Reviewerly/exHarmony huggingface dataset:
Comparative Study of Type-1 and Interval Type-2 Fuzzy Logic Systems in Parameter Adaptation for the Fuzzy Discrete Mycorrhiza Optimization Algorithm

The Fuzzy Discrete Mycorrhiza Optimization (FDMOA) Algorithm is a new hybrid optimization method using the Discrete Mycorrhiza Optimization Algorithm (DMOA) in combination with type-1 or interval type-2 fuzzy logic system. In this new research, when using T1FLS, membership functions are defined by type-1 fuzzy sets, which allows for a more flexible and natural representation of uncertain and imprecise data. This approach has been successfully applied to several optimization problems, such as in feature selection, image segmentation, and data clustering. On the other hand, when DMOA is using IT2FLS, membership functions are represented by interval type-2 fuzzy sets, which allows for a more robust and accurate representation of uncertainty. This approach has been shown to handle higher levels of uncertainty and noise in the input data and has been successfully applied to various optimization problems, including control systems, pattern recognition, and decision-making. Both DMOA using T1FLS and DMOA using IT2FLS have shown better performance than the original DMOA algorithm in many applications. The combination of DMOA with fuzzy logic systems provides a powerful and flexible optimization framework that can be adapted to various problem domains. In addition, these techniques have the potential to more efficiently and effectively solve real-world problems.
RLSS: real-time, decentralized, cooperative, networkless multi-robot trajectory planning using linear spatial separations

Abstract Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots’ dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm’s performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.
Integrating ADTs in KeY and their application to history-based reasoning about collection

Abstract We discuss integrating abstract data types (ADTs) in the KeY theorem prover by a new approach to model data types using Isabelle/HOL as an interactive back-end, and represent Isabelle theorems as user-defined taclets in KeY. As a case study of this new approach, we reason about Java’s interface using histories, and we prove the correctness of several clients that operate on multiple objects, thereby significantly improving the state-of-the-art of history-based reasoning. Open Science. Includes video material (Bian and Hiep in FigShare, 2021. https://doi.org/10.6084/m9.figshare.c.5413263 ) and a source code artifact (Bian et al. in Zenodo, 2022. https://doi.org/10.5281/zenodo.7079126 ).
UAV path recognition and tracking based on image

Abstract This paper firstly analyzes and establishes the motion model of the quadrotor UAV, and then designs the image recognition and control system scheme for the path tracking task. In the image recognition part, the path information is obtained and the desired position signal is obtained by the steps of image optimization, Hough transformation, determining flight direction, identifying the drop-point area, and the PID control is used in the control system part. From the simulation results and 3D visualization module, it can be seen that the UAV can complete the straight-line flight, curve flight, and landing action well, and the image recognition and flight control scheme designed in this paper has good performance.
Deep learning in crowd counting: A survey

Abstract Counting high‐density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence. Despite many advancements in this field, many of them are not widely known, especially in terms of research data. The authors proposed a three‐tier standardised dataset taxonomy (TSDT). The Taxonomy divides datasets into small‐scale, large‐scale and hyper‐scale, according to different application scenarios. This theory can help researchers make more efficient use of datasets and improve the performance of AI algorithms in specific fields. Additionally, the authors proposed a new evaluation index for the clarity of the dataset: average pixel occupied by each object (APO). This new evaluation index is more suitable for evaluating the clarity of the dataset in the object counting task than the image resolution. Moreover, the authors classified the crowd counting methods from a data‐driven perspective: multi‐scale networks, single‐column networks, multi‐column networks, multi‐task networks, attention networks and weak‐supervised networks and introduced the classic crowd counting methods of each class. The authors classified the existing 36 datasets according to the theory of three‐tier standardised dataset taxonomy and discussed and evaluated these datasets. The authors evaluated the performance of more than 100 methods in the past five years on different levels of popular datasets. Recently, progress in research on small‐scale datasets has slowed down. There are few new datasets and algorithms on small‐scale datasets. The studies focused on large or hyper‐scale datasets appear to be reaching a saturation point. The combined use of multiple approaches began to be a major research direction. The authors discussed the theoretical and practical challenges of crowd counting from the perspective of data, algorithms and computing resources. The field of crowd counting is moving towards combining multiple methods and requires fresh, targeted datasets. Despite advancements, the field still faces challenges such as handling real‐world scenarios and processing large crowds in real‐time. Researchers are exploring transfer learning to overcome the limitations of small datasets. The development of effective algorithms for crowd counting remains a challenging and important task in computer vision and AI, with many opportunities for future research.
Noisy Random Quantum Circuit Sampling and its Classical Simulation

Abstract Demonstrating quantum advantage on noisy quantum devices is one of the most important tasks of quantum computing research in the near future. Random quantum circuit sampling is proposed as the most promising approach to the task and has been implemented in experiments for achieving quantum advantage. A series of encouraging computational complexity results, based on some plausible assumptions, show that this task is impossible to complete efficiently by classical computers. However, in practical experiments on noisy quantum devices, the approximate average‐case hardness and the effects of the noise need to be further checked. The competition between the classical simulation algorithm (mainly based on tensor network algorithms) and noisy quantum devices is the explicit way to understand quantum advantage in practice. This review briefly overviews the computational complexity arguments for the hardness of classical simulation of random quantum circuits sampling, then focus on various methods of classical simulation of quantum circuits based on tensor network method.
ULS4US: universal lesion segmentation framework for 2D ultrasound images

Objective. Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs.Approach. The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively.Main Results. Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data.Significance. The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy.
Trusted Deep Neural Execution—A Survey

The growing use of deep neural networks (DNNs) in various applications has raised concerns about the security and privacy of model parameters and runtime execution.To address these concerns, researchers have proposed using trusted execution environments (TEEs) to build trustworthy neural network execution.This paper comprehensively surveys the literature on trusted neural networks, viz., answering how to efficiently execute neural models inside trusted enclaves.We review the various TEE architectures and techniques employed to achieve secure neural network execution and provide a classification of existing work.Additionally, we discuss the challenges and present a few open issues.We intend that this review will assist researchers and practitioners in understanding the state-of-the-art and identifying research problems.
Participating Citizens = Smart Citizens? Applying the Human-centered Design Approach on a Digital Care Platform

New digital innovations are becoming increasingly relevant in the healthcare sector of smart cities. Although the healthcare sector has not been the focus of smart city strategies so far, this sector offers a high potential to save resources. One such measure is the development of a digital care platform. A digital care platform is intended to counteract the shortage of skilled care staff through the targeted deployment of caregivers. To meet the needs of users and service providers, a target group-oriented, human-centered design approach is applied to evaluate the benefits of a care platform for users and service providers. The results are recorded with the help of a Benefit Model. During the surveys, it became clear that users and service providers benefit from the use of a care platform in technical, economic, and social aspects, among others. In the process, stakeholders not only save important resources but also can build valuable networks for healthy and successful aging.
Beyond isolation: OS verification as a foundation for correct applications

Verified systems software has generally had to assume the correctness of the operating system and its provided services (like networking and the file system). Even though there exist verified operating systems and file systems, the specifications for these components do not compose with applications to produce a fully verified high-performance software stack.
Why write address translation OS code yourself when you can synthesize it?

Address translation hardware is at the cornerstone of modern computer systems. It provides a wide range of security-relevant features and abstractions such as memory partitioning, address space isolation, and virtual memory. Hardware designers have developed different memory protection schemes with varying features and means of configuration.
Unleashing True Utility Computing with Quicksand

Today's clouds are inefficient: their utilization of resources like CPUs, GPUs, memory, and storage is low. This inefficiency occurs because applications consume resources at variable rates and ratios, while clouds offer resources at fixed rates and ratios. This mismatch of offering and consumption styles prevents fully realizing the utility computing vision.
Putting out the hardware dumpster fire

The immense hardware complexity of modern computers, both mobile phones and datacenter servers, is a seemingly endless source of bugs and vulnerabilities in system software.
Learning to Live with Privacy-Preserving Analytics

Seeking to close the gap between research and real-world applications of PPAs.
Fabric-Centric Computing

Emerging memory fabrics and the resulting composable infrastructures have fundamentally challenged our conventional wisdom on how to build efficient rack/cluster-scale systems atop. This position paper proposes a new computing paradigm-called Fabric-Centric Computing (FCC)-that views the memory fabric as a first-class citizen to instantiate, orchestrate, and reclaim computations over composable infrastructures. We describe its design principles, report our early experiences, and discuss a new intermediate system stack proposal that harnesses the uniqueness of this cluster interconnect and realizes the vision of FCC.
Quantum computing for social business optimization: a practitioner’s perspective

Currently, E-commerce is widely adopted as it is important for business management and economic growth in the new global economy, and to reach the rapid increasing population. To better manage the e-commerce, it is important to collect and evaluate the consumer behavior data for decision making and optimization. The conventional computing technologies need high amount of power and time for large data analysis. Quantum computing has the potential to analyze the large amount of data more efficiently than classical computing. This paper aims to explore the core process areas that need to be considered by the practitioners for adopting quantum computing in social business. To address the objective of this study, we conducted a literature review and empirical study to explore the core process areas that need to be considered for the consideration of quantum computing in social business. The results show that limited scalability, regulatory challenges, high initial cost, limited availability of quantum resources, education, and training and security are the most important process areas that need to be focused by the industry practitioners. The result of this study gives a knowledge base for research and practitioners community to develop the tools and strategies for the adoption of quantum computing in social business.
Turning traffic surveillance cameras into intelligent sensors for traffic density estimation

