Reviewerly/exHarmony huggingface dataset:
A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps

The goal of full-reference image quality assessment (FR-IQA) is to predict the quality of an image as perceived by human observers with using its pristine, reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulted feature vector is mapped onto perceptual quality scores with the help of a trained support vector regressor. A detailed parameter study is also presented in which the design choices of the proposed method is reasoned. Furthermore, we study the relationship between the amount of training images and the prediction performance. Specifically, it is demonstrated that the proposed method can be trained with few amount of data to reach high prediction performance. Our best proposal - ActMapFeat - is compared to the state-of-the-art on six publicly available benchmark IQA databases, such as KADID-10k, TID2013, TID2008, MDID, CSIQ, and VCL-FER. Specifically, our method is able to significantly outperform the state-of-the-art on these benchmark databases.
Scale-Adaptive NN-Based Similarity for Robust Template Matching

Template matching in unconstrained environment with complex deformation, occlusion, and background clutter is a challenging task. Recently, some measures which are robust to outliers were presented, however, they fix the window size and thus cannot handle large-scale change. In this article, a multiscale template matching method based on nearest neighbor (NN) search is proposed. To discover the effect of scale to the measure, the expectation of the diversity similarity (DIS) is derived by probabilistic analysis. Then, a scale-adaptive measure is provided by extending DIS and penalizing the deformation explicitly. Moreover, for rectangular template, weights are appended to points to suppress the negative effect of background pixels, and for masked template, foreground pixels are separated from the candidate window based on NN field. In addition, a scheme for preselecting the candidate positions of object detection is given. Experiment on the real-world scenario benchmarks and surface-mount component (SMC) positioning shows that the proposed method is robust to scale changes along with other challenging aspects and outperforms the state-of-the-arts using both color and deep features.
Building Scalable Blockchain Applications - A Decision Process

The blockchain technology faces many challenges before experiencing widespread adoption. One major challenge for many applications is the lack of scalability of the underlying architecture. While various technologies exist, that provide solutions to this issue, they are often overlooked when new applications are developed. To solve this problem we used a design science research approach to develop a decision process, that enables developers to choose the right technologies and ensure the scalability of their blockchain applications. The result is a four-step process, that helps to find the appropriate scalability solutions while still taking the business and technology environment into account. In addition the developed framework provides an overview of existing solutions and highlights gaps, where no solutions exist yet, providing a starting point for further research.
Enhanced Artificial Potential Field-based Moving Obstacle Avoidance for UAV in Three-Dimensional Environment

The obstacle avoidance problem plays an essential role in the issues of unmanned aerial vehicle(UAV). An enhanced artificial potential field(EAPF) model is proposed in this paper, considering the three-dimensional vector field and focusing on avoiding the moving obstacle. In the proposed EAPF model, a steering force is designed to change the orientation of UAV's velocity, which can ensure that UAV gets rid of the local minimum situation. And a restoring force considering a slowing down strategy is designed to drag UAV back on the given trajectory. Moreover, an EAPF-based obstacle avoidance(EAPF-OA) algorithm is also proposed to help UAV avoid moving obstacle in flight. Simulation results demonstrate that the proposed EAPFOA algorithm significantly outperforms the traditional APF-based algorithm on the performance in terms of smoothness, safety and energy consumption.
Learning Local and Global Priors for JPEG Image Artifacts Removal

Lossy compression will inevitably introduce image artifacts in the decoded image and degrade the image quality. In recent years, convolutional neural network (CNN) has been exploited for removing compression artifacts with great success. However, most existing CNN-based methods only utilize image's local prior without considering the global prior on the training of their networks. In this letter, a novel CNN, called the local and global priors network (LGPNet), is proposed that simultaneously learns both the local and the global priors for removing compression image artifacts. To achieve this goal, a dual-attention unit (DAU) is developed and incorporated into the well-known U-Net architecture for learning a better local prior. Meanwhile, the global prior is also learned from the entire image via our proposed global prior network. Extensive experimental results have clearly demonstrated that our proposed LGPNet is able to effectively remove image artifacts and greatly improve the image quality of JPEG-compressed images.
An integrated neural network with nonlinear output structure for interval-valued data

