Reviewerly/exHarmony huggingface dataset:
Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification

Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong dis-crimination ability in supervised person re-identification (Re-ID) tasks. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsupervised Re-ID problem that is label-free but much more challenging. To this end, we build a dual-branch network architecture based upon a modified Vision Transformer (ViT). The local tokens output in each branch are reshaped and then uniformly partitioned into multiple stripes to generate part-level features, while the global tokens of two branches are averaged to produce a global feature. Further, based upon offline-online associated camera-aware proxies (02CAP) that is a top-performing unsupervised Re-ID method, we define offline and online contrastive learning losses with respect to both global and part-level features to conduct unsupervised learning. Extensive experiments on three person Re-ID datasets show that the proposed method outperforms state-of-the-art unsupervised methods by a considerable margin, greatly mitigating the gap to supervised counterparts. Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.
Learning Pairwise Interaction for Generalizable DeepFake Detection

A fast-paced development of DeepFake generation techniques challenge the detection schemes designed for known type DeepFakes. A reliable Deepfake detection approach must be agnostic to generation types, which can present diverse quality and appearance. Limited generalizability across different generation schemes will restrict the wide-scale deployment of detectors if they fail to handle unseen attacks in an open set scenario. We propose a new approach, Multi-Channel Xception Attention Pairwise Interaction (MCX-API), that exploits the power of pairwise learning and complementary information from different color space representations in a fine-grained manner. We first validate our idea on a publicly available dataset in a intra-class setting (closed set) with four different Deepfake schemes. Further, we report all the results using balanced-open-set-classification (BOSC) accuracy in an inter-class setting (open-set) using three public datasets. Our experiments indicate that our proposed method can generalize better than the state-of-the-art Deepfakes detectors. We obtain 98.48% BOSC accuracy on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$FF++$</tex> dataset and 90.87% BOSC accuracy on the CelebDF dataset suggesting a promising direction for generalization of DeepFake detection. We further utilize t-SNE and attention maps to interpret and visualize the decision-making process of our proposed network.
Clinical-GAN: Trajectory Forecasting of Clinical Events using Transformer and Generative Adversarial Networks

Predicting the trajectory of a disease at an early stage can aid physicians in offering effective treatment, prompt care to patients, and also avoid misdiagnosis. However, forecasting patient trajectories is challenging due to long-range dependencies, irregular intervals between consecutive admissions, and non-stationarity data. To address these challenges, we propose a novel method called Clinical-GAN, a Transformer-based Generative Adversarial Networks (GAN) to forecast the patients' medical codes for subsequent visits. First, we represent the patients' medical codes as a time-ordered sequence of tokens akin to language models. Then, a Transformer mechanism is used as a Generator to learn from existing patients' medical history and is trained adversarially against a Transformer-based Discriminator. We address the above mentioned challenges based on our data modeling and Transformer-based GAN architecture. Additionally, we enable the local interpretation of the model's prediction using a multi-head attention mechanism. We evaluated our method using a publicly available dataset, Medical Information Mart for Intensive Care IV v1.0 (MIMIC-IV), with more than 500,000 visits completed by around 196,000 adult patients over an 11-year period from 2008-2019. Clinical-GAN significantly outperforms baseline methods and existing works, as demonstrated through various experiments. Source code is at https://github.com/vigi30/Clinical-GAN.
HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks

Heterogeneous graphs are commonly used to describe networked data with multiple types of nodes and edges. Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for analyzing heterogeneous graphs. However, designing neural architectures of HGNNs requires extensive domain knowledge and time-consuming manual work. Recently, neural architecture search algorithms have become popular in automatically designing neural architectures for homogeneous graph neural networks. In this paper, we present a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which allows the automatic design of heterogeneous graph neural architectures. Specifically, HGNAS first designs a new search space based on existing popular HGNNs. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a given validation dataset. Moreover, we design a new method HGNAS++ to improve the efficiency of HGNAS by training the RNN controller within a generative adversarial learning framework. The basic idea of HGNAS++ is to embed a pairwise ranker into the reinforcement learning based architecture search algorithm. The pairwise ranker can be taken as a discriminator which selects more accurate architectures between pairs of candidate architectures. Then, the RNN controller can be updated more efficiently by only using a relatively small number of candidate architectures selected by the pairwise ranker. Experiments on real-world heterogeneous graph datasets show that HGNAS is capable of designing novel HGNNs that beat the best human-invented HGNNs. On the benchmark datasets, HGNAS++ improves HGNAS in terms of evaluation cost, with a reduction of 50% of the evaluated candidate architectures and a decrease of 24% in search time on average. As a byproduct, HGNAS++ can find sparse yet powerful neural architectures for HGNNs.
Blockchain Based Optimized Energy Trading for E-Mobility Using Quantum Reinforcement Learning

Using microgrids to charge Electric Vehicles (EVs) is a significant step toward achieving Electric mobility. The microgrids generate electricity for self-use and sell surplus energy locally in Peer-to-Peer (P2P) manner, where seller and buyer meet to trade electricity directly on agreed terms without any intermediary. The energy trading decision for the microgrid is a challenging issue due to uncertainty of renewable energy yield, the electric demands of EVs and without knowing the offers of other competitive microgrids together makes it hard to decide the selling price of energy per unit. Further, there is a need to audit and verify the energy trading and store energy transactions securely in distributed manner to avoid collapse of the system in case of single point of failure. In this context, this paper presents a Blockchain and Quantum Reinforcement Learning based optimized Energy Trading (BQL-ET) model for E-mobility. Firstly, a double-auction mechanism is proposed to set optimal market-trading price by observing the selling price of each microgrid and the demand of EV's. Secondly, using smart contracts, consortium blockchain is deployed for the evaluation of overall utility, which includes energy supply, demand, and cost for both microgrids and EVs. Finally, Utility maximization problem is transformed into a Markov Decision Process (MDP), and in order to develop the learning policy and maximize overall utility, a QRL optimization for solving the MDP problem is proposed. Convergence analysis and performance results attest that BQL-ET convergences faster, maximizes the utility of both microgrids an EVs with lower transaction confirmation time and setting of the optimal market-trading price compared to state-of-the art models.
A Novel Method for Authentication Using Chaotic Behaviour of Chua’s Oscillator in (n,k) Secret Shared Data Scheme for Secure Communication

In the current scenario, the utilization, and advancement of security algorithms in communication models have been increased. The use of chaotic circuit-based secure algorithms can give a new direction to develop a highly secure communication model. Security in any communication model can be provided by various available methods. The main feature of any security method is to introduce randomness at every step of the algorithm. Generally, the security algorithms deal with random numbers that are generated using a random number generator with predefined logic in the conventional secure communication model. In this article, the chaotic behavioral circuit is used to handle the randomness. The three main classifications of security control are management security, operational security, and physical security controls. The chaotic behavior of high-dimensional Chua’s circuit has been used to implement these three security controls. The challenging task is to design and deploy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n,k$ </tex-math></inline-formula> ) secret sharing scheme using Chua’s oscillator. The novel authenticator code function based on Chua’s oscillator is proposed in this paper. This authenticator code function is having almost the same characteristics as of hash code, but with a higher degree of security.
DeepFake Detection Through Key Video Frame Extraction using GAN

