Reviewerly/exHarmony huggingface dataset:
Spectral stabilizability

Decoherence represents a major obstacle towards realizing reliable quantum technologies. Identifying states that can be upheld against decoherence by purely coherent means, i.e., stabilizable states, for which the dissipation-induced decay can be completely compensated by suitable control Hamiltonians, can help to optimize the exploitation of fragile quantum resources and to understand the ultimate limits of coherent control for this purpose. In this work, we develop conditions for stabilizability based on the target state's eigendecomposition, both for general density operators and for the covariance matrix parametrization of Gaussian states. Unlike previous conditions for stabilizability, these spectral conditions are both necessary and sufficient and are typically easier to use, extending their scope of applicability. To demonstrate its viability, we use the spectral approach to derive upper bounds on stabilizability for a number of exemplary open system scenarios, including stabilization of generalized Greenberger-Horne-Zeilinger (GHZ) and W states in the presence of local dissipation and stabilization of squeezed thermal states under collective damping.
Novel insights on atomic synchronization for sort-based group-by on GPUs

Abstract Using heterogeneous processing devices, like GPUs, to accelerate relational database operations is a well-known strategy. In this context, the operation is highly interesting for two reasons. Firstly, it incurs large processing costs. Secondly, its results (i.e., aggregates) are usually small, reducing data movement costs whose compensation is a major challenge for heterogeneous computing. Generally, for computation on GPUs, one relies either on sorting or hashing. Today, empirical results suggest that hash-based approaches are superior. However, by concept, hashing induces an unpredictable memory access pattern conflicting with the architecture of GPUs. This motivates studying why current sort-based approaches are generally inferior. Our results indicate that current sorting solutions cannot exploit the full parallel power of modern GPUs. Experimentally, we show that the issue arises from the need to synchronize parallel threads that access the shared memory location containing the aggregates via . Our quantification of the optimal performance motivates us to investigate how to minimize the overhead of atomics. This results in different variants using atomics, where the best variants almost mitigate the atomics overhead entirely. The results of a large-scale evaluation reveal that our approach achieves a 3x speed-up over existing sort-based approaches and up to 2x speed-up over hash-based approaches.
The Effect of Mobile Applied Anatomy Learning on Students’ Academic Success, Cognitive Loads, and Attitudes

Anatomy has always been one of the most important components of Health Science education. Worldwide, anatomy education is given in an environment based on cadaver, touch and 3D designs. However, this process has become quite difficult as the pandemic restricted use of laboratory procedures, models, and other learning materials. Therefore, education with mobile applications has become much more important. The aim of this study was to measure the effect of mobile applications used in anatomy course, which is one of the courses that form the basis of medical science, on the success levels of students, and to evaluate their perspectives on this method.In this study, a real experimental research model with pretest-posttest control group was used in order to determine the difference that may occur between academic achievement and cognitive load when anatomy course students use traditional method or mobile application technology learning method.The findings of the study showed that the students in the experimental group, in which mobile applications were used in the anatomy course, had higher achievement levels and lower cognitive loads than the students in the control group. Another point that was determined was that the students in the experimental group were satisfied with the fact that the use of the mobile application facilitated learning, and they learned better as the ease of use in the mobile application increased.
Applications of Deep Learning for Vehicle Detection Using Geospatial Data

This chapter details initially acquiring an open-source UAV dataset and creating a Google Earth dataset of vehicles, and creating the metadata for these images. Then training a deep learning object detection model, YOLOv4, to generate the best training weight files, having a very high mean average precision (mAP). It is the measure of how precisely the model is detecting the objects specified in the metadata of the validation dataset. The higher this value, the more accurate the model is, with the specific data it has been trained upon. Then deploying the model on various satellite data of certain areas like parking lots, toll booths, and roads, over a certain period of time, to count the number of vehicles in RGB images and from those images calculate factors like maximum capacity of parking lots, average vehicle density of roads, congestion rate in toll booths, length of congestion in toll booths, etc. The model trained on the UAV Dataset at various conditions of weather, daytime, and different resolutions are tested over other UAV Datasets and the trained weights are uploaded to GitHub for future use.
Artificial Intelligence for Defence

Technology is rapidly improving.The advancements in use have enabled research in previously unexplored areas.These include military, Big Data, Robotics, Blockchain, and other fields. In these areas, we employ AI and machine learning. One might think that AI and ML are the same, but they are not. Artificial intelligence is defined as a computer or robot's ability to perform any task assigned to humans, thereby simplifying our work. Once the work is completed, one must ensure that it is accurate and precise. This was accomplished through Machine Learning, a type of AI. As previously stated, AI and ML use various settings, including the military, hospitals, banking, video games, and others. In this paper, the use of AI and ML is mainly concentrated in the military. As a result, we have focused on the developments of AI and ML in the military rather than the trends of AI and ML in other areas in this chapter. With a basic understanding of how everything works, we provide a brief overview of military operations and developments in a few countries, such as China, Russia,United States
Spatio-Temporal Outlier Detection