Abstract Accurate traffic density plays a pivotal role in the Intelligent Transportation Systems (ITS). The current practice to obtain the traffic density is through specialized sensors. However, those sensors are placed in limited locations due to the cost of installation and maintenance. In most metropolitan areas, traffic surveillance cameras are widespread in road networks, and they are the potential data sources for estimating traffic density in the whole city. Unfortunately, such an application is challenging since surveillance cameras are affected by the 4L characteristics: L ow frame rate, L ow resolution, L ack of annotated data, and L ocated in complex road environments. To the best of our knowledge, there is a lack of holistic frameworks for estimating traffic density from traffic surveillance camera data with 4 L characteristics. Therefore, we propose a framework for estimating traffic density using uncalibrated traffic surveillance cameras. The proposed framework consists of two major components: camera calibration and vehicle detection. The camera calibration method estimates the actual length between pixels in the images and videos, and the vehicle counts are extracted from the deep-learning-based vehicle detection method. Combining the two components, high-granular traffic density can be estimated. To validate the proposed framework, two case studies were conducted in Hong Kong and Sacramento. The results show that the Mean Absolute Error (MAE) for the estimated traffic density is 9.04 veh/km/lane in Hong Kong and 7.03 veh/km/lane in Sacramento. The research outcomes can provide accurate traffic density without installing additional sensors.
An LA-group based design of the non-linear component of block cipher

An S-box is a crucial component of modern block ciphers, providing confusion and making them attack-resistant. For the last three decades, researchers have designed S-boxes using techniques mainly based on associative algebras of Galois fields. In this work, the non-associative structure of an LA-group (left almost group) is employed to design 8 × 8 S-boxes. An LA-group-based design S-box offers appealing properties, such as the inverse of zero element, non-associativity, and fewer constraints. These characteristics increase robustness and expand the key space compared to cyclic groups and Galois fields. Furthermore, an RGB image encryption scheme is also presented in this work to judge the impact of proposed S-boxes. The proposed S-boxes create confusion in a plain image "Red, Green, and Blue" channels, whereas for diffusion, the linear transformation K=(i×32)mod127 is operationalized. In the last, we compared our encryption scheme to some existing benchmarks, revealing that our encryption technique is adequate for image security.
Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration

Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fβw, and MAE).
Extraction of product and higher moment weak values: Applications in quantum state reconstruction and entanglement detection

Weak values of product observables or higher moments of an observable are informationally significant because of their ability to solve some paradoxes, realise strange quantum effects, reconstruct density matrices, etc. In this work, we demonstrate that pairwise orthogonal post-selections can be used to obtain higher moment weak values. By measuring only local weak values (defined as single system weak values in a multipartite scenario), product weak values can be obtained. As applications, we use product and higher moment weak values to reconstruct quantum states showing advantages over previous works in terms of number of required measurement operators and experimental feasibility. Additionally, a necessary separability criteria is given using product weak values to detect entanglement. For some classes of entangled states, positive partial transpose (PPT) criteria is achieved by cleverly choosing product observables and post selections. Robustness of our method against inappropriate choices of observables and noisy post-selections is also discussed.
Visual saliency assistance mechanism based on visually impaired navigation systems

Visually impaired people face significant challenges in their work, studies, and daily lives. Nowadays, numerous visually impaired navigation devices are available to help visually impaired people solve daily life problems. These devices typically include modules for target recognition, distance measurement, and text reading aloud. They are designed to help visually impaired people avoid obstacles and understand the presence of things around them through text reading aloud. Due to the need to avoid all potential obstacles, the target recognition algorithms embedded in these devices must recognize a wide range of targets. However, the text reading aloud module cannot read all of them. Therefore, we designed a visual saliency assistance mechanism that simulates the regions that humans may pay the most attention to in the whole picture. The output of the visual saliency assistance mechanism is overlaid with the target recognition result, which can greatly reduce the target number of text reading aloud. This way, the visually impaired navigation device can not only help to avoid obstacles but also help visually impaired people understand the interest targets of most people in the whole picture. The visual saliency assistance mechanism we designed consists of three components: a spatio-temporal feature extraction (STFE) module, a spatio-temporal feature fusion (STFF) module, and a multi-scale feature fusion (MSFF) module. The STFF module fuses long-term spatio-temporal features and improves the temporal memory between frames. The MSFF module fully integrates information at different scales to improve the accuracy of saliency prediction. Therefore, this proposed visual saliency model can assist in the efficient operation of visually impaired navigation systems. The area under roc curve judd (AUC-J) metric of the proposed model was 93.9%, 93.8%, and 91.5% on three widely used saliency datasets: Holly-wood2, UCF Sports, and DHF1K, respectively. The results show that our proposed model outperforms the current state-of-the-art models.