Reviewerly/exHarmony huggingface dataset:
Domain-Invariant Region Proposal Network For Cross-Domain Detection

The performances of object detectors are highly impacted by the discrepancy between existing data sets and application scenarios, leading to the so-called domain shift problem. Previous works, based on Faster R-CNN, focus on aligning the image-level features and the region-level features. However, the Region Proposal Network (RPN), as a key module between the image-level and the region-level modules, still has the problem of domain shift that leads to inaccurate or even false detected results. To tackle this issue, we propose a new design, Domain-Invariant RPN (DIR), which adopts adversarial learning to eliminate the domain shift in RPN, and thereby, significantly improving the accuracy and robustness of bounding box proposals. Furthermore, we propose a Double-Consistency Regularization (DCR) to improve the overall feature alignment. Extensive experiments show that our approach outperforms state-of-the-art methods.
Region Adaptive Two-Shot Network For Single Image Dehazing

Existing single image dehazing