Reviewerly/exHarmony huggingface dataset:
Detecting Arbitrary Intermediate Keypoints for Human Pose Estimation with Vision Transformers

Most human pose estimation datasets have a fixed set of keypoints. Hence, trained models are only capable of detecting these defined points. Adding new points to the dataset requires a full retraining of the model. We present a model based on the Vision Transformer architecture that can detect these fixed points and arbitrary intermediate points without any computational overhead during inference time. Furthermore, independently detected intermediate keypoints can improve analyses derived from the keypoints such as the calculation of body angles. Our approach is based on TokenPose [9] and replaces the fixed keypoint tokens with an embedding of human readable keypoint vectors to keypoint tokens. For ski jumpers, who benefit from intermediate detections especially of their skis, this model achieves the same performance as TokenPose on the fixed keypoints and can detect any intermediate keypoint directly.
Residual YOLOX-based Ship Object Detection Method

With the development of the times, Shipping and logistics-related industries have higher requirements for the accuracy and speed of ship detection algorithms. This paper proposes a ship object detection method based on YOLOX algorithm, which can be used for ship image detection in ports. Refer to the COCO dataset format, construct the ship object detection data set, introduce the residual structure and the CIOU loss function to improve and optimize the algorithm, and compare the model performance with the original YOLOX algorithm. The test results show that the Res-YOLOX algorithm is significantly better than the original YOLOX algorithm, for a single class ship object detection, the AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> on the test set reached 88.7%, increased by3.5%, and for multiclass ship object detection, the AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> reached 83.6%, 54.3%, 40.3% respectively. Finally, model compression is used for trained model. After compressing model, the speed is increased by 15.4ms.
A Pipeline Approach to Context-Aware Handwritten Text Recognition

Despite concerted efforts towards handwritten text recognition, the automatic location and transcription of handwritten text remain a challenging task. Text detection and segmentation methods are often prone to errors, affecting the accuracy of the subsequent recognition procedure. In this paper, a pipeline that locates texts on a page and recognizes the text types, as well as the context of the texts within the detected region, is proposed. Clinical receipts are used as the subject of study. The proposed model is comprised of an object detection neural network that extracts text sequences present on the page regardless of size, orientation, and type (handwritten text, printed text, or non-text). After that, the text sequences are fed to a Residual Network with a Transformer (ResNet-101T) model to perform transcription. Next, the transcribed text sequences are analyzed using a Named Entity Recognition (NER) model to classify the text sequences into their corresponding contexts (e.g., name, address, prescription, and bill amount). In the proposed pipeline, all the processes are implicitly learned from data. Experiments performed on 500 self-collected clinical receipts containing 15,297 text segments reported a character error rate (CER) and word error rate (WER) of 7.77% and 10.77%, respectively.
Three Intuitionistic Fuzzy Estimations of Uncertainty in Service Compositions

Three estimations of uncertainty of the service of requests by service devices are proposed: intuitionistic fuzzy traffic characterization, intuitionistic fuzzy flow characterization and intuitionistic fuzzy time characterization. They are defined in the form of intuitionistic fuzzy pairs. The proposed characterizations are applied to a service device consisting of two consecutively connected service devices. One of the advantages of the proposed approach is that an estimation of the uncertainty in comprise virtual devices consisting of subdevices on several levels can be obtained using the operations defined over intuitionistic fuzzy pairs.
A Deep Attention Model for Action Recognition from Skeleton Data

This paper presents a new IndRNN-based deep attention model, termed DA-IndRNN, for skeleton-based action recognition to effectively model the fact that different joints are usually of different degrees of importance to different action categories. The model consists of (a) a deep IndRNN as the main classification network to overcome the limitation of a shallow RNN network in order to obtain deeper and longer features, and (b) a deep attention network with multiple fully connected layers to estimate reliable attention weights. To train the DA-IndRNN, a new triplet loss function is proposed to guide the learning of the attention among different action categories. Specifically, this triplet loss enforces intra-class attention distances to be smaller than inter-class attention distances and at the same time to allow multiple attention weight patterns to exist for the same class. The proposed DA-IndRNN can be trained end-to-end. Experiments on the widely used datasets, including the NTU RGB + D dataset and UOW Large-Scale Combined (LSC) Dataset, have demonstrated that the proposed method can achieve better and stable performance than the state-of-the-art attention models.
Novel technique for control of industrial robots with wearable and contactless technologies

