Reviewerly/exHarmony huggingface dataset:
Automatic Speech Recognition Post-Processing for Readability: Task, Dataset and a Two-Stage Pre-Trained Approach

Nowadays Automatic Speech Recognition (ASR) systems can accurately recognize which words are said. However, due to the disfluency, grammatical error, and other phenomena in spontaneous speech, the verbatim transcription of ASR impairs its readability, which is crucial for human comprehension and downstream tasks processing that need to understand the meaning and purpose of what is spoken. In this work, we formulate the ASR post-processing for readability (APR) as a sequence-to-sequence text generation problem that aims to transform the incorrect and noisy ASR output into readable text for humans and downstream tasks. We leverage the Metadata Extraction (MDE) corpus to construct a task-specific dataset for our study. To solve the problem of too little training data, we propose a novel data augmentation method that synthesizes large-scale training data from the grammatical error correction dataset. We propose a model based on the pre-trained language model to perform the APR task and train the model with a two-stage training strategy to better exploit the augmented data. On the constructed test set, our approach outperforms the best baseline system by a large margin of 17.53 on BLEU and 13.26 on readability-aware WER (RA-WER). The human evaluation also shows that our model can generate more human-readable transcripts than the baseline method.
Intelligence in Countering Hoaxes in Indonesia

This research is motivated by the existence of many potential people who are consumed by hoax information that is available on various social media that are freely circulating in cyberspace. The potential for spreading hoax information in the Salatiga area is very disturbing to the community so that appropriate efforts are needed to overcome these problems. The purpose of this study is to explain the function of gathering intelligence in preventing the spread of hoaxes ahead of the presidential election in the jurisdiction of the Salatiga Police, the mechanism for raising intelligence and explaining the factors that influence it. The knife of analysis in this research is the theory of strategic management, management theory and the concept of raising intelligence. This research was conducted with a qualitative approach with a case study research method, which was then carried out through interviews, observations and document studies and analyzed by reducing data, presenting data and verifying data. The results of this study indicate the function of gathering intelligence in preventing the spread of hoaxes ahead of the presidential election in the jurisdiction of the Salatiga Police. The intelligence-raising mechanism is implemented with planning steps, the implementation of which includes the stages of infiltration, dispersal, denial, direction, investigation of allegiances, shifts, and mergers, as well as evaluating and reporting intelligence-raising actions to the leadership. The influencing factors come from the action agents/intruders who do not have reliable capabilities evenly in the implementation of intelligence-raising actions, the limited budget for intelligence-raising operations, the lack of infrastructure facilities used for intelligence-raising operations and the lack of appropriate methods in implementing the action-raising activities. intelligence, as well as the sensitivity of the target to the actions of infiltrating agents who carry out these intelligence-raising actions. Suggestions in this study are to provide intruders with various intelligence-raising action training standards, and provide intruders with various abilities to approach the public and the ability to utilize technology that can be used for intelligence-raising actions.
Fuzzy Joint Gaussian-Impulsive Noise Removal Using Joint Distribution Modelling in Sparse Domain

Image denoising is trivial. It is considered that when multiple sources of noise act simultaneously such a task tends to be more critical. The distribution of resulting noise will possess irregular structure with heavy tail leading to fuzzy in detection and removal of noise from images. Most mixed noise removal schemes first detect the pixels with noise attack and then attempt to remove the noise. The proposed scheme is a single phase mechanism where the noise detection phase is absent. The proposed scheme uses sparse coding as a base and modifies the weight of the fidelity term so that the heavy tail of mixed noise distribution is approximated to Gaussian distribution. The simulation results prove the superiority of the proposed scheme using peak signal to noise ratio and feature similarity index. Results show that in the severe mixed noise case a PSNR improvement of 1% is achieved, whereas in the intermediate and little mixed noise cases a PSNR improvement of about 4% and 5% ae achieved.
Deception Detection And Vulnerability Analysis Using A Multi-Level Clustering Machine Learning Algorithm In Business Transactions

To protect criminals, fraud detection techniques are regularly improved. They make it possible for us to spot frauds quickly and simply. We will concentrate on the issue of detecting fraud in financial transactions in this paper. Every problem may not be best served by a single algorithm. Choosing an algorithm that works well in a particular context is therefore vital. Frauds are becoming more prevalent on a daily basis in industries including banking, finance, and government. Fraud detection requires precise analysis. Techniques for data mining aid in the prevention and detection of fraud. Tools for data mining can be used to identify trends and fraudulent transactions. Data mining may be used to identify fraud-causing elements. The metrics of Total Running Time and Accuracy are used to analyse the performance. The outcomes demonstrated that the straightforward anomaly detection method produced the poorest outcomes while the multi-level clustering machine learning technique produced the greatest outcomes.
Hand-Crafted Features for Floating Plastic Detection

Plastic waste is a global concern that has a negative impact on the oceans and wildlife health. This paper focuses on detection of floating plastics in aerial images taken from unmanned aerial vehicles (UAVs). It proposes a new method for plastic detection in marine environments, based on SIFT descriptor and color histograms for feature extraction, as an alternative to state-of-the-art object detectors based on convolutional neural networks (CNNs), Our approach is named SURFACE: “SIFT featURes For plAstiC dEtection”. We investigate how different color-spaces and image resolutions impact the extraction of SIFT features and compare SURFACE to ResNet CNN. Also, we provide a detailed comparison with YOLO and Faster-RCNN object detection models and show that SURFACE achieves approximately the same accuracy while being faster and less memory consuming. The dataset acquired during this research will be publicly available.
Professional informational autonomy’s formation of higher school students

The relevance of the research is determined by the rapid development of all modern life spheres’ digitalization, establishment of which is seen also in the: updated path of the information technology use, educational field, development of contacts, integration processes, information exchange. Above-mentioned influence the cause of creation the conditions for the formation of students professional autonomy, which in serves as a guarantee of success and effectiveness of educational activities, and, as a consequence, successful future professional activities. Analysis of methodological, didactical and psychological-pedagogical papers, the observation of students’ teaching process, and the study of domestic and foreign experience in higher education revealed a deficit of students’ autonomy. Which is observed in their inability independently formulate and solve professional problems in the conditions of the changing informational needs of society and professional environment. In the process of developing the methodology of professional informational autonomy of higher school students’, the approaches of professional autonomy have been methodically interpreted and analyzed, have been developed a learning book, training courses and electronic information environment.
Masked Face Recognition Using MobileNetV2

Masked face recognition has made great progress in the field of computer vision since the popularity of COVID-19 epidemic in 2020. In countries with severe outbreaks, people are required to wear masks in public. The current face recognition methods, which take use of the whole face as input data, are quite well established. However, while people are use of face masks, it will reduce the accuracy of face recognition. Therefore, we propose a mask wearing recognition method based on MobileNetV2 and solve the problem that many of models cannot be applied to portable devices or mobile terminals. The results indicate that this method has 98.30% accuracy in identifying the masked face. Simultaneously, a higher accuracy is obtained compared to VGG16. This approach has proven to be working well for the practical needs.
Multiscale Spatio-Temporal Network for Aerial Video Event Recognition

Unmanned aerial vehicles (UAVs) are widely used in the field of remote sensing because of their advantages of providing real-time and high-resolution videos at a low cost. Compared with generic video understanding, aerial video event recognition is faced with emerging challenges: 1) aerial videos contain richer scene information; 2) the scale variations between different videos are large. To address these issues, we propose a Multiscale Spatio-Temporal Network (MSTN) in this paper. More precisely, the MSTN consists of a Pyramid Spatio-Temporal (PST) module and a Multi-Time Scale Decision (MTSD) module, which learn multi-scale spatio-temporal features together. The two modules can better learn spatio-temporal characteristics and boost the performance by 3.7% compared with the baseline method. In ERA, an aerial event recognition dataset, our method achieves the state-of-the-art results.
A Weighted <i>k</i>-Nearest Neighbours Ensemble With Added Accuracy and Diversity

Ensembles based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN models are considered effective in reducing the adverse effect of outliers, primarily, by identifying the closest observations to a test point in a given training data. Class label of the test point is estimated by taking a majority vote of the nearest observations’ class labels. While identifying the closest observations, certain training patterns might possess high regulatory power than the others. Therefore, assigning weights to observations and then calculating weighted distances are deemed important. This paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN ensemble that identifies nearest observations based on their weighted distance in relation to the response variable via support vectors. This is done while building a large number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN models each on a bootstrap sample from the training data along with a randomly selected subset of features from the given feature space. The estimated class of the test observation is decided via majority voting based on the estimates given by all the base <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN models.The ensemble is assessed on 14 benchmark and simulated datasets against other classical methods, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN based models using Brier score, classification accuracy and Kappa as performance measures. Average values of the given measures along with boxplots are given for the proposed and the other competitors. On both benchmark and simulated datasets, the proposed ensemble outperformed the other competitive methods in majority of the cases. The suggested ensemble gave overall better classification performance than the other methods on 8 datasets. The ensemble performed better than the other methods in simulation study that considered the presence of noisy features in a dataset. The analyses reveal that the proposed method is effective in classification problems that involve noisy features in the data. Furthermore, feature weighting and randomization also make the method robust to the choice of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , i.e., the number of nearest observations in a base model.
Wearable Exercise Protocol Monitoring System for Athletic Training

Physical exercises have become an essential part of day-to-day life. WHO recommends that an average adult should spend at least 150-300 minutes a week of moderate-intensity or 75-150 minutes a week of vigorous-intensity physical training for improved health benefits and lower risk of bodily function disorders. For athletes, a strict exercise regime is required to improve their body movements and fitness.This paper presents a wearable fitness device that can be used to monitor walking, running, shuttle-run, push-up, and sit-up exercises performed by a subject. The paper emphasizes on placement location of sensor to obtain accurate exercise results, and software algorithms developed using statistical tools to calculate the exercise results. Continuous built-in tests implemented in the device are useful to ensure the functional reliability of the device during exercises. Real-time algorithm results have been verified through algorithms implemented in a desktop computer and are found to be in a high degree of agreement.
Using Stochastic Gradient Descent On Parallel Recommender System with Stream Data

Stochastic gradient descent (SGD) and Alternating least squares (ALS) are two popular algorithms applied on matrix factorization. Moreover recent researches pay attention to how to parallelize them on daily increading data. About large-scale datasets issue, however, SGD still suffers with low convergence by depending on the parameters. While ALS is not scalable due to the cubic complexity with the target time rank. The remaining issue, how to operate system, almost parallel algorithms conduct matrix factorization on a batch of training data while the system data is real-time. In this work, the authors proposed FSGD algorithm overcomes drawbacks in large-scale issue base on coordinate descent, a novel optimization approach. According to that, algorithm updates rank-one factors one by one to get faster and more stable convergence than SGD and ALS. In addition, FSGD is feasible to paralleize and operates on a stream of incoming data. The experimental results show that FSGD performs much better in solving the matrix factorization issue compared to existing state-of-the-art parallel models.
AgentFuzz: Fuzzing for Deep Reinforcement Learning Systems

In recent years, deep reinforcement learning (DRL) technology has developed rapidly, and the application of DRL has been extended to many fields such as game gaming, au-tonomous driving, financial transactions, and robot control. As DRL applications expand and enrich, quality assurance of DRL software is increasingly important, especially in safety -critical areas. Therefore, it is necessary and urgent to adequately test DRL models to ensure the reliability and security of DRL systems. However, due to fundamental differences, traditional software testing methods cannot be directly applied to D RL systems. To bridge this gap, we introduce a new DRL system testing framework in this proposal, which aims to generate various test cases that can cause D RL systems to fail. The proposed testing framework is the first fuzzing framework for systematically testing DRL systems which we call AgentFuzz.
Deepfake Detection Method Based on Face Edge Bands

The rapid development of face forgery technology and the generation of realistic fake videos have caused serious harm to individuals, society and even the country, so it is important to detect deepfake videos. There are many detection methods available for forged videos, but the overall performance is yet to be improved and does not cope well with high quality forged images or videos. Observing that existing forgery algorithms leave synthetic forgery traces at the edges of faces when creating videos, this paper proposes a new method for detecting forged videos. It first finds the face edges from the video frames, then extracts the face edge bands as deep learning inputs and trains them based on EfficientNet-B3 to achieve effective detection of deepfake videos. Experiments show that the method in this paper can achieve more than 99.8% AUC values on all four forgery methods of the Face-Forensics++ dataset.
Multimodal Representation Learning:Cross-modality and Shared Representation

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts
Generalising Semantics to Weighted Bipolar Argumentation Frameworks

In Bipolar Argumentation Frameworks ( $$\textit{BAF}$$ s), arguments cannot only attack, but support other arguments. In this paper, we generalise the fundamental semantics for $$\textit{BAF}$$ s to deal with Weighted Bipolar Argumentation Frameworks ( $$ WBAF $$ s), in which real numbers are assigned as weights to arguments. A distinguishing aspect of our approach is that our semantics are determined in terms of two measures: one is employed to assess the degree of acceptability of an argument, and the other, its degree of rejection. Then, as opposed to previous proposals, we have more expressive semantics that naturally generalise their corresponding semantics for $$\textit{BAF}$$ s and are defined for each $$ WBAF $$ .
Smart Wheelchair for Specially Abled People

This paper is focused on the speech recognition technology. In recent years, it was not possible to control our machineries through the help of voice commands. As the home automation is very popular nowadays. In this method, all home appliances are controlled by android phone through voice command. Keeping this automation in mind a wheelchair is also automated here by android phone through voice command. This special feature is added in traditional wheelchair to make especially abled persons independent. In this project, a small model of voice-controlled wheelchair is designed for especially abled persons. The wheelchair can be easily controlled by speech recognition module. It is very helpful for elderly people also who cannot walk well due to aging effect. Here wheelchair is trained with forward, backward, left, right, and stop commands. These voice commands are given by user via android application which transfers to microcontroller through Bluetooth module. After receiving the command, wheelchair will respond by performing proper movement to the proper direction according to the received data.
Business Tech in Media and Entertainment

Businesses are using innovation to communicate their story online so they can reach hundreds of thousands of users at once. The technology trend is probably here to stay. This means that innovation is still in its infancy and that new types of technical advances are appearing every day. Video content is becoming more popular as a result of the use of cutting-edge technologies in the media and entertainment industries. However, the creation of novel technical ideas may result in a wide range of previously unheard-of digitalized tools and functionalities. Films, print, television, and radio were the key components of media and entertainment. The usage of gesture recognition technology, picture analysis in journalism, film screenplay analysis, and music composition and generation are a few of the new components that make up the media and entertainment environment. Due to the incorporation of innovation and technology, the media and entertainment sector has seen dramatic changes in recent years.
EFG-Net: A Unified Framework for Estimating Eye Gaze and Face Gaze Simultaneously

Gaze is of vital importance for understanding human purpose and intention. Recent works have gained tremendous progress in appearance-based gaze estimation. However, all these works deal with eye gaze estimation or face gaze estimation separately, ignoring the mutual benefit of the fact that eye gaze and face gaze are roughly the same with a slight difference in the starting point. For the first time, we propose an Eye gaze and Face Gaze Network (EFG-Net), which makes eye gaze estimation and face gaze estimation take advantage of each other, leading to a win-win situation. Our EFG-Net consists of three feature extractors, a feature communication module named GazeMixer, and three predicting heads. The GazeMixer is designed to propagate coarse gaze features from face gaze to eye gaze and fine gaze features from eye gaze to face gaze. The predicting heads are capable of estimating gazes from the corresponding features more finely and stably. Experiments show that our method achieves state-of-the-art performance of 3.90° (by $${\sim } 4 \% $$ ) eye gaze error and 3.93° (by $${\sim } 2 \% $$ ) face gaze error on MPIIFaceGaze dataset, 3.03° eye gaze error and 3.17° (by $${\sim } 5 \% $$ ) face gaze error on GazeCapture dataset respectively.
Pcwin Transformer: Permuted Channel Window based Attention for Image Classification

The Transformer is one of the mainstream methods in computer vision. Most Transformer based architectures focus on the design of spatial attention and optimizing the computational complexity of high resolution of pixels in image but pay little attention to modeling channel dependencies and optimizing the computational complexity associated with a large number of channels. In this paper, we propose a new channel window based self-attention mechanism and apply two consecutive transformer layers to capture global channel dependencies through permuting channel layer, which can greatly reduce the computational complexity caused by a large number of channels. Meanwhile, a new linear layer for channel attention is proposed, which eliminates the need for position bias in Transformer. The proposed method can be conveniently appended to the existing image classification architectures in parallel with minimal modification. We demonstrate the feasibility of the proposed method on the state-of-the-art transformer-based architecture for image classification and improve the results on ImageNet-1K. The code will be publicly available at GitHub.