In this paper, an integrated model combining interval deep belief network (IDBN) and neural network with nonlinear weights, called IDBN-NN, is proposed for interval-valued data modeling. Firstly, the IDBN with variable learning rate is designed to initialize parameters of each sub-model. Based on a modified contrastive divergence algorithm the least square method is adopted to identify the coefficients of nonlinear weights in the output layer. Then, to improve the modeling accuracy, the Fuzzy C-Means (FCM) method and the Particle Swarm Optimization (PSO) algorithm are applied to tune the weights of sub-models. Though each sub-model can capture the nonlinear feature of the original system, by intersecting cut sets the synthesizing modeling scheme can further improve the performance of the proposed model. Some numerical examples show that the IDBN-NN with nonlinear output structure can achieve higher accuracy than some interval-valued data modeling methods.
I-Scheduler: Iterative scheduling for distributed stream processing systems

Task allocation in Data Stream Processing Systems (DSPSs) has a significant impact on performance metrics such as data processing latency and system throughput. An application processed by DSPSs can be represented as a Directed Acyclic Graph (DAG), where each vertex represents a task and the edges show the dataflow between the tasks. Task allocation can be defined as the assignment of the vertices in the DAG to the physical compute nodes such that the data movement between the nodes is minimised. Finding an optimal task placement for DSPSs is NP-hard. Thus, approximate scheduling approaches are required to improve the performance of DSPSs. In this paper, we propose a heuristic scheduling algorithm which reliably and efficiently finds highly communicating tasks by exploiting graph partitioning algorithms and a mathematical optimisation software package. We evaluate the communication cost of our method using three micro-benchmarks, showing that we can achieve results that are close to optimal. We further compare our scheduler with two popular existing schedulers, R-Storm and Aniello et al.'s 'Online scheduler' using two real-world applications. Our experimental results show that our proposed scheduler outperforms R-Storm, increasing throughput by up to 30%, and improves on the Online scheduler by 20%–86% as a result of finding a more efficient schedule.1
Burst Denoising via Temporally Shifted Wavelet Transforms

Mobile photography has made great strides in recent years. However, low light imaging remains a challenge. Long exposures can improve signal-to-noise ratio (SNR) but undesirable motion blur can occur when capturing dynamic scenes. Consequently, imaging pipelines often rely on computational photography to improve SNR by fusing multiple short exposures. Recent deep network-based methods have been shown to generate visually pleasing results by fusing these exposures in a sophisticated manner, but often at a higher computational cost. We propose an end-to-end trainable burst denoising pipeline which jointly captures high-resolution and high-frequency deep features derived from wavelet transforms. In our model, precious local details are preserved in high-frequency sub-band features to enhance the final perceptual quality, while the low-frequency sub-band features carry structural information for faithful reconstruction and final objective quality. The model is designed to accommodate variable-length burst captures via temporal feature shifting while incurring only marginal computational overhead, and further trained with a realistic noise model for the generalization to real environments. Using these techniques, our method attains state-of-the-art performance on perceptual quality, while being an order of magnitude faster.
Using Convolutional Neural Networks with Multiple Thermal Sensors for Unobtrusive Pose Recognition

The desire to remain living in one’s own home rather than a care home by those in need of 24/7 care is one that requires a level of understanding for the actions of an environment’s inhabitants. This can potentially be accomplished with the ability to recognise Activities of Daily Living (ADLs); however, this research focuses first on producing an unobtrusive solution for pose recognition where the preservation of privacy is a primary aim. With an accurate manner of predicting an inhabitant’s poses, their interactions with objects within the environment and, therefore, the activities they are performing, can begin to be understood. This research implements a Convolutional Neural Network (CNN), which has been designed with an original architecture derived from the popular AlexNet, to predict poses from thermal imagery that have been captured using thermopile infrared sensors (TISs). Five TISs have been deployed within the smart kitchen in Ulster University where each provides input to a corresponding trained CNN. The approach is evaluated using an original dataset and an F1-score of 0.9920 was achieved with all five TISs. The limitations of utilising a ceiling-based TIS are investigated and each possible permutation of corner-based TISs is evaluated to satisfy a trade-off between the number of TISs, the total sensor cost and the performances. These tests are also promising as F1-scores of 0.9266, 0.9149 and 0.8468 were achieved with the isolated use of four, three, and two corner TISs, respectively.
An intelligent electromagnetic environment reconstruction method based on super-resolution generative adversarial network

