Reviewerly/exHarmony huggingface dataset:
Image Processing Robustness Assessment of Small-Body Shapes

Abstract Asteroids and comets are triggering interest due to the richness of precious materials, their scientific value as well as for their potential hazardousness. Owing to their significant diversity, minor bodies do not exhibit uniform shapes: they can range from spherical to irregularly shaped objects with rocky, uneven, and cratered surface. Nowadays, space probes rely more and more on optical navigation techniques, due to the increasing demand for autonomy. When dealing with minor bodies, the diversified range of shapes can significantly affect the performance of these techniques. In order to enable deep space probes to confidently deal with uncertainties, the need for robust image processing methods arises. Commonly, few image processing methods are designed and tested with limited shapes to meet mission requirements. In this work, we depart from this paradigm by developing a new framework, which includes extensive testing of the image processing algorithms with various shapes. The shapes are not randomly analyzed, yet they are arranged in a hierarchical structure called hyper-cube. The cube allows for a better understanding of the methods performance and to infer the way they shift from one shape to the other. The novelty of this approach lies both in the cube representation, which allows a better understanding of the link between the image processing algorithms and shape of the object, but also in the extensive number of shapes that have been tested, which has never been done before. In this analysis, four methods are considered, namely: center of brightness, intensity weighted centroiding, correlation with Lambertian spheres, and least-squares-based ellipse fitting. Results from this test allow us correlating the methods performances to the bodies shape, to suggest the best performing method for each shape family, and to assess their robustness.
Playing With Sound Beings: A Sonic Playfield

How would you play with a friend that you cannot see, you can only listen to? We present an auditory experience designed as a sound-only, touch-based journey through a playfield of sound beings. The sound beings are auditory entities living in community, evolving their sonic presence and behaviour through interactive exchanges with their surroundings. By combining multiple levels of reactivity and openness to interaction, we explore the notion of sonic dialogues established between the sound beings and each human participant who dwells in this space. The dialogic exchanges are conceived from the concept of musical phonemes, as linguistic pieces that are combined, merged, extended and synchronised with each interchange to produce an interactive, orchestral piece. With this experience, we intend to incite experimentation of a sonic playfield, as a generative sonic space to be uncovered and transformed through interaction.
TouchMate: Understanding the Design of Body Actuating Games using Physical Touch

Body-actuating technologies such as Electrical Muscle Stimulation (EMS) can actuate multiple players simultaneously via physical touch. To investigate this opportunity, we designed a game called “Touchmate”. Here, one guesser and two suspects sit across with their legs hidden under a table. The guesser attaches a ground electrode from one EMS channel, and each suspect attaches one active electrode from the same channel on their forearms. When a suspect touches the guesser’s leg, their bodies complete the electrical circuit, actuating both their hands involuntarily via the EMS. The guesser’s goal is to determine who touched their leg. In this paper, we present the results from our initial study and articulate three player experience themes. Ultimately, we hope our work inspires game designers to create physical touch games using body-actuating technologies.
Hospital Information Systems in Malaysia: Current Issues and Blockchain Technology as a Solution

Healthcare data is seen as a very useful source of information that may help healthcare systems become more intelligent and enhance the quality of services delivered. The main aim of this paper is to outline present challenges in hospital information systems while also highlighting the potential for blockchain technology to address these issues in Malaysian healthcare. It begins by reviewing current hospital information systems and the issues that health providers face, specifically concerning data security, patient life-long health records, and healthcare information sharing. These problems in handling healthcare data have forced the implementation of a system that would offer medical professionals access to up-to-date healthcare information. Blockchain technology has sparked interest in the healthcare industry as a potential solution, particularly after the technology was successfully implemented in the finance sector to increase the security of financial transactions using digital currencies such as Bitcoin. Furthermore, this paper discusses the possible technological, organizational, and environmental constraints that may impact Malaysia's adoption of blockchain-based hospital information systems. This study may serve as the foundation for a series of studies investigating, assessing, and answering such requirements of blockchain-based hospital information systems in Malaysia. Additionally, this study may help in policy and decision-making procedures for reliable and sustainable hospital information systems in Malaysian healthcare organizations.
HRD-GKV-CCNet: A Deep Learning-based Multitask Method for Human Crowd Management