Wearable and non-contact sensor technologies have spread to many diverse areas with their ease of use and application, and the variety of applications has been increasing rapidly thanks to technological developments in the recent years. This work ensures the usability of wearable and non-contact sensor technologies in various industrial fields. In this study, it is aimed that operators can easily control industrial robots in real-time without the need for a specialized programming environment. For this purpose, a new human–robot interface, in which industrial robots can be controlled by hand movements, has been created. In this interface, Leap Motion Controller (LMC), which uses an IR Camera for the control of robot arm positioning and motion trajectories, and Myo armband, which detects wrist and hand movements with EMG sensors, is used to control of robot end effector (tool) actions. By means of both devices, robot movements are controlled by industrial communication after hand movements, classification and data processing stages. The data processing, control and positioning results of the platform developed in the article are presented in comparison with similar studies in the literature. As a result, it has been observed that the developed platform works with high accuracy. EMG classification success was determined as 96.9% and hand tracking error between 2.45–2.85 mm. Thanks to this new platform, various robotic processes such as pick-place, object movement, painting applications, etc. will be carried out easily and flexibly without the need for programming.
AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities

Digital forgery has become one of the attractive research fields in today's technology. There are several types of forgery in digital media transmission, especially digital image transmission. A common type of forgery is copy-move forgery (CMF). The CMF may be encountered in streets, railway stations, underground stations, or festivals. This type of forgery may lead to hugger-mugger in some cases. Therefore, there is a need to find a sufficient countermeasure mechanism to detect image forgeries. This paper presents a new CMFD approach that depends on deep learning for IoT based smart cities. Two well-known deep learning models, namely CNN and ConvLSTM, are adopted for CMFD. The proposed models are tested on MICC-220, MICC-600 and MICC 2000 datasets for validation. Several tests are performed to verify the effectiveness of the proposed models. The simulation results reveal that the testing accuracy reaches 95%, 73%, and 94% for MICC-F220, MICC-F600 and MICC-F2000 datasets. In addition, the proposed approach achieves an accuracy of 85% for a combined set of all datasets.
Quantum Dilated Convolutional Neural Networks

In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).
Fake Product Detection using Blockchain

The availability of fake product in the Market is one of the biggest challenges of the online retail industry. These products appear to be genuine but they are imitations of the original branded products. Almost 20&#x0025; of the products sold on online websites are fake. In recent times, block chain is receiving more engagement and various applications are been emerged from this technology. In this paper, to ensure that consumers need not depend on the distributers to know whether their products are authentic or not, we are using the decentralized Block chain technology approach. We describe a decentralization Block chain network with anti-counterfeiting items, which allows producers to deliver items without having to run clear outlets, lowering product quality assurance costs dramatically.
Bipolar equations on complete distributive symmetric residuated lattices: The case of a join-irreducible right-hand side

Bipolar max-⁎ equations, with ⁎ a triangular norm, have recently become a popular research topic embedded in the broad field of fuzzy relational equations. In this paper, we lift the work from the restrictive setting of the real unit interval — obfuscating the underlying lattice-theoretical essence — to the general setting of complete distributive symmetric residuated lattices, allowing to build upon the vast body of knowledge on unipolar sup-⁎ equations on complete distributive residuated lattices. We determine the full solution set, with particular emphasis on the extremal solutions, of a bipolar sup-⁎ equation in case the right-hand side is a join-irreducible element. The results are illustrated by means of ample examples.
Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