Nowadays, GPS-enabled devices play a vital role in location-based services and daily basis activities such as: locating where your vehicle is at any given time, recovering a stolen vehicle, monitoring your children's location, and traffic or weather alerts. Indeed, precise locations generated from these GPS devices are a must to ensure that the location-based services work accurately. For this purpose, the discovery of abnormal patterns in spatio-temporal data is a significant topic for many kinds of researchers. In this paper, the methods of spatio-temporal outlier detection are categorized into four types: distance and density-based outlier detection, pattern outlier detection, supervised and semi-supervised learning, and statistical and probabilistic techniques. This chapter describes the datasets used in the approaches of spatio-temporal outlier detection and explores its popular applications. The main contribution of this study is to guide researchers to define research gaps in trajectories outlier detection and choose the proper techniques that cope with their research problems.
Geometry-guided multilevel RGBD fusion for surface normal estimation

Developments in 3D computer vision have advanced scene understanding and 3D modeling. Surface normal estimation is a basic task in these fields. In this paper, we propose a geometry-guided multilevel fusion scheme for high-quality surface normal estimation by exploiting texture and geometry information from color and depth images. The surface normal is progressively predicted with a coarse-to-fine strategy. First, an initial surface normal (IniNormal) Nini is predicted by a hierarchical confidence reweighting convolution neural network to merge texture and geometry information in a CNN feature level. Although a general accuracy is achieved, the long tail problem makes the IniNormal always fails in special areas where the depth map is high-quality while the intensity interference is challenging, such as repeating textures and abnormal exposures. Further, a traditional geometry-consistent based surface normal(GeoNormal) Ngeo is calculated based on traditional constraints, and a surface normal level fusion module is designed to remap the depth to different representations and reconsider scene information. Then, the final clear surface normal N is estimated by adaptively reintegrating the IniNormal and GeoNormal in a decision level. To overcome disturbances in the dataset and ensure the trainability of the network, a carefully designed hybrid objective function and an annealed term are applied. An explainable analysis is attached. The experimental results on two benchmark datasets demonstrate that the proposed GMLF(geometry-guided multilevel RGBD fusion for surface normal estimation) can achieve better quantitative and qualitative performance. The proposed method may be useful for robots and auto-driving which can be applied in the next-generation Internet-of-Things (NG-IoT).
Domain adaptive multigranularity proposal network for text detection under extreme traffic scenes

Traffic text detection is an important and meaningful research task as it can provide abundant semantic information for autonomous driving. Although major breakthroughs have been achieved on conventional datasets, existing scene text detectors generally suffer from significant performance degradation in real-life traffic scenes since various extreme scenarios may cause a large domain shift. To realize robust traffic text detection under scene changes, we propose a novel network for cross-domain traffic text detection, which integrates both text detection and domain adaptation into one framework. In the text detection pipeline, we introduce a Multigranularity Text Proposal Network (MG-TPN) to generate fine-grained and coarse-grained text proposals, which could deeply interact during both training and inference stages, benefiting the pipeline in learning more robust text features and generating accurate detection results. To transfer the text detection ability from common scenes to unlabeled extreme traffic scenes, we propose an inter&intradomain adaptation (I2-DA) strategy, which adequately excavates domain-invariant features between the source domain and target domain (interdomain), as well as multiple extreme scenarios of the target domain (intradomain). To the best of our knowledge, this is the first study on cross-domain text detection under extreme traffic scenes. Extensive experiments on the traffic text datasets and standard benchmarks, including SynthText, VISD, ICDAR2013 and ICDAR2015 validate the superiority of our method. The proposed two datasets (CTST and ES-CTST) are available at https://github.com/pummi823/test.
Application of embedded voice and digital forensics system in financial cost management

This article first describes the embedded voice processing process and uses the two-door restriction method for endpoint detection. Next, the wavelet + MFCC algorithm is proposed. The Fourier transform will replace it with ripple. The disadvantage of the ripple is that the size of the Fourier transform window cannot move with frequency. On this basis, the overall goal of the system is proposed, the overall structure is constructed, and the functions of the system are divided. Android mobile phone data collection technology has been extensively researched, including logical search, temporary access to permissions, JTAG search and ISP search. The design and implementation of the logical search engine based on root authority, the physical search engine based on Qualcomm processor, and the data analysis system have been completed, and the evidence collection of e-commerce applications has been completed. Based on the digital forensics system, this article studies and designs an Android device digital forensics system suitable for China's national conditions and laws from the three perspectives of data collection, analysis and storage. At the same time, brainstorming methods must be used between corporate financial management and cost management personnel. Finally, through research, scientifically construct the framework of the financial cost management system, distinguish the various requirements of the construction system, classify the importance, urgency, and feasibility of the requirements, establish the basic requirements and extension requirements, set the demand function to complete the schedule and Perform as planned. Through the research of embedded voice and digital forensics system, this paper applies it to the research of financial cost management, thereby promoting the development of enterprise financial management.
Accelerating distributed machine learning with model compression and graph partition

The rapid growth of data and parameter sizes of machine learning models makes it necessary to improve the efficiency of distributed training. It is observed that the communication cost usually is the bottleneck of distributed training systems. In this paper, we focus on the parameter server framework which is a widely deployed distributed learning framework. The frequent parameter pull, push, and synchronization among multiple machines leads to a huge communication volume. We aim to reduce the communication cost for the parameter server framework. Compressing the training model and optimizing the data and parameter allocation are two existing approaches to reducing communication costs. We jointly consider these two approaches and propose to optimize the data and parameter allocation after compression. Different from previous allocation schemes, the data sparsity property may no longer hold after compression. It brings additional opportunities and challenges for the allocation problem. We also consider the allocation problem for both linear and deep neural network (DNN) models. Fixed and dynamic partition algorithms are proposed accordingly. Experiments on real-world datasets show that our joint compression and partition scheme can efficiently reduce communication overhead for linear and DNN models.
mrNISC from LWE with polynomial modulus

Introduced by Benhamouda and Lin [TCC'20], a multi-party reusable non-interactive secure computation protocol (mrNISC) consists of a commitment phase and an unbounded number of computation phases. In the commitment phase, a number of parties first commit to their input in a single broadcast round. Later in a computation phase, any subset of the parties can compute a function on their joint input by each sending a single broadcast message. Benhamouda and Lin [TCC'20] constructed the first mrNISC for all functions based on standard hardness assumptions in pairing groups. Soon after their work, two concurrent papers by Benhamouda et al. [EUROCRYPT'21] and Ananth et al. [EUROCRYPT'21] constructed mrNISC for all functions based on the hardness of LWE with super-polynomial modulus-to-noise ratio. In this work we build the first mrNISC for all functions based solely on LWE with polynomial modulus-to-noise ratio. We thus place mrNISC in the same category as public-key encryption and leveled fully homomorphic encryption in terms of the required LWE hardness assumption. We achieve our result by carefully introducing a bootstrapping step in the construction of Benhamouda et al.
FDCT: Fusion-Guided Dual-View Consistency Training for semi-supervised tissue segmentation on MRI

Accurate tissue segmentation on MRI is important for physicians to make diagnosis and treatment for patients. However, most of the models are only designed for single-task tissue segmentation, and tend to lack generality to other MRI tissue segmentation tasks. Not only that, the acquisition of labels is time-consuming and laborious, which remains a challenge to be solved. In this study, we propose the universal Fusion-Guided Dual-View Consistency Training(FDCT) for semi-supervised tissue segmentation on MRI. It can obtain accurate and robust tissue segmentation for multiple tasks, and alleviates the problem of insufficient labeled data. Especially, for building bidirectional consistency, we feed dual-view images into a single-encoder dual-decoder structure to obtain view-level predictions, then put them into a fusion module to generate image-level pseudo-label. Moreover, to improve boundary segmentation quality, we propose the Soft-label Boundary Optimization Module(SBOM). We have conducted extensive experiments on three MRI datasets to evaluate the effectiveness of our method. Experimental results demonstrate that our method outperforms the state-of-the-art semi-supervised medical image segmentation methods.
Towards optimization of anomaly detection in DevOps

DevOps has recently become a mainstream solution for bridging the gaps between development (Dev) and operations (Ops) enabling cross-functional collaboration. The DevOps concept of continuous monitoring may bring a lot of benefits to development teams such as early detection of run-time errors and various performance anomalies. We aim to explore deep learning (DL) solutions for detection of anomalous systems behavior based on collected monitoring data that consists of applications’ and systems’ performance metrics. Moreover, we specifically address a shortage of approaches for evaluating DL models without any ground truth data. We perform a case study in a real DevOps environment, following the principles of the design science paradigm. The research activities span from practice to theory and from problem to solution domain, including problem conceptualization, solution design, instantiation, and empirical validation. We proposed and implemented a cloud solution for DL model deployment and evaluation empowered by feedback from the development team. The labeled data generated through the feedback was used for evaluation of current and training of new DL models in several iterations. The overall results showed that reconstruction-based models such as autoencoders, are quite robust to any parameter modification and are among the preferred for anomaly detection in multivariate monitoring data. Leveraging raw monitoring data and DL-inspired solutions, DevOps teams may get critical insights into the software and its operation. In our case, this proved to be an efficient way of discovering early signs of production failures.
Design and evaluation of a self-paced cybersecurity tool

Purpose This paper aims to present the evaluation of a self-paced tool, CyberSecurity Coach (CYSEC), and discuss the adoption of CYSEC for cybersecurity capability improvement in small- and medium-sized enterprises (SMEs). Cybersecurity is increasingly a concern for SMEs. Previous literature has explored the role of tools for awareness raising. However, few studies validated the effectiveness and usefulness of cybersecurity tools for SMEs in real-world practices. Design/methodology/approach This study is built on a qualitative approach to investigating how CYSEC is used in SMEs to support awareness raising and capability improvement. CYSEC was placed in operation in 12 SMEs. This study first conducted a survey study and then nine structured interviews with chief executive officers (CEOs) and chief information security officers (CISO). Findings The results emphasise that SMEs are heterogeneous. Thus, one cybersecurity solution may not suit all SMEs. The findings specify that the tool’s adoption varied quite widely. Four factors are primary determinants influencing the adoption of CYSEC: personalisation features, CEOs’ or CISOs’ awareness level, CEOs’ or CISOs’ cybersecurity and IT knowledge and skill and connection to cybersecurity expertise. Originality/value This empirical study provides new insights into how a self-paced tool has been used in SMEs. This study advances the understanding of cybersecurity activities in SMEs by studying the adoption of CYSEC. Moreover, this study proposes significant dimensions for future research.
Autonomous Navigation of Rovers Using ML and DL Techniques

The growth in the sector of autonomous robots in the field of transportation has increased since the 2000s. Improving the dynamics of a robot is a continuous process to tailor its user experience. This field involves the use of planetary and extra-terrestrial robots' (called rovers) autonomous navigation capabilities. It allows analysis of terrain irregularities, climate and weather monitoring, sample collection, etc. As rovers require a significant investment, therefore, it is essential that the rover performs autonomously according to the expectations while ensuring its own safety. It is achieved by the use of complex mathematical models, image analysis techniques, machine learning (ML) and deep learning (DL) models, and allowing execution of the required tasks efficiently. Further, it provides insight on various aspects of rovers' navigation such as intended missions of rover, ML and DL models, comparison in terms of precision and accuracy, merits, and demerits along with future scope.
Supervised Learning by Evolutionary Computation Tuning: An Application to Blockchain-Based Pharmaceutical Supply Chain Cost Model

A pharmaceutical supply chain (PSC) is a system of processes, operations, and organisations for drug delivery. This paper provides a new PSC mathematical cost model, which includes Blockchain technology (BT), that can improve the safety, performance, and transparency of medical information sharing in a healthcare system. We aim to estimate the costs of the BT-based PSC model, select algorithms with minimum prediction errors, and determine the cost components of the model. After the data generation, we applied four Supervised Learning algorithms (k-nearest neighbour, decision tree, support vector machine, and naive Bayes) combined with two Evolutionary Computation algorithms (ant colony optimization and the firefly algorithm). We also used the Feature Weighting approach to assign appropriate weights to all cost model components, revealing their importance. Four performance metrics were used to evaluate the cost model, and the total ranking score (TRS) was used to determine the most reliable predictive algorithms. Our findings show that the ACO-NB and FA-NB algorithms perform better than the other six algorithms in estimating the costs of the model with lower errors, whereas ACO-DT and FA-DT show the worst performance. The findings also indicate that the shortage cost, holding cost, and expired medication cost more strongly influence the cost model than other cost components.
Introduction to the Special Issue on Logic Rules and Reasoning: Selected Papers from the 4th International Joint Conference on Rules and Reasoning (RuleML+RR 2020)

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Autonomous Remote Control of Small Unmanned Aircraft

In this paper we present a novel approach to controlling small unmanned aircraft in third-person perspective scenarios. Our approach uses monocular imagery to mimic the human ability to perceive and control a remote aircraft. This allows for the autonomous control of a wide variety of aircraft with minimal on-board sensors. We improve upon other 2D single-shot detection methods by estimating a 3D box containing the aircraft, which allows us to capture a vehicle’s position and attitude as measurements. Using a constant-jerk Kalman filter, we then estimate the complete aircraft state from these measurements. Finally a proportional-integral-derivative controller is used to provide closed-loop autonomous flight of an aircraft from a remote location. Hardware experiments of a completely autonomous vehicle flying waypoints illustrate the validity of this method.