Reviewerly/exHarmony huggingface dataset:
LBAC: A lightweight blockchain-based access control scheme for the internet of things

Attribute-based encryption (ABE) is considered to be one of the most suitable schemes for cryptographic access control in the big data environment. But it still faces many challenges to be applied in the Internet of Things (IoT). ABE is implemented based on bilinear pairing, which is considered to be an expensive operation. However, there are lots of IoT devices with constrained resources. Proxy re-encryption methods based on the cloud are proposed to reduce computing overhead on the user side, but an untrusted cloud may give incorrect computing results to mislead users. To solve this problem, an access control scheme with lightweight decryption based on ABE and blockchain technologies is proposed. We assume that the cloud is untrusted, and guarantee the accuracy of proxy re-encryption calculation based on blockchain. In addition, how to encourage users to obtain authorization through blockchain and record access behavior on the blockchain is a problem worthy of attention. This paper proposes a user credibility incentive mechanism, which calculates the user’s credibility according to the user’s access behavior and gives a reputation score, so as to dynamically adjust the endorsement protocol. Security analysis and experimental results show that the proposed method is reliable and efficient.
MARMA: A Mobile Augmented Reality Maintenance Assistant for Fast-Track Repair Procedures in the Context of Industry 4.0

The integration of exponential technologies in the traditional manufacturing processes constitutes a noteworthy trend of the past two decades, aiming to reshape the industrial environment. This kind of digital transformation, which is driven by the Industry 4.0 initiative, not only affects the individual manufacturing assets, but the involved human workforce, as well. Since human operators should be placed in the centre of this revolution, they ought to be endowed with new tools and through-engineering solutions that improve their efficiency. In addition, vivid visualization techniques must be utilized, in order to support them during their daily operations in an auxiliary and comprehensive way. Towards this end, we describe a user-centered methodology, which utilizes augmented reality (AR) and computer vision (CV) techniques, supporting low-skilled operators in the maintenance procedures. The described mobile augmented reality maintenance assistant (MARMA) makes use of the handheld’s camera and locates the asset on the shop floor and generates AR maintenance instructions. We evaluate the performance of MARMA in a real use case scenario, using an automotive industrial asset provided by a collaborative manufacturer. During the evaluation procedure, manufacturer experts confirmed its contribution as an application that can effectively support the maintenance engineers.
A Privacy-Preserving and Untraceable Group Data Sharing Scheme in Cloud Computing

With the development of cloud computing, the great amount of storage data requires safe and efficient data sharing. In multiparty storage data sharing, first, the confidentiality of shared data is ensured to achieve data privacy preservation. Second, the security of stored data is ensured. That is, when stored shared data are subject to frequent access operations, the address sequence or access pattern of data is hidden. Therefore, determining how to ensure the untraceability of stored data or efficient hide the data access pattern in sharing stored data is a challenge. By employing the proxy re-encryption algorithm and oblivious random access memory (ORAM), a privacy-preserving and untraceable scheme is proposed to support multiple users in sharing data in cloud computing. On the one hand, group members and a proxy use the key exchange phase to obtain keys and resist multiparty collusion if necessary. The ciphertext obtained according to the proxy re-encryption phase enables group members to implement access control and store data, thereby completing secure data sharing. On the other hand, this article realizes data untraceability and a hidden data access pattern through a one-way circular linked table in a binary tree (OCLT) and obfuscation operation. Additionally, based on the designed structure and pointer tuple, malicious users are identified and data tampering is prevented. The security analysis shows that the protocol designed in this article can meet the security requirements of proxy re-encryption and ORAM. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.
How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals

Fake portrait video generation techniques have been posing a new threat to the society with photorealistic deep fakes for political propaganda, celebrity imitation, forged evidences, and other identity related manipulations. Following these generation techniques, some detection approaches have also been proved useful due to their high classification accuracy. Nevertheless, almost no effort was spent to track down the source of deep fakes. We propose an approach not only to separate deep fakes from real videos, but also to discover the specific generative model behind a deep fake. Some pure deep learning based approaches try to classify deep fakes using CNNs where they actually learn the residuals of the generator. We believe that these residuals contain more information and we can reveal these manipulation artifacts by disentangling them with biological signals. Our key observation yields that the spatiotemporal patterns in biological signals can be conceived as a representative projection of residuals. To justify this observation, we extract PPG cells from real and fake videos and feed these to a state-of-the-art classification network for detecting the generative model per video. Our results indicate that our approach can detect fake videos with 97.29% accuracy, and the source model with 93.39% accuracy.
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes

In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-the-art deepfake detections are based on black-box models that process videos frame-by-frame for inference, and few closely examine their temporal inconsistencies. However, the existence of such temporal artifacts within deepfake videos is key in detecting and explaining deepfakes to a supervising human. To this end, we propose Dynamic Prototype Network (DPNet) - an interpretable and effective solution that utilizes dynamic representations (i.e., prototypes) to explain deepfake temporal artifacts. Extensive experimental results show that DPNet achieves competitive predictive performance, even on unseen testing datasets such as Google's DeepFakeDetection, DeeperForensics, and Celeb-DF, while providing easy referential explanations of deepfake dynamics. On top of DPNet's prototypical framework, we further formulate temporal logic specifications based on these dynamics to check our model's compliance to desired temporal behaviors, hence providing trustworthiness for such critical detection systems.
Application of artificial intelligence and machine learning for prediction of oral cancer risk

Abstract Background Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic‐pathological features. Methods A popular data mining algorithm artificial neural network was used for developing the artificial intelligence‐based prediction model. A total of 29 variables that were associated with the patients were used for developing the model. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. All records and observations were reviewed by Board‐certified oral pathologist. Results A total of 73 patients met the eligibility criteria. Twenty‐two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. Thirty‐seven were female, and 36 were male, with a mean age of 63.09 years. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10‐fold cross‐validation analysis was 85.71% (95% confidence interval [CI], 57.19‐98.22) and 60.00% (95% CI, 14.66‐94.73), respectively. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43‐931.95). Conclusion Our results suggest that this machine‐learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic‐pathological data.
Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. In coping with it, histopathology image analysis (HIA) provides key information for clinical diagnosis of CRC. Nowadays, the deep learning methods are widely used in improving cancer classification and localization of tumor-regions in HIA. However, these efforts are both time-consuming and labor-intensive due to the manual annotation of tumor-regions in the whole slide images (WSIs). Furthermore, classical deep learning methods to analyze thousands of patches extracted from WSIs may cause loss of integrated information of image. Herein, a novel method was developed, which used only global labels to achieve WSI classification and localization of carcinoma by combining features from different magnifications of WSIs. The model was trained and tested using 1346 colorectal cancer WSIs from the Cancer Genome Atlas (TCGA). Our method classified colorectal cancer with an accuracy of 94.6 %, which slightly outperforms most of the existing methods. Its cancerous-location probability maps were in good agreement with annotations from three individual expert pathologists. Independent tests on 50 newly-collected colorectal cancer WSIs from hospitals produced 92.0 % accuracy and cancerous-location probability maps were in good agreement with the three pathologists. The results thereby demonstrated that the method sufficiently achieved WSI classification and localization utilizing only global labels. This weakly supervised deep learning method is effective in time and cost, as it delivered a better performance in comparison with the state-of-the-art methods.
Digital Image Noise Estimation Using DWT Coefficients

Noise type and strength estimation are important in many image processing applications like denoising, compression, video tracking, etc. There are many existing methods for estimation of the type of noise and its strength in digital images. These methods mostly rely on the transform or spatial domain information of images. We propose a hybrid Discrete Wavelet Transform (DWT) and edge information removal based algorithm to estimate the strength of Gaussian noise in digital images. The wavelet coefficients corresponding to spatial domain edges are excluded from noise estimate calculation using a Sobel edge detector. The accuracy of the proposed algorithm is further increased using polynomial regression. Parseval’s theorem mathematically validates the proposed algorithm. The performance of the proposed algorithm is evaluated on a standard LIVE image dataset. Benchmarking results show that the proposed algorithm outperforms all other state of the art algorithms by a large margin over a wide range of noise.
Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition

Benefited from the powerful ability of spatial temporal Graph Convolutional Networks (ST-GCNs), skeleton-based human action recognition has gained promising success. However, the node interaction through message propagation does not always provide complementary information. Instead, it May even produce destructive noise and thus make learned representations indistinguishable. Inevitably, the graph representation would also become over-smoothing especially when multiple GCN layers are stacked. This paper proposes spatial-temporal graph deconvolutional networks (ST-GDNs), a novel and flexible graph deconvolution technique, to alleviate this issue. At its core, this method provides a better message aggregation by removing the embedding redundancy of the input graphs from either node-wise, frame-wise or element-wise at different network layers. Extensive experiments on three current most challenging benchmarks verify that ST-GDN consistently improves the performance and largely reduce the model size on these datasets.
Facial emotion recognition using modified HOG and LBP features with deep stacked autoencoders

Recognition of emotions using facial expression is an active research topic in the field of computer vision. In this paper, a novel feature descriptor proposed for facial expression recognition using modified Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) feature descriptor. Firstly, viola-Jones face detection used to detect the face region, Then, Butterworth high pass filter utilized to enhance the detected region to find the eye, nose and mouth region detection using Viola-Jones approach. Secondly, the proposed modified HOG and LBP feature descriptor are used to extract the features of the detected eye, nose and mouth regions. The extracted features of these three regions are concatenated and reduced its dimensionality using Deep Stacked AutoEncoders. Finally, multi-class Support Vector Machine is used for classification and recognition. Experimental results show that the proposed modified feature descriptors can effectively recognize emotions on CK+ dataset and JAFFE dataset.
Biodigital Philosophy, Technological Convergence, and Postdigital Knowledge Ecologies