The emergence of deepfake videos in recent years has made image falsification a real threat. A deepfake video uses deep learning technology to substitute a person's face, emotion, or speech with the face, emotion, or speech of another person. Finding such deceptive deepfake videos on social media is the first step in preventing them. A robust neural network-based technique to identify false videos is presented in this paper. An important video frame extraction approach is used to speed up the process of finding deep fake videos. A model made up of a convolutional neural network (CNN) and a classifier network consisting of GAN technology is provided. Resnet, Resnext50 and LSTM were passed over in favor of the Confusion Matrix when deciding which structure to pair with the classifier while detecting the fake video. The model is a method for detecting visual artefacts. The subsequent classifier network uses the feature vectors from the CNN module as this is the input to categorize the video whether it is fake or real one. The dataset is considered from DeepFake Detection Challenge to get the best model. The key goal is to get high accuracy without using a lot of data to train the model. In comparison to earlier efforts, the key video frame extraction method dramatically decreases computations by achieving 97.2% accuracy using the Deepfake Detection Challenge dataset.
Twitter Sentiment Analysis with Machine Learning

Sentiment analysis plays an important role in assessing the human emotions and feelings by using Natural Language Processing (NLP) technique. Researchers have recently developed different models to accurately detect and analyze the human emotions. Sentiment analysis overcomes the Natural Language Processing (NLP) challenge by using Machine Learning (ML) models to perform classification, text mining, text analysis, data analysis, and data visualization to identify positive and negative tweets. Initially, the plain text present in the tweets will be cleaned and pre-processed. The tweets are then analyzed from the pre-processed text. Followed by this, the proposed model extracts the numerical features from the data and combine them with tweet sentiments to train and detect different human sentiments. The main purpose of the proposed model is to find the offensive content in tweets. For the sake of simplicity, the proposed model considers a tweet to be vulgar content if it incorporates offensive or hateful sentiments.
Analysis of CNN Model with Traditional Approach and Cloud AI based Approach

A plethora of work has been done in the field of AI, including research and real-time applications; however, cloud computing and its technologies have amended Artificial Intelligence applications; cloud computing has aided a lot in deep learning, which is a subset of AI; with the use of cloud computing, the performance and improvement of data set and model, the time and resource consuming process has become much more facile. Making data sets suitable for model training is a time-consuming operation; hence, a transfer learning idea is proposed for simplification, but it must be concrete to the desired data set. The advancement of cloud AI and the usage of resources such as the GPU made it possible to train and evaluate CNN models in less time. This study compares the performance and feasibility of CPU-based local computers and GPU-based cloud platforms like Google colaboratory in terms of speed, power, and security.
Software engineering learning landscape

This paper gives an overview of the software engineering activities of Siemens Healthineers that are related to education and learning. Our training activities have a long history and are done globally throughout the company. We expect that experience and lessons learned are useful for others. Our software engineering education activities range from onboarding of new employees to approaches for continuous learning for highly experienced professionals. We start already very early with training the "next generation" software engineers and focus on growing that talent throughout their career.
Towards Interoperability of Open and Permissionless Blockchains: A Cross-Chain Query Language

The rise of open and permissionless blockchains has introduced novel platforms for applications based on distributed data storage. At the application and business levels, long-established query languages such as SQL provide interoperability that can be complemented by blockchain-based data storage today, enabling permissionless and verifiable data storage along with decentralized execution across tens of thousands of nodes. However, when accessing one or more blockchains, interoperability is not provided today, posing challenges such as inhomogeneous data access in addition to different features and trade-offs, e.g. in data and distribution, scalability, and security. Towards interoperability in data access among the increasing number of blockchain platforms, this paper introduces a cross-chain query language for data access across blockchains. Similar to SQL, the language abstracts from implementation based on a data model compatible with the largest open and permission-less blockchains (OPB) today. The language, data model, and processing architecture are demonstrated and evaluated with an implemented prototype, aiming to contribute to the discussion on blockchain interoperability among OPB.
E-prescription using blockchain technology

The electronic transfer of a prescription from a health-care provider to a pharmacy for the patient's retrieval is referred to as E-Prescription [1]. Such a system would facilitate overall better management of medical prescriptions by publicly registering them using blockchain technology in such a way that an electronically issued medical prescription could be validated by any pharmacist or healthcare provider. The paper presents a proposed implementation for a system suitable for issuing and keeping track of electronic medical prescriptions.
Towards Sustainable Virtual Reality: Gathering Design Guidelines for Intuitive Authoring Tools

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who do not have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This study compiles design guidelines derived from a systematic literature review to contribute to the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science knowledge databases for studies published between 2018 and 2021 and discovered fourteen articles. We compiled fourteen requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The gathered guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse since virtual content creation is one of its bases.
Multi-Stage Validation of a Solar Irradiance Model Chain: An Application at High Latitudes

Evaluating how the sources of uncertainty in solar modelling (e.g., input parameters, developed model chain) can influence the results’ accuracy is one of the main challenges when applied at high latitudes. In this study, a multi-stage validation workflow is implemented around five main stages: data acquisition, data quality check, solar radiation modelling, photovoltaic energy modelling, and experimental validation. Different data sources such as satellite observations, numerical reanalysis, and on-site ground measurements are considered as inputs, while the outcomes from each step of the model chain (e.g., decomposition modelling, transposition modelling, photovoltaic energy modelling) are compared against observations recorded from the solar radiation network at the Norwegian University of Science and Technology (NTNU-Solarnet) in Trondheim (Norway). In the first and second validation stages, the decomposition and transposition models with measured input parameters show the best accuracy indicators, but they do not fulfill the validation criteria. Conversely, in the third validation stage, the photovoltaic energy models with on-site ground measurements as inputs are experimentally validated. In conclusion, at high latitudes, the most accurate results are obtained when monitored solar irradiation data are used instead of satellite observations and numerical reanalysis. Furthermore, the shortest model chain is preferred, with equal data sources.
A differential evolutionary chromosomal gene expression programming technique for electronic nose applications

The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical Gene Expression Programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.
Learning efficient facial landmark model for human attractiveness analysis

Existing geometric features on facial attractiveness analysis only focus on the ratios and distances, which is incomplete to represent all the information of a face. In this paper, we introduce a new category of feature, i.e., the angle features, to describe the angle of different organs such as the chin and eyes, which help boost the analysis performance in experiment. In addition, existing facial beauty analysis papers usually apply existing landmark models and extract their own different geometric feature sets on the landmarks. On the one hand, the geometric features are quite chaotic between different papers. On the other hand, most of the landmarks in the existing landmark model are useless for geometric feature extraction which wastes a lot of computational resources. To tackle these issues, we suggest to define a common geometric feature set and learn a special landmark model for attractiveness analysis. Specially, we collect all the available geometric features from the previous jobs and introduce a genetic feature selection algorithm to select the most effective geometric features. Furthermore, we introduce a special landmark model which exactly covers all the extracted geometric features. The experiments show that our method with the introduced angle features and the common feature set can outperform state-of-art facial beauty estimation methods with geometric features.
Rethinking 3D cost aggregation in stereo matching

In the stereo matching task, the 3D convolution network can effectively aggregate the cost volume with the strong representation ability to model the spatial and depth dimensions but with the disadvantage of a high computational cost. In this letter, we revisit the 3D convolution network and its common variant, and then propose the Depth Shift Module (DSM) to model the cost volume in the depth dimension which could imitate the 3D convolution function with the computational complexity of the 2D convolution. The proposed DSM is easy to extend to present 3D cost aggregation methods in stereo matching with less inference time, lower computational complexity, and minor precision loss. Moreover, a novel compact but efficient stereo matching framework named HybridNet is proposed. This framework can hybridize the 2D convolution layer with the proposed DSM to effectively aggregate the cost volume. The proposed HybridNet achieves a better trade-off between the performance, computational complexity, and model size (e.g., 30% less than the size of AANet and 25% less than the size of PSMNet) in public open-source datasets (e.g., Scene Flow and KITTI Stereo 2015). The relevant code is available at https://github.com/GANWANSHUI/HybridNet.
Robust weighted co-clustering with global and local discrimination

In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones.