Reviewerly/exHarmony huggingface dataset:
Optical double-image cryptosystem based on generalized singular value decomposition and five-dimensional hyperchaotic maps

In this paper, we propose an asymmetric optical double-image cryptosystem based on generalized singular value decomposition (GSVD) and five-dimensional (5D) hyperchaotic maps. In the proposed cryptosystem, the two plain images are first decomposed into five components by the GSVD operation. The two unitary matrices obtained by GSVD are encoded as a complex function, which is then modulated by the chaotic random phase masks (CRPMs). The private key and the final encryption result are generated by phase-truncation and amplitude-truncation operations. The GSVD operation can decompose two images at the same time and is used to generate the private key that enables the encryption process to be asymmetric. Compared with the existing phase-truncated-based cryptosystems, our cryptosystem can improve security against a special attack. In addition, the CRPMs are generated by 5D hyperchaotic maps, which have a larger parameter space and better randomness. Numerical simulation results are shown to verify the feasibility and robustness of our cryptosystem. Furthermore, the proposed cryptosystem can be extended to encrypt multiple images conveniently.
Improving Inertial Sensor-Based Activity Recognition in Neurological Populations

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued.
Digital footprint in education as a driver of professional growth in the digital age

The large-scale global digital transformation of the economy is changing all spheres of human activity, introducing fundamental changes, including all the levels of educational system. The modern educational process is not limited to the digitisation of educational materials or to the introduction of remote education. Digital transformation impacts the educational activities in higher education, secondary, professional, and even preschool education. Development and implementation of digital learning environments is a prerequisite for obtaining detailed learning data of an individual for the entire time of his education, that caused the appearance of the lifelong digital educational footprint. The developers of digital educational courses plan to analyze the digital footprint and to use the results to adjust an educational trajectory when studying at higher educational institutions. The integration with social networks and messengers provides the data needed to create virtual assistant based on the individual behavior, that helps the student to master the course material. The collected data is also useful to create student’s digital twin, that is used by the teacher to conduct educational experimental research on the implementation of new educational technologies, or to adjust learning path to achieve maximum educational results of the particular student. The authors conducted the research on possibilities to use digital footprint for assessing the current state of the student’s competencies and for keeping the student’s competencies in an active state taken into account lifelong learning strategy. The proposed by the authors’ hypotheses could be applied to improve the effectiveness of adaptive systems of professional adult education.
Genetic programming benchmarks

The top image shows a set of scales, which are intended to bring to mind the ideas of balance and fair experimentation which are the focus of our article on genetic programming benchmarks in this issue. Image by Elena Mozhvilo and made available under the Unsplash license on https://unsplash.com/photos/j06gLuKK0GM.
SIOD: Single Instance Annotated Per Category Per Image for Object Detection

Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled and unlabeled data. In this study, we propose the Single Instance annotated Object Detection (SIOD), requiring only one instance annotation for each existing category in an image. Degraded from inter-task (WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD provides more reliable and rich prior knowledge for mining the rest of unlabeled instances and trades off the annotation cost and performance. Under the SIOD setting, we propose a simple yet effective framework, termed Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label Generating module (SPLG) and a Pixel-level Group Contrastive Learning module (PGCL). SPLG firstly mines latent instances from feature representation space to alleviate the annotation missing problem. To avoid being misled by inaccurate pseudo labels, we propose PGCL to boost the tolerance to false pseudo labels. Extensive experiments on MS COCO verify the feasibility of the SIOD setting and the superiority of the proposed method, which obtains consistent and significant improvements compared to baseline methods and achieves comparable results with fully supervised object detection (FSOD) methods with only 40% instances annotated. Code is available at https://github.com/solicucu/SIOD.
Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation

Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method.A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function.In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method.Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.
Towards Comprehensive Representation Enhancement in Semantics-Guided Self-supervised Monocular Depth Estimation

Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics. However, since depth estimation and semantic segmentation are fundamentally two types of tasks: one is regression while the other is classification, the distribution of depth feature and semantic feature are naturally different. Previous works that leverage semantic information in depth estimation mostly neglect such representational discrimination, which leads to insufficient representation enhancement of depth feature. In this work, we propose an attention-based module to enhance task-specific feature by addressing their feature uniqueness within instances. Additionally, we propose a metric learning based approach to accomplish comprehensive enhancement on depth feature by creating a separation between instances in feature space. Extensive experiments and analysis demonstrate the effectiveness of our proposed method. In the end, our method achieves the state-of-the-art performance on KITTI dataset.
Secure multi-party computations for privacy-preserving machine learning

