Reviewerly/exHarmony huggingface dataset:
Learning a Frequency Separation Network with Hybrid Convolution and Adaptive Aggregation for Low-dose CT Denoising

Low-dose computed tomography (CT) has attracted widespread attention in the medical imaging field due to its mild radiation hazards to the human body. However, the image may suffer from unpleasing noises under the low-dose radiation condition, which is not conducive to accurate analysis and diagnosis of diseases. Recently, deep learning has shown great potential in low-dose CT denoising. However, current approaches neglect that noise causes varying degrees of damage to low-/high-frequency components of the LDCT image, which hinders further improvement in denoising accuracy. In this paper, we propose a novel frequency separation network (FSNet) for low-dose CT image denoising, which recovers low-/high-frequency components separately, and takes full advantage of them for reconstructing high-quality CT images. To recover clean frequency components effectively, we design hybrid convolution module (HCM) that exploits parallel cascaded convolution path and encoder-decoder path to eliminate noises and preserve image structures. To fuse clean frequency components adaptively, we introduce content attention module (CAM) to adjust the contribution of features across valuable channels and regions, which encourages FSNet to restore image contents based on their frequency characteristics. Extensive experimental results on the Mayo Clinic low-dose CT image dataset show that our proposed FSNet outperforms state-of-the-art denoising methods.
Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System

Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.
Efficient 3D observation of steel microstructure using serial sectioning with precision cutting and on-site etching

This paper describes a fast and precise 3D observation method for steel microstructures using a cutting-based serial sectioning system with on-site etching. The method employs automated 3D internal structure microscopy for ferrous materials and on-site manual etching without detaching the specimen during the process. A notable feature of this system is that it uses precision cutting instead of polishing for surface fabrication, which has advantages in the fabrication speed, applicability in dry conditions, and controllability of the fabrication depth. The precision cutting of a dual-phase (DP) steel achieved a low roughness comparable to polishing. Furthermore, the on-site etching process by dropping etchant yielded sufficient contrast between ferrite and martensite phases. Observation of DP steel cross sections at 1 μm intervals afforded a 3D image in the range of 690 × 518 × 103 μm3 with a resolution of 0.144 × 0.144 × 1.0 μm3. The processing time was approximately ﬁve minutes for each cross section and almost half a day for the whole image, which was much more efficient than manual serial sectioning that requires a few weeks. The microstructures were also visible in reconstructed lateral cross sections of the 3D image, which proved the sufficient quality of our imaging. In addition, the segmented volume revealed the non-uniform martensite distribution among cross sections quantitatively. Most of them were connected in 3D, while they seemed to be separated in 2D. These results demonstrated the usefulness of our method with precision cutting and on-site etching.
Cloud Key Management Based on Verifiable Secret Sharing

Managing encryption keys in cloud computing is a very challenging task, especially where the model is shared and entirely controlled by the cloud providers. Hardware Security Module (HSM) solutions turned out to be an efficient approach for delivering cloud key management services. Unfortunately, the HSM approach has shown some shortcomings related to key migration when it comes to widespread cloud deployment. Recent systems based on homomorphic encryption and multiparty computation suffer from security issues or heavy overhead costs inherent to underlying cryptographic techniques. In this paper, we introduce a new software cloud key management system based on a dedicated (t, n) verifiable secret sharing protocol that tolerates up to t byzantine adversaries. The proposed design meets the requirements of BYOK (Bring Your Own Keys) model and multi-clouds deployment that are gaining more attraction among the biggest cloud industry players. Taking advantage of our verifiable secret sharing protocol, that reduces by a factor t the opening phase of the VSS protocols known in the literature, the proposed design offers promising performances. We also provide a formal model of our construction and proof of security. Finally, we implement a prototype of our design and give some experimental results about its performance along with some optimizations that make it efficient enough to be deployed in real-world applications.
A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.
Violence Detection System Using Resnet

In recent years, human motion analysis has become the center of video processing, especially in motion detection and security surveillance. When performing specific tasks, it is always a tool to minimize human resources. However, earlier methods of detection often descriptors are extracted around spatiotemporal attention points or statistical features of motion regions are extracted, resulting in limited ability to effectively detect violence in videos. This paper is intended to use a Pre-trained Convolutional Neural Network Model (ResNet), where the video stream is sent to the ResNet together with a single shot detector (S S D) to detect violence and objects in the video. In addition, if an intense incident is identified, the relevant departments will be notified. This proposed methodology would be beneficial in monitoring scenarios such as detention center & asylum. Also, on mobile phones with cameras.
An Approach to Air-to-Surface Mission Planner on 3D Environments for an Unmanned Combat Aerial Vehicle

Recently, interest in mission autonomy related to Unmanned Combat Aerial Vehicles(UCAVs) for performing highly dangerous Air-to-Surface Missions(ASMs) has been increasing. Regarding autonomous mission planners, studies currently being conducted in this field have been mainly focused on creating a path from a macroscopic 2D environment to a dense target area or proposing a route for intercepting a target. For further improvement, this paper treats a mission planning algorithm on an ASM which can plan the path to the target dense area in consideration of threats spread in a 3D terrain environment while planning the shortest path to intercept multiple targets. To do so, ASMs are considered three sequential mission elements: ingress, intercept, and egress. The ingress and egress elements require a terrain flight path to penetrate deep into the enemy territory. Thus, the proposed terrain flight path planner generates a nap-of-the-earth path to avoid detection by enemy radar while avoiding enemy air defense threats. In the intercept element, the shortest intercept path planner based on the Dubins path concept combined with nonlinear programming is developed to minimize exposure time for survivability. Finally, the integrated ASM planner is applied to several mission scenarios and validated by simulations using a rotorcraft model.
Taking stock of COVID-19 health status certificates: Legal implications for data privacy and human rights

The technological solutions adopted during the current pandemic will have a lasting impact on our societies. Currently, COVID-19 health status certificates are being deployed around the world, including in Europe, the United States and China. When combined with identity verification, these digital and paper-based certificates allow individuals to prove their health status by showing recent COVID-19 tests results, full vaccination records or evidence of recovery from COVID-19. Most countries in the Global South, where vaccination rates are low, have not yet fully implemented such certificates, although several initiatives are currently underway. That is, for instance, the case in the African Union. Yet, it is not sufficient to develop technical solutions for the verification of COVID-19 health status. Because technologies do not evolve in a legal vacuum, the existing laws and regulations must be respected. The risks of implementing such technologies must be anticipated and mitigated as much as possible before any large-scale deployment. Risk mitigation should also underpin strategies throughout the deployment of these certificates. This article evaluates the key legal implications of COVID-19 health status certificates for data privacy and human rights. In doing so, it contributes to the current debates, thus informing policymakers in this area of vital national and international interest
Differences in Personal Innovativeness in the Domain of Information Technology Among University Students and Teachers

Two online surveys among 1105 university students and 656 employees were conducted with the inclusion of the construct Personal Innovativeness in the domain of Information Technologies (PIIT). After calculating descriptive statistics, statistically significant differences between personal innovativeness of university students and teachers were sought by the application of one-way ANOVA. The first and most important finding was that average perceived PIIT of teachers and students falls around the middle of the seven-point scale, which cannot be regarded as a plausible predictor of upgrading the University as an Innovative Ecosystem. The second was that university teachers scored higher than their students, a situation that could produce an expectancy conflict between those who want to work in an innovative way and those who would prefer study by the book. Teaching assistants, who should belong to the generation of digital natives, are only slightly more innovative than university teachers, who can be regarded as digital immigrants. Assuming that innovativeness can be upgraded by learning, means that efforts should be made by University Management to encourage and support Personal Innovativeness (and other creativities, as well) as a preferred teaching practice.
Fine-Grained Hashing With Double Filtering

Fine-grained hashing is a new topic in the field of hashing-based retrieval and has not been well explored up to now. In this paper, we raise three key issues that fine-grained hashing should address simultaneously, i.e., fine-grained feature extraction, feature refinement as well as a well-designed loss function. In order to address these issues, we propose a novel Fine-graIned haSHing method with a double-filtering mechanism and a proxy-based loss function, FISH for short. Specifically, the double-filtering mechanism consists of two modules, i.e., Space Filtering module and Feature Filtering module, which address the fine-grained feature extraction and feature refinement issues, respectively. Thereinto, the Space Filtering module is designed to highlight the critical regions in images and help the model to capture more subtle and discriminative details; the Feature Filtering module is the key of FISH and aims to further refine extracted features by supervised re- weighting and enhancing. Moreover, the proxy-based loss is adopted to train the model by preserving similarity relationships between data instances and proxy-vectors of each class rather than other data instances, further making FISH much efficient and effective. Experimental results demonstrate that FISH achieves much better retrieval performance compared with state-of-the-art fine-grained hashing methods, and converges very fast. The source code is publicly available: https://github.com/chenzhenduo/FISH.
Using Statistical and Artificial Neural Networks Meta-learning Approaches for Uncertainty Isolation in Face Recognition by the Established Convolutional Models

We investigate ways of increasing trust in verdicts of the established Convolutional Neural Network models for the face recognition task. In the mission-critical application settings, additional metrics of the models’ uncertainty in their verdicts can be used for isolating low-trust verdicts in the additional ‘uncertain’ class, thus increasing trusted accuracy of the model at the expense of the sheer number of the ‘certain’ verdicts. In the study, six established Convolutional Neural Network models are tested on the makeup and occlusions data set partitioned to emulate and exaggerate the usual for real-life conditions training and test set disparity. Simple A/B and meta-learning supervisor Artificial Neural Network solutions are tested to learn the error patterns of the underlying Convolutional Neural Networks.
Constructing Quantum Surface Codes for Arbitrary Surface Forms

Quantum technology has been considered as one of the key enablers for the sixth-generation (6G) wireless commu-nication systems. Since we rely on vulnerable quantum states, the main challenge in quantum communication is to compute and send information in a reliable manner, which requires highly reliable quantum error corrections (QEC) with high fidelity. This paper proposes counter-clockwise standard forms to construct quantum surface codes for arbitrary surface forms. We show that our procedure can give result to the same codes as the currently available quantum surface codes. We investigate the matrix generated from Roffe <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[5,1,2]]$</tex> quantum surface codes and compare to that of the proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[5,1,2]]$</tex> quantum surface codes. We perform a series of computer simulations to evaluate the codes in terms of quantum word error rate (QWER) under depolarizing channel model. Our results confirm that we can construct good performance quantum surface codes using counter-clockwise standard forms that helps the design of any arbitrary forms of quantum surface codes.
Video retrieval framework based on color co-occurrence feature of adaptive low rank extracted keyframes and graph pattern matching

In recent times, exploration of multimedia required ever increasing demand and application for intelligent video retrieval from repositories. This paper presents an efficient video retrieval framework by employing the effective singular value decomposition and computationally low complex ordered dither block truncation coding to extract simple, compact, and well discriminative Color Co-occurrence Feature (CCF). In this context, the occurrence probability of a video frame pixel in the neighborhood is employed to formulate this specific and distinct feature. Moreover, we applied a new adaptive low rank thresholding based on energy concentricity, transposition, and replacement invariance characteristics to formulate a unified fast shot boundary detection approach to solve the protuberant bottleneck problem for real-time cut and gradual transition that eventually contributes for effective keyframes extraction. Therefore, we can assert that the keyframes are distinct and discriminative to represent the whole video content. For effective indexing and retrieval, it is imperative to formulate similarity score evaluator for the encapsulated contextual video information with substantial temporal consistency, least computation, and post-processing. Therefore, we introduced graph-based pattern matching for video retrieval with an aim to sustain temporal consistency, accuracy and time overhead. Experimental results signify that the proposed method on average provides 7.40% and 17.91% better retrieval accuracy and 23.21% and 20.44% faster than the recent state-of-the-art methods for UCF11 and HMDB51 standard video dataset, respectively.
Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm Optimization

Fundamentally, a strategy considering the effective utilization of resources results in the better energy efficiency of the system. The aroused interest of users in cloud computing has led to an increased power consumption making the network operation costly. The frequent requests from the users asking for computing resources can lead to instability in the load of the computing system. To perform the load balancing in the host, migration of the virtual machines from the overloaded and underloaded hosts needs to be done, which is considered an important facet concerning energy consumption. The proposed Particle Swarm Optimization based Resource Aware VM Placement (RAPSO_VMP) scheme aims to place the migrated virtual machines. RAPSO_VMP takes into consideration multiple resources like CPU, storage, and memory while trying to optimize the overall resource utilization of the system. According to the simulation analysis, the proposed RAPSO_VMP scheme shows an improvement of 5.51% in energy consumption, reduced the number of migrations by 9.12%, and the number of hosts shutdowns 22.74%.
QoS and Energy Efficiency Using Green Cloud Computing

Green cloud computing is an emerging technology. Cloud computing is already well accepted in the industry. Green cloud computing brings green computing into the cloud computing environment. The goal of this collaborative technology is to provide environment sustainability while providing cloud services in a cost-effective manner. Traditional data centers consume huge electricity generated from fossil fuels that increases the carbon footprint in the environment. Green cloud computing will ensure efficient service delivery with minimal energy consumption. Several security concerns also arise with new technology. Those security concerns and some solutions were discussed in this book chapter.
Contextual Detection of Pedestrians and Vehicles in Orthophotography by Fusion of Deep Learning Algorithms

In the context of smart cities, monitoring pedestrian and vehicle movements is essential to recognize abnormal events and prevent accidents. The proposed method in this work focuses on analyzing video streams captured from a vertically installed camera, and performing contextual road user detection. The final detection is based on the fusion of the outputs of three different convolutional neural networks. We are simultaneously interested in detecting road users, their motion, and their location respecting the static environment. We use YOLOv4 for object detection, FC-HarDNet for background semantic segmentation, and FlowNet 2.0 for motion detection. FC-HarDNet and YOLOv4 were retrained with our orthophotographs dataset. The last step involves a data fusion module. The presented results show that the method allows one to detect road users, identify the surfaces on which they move, quantify their apparent velocity, and estimate their actual velocity.
Semi-supervised Learning via Bipartite Graph Construction with Adaptive Neighbors

Graph-based semi-supervised learning, which further utilizes graph structure behind samples for boosting semi-supervised learning, gains convincing results in several machine learning tasks. Nevertheless, existing graph-based methods have shortcomings from two aspects. On the one hand, many of them concentrate on improving label propagation over the constructed graph through time-saving methods, e.g. path searching, without giving insights on constructing a proper graph accommodated to samples. On the other hand, some models are only devoted to constructing the appropriate graph resulting in a two-stage procedure, which may incur a suboptimal scenario. In this paper, we develop a joint learning method that considers both bipartite graph construction and label propagation simultaneously. With this configuration, the constructed graph is constantly adjusted by the smoothness term in the objective as the algorithm proceeds. The time complexity of our method gets significant improvement compared with traditional graph-based methods, and the experimental results on one synthetic dataset and several real-world benchmarks demonstrate the effectiveness and scalability of our proposed method.