In a unified framework, we offer a multitasking method for crowd counting, classification, and localization. As the detection, classification, counting, and localization tasks are highly associated and may be performed concurrently, our model benefits from a multitask approach of encoded crowd images and then fusing them. In contrast to the somewhat more prevalent density-based techniques, our model uses point and bounding box-based annotations to precisely identify human crowd situations. We evaluate our model on recorded videos during Kumbh Mela 2021 and demonstrate that our method achieves strong results on counting, classification, and localization tasks, with MAE measures of 105.79% for crowd counting, a precision measure of 96% for crowd classification. Our exhaustive ablation experiments demonstrate the efficacy of our multitask method.
A Comparison of Several Implementations of B92 Quantum Key Distribution Protocol

This paper presents the development, comparison, and analysis of several implementations of the B92 Quantum Key Distribution (QKD) protocol. To achieve this goal three prototypes which consists of traditional simulators (non-quantum) were created, one for the B92 protocol, one for the B92 protocol with an eavesdropper, and one for the B92 protocol with the Quantum Bit Travel Time (QBTT) eavesdropper detection method. The principles of quantum mechanics were studied, as a foundation of quantum cryptography, for the implementation of the simulation programs that were written in C++, focusing mainly on the B92 protocol and QBTT eavesdropper detection method. We compared the Quantum Bit Error Rate (QBER) for the implementation of the B92 protocol without an eavesdropper, the B92 protocol with an eavesdropper and the B92 protocol with the QBTT eavesdropper detection method and found that the latest one significantly reduces the QBER from the final key.
Poster

As transaction fees skyrocket today, blockchains become increasingly expensive, hurting their adoption in broader applications. This work tackles the saving of transaction fees for economic blockchain applications. The key insight is that other than the existing "default'' mode to execute application logic fully on-chain, i.e., in smart contracts, and in fine granularity, i.e., user request per transaction, there are alternative execution modes with advantages in cost-effectiveness. On Ethereum, we propose a holistic middleware platform supporting flexible and secure transaction executions, including off-chain states and batching of user requests. Furthermore, we propose control-plane schemes to adapt the execution mode to the current workload for optimal runtime cost. We present a case study on the institutional accounts (e.g., coinbase.com) intensively sending Ether on Ethereum blockchains. By collecting real-life transactions, we construct workload benchmarks and show that our work saves 18%\sim 47%18%-47% per invocation than the default baseline while introducing 1.81%\sim 16.59%1.81%-16.59% blocks delay.
Object-aware bounding box regression for online multi-object tracking

Based on the detection technology, regressing predicted bounding boxes provides an effective approach in multiple object tracking. However, if only the information in the current frame is considered, identity (ID) switch is easy to happen when objects interact. In this paper, we propose an Object-Aware Bounding Box Regression (OABBR) for online multi-object tracking. We first propose an Object-Aware Spatial-Temporal Understanding (OASTU) module to mine the correlated information in corresponding object’s trajectory. OASTU updates features of predictions by the correlated information. By using the updated features, we further perform bounding box regression. Besides, to make features extracted by the backbone network contain more ID information, we construct a weak ID constraint in the training phase. The introduced weak ID constraint facilitates OASTU to be ID consistent and further alleviates ID switch. By exploring the spatial-temporal information in corresponding object’s trajectory, each prediction is able to know the information of the corresponding object, which makes the purpose of its regression clearer. Experimental results on four public benchmarks demonstrate the effectiveness of the proposed method.
Two-Phase Mitotic Detection Using Deep Learning Techniques

Mitosis identification in breast cancer pictures is critical for determining the tumour’s aggressiveness. In cancer grading, the mitotic count is an important metric. Currently, a pathologist visualizes several high power fields (HPFs) on a glass slide under a super microscope in a clinic setting, which is a difficult and time-consuming operation. The development of automated mitosis detection techniques is critical, but it also faces challenges such as size invariance, data scarcity, poor image staining, and an uneven sample class. These constraints nevertheless restrict the automated histopathology picture interpretation to be employed in clinical practice. The proposed work is a deep learning model, which is combination of object detection network and classification network. The validation of proposed work was carried out on the MITOS-ATYPIA-14 data set. The object detection network which is yolov5 architecture detects the nuclei. Then with the detected nuclei, bbox images are extracted and fed to classification layer which is EfficientNetV2-S, which decides whether the detected nuclei are mitotic or not. Finally, the predicted label class along with its confidence score is plotted on test image. The nuclei detection model which is yolo network achieved mAP@0.5 of 0.93 and classification network of proposed architecture achieved accuracy of 97.1%
Counterfactual explanations as interventions in latent space

Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems. XAI is crucial to help satisfying the increasingly important demand of trustworthy Artificial Intelligence, characterized by fundamental aspects such as respect of human autonomy, prevention of harm, transparency, accountability, etc. Within XAI techniques, counterfactual explanations aim to provide to end users a set of features (and their corresponding values) that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations, and in particular, they fall short of considering the causal impact of such actions. In this paper, we present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations capturing by design the underlying causal relations from the data, and at the same time to provide feasible recommendations to reach the proposed profile. Moreover, our methodology has the advantage that it can be set on top of existing counterfactuals generator algorithms, thus minimising the complexity of imposing additional causal constrains. We demonstrate the effectiveness of our approach with a set of different experiments using synthetic and real datasets (including a proprietary dataset of the financial domain).
A corpus-based search for machine translationese in terms of discourse coherence

Abstract Earlier studies have corroborated that human translation exhibits unique linguistic features, usually referred to as translationese. However, research on machine translationese, in spite of some sparse efforts, is still in its infancy. By comparing machine translation with human translation and original target language texts, this study aims to investigate if machine translation has unique linguistic features of its own too, to what extent machine translations are different from human translations and target-language originals, and what characteristics are typical of machine translations. To this end, we collected a corpus containing English translations of modern Chinese literary texts produced by neural machine translation systems and human professional translators and comparable original texts in the target language. Based on the corpus, a quantitative study of discourse coherence was conducted by observing metrics in three dimensions borrowed from Coh-Metrix, including connectives, latent semantic analysis and the situation/mental model. The results support the existence of translationese in both human and machine translations when they are compared with original texts. However, machine translationese is not the same as human translationese in some metrics of discourse coherence. Additionally, machine translation systems, such as Google and DeepL, when compared with each other, show unique features in some coherence metrics, although on the whole they are not significantly different from each other in those coherence metrics.
Research on Logistics Distribution Center Location Based on Hybrid Beetle Antennae Search and Rain Algorithm

The location of logistics distribution centers is a crucial issue in modern logistics distribution systems. In order to obtain a more reasonable solution, an effective optimization algorithm is essential. This paper proposes a new hybrid method, named the beetle antennae search–rain algorithm (BRA), for the problem of logistics distribution centers’ location. The innovation of the BRA is embodied in three aspects. Firstly, the beetle antennae search (BAS) algorithm is embedded into the rain algorithm (RA); thus, the BAS is improved from an individual search to a swarm intelligent search and the global search ability is improved. Secondly, the search direction strategy of the BAS algorithm is incorporated into the RA, which can improve response speed while ensuring optimization performance. Finally, the search precision is improved by the mechanism of eliminating the inferior solution and generating a new solution. The BRA is tested on 10 benchmark functions and applied to solve the logistics distribution centers’ location problem. The performance of the BRA is compared to that of several classical heuristics by using relevant evaluation indexes and dynamic optimization convergence graphs. Experimental results show that the BRA outperforms the BAS algorithm, the RA and some other classic heuristics. It is also revealed that the BRA is an effective and competitive algorithm for logistics distribution centers’ location.
Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

Cigarette filter rods are a kind of easily deformed and irregularly shaped parts, and the quality of cigarette filter rods is often measured by the filter rod roundness index, although they are affected by factors such as environment and humidity, which can affect the detection of filter rod roundness and show blurred images. For the detection of blurred filter rod image roundness, an improved active contour snake model algorithm is proposed. The experimental results show that in the analysis of the algorithm iteration number on the model performance, the model performance gradually leveled off at the iteration number of 200. Comparing the PR of the improved algorithm with the traditional snake algorithm with the balloon force snake algorithm, the accuracy and recall rates obtained were 0.803 and 0.798, respectively, which were greater than the other two algorithms; the error of the value of the residual under the improved snake algorithm was always smaller than the other algorithms, which indicated that the convergence effect of the improved snake algorithm on the error of filter bar roundness was better than the other experimental algorithms. The abovementioned results indicate that the improved snake algorithm has high feasibility and effectiveness in the application of filter bar roundness error detection and has a significant value.
Hyperchaotic System with Application to Image Encryption

