationship of the angle to time. The experimental results indicate that the deep learning based method outperforms the traditional object tracking methods in term of accuracy for the application of measuring the oscillation of the magnetic needle.
Chemostratigraphic Automations: Chemostratigraphic Data Analytics and Interpretations

Summary Chemostratigraphy and the interpretation of geological boundaries is a very time consuming and multivariate and multidimensional problem. Standard manual workflows in chemostratigraphy can be fully automated, reducing uncertainty in the interpretation and use of all the data. This paper presents a new workflow that is based on a five-step approach that automates this process. It begins with data QCing and Principle Component analysis that discards the bad data and creates new set of a few new variables that include all the available variability. It then performs Statistical picking and a Quartile Analysis, that picks stratigraphic tops and identifies chemical components that are important for the correlation. Through this process the zones and robust variables are used for training through mashine learning and Discriminant Function Analysis (DFA). The classifiers are used for training and validation of the models, prediction and correlation of stratigraphic boundaries in uninterpreted wells. The approach was used on an eleven well geological study and produced similar results to those of the standard workflow in minutes rather than weeks. This method, used in combination with the standard workflow, can deliver more robust and accurate geological correlations and assistance for the correlation when there is uncertainty.
Detection of Audio Copy-Move-Forgery with Novel Feature Matching on Mel Spectrogram

Audio copy-move-forgery created by copying one or more segments of an audio file and pasting it in a different position within the same audio is one of the most widely used methods in the field of audio forensics. This type of forgery is ease to apply but difficult to detect in case of post-processing operations applied to forged speech to hide traces of forgeries. In this paper, for detection and localization of the audio copy-move forgery, a robust method using a keypoint-based approach on the Mel spectrogram representation of audio is proposed. In the proposed method, first, Mel spectrogram image is created from the input audio. Then, SIFT keypoints are obtained from each RBG color channel of this image. The obtained keypoints from each channel are matched via feature vectors to reveal the clues of the forgery regions and the image sub-blocks whose keypoints are determined to be the center are labeled as forged blocks and then the blocks in the neighborhood of the forged blocks are investigated whether forged or not. The determination of the forged regions is completed with the post-processing stage. In this stage, possible false positives are eliminated and the forged regions in the spectrogram image are marked. By utilizing the positions of the forged regions in the spectrogram image, the forged segments are marked in the audio file. Experimental studies are carried out on the pitch-based dataset. The detailed performance results of popular referenced studies on this dataset are presented. The performance results prove that the proposed method is more robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.