New technological ability is leading postdigital science, where biology as digital information, and digital information as biology, are now dialectically interconnected. In this article we firstly explore a philosophy of biodigitalism as a new paradigm closely linked to bioinformationalism. Both involve the mutual interaction and integration of information and biology, which leads us into discussion of biodigital convergence. As a unified ecosystem, this allows us to resolve problems that isolated disciplinary capabilities cannot, creating new knowledge ecologies within a constellation of technoscience. To illustrate our arrival at this historical flash point via several major epistemological shifts in the post-war period, we venture a tentative typology. The convergence between biology and information reconfigures all levels of theory and practice, and even critical reason itself now requires a biodigital interpretation oriented towards ecosystems and coordinated Earth systems. In this understanding, neither the digital humanities, the biohumanities, nor the posthumanities sit outside of biodigitalism. Instead, posthumanism is but one form of biodigitalism that mediates the biohumanities and the digital humanities, no longer preoccupied with the tradition of the subject, but with the constellation of forces shaping the future of human ontologies. This heralds a new biopolitics which brings the philosophy of race, class, gender, and intelligence, into a compelling dialog with genomics and information.
Representation Learning with Statistical Independence to Mitigate Bias

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years. Such challenges range from spurious associations between variables in medical studies to the bias of race in gender or face recognition systems. Controlling for all types of biases in the dataset curation stage is cumbersome and sometimes impossible. The alternative is to use the available data and build models incorporating fair representation learning. In this paper, we propose such a model based on adversarial training with two competing objectives to learn features that have (1) maximum discriminative power with respect to the task and (2) minimal statistical mean dependence with the protected (bias) variable(s). Our approach does so by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and the learned features. We apply our method to synthetic data, medical images (containing task bias), and a dataset for gender classification (containing dataset bias). Our results show that the learned features by our method not only result in superior prediction performance but also are unbiased.
An ensemble of autonomous auto-encoders for human activity recognition

Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model.
A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

We develop computationally affordable and encoding independent gradient evaluation procedures for unitary coupled-cluster type operators, applicable on quantum computers.
Explainable AI and Multi-Modal Causability in Medicine

Progress in statistical machine learning made AI in medicine successful, in certain classification tasks even beyond human level performance. Nevertheless, correlation is not causation and successful models are often complex "black-boxes", which make it hard to understand why a result has been achieved. The explainable AI (xAI) community develops methods, e. g. to highlight which input parameters are relevant for a result; however, in the medical domain there is a need for causability: In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations produced by xAI. The key for future human-AI interfaces is to map explainability with causability and to allow a domain expert to ask questions to understand why an AI came up with a result, and also to ask "what-if" questions (counterfactuals) to gain insight into the underlying independent explanatory factors of a result. A multi-modal causability is important in the medical domain because often different modalities contribute to a result.
Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.
On powerpointers, clickerers, and digital pros: Investigating the initiation of digital learning activities by teachers in higher education

This study investigated the initiation of digitally supported learning activities and personal and institutional factors associated with them in different higher education courses, based on the C♭-model. The C♭-model is a theoretical framework that systematizes contextual factors, which influence students‘ learning activities as the most important facilitator of students’ learning success. Using a self-assessment instrument with anchored scenarios in a sample of 1625 higher education teachers, we were able to identify three levels at which higher education teachers initiated digital learning activities: a low level (powerpointers), a moderate level (clickerers), and a high level (digital pros). The findings also support the relevance of the contextual factors specified in the C♭-model for initiating a high level of digital learning activities, namely digitalization policy and commitment of university administration, institutional equipment, technical and educational support, self-assessed basic digital skills, and self-assessed technology-related teaching skills. All of these factors explain a substantial amount of variance in the level of initiated digital learning activities. We conclude that a comprehensive approach rather than isolated measures might contribute to successful teaching and learning in higher education.
Dollarization and the “unbundling” of globalization in sub-Saharan Africa

This study contributes to the dollarization literature by expanding its determinants to account for different dimensions of globalization, using the widely employed KOF index of globalization. Specifically, globalization is “unbundled” into three different layers namely: economic, social and political dimensions. The study focuses on 25 sub-Saharan African (SSA) countries for the period 2001–2012. Using the Tobit regression approach, the following findings are established. First, from both economic and statistical relevance, the social and political dimensions of globalization constitute the key dollarization amplifiers, while the explanatory power of the economic component is weaker on dollarization. Second, consistent with the theoretical underpinnings, macroeconomic instabilities (such as inflation and exchange rate volatilities) have the positive expected signs. Third, the positive association between the accumulation of international reserves and dollarization is also apparent. Policy implications are discussed.