Abstract Online hate is a growing concern on many social media platforms, making them unwelcoming and unsafe. To combat this, technology companies are increasingly developing techniques to automatically identify and sanction hateful users. However, accurate detection of such users remains a challenge due to the contextual nature of speech, whose meaning depends on the social setting in which it is used. This contextual nature of speech has also led to minoritized users, especially African–Americans, to be unfairly detected as ‘hateful’ by the very algorithms designed to protect them. To resolve this problem of inaccurate and unfair hate detection, research has focused on developing machine learning (ML) systems that better understand textual context. Incorporating social networks of hateful users has not received as much attention, despite social science research suggesting it provides rich contextual information. We present a system for more accurately and fairly detecting hateful users by incorporating social network information through geometric deep learning. Geometric deep learning is a ML technique that dynamically learns information-rich network representations. We make two main contributions: first, we demonstrate that adding network information with geometric deep learning produces a more accurate classifier compared with other techniques that either exclude network information entirely or incorporate it through manual feature engineering. Our best performing model achieves an AUC score of 90.8% on a previously released hateful user dataset. Second, we show that such information also leads to fairer outcomes: using the ‘predictive equality’ fairness criteria, we compare the false positive rates of our geometric learning algorithm to other ML techniques and find that our best-performing classifier has no false positives among a subset of African–American users. A neural network without network information has the largest number of false positives at 26, while a neural network incorporating manual network features has 13 false positives among African–American users. The system we present highlights the importance of effectively incorporating social network features in automated hateful user detection, raising new opportunities to improve how online hate is tackled.
Optimization of 3-D Pose Measurement Method Based on Binocular Vision

To improve the accuracy and speed of existing binocular vision methods in practical environments, this article presents an optimization method for accurate and stable 3-D pose measurement based on binocular vision. First, in the stereo-image-matching stage, we introduce a guide-point definition and propose an optimal path-searching method for dynamic programming (DP). Second, a distance-based adaptive-filtering method is added to remove noise points and outliers around the target so that the environmental noise interference in practical environments is reduced before registration. Third, an adaptive-sampling method based on distance is proposed to extract the key points of the target point-cloud, and finally a target surface model based on a hash table is established as an offline matching data structure of point-cloud registration to improve the matching speed and accuracy of the 3-D pose calculation. A series of experiments showed the translation error of the proposed method was less than 1 cm relative to the measurement of 100 cm, and the rotation error was less than 2&#x00B0; relative to the measurement of 180&#x00B0;. Compared with traditional point-cloud registration algorithms, our method exhibited higher accuracy and stability, indicating great potential for use in unstructured environments.
The unintended consequences of the regulation of cryptocurrencies

Abstract This paper investigates whether the application of the latest guidance of the Financial Action Task Force (FATF) for regulating cryptocurrencies may engender unintended consequences at odds with the initial purposes of transparency and technology neutrality. For instance, we will ask whether regulation strengthening may incite a category of investors to flight to unregulated and non-compliant decentralised exchange platforms to stay under the radar of regulators. Furthermore, we ask whether regulation may lead to a two-tier industry, fragmented between compliant trading venues that attract mainstream users and non-compliant trading venues that attract privacy-centric users. We argue that somewhat paradoxically, regulation may push part of the crypto-industry to the ‘dark side’ of financial innovation and drives privacy-centric investors out of the scope of regulators.
An ROI-based robust video steganography technique using SVD in wavelet domain

Abstract Steganography is a technique that embeds secret information in a suitable cover file such as text, image, audio, and video in such a manner that secret information remains invisible to the outside world. The study of the literature relevant to video steganography reveals that a tradeoff exists in attaining the acceptable values of various evaluation parameters such as a higher capacity usually results in lesser robustness or imperceptibility. In this article, we propose a technique that achieves high capacity along with required robustness. The embedding capacity is increased using singular value decomposition compression. To achieve the desired robustness, we constrain the embedding of the secret message in the region of interest in the cover video file. In this manner, we also succeed in maintaining the required imperceptibility. We prefer Haar-based lifting scheme in the wavelet domain for embedding the information because of its intrinsic benefits. We have implemented our suggested technique using MATLAB. The analysis of results on the prespecified parameters of the steganography justifies the effectiveness of the proposed technique.
AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications

Natural user interfaces based on hand gestures are becoming increasingly popular. The need for expensive hardware left a wide range of interaction possibilities that hand tracking enables largely unexplored. Recently, hand tracking has been built into inexpensive and widely available hardware, allowing more and more people access to this technology. This work provides researchers and users with a simple yet effective way to implement various one-handed gestures to enable deeper exploration of gesture-based interactions and interfaces. To this end, this work provides a framework for design, prototyping, testing, and implementation of one-handed gestures. The proposed framework was implemented with two main goals: First, it should be able to recognize any one-handed gesture. Secondly, the design and implementation of gestures should be as simple as performing the gesture and pressing a button to record it. The contribution of this paper is a simple yet unique way to record and recognize static and dynamic one-handed gestures. A static gesture can be captured with a template matching approach, while dynamic gestures use previously captured spatial information. The presented approach was evaluated in a user study with 33 participants and the implementable gestures received high accuracy and user acceptance.
Dataset of British English speech recordings for psychoacoustics and speech processing research: The clarity speech corpus

This paper presents the Clarity Speech Corpus, a publicly available, forty speaker British English speech dataset. The corpus was created for the purpose of running listening tests to gauge speech intelligibility and quality in the Clarity Project, which has the goal of advancing speech signal processing by hearing aids through a series of challenges. The dataset is suitable for machine learning and other uses in speech and hearing technology, acoustics and psychoacoustics. The data comprises recordings of approximately 10,000 sentences drawn from the British National Corpus (BNC) with suitable length, words and grammatical construction for speech intelligibility testing. The collection process involved the selection of a subset of BNC sentences, the recording of these produced by 40 British English speakers, and the processing of these recordings to create individual sentence recordings with associated transcripts and metadata.
The Use of Genetic Algorithm, Multikernel Learning, and Least-Squares Support Vector Machine for Evaluating Quality of Teaching

The educational data mining (EDM) methods are increasingly diversified. In this research, a hybrid method of multikernel learning (MKL), least-squares support vector machine (LSSVM), and genetic algorithm (GA) is employed to evaluate teaching quality through nine indicators; the reliability of our proposed method is evaluated by confidence interval and prediction interval. First, English teaching quality samples occurring from three age groups at Guizhou Normal University are collected. Next, an intelligent method MK-LSSVM is proposed. Finally, the test sets are regression by the proposed model, and regression results are evaluated by confidence interval, prediction interval, and several error calculation methods; we also develop an ablation experiment for our proposed model. The experiment indicates that the MKL-LSSVM-GA outperforms other benchmark methods at three age-group levels. Additionally, at all three age-group levels, the experiment indicates that three indicators are crucial for the evaluation of teaching quality. Therefore, the proposed model in this paper can evaluate the English teaching quality effectively.
An Efficient Approach for Encrypted Traffic Classification using CNN and Bidirectional GRU

With the rapid development of the Internet, the amount and types of network traffic have increased dramatically in tandem. Therefore, precise network traffic classification has become an essential aspect of network management. Furthermore, as user privacy and data encryption requirements have grown, more encrypted traffic has evolved. Because of the wide variety of encryption techniques and methodologies, network traffic categorization has become extremely challenging. Traditional traffic classification methods have been unable to meet the demand for classification accuracy. In this study, we propose a network structure that combines CNN and Bi-GRU to learn the temporal and spatial features of encrypted traffic data. We utilize the public dataset ISCX VPN-non VPN to evaluate the effect of our model, and we use accuracy, recall, and F1 score as criteria. Finally, our model had a classification accuracy of93.1 %, with a recall rate of 93.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and an F1 score of 93.6%. We also discussed about the differences between Bi-GRU and LSTM in terms of model parameter scale and time efficiency. Experiments show that Bi-GRU has the greatest classification effect and efficiency.
Virtual reservoir computer using an optical resonator

Reservoir computing is a machine learning approach that enables us to use recurrent neural networks without involving the complexity of training algorithms and make hardware implementation possible. We present a novel photonic architecture of a reservoir computer that employs a nonlinear node and a resonator to implement a virtual recurrent neural network. This resonator behaves as an echo generator component that substitutes the delay line in delay-based reservoir computers available in the literature. The virtual neural network formed in our implementation is fundamentally different from the delay-based reservoir computers. Different virtual architectures based on the FSR and the Finesse of the resonator are investigated to provide higher performance depending on the task. We test the performance of our proposed reservoir by 3 benchmark tasks, signal classification, nonlinear channel equalization, and memory capacity. Our system enhances the memory capacity of the reservoir compared to the delay-based systems and shows reliable results in signal classification and nonlinear channel equalization.