This paper introduces a novel hyperchaotic circuit system that is extremely sensitive to circuit parameters and system initial values. The typical dynamical behaviors of the hyperchaotic system such as one-parameter bifurcation diagrams, two-parameter bifurcation diagrams and Lyapunov exponent spectrum are discussed in detail. Then an image encryption algorithm is designed by using the hyperchaotic sequences. In this algorithm, Zigzag algorithm is used to scramble pixel positions, and backward diffusion and forward diffusion algorithms are used to diffuse pixel values. The results indicate that the proposed algorithm can improve the security of the image encryption algorithm. In addition, some circuit simulation experiments are performed to verify the sensitivity of the hyperchaotic circuit to parameters by Multisim. Finally, the hyperchaotic circuit is implemented by using physical elements. The simulation results are consistent with the experimental results of hardware circuit, which effectively verifies the existence of hyperchaotic attractors.
SwitchNet: A modular neural network for adaptive relation extraction

This paper presents a portable toolkit, SwitchNet, for extracting relations from textual input. We summarize four data protocols for relation extraction tasks, including relation classification, relation extraction, triple extraction, and distant supervision relation extraction. This neural architecture is modular, so it can take as input data at different stages of the information extraction process (simple text, text and entities or entity pairs as relation candidates) and compute the rest of the process (named entity recognition and relation classification). We systematically design four information flows to integrate the above protocols by sharing network building blocks and switching different information flows. This framework can extract multiple triples (subject, predicate, object) in one pass. This framework enhances the use of relation classification models in end-to-end triple extraction by inferring pairs of entities of interest and using the shared representation mechanism.
Stable and compact face recognition via unlabeled data driven sparse representation-based classification

Sparse representation-based classification (SRC) has attracted much attention by casting the recognition problem as simple linear regression problem. SRC methods, however, still is limited to enough labeled samples per category, insufficient use of unlabeled samples, and instability of representation. For tackling these problems, an unlabeled data driven inverse projection pseudo-full-space representation-based classification model is proposed with low-rank sparse constraints. The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition. The mixed Gauss–Seidel and Jacobian ADMM algorithm is introduced to solve the model. The convergence, representation capability and stability of the model are analyzed. Experiments on three public datasets show that the proposed LR-S-PFSRC model achieves stable results, especially for proportion imbalance of samples.
A multi-scale reconstruction method for the anomaly detection in stochastic dynamic networks

Detecting anomalous edges and/or nodes is a challenging problem for dynamic networks, due to the spatio-temporal (ST) patterns and randomness underneath the time-varying topology and node attributes. Existing methods commonly ignore randomness, and the anomaly detectors would be sensitive, especially in highly variable dynamic networks. In this paper, a new reconstruction method, the multi-scale variational graph recurrent autoencoder (M-VGRAE), is designed to detect anomalies in stochastic dynamic networks. Overall, upon the framework of Learning from Pure Normal (LPN), the M-VGRAE is trained to reconstruct the normal nodes and edges, and is used to detect the anomalies that cannot be reconstructed well by the trained M-VGRAE. To reduce sensitivity against the high variability, the reconstruction of stochastic dynamic networks is performed in a multi-scale manner. Specifically, the randomness is modeled by introducing multiple latent random variables at each node, each of which models the local randomness of attributes and connectivity within the temporal neighborhoods in a specific hop and historical interval. Then, the local randomness is approximated by employing the variational autoencoder (VAE) with a designed multi-scale ST feature extractor based on graph neural networks and recurrent neural networks. By such design, the capture of ST patterns and the modeling of randomness are jointly achieved. To show the effectiveness of the M-VGRAE in detecting anomalous edges and nodes, experiments are conducted on four real-world datasets of dynamic networks. The results demonstrate that the M-VGRAE consistently outperforms the existing baselines in terms of AUC score on anomaly detection.
DECAF: An interpretable deep cascading framework for ICU mortality prediction

Medical risk detection is an important topic and a challenging task to improve the performance of clinical practices in Intensive Care Units (ICU). Although many bio-statistical learning and deep learning approaches have provided patient-specific mortality predictions, these existing methods lack interpretability that is crucial to gain adequate insight on why such predictions would work. In this paper, we introduce cascading theory to model the physiological domino effect and provide a novel approach to dynamically simulate the deterioration of patients' conditions. We propose a general DEep CAscading Framework (DECAF) to predict the potential risks of all physiological functions at each clinical stage. Compared with other feature-based and/or score-based models, our approach has a range of desirable properties, such as being interpretable, applicable with multi prediction tasks, and learnable from medical common sense and/or clinical experience knowledge. Experiments on a medical dataset (MIMIC-III) of 21,828 ICU patients show that DECAF reaches up to 89.30 % on AUROC, which surpasses the best competing methods for mortality prediction.