In this paper, an intelligent electromagnetic environment reconstruction method is proposed based on a super-resolution generative adversarial network (SRGAN). The altitude matrices together with the low-resolution matrices obtained by measured power values are employed as inputs. Then, an electromagnetic environment reconstruction method capable of generating the high-resolution power coverage matrix in the selected area is designed. Data enhancement is employed to expand the dataset and a modified generator network with squeeze and excitation modules is used during the training process. To validate the proposed method, the simulation analyses are carried out in typical suburb environments based on a ray-tracing tool. Numerical results indicate that, compared with classical methods such as nearest-neighbor interpolation, bilinear interpolation, and bicubic interpolation, the proposed method can provide more accurate reconstruction results for power coverage. In addition, the peak signal to noise ratio (PSNR) of the proposed method is higher than those of the classical methods. The proposed intelligent electromagnetic environment reconstruction method can be useful for the planning, deployment, and optimization of wireless networks.
A Generic Technique for Automatically Finding Defense-Aware Code Reuse Attacks

Code reuse attacks have been the subject of a substantial amount of research during the past decade. This research largely resulted from early work on Return-Oriented Programming (ROP), which showed that the then newly proposed Non-Executable Memory (NX) defense could be bypassed. More recently, the research community has been simultaneously investigating new defenses that are believed to thwart code reuse attacks, such as Control Flow Integrity (CFI), and defense-aware techniques for attacking these defenses, such as Data-Oriented Programming (DOP). Unfortunately, the feasibility of defense-aware attacks are very dependent on the behaviors of the attacked program, which makes it difficult for defenders to understand how much protection a defense such as CFI may provide. To better understand this, researchers have introduced automated defense-aware code reuse attack systems. Unfortunately, the handful of existing systems implement a single fixed, defense-specific strategy that is complex and cannot be used to consider other defenses.
Auxiliary Networks for Joint Speaker Adaptation and Speaker Change Detection

Speaker adaptation and speaker change detection have both been studied extensively to improve automatic speech recognition (ASR). In many cases, these two problems are investigated separately: speaker change detection is implemented first to obtain single-speaker regions, and speaker adaptation is then performed using the derived speaker segments for improved ASR. However, in an online setting, we want to achieve both goals in a single pass. In this study, we propose a neural network architecture that learns a speaker embedding from which it can perform both speaker adaptation for ASR and speaker change detection. The proposed speaker embedding is computed using self-attention based on an auxiliary network attached to a main ASR network. ASR adaptation is then performed by subtracting, from the main network activations, a segment dependent affine transformation of the learned speaker embedding. In experiments on a broadcast news dataset and the Switchboard conversational dataset, we test our system on utterances with a change point in them and show that the proposed method achieves significantly better performance as compared to the unadapted main network (10-14% relative reduction in word error rate (WER)). The proposed architecture also outperforms three different speaker segmentation methods followed by ASR (around 10% relative reduction in WER).
The 3D reconstruction method of a line-structured light vision sensor based on composite depth images

Abstract In order to simplify the three-dimensional (3D) reconstruction process of a rotating scanning line-structured light vision sensor, a 3D reconstruction method of composite depth images is proposed. First, a frame of an image acquired by a camera at a constant speed is converted into a column of data in the composite pixel image. Second, the composite pixel image is transformed into a depth image via coordinate transformation. Finally, the 3D coordinates of the object are calculated from the depth image by perspective transformation. The parameters of the light plane are calibrated by using a one-dimensional target, and the focal length of the depth image is calibrated by using a ball target. Compared with the traditional 3D reconstruction method of a structured light vision sensor, this method uses a composite depth image instead of point cloud splicing, which is simple to operate and flexible to calibrate, and further improves the efficiency of the 3D reconstruction of a structured light vision sensor. Experiments show that the absolute error of the 3D measurement in the measurement range of 380–4000 mm is less than 0.21 mm, and the accuracy is higher than 0.26%, which significantly improves the measurement efficiency and point cloud density of the system, and can meet the general 3D reconstruction requirements of the scene.
Monitoring System of OpenStack Cloud Platform Based on Prometheus

Prometheus is currently the hottest open source monitoring tool in the world. With the continuous development of cloud computing, OpenStack has become the most widely used open source cloud platform. Cloud platform as a production system, comprehensive system monitoring is an essential link and an important means to maintain the reliability and stability of cloud platform. Combined with the characteristics of OpenStack cloud platform, this paper uses prometheus to collect the monitoring data of OpenStack cloud platform, and uses the visualization tool grafana to display the monitoring data in real time, designs and implements a comprehensive, intelligent and efficient monitoring system. Through testing, the system can effectively improve the reliability and stability of OpenStack cloud platform.
Blind Source Camera Identification of Online Social Network Images Using Adaptive Thresholding Technique