The paper is devoted to the analysis of privacy-preserving machine learning (PPML) systems based on secure multi-party computations. It reviews PPML systems, analyses the goals and objectives of its application. A generalized model of PPML architecture is proposed, reflecting the main functional blocks of such systems. The formulation of the problem of secure multi-party computation is considered. The descriptions of cryptographic primitives and protocols used to implement multi-party secure computation protocols, including garbled circuits, secret sharing schemes, and homomorphic encryption are given. The current PPML systems based on two-, three-, and four-party secure computations are analyzed. The main attention is paid to algorithmic aspects of systems, methods and protocols of securing information. Systems secure against semi-honest and active adversaries are considered, both based on universal modules for secure multi-party computations, and specialized ones designed to ensure the privacy of specific machine learning technologies, such as convolutional neural networks. We consider examples of implemented prototypes of several PPML systems. Based on the results of the analysis, conclusions are formulated about the features of the future PPML systems.
Modification in Functional Discourse Grammar: State of the art and issues addressed

Abstract This is an introduction to the Special Issue on Modification in Functional Discourse Grammar.
HArMoNICS: High-Assurance Microgrid Network Infrastructure Case Study

Modern Intelligent Infrastructures (II) are highly complex, interconnected systems that are now emerging. For instance, II can integrate technologies and processes to provide citizens with faster services and better goods. An average II can include many technologies, e.g., Cloud applications and IoT devices, under different environments, e.g., industry 4.0 production plants and smart buildings. Although II bring concrete benefits to all of these contexts, they also carry security concerns. Reasoning about threats and security exposures that might affect II is non trivial. This is only partially due to their inherent complexity. As a matter of fact, real II are typically in charge of some critical operations that cannot be interrupted or compromised for experimental purposes. An alternative solution is to rely on digital replicas which can provide a good trade off between realism and usability. These assets represent a strategic and highly demanded resource for the security community. In this paper we present HArMoNICS, a case study infrastructure meant to provide a playground for security experts interested in II security. HArMoNICS revolves around a digital replica of a real Smart Polygeneration Microgrid (SPM) located in Italy. Although most of the components are based on or inspired to the real system, HArMoNICS has been enriched with further security-relevant features. As a result, the case study includes vertical uses cases focusing on specific security topics. Security researchers can use it to assess the effectiveness of new methodologies, to carry out security training activities, or even to extend it with new elements.
Automata Learning Meets Shielding

Safety is still one of the major research challenges in reinforcement learning (RL). In this paper, we address the problem of how to avoid safety violations of RL agents during exploration in probabilistic and partially unknown environments. Our approach combines automata learning for Markov Decision Processes (MDPs) and shield synthesis in an iterative approach. Initially, the MDP representing the environment is unknown. The agent starts exploring the environment and collects traces. From the collected traces, we passively learn MDPs that abstractly represent the safety-relevant aspects of the environment. Given a learned MDP and a safety specification, we construct a shield. For each state-action pair within a learned MDP, the shield computes exact probabilities on how likely it is that executing the action results in violating the specification from the current state within the next k steps. After the shield is constructed, the shield is used during runtime and blocks any actions that induce a too large risk from the agent. The shielded agent continues to explore the environment and collects new data on the environment. Iteratively, we use the collected data to learn new MDPs with higher accuracy, resulting in turn in shields able to prevent more safety violations. We implemented our approach and present a detailed case study of a Q-learning agent exploring slippery Gridworlds. In our experiments, we show that as the agent explores more and more of the environment during training, the improved learned models lead to shields that are able to prevent many safety violations.
Discrete-Constrained Regression for Local Counting Models

Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression. Code is available at https://github.com/xhp-hust-2018-2011/dcreg .
Self-Taught Metric Learning without Labels

We present a novel self-taught framework for unsuper-vised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the embedding space to predict their class-equivalence relations as pseudo labels. The algorithm enables efficient end-to-end training since it demands no off-the-shelf module for pseudo labeling. Also, the class-equivalence relations provide rich supervisory signals for learning an embedding space. On standard benchmarks for metric learning, it clearly outperforms existing unsupervised learning methods and sometimes even beats supervised learning models using the same backbone network. It is also applied to semi-supervised metric learning as a way of exploiting additional unlabeled data, and achieves the state of the art by boosting performance of supervised learning substantially.
Contrastive Vision-Language Pre-training with Limited Resources

Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these works require a tremendous amount of data and computational resources (e.g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration. To this end, we propose a stack of novel methods, which significantly cut down the heavy resource dependency and allow us to conduct dual-encoder multi-modal representation alignment with limited resources. Besides, we provide a reproducible baseline of competitive results, namely ZeroVL, with only 14M publicly accessible academic datasets and 8 V100 GPUs. Additionally, we collect 100M web data for pre-training, and achieve comparable or superior results than state-of-the-art methods, further proving the effectiveness of our methods on large-scale data. We hope that this work will provide useful data points and experience for future research in contrastive vision-language pre-training. Code is available at https://github.com/zerovl/ZeroVL .
Addressing Heterogeneity in Federated Learning via Distributional Transformation

Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs from one client to another. Existing personalized FL algorithms are only applicable to narrow cases, e.g., one or two data classes per client, and therefore they do not satisfactorily address FL under varying levels of data heterogeneity. In this paper, we propose a novel framework, called DisTrans, to improve FL performance (i.e., model accuracy) via train and test-time distributional transformations along with a double-input-channel model structure. DisTrans works by optimizing distributional offsets and models for each FL client to shift their data distribution, and aggregates these offsets at the FL server to further improve performance in case of distributional heterogeneity. Our evaluation on multiple benchmark datasets shows that DisTrans outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity (e.g., for CelebA and 100% heterogeneity DisTrans has accuracy of 80.4% vs. 72.1% or lower for other SOTA approaches).
PNB-Focused Differential Cryptanalysis of ChaCha Stream Cipher

This study focuses on differential cryptanalysis of the ChaCha stream cipher. In the conventional approach, an adversary first searches for an input/output differential pair with the highest differential bias and then analyzes the probabilistic neutral bits (PNB) based on the obtained input/output differential pair. However, although the time and data complexities for the attack can be estimated by the differential bias and PNB obtained by this approach, the combination of the differential bias and PNB is not always optimal. In addition, the existing studies have not performed a comprehensive analysis of the PNB; thus, they have not provided an upper bound on the number of rounds required for a differential attack that uses a single-bit truncated differential to be successful. To address these limitations, we propose a PNB-focused differential attack on reduced-round ChaCha by first comprehensively analyzing the PNB for all possible single-bit truncated output differences and then searching for the input/output differential pair with the highest differential bias based on the obtained PNB. The best existing attack on ChaCha, proposed by Beierle et al. at CRYPTO 2020, works on up to 7 rounds, whereas the most extended attack we observed works on up to 7.25 rounds using the proposed PNB-focused approach. The time complexity, data complexity, and success probability of the proposed attack are $$2^{255.62}$$ , $$2^{48.36}$$ , and 0.5, respectively. Although the proposed attack is less efficient than a brute force attack, it is the first dedicated attack on the target and provides both a baseline and useful components (i.e., differential bias and PNB) for improved attacks.
A Challenge for the Next 50 Years of Automated Service Composition

Automated Service Composition emerged as a promising area of research at the beginning of the century. After twenty years, it appears to have reached a stagnating state where only little progress is made. In the present vision paper, I propose a challenge for automated service composition to be achieved in the next 50 years. I set a scene in 2052 that service composition should be able to handle by then. Finally, I draw a parallel with autonomous driving to identify the major milestones in the quest to fully autonomous service composition systems.
Identification and IDNs in the Metaverse: Who Would We Like to Be?

One’s digital identity on the Metaverse is critical enough to warrant EU regulation. Suggesting Interactive Digital Narratives as having a role to play in the Metaverse, we focus on the identity of the Virtual Reality interactor in such virtual spaces, and the potential impact this may have on the self-identity of the interactor. Building upon the notions of identity and the interactor’s construction of their narrative identity, we revisit identification in the context of VR Interactive Narratives (VRINs) and explore authenticity and character similarity as its dimensions. We interpret the construction of a narrative identity in VR as a vehicle for identity shift between the interactor’s self-identity and identification with the character. Based on the theoretical framework, we present a conceptual model for identity shift in VRINs which we then apply to a number of case studies to exemplify its utility and provide some guidelines for VRIN authors in how to use this model.