The ubiquitous use of digital images has led to a new provocation in digital image forensics. Source camera identification authorizes forensic investigators to discover the probable source model that are appointed to acquire the image under investigation in criminal cases like terrorism, child pornography, etc. The techniques proposed till date, to identify the source camera, are inappropriate when applied to images captured with smartphones, subsequently uploaded and downloaded from these online social networks (OSN) such as Facebook, WhatsApp, etc. Such images lose their original quality, resolution, and statistical properties as online social platforms compress the images substantially, for efficient storage and transmission. Here, we present an efficient forensic image-source identification procedure, which follows an adaptive thresholding mechanism to differentiate between high and low image-camera correlation. The results of our experiments demonstrate that the proposed method drastically improves the performance of blind source camera identification of compressed OSN images as compared to traditional global threshold approach.
DMCR-GAN: Adversarial Denoising for Monte Carlo Renderings with Residual Attention Networks and Hierarchical Features Modulation of Auxiliary Buffers

Learning-based denoising single-frame Monte Carlo rendering methods have achieved better rendering quality in the photo-realistic rendering research. However, most of the works ignore the rich information of auxiliary buffers and treat all features equally. In this paper, we propose an adversarial approach for denoising Monte Carlo renderings (DMCR-GAN) with residual attention networks and hierarchical features modulation of auxiliary buffers. Specifically, we use a residual in residual (RIR) structure to make the network deeper and ease the flow of low-frequency information. Moreover, we propose a convolution dense block group (CDBG) to extract hierarchical features of auxiliary buffers and then to modulate the noisy features in RIR structure. Furthermore, we propose a channel attention (CA) and spatial attention (SA) mechanism to exploit the inter-channel and inter-spatial dependencies of features. Compared with the state-of-the-art methods, our approach can restore more high-frequency information of images.
Taming type annotations in gradual typing

Gradual typing provides a methodology to integrate static and dynamic typing, harmonizing their often conflicting advantages in a single language. When a user wants to enjoy the advantages of static typing, most gradual languages require that they add type annotations. Many nontrivial tasks must be undertaken while adding type annotations, including understanding program behaviors and invariants. Unfortunately, if this is done incorrectly then the added type annotations can be wrong–leading to inconsistencies between the program and the type annotations. Gradual typing implementations detect such inconsistencies at runtime, raise cast errors, and generate messages. However, solely relying on such error messages for understanding and fixing inconsistencies and their resulting cast errors is often insufficient for multiple reasons. One reason is that while such messages cover inconsistencies in one execution path, fixing them often requires reconciling information from multiple paths. Another is that users may add many wrong type annotations that they later find difficult to identify and fix, when considering all added annotations. Recent studies provide evidence that type annotations added during program migration are often wrong and that many programmers prefer compile-time warnings about wrong annotations. Motivated by these results, we develop exploratory typing to help with the static detection, understanding, and fixing of inconsistencies. The key idea of exploratory typing is that it systematically removes dynamic types and explores alternative types for static type annotations that can remedy inconsistencies. To demonstrate the feasibility of exploratory typing, we have implemented it in PyHound, which targets programs written in Reticulated Python, a gradual variant of Python. We have evaluated PyHound on a set of Python programs, and the evaluation results demonstrate that our idea can effectively detect inconsistencies in 98% of the tested programs and fix 93% of inconsistencies, significantly outperforming pytype, a widely used Python tool for enforcing type annotations.
A Research Agenda Is Needed for Designing for the User Experience of Augmented and Mixed Reality: A Position Paper

With this position paper, we want to draw the attention of the community toward theoretical work and practical opportunities that have been overlooked so far regarding concepts, principles, and design knowledge for the user experience of Augmented and Mixed Reality content, I/O devices, interactions, applications, and systems. Despite considerable innovations in commercial products and research prototypes enabling Augmented and Mixed Reality worlds, how to design great user experiences in such worlds has been overall neglected at core, while the information and knowledge currently available to practitioners cover usability aspects mostly. Therefore, we advocate for theoretical foundations of the user experience in Augmented and Mixed Reality and propose several directions for more scientific research in this regard.
NFC Technology: Assessment Effective of Security towards Protecting NFC Devices &amp; Services

Today with rapid development and adoption of information technology's applications and services, the way we do business changed significantly and this is making our daily lives more prosperous and hassle-free. Near Field Communication (NFC) has become one of the promising technological developments. This technology is vulnerable to security attacks. These attacks lead to leakage of user's important data which could affect any organization adopting NFC technologies and applications. The basic objective of this paper is to discuss NFC in general and compare this technology with RFID. Further, we reviewed various attack types and finally we proposed scientific mechanisms that can help to increase the security efficiency of NFC and to provide information protection to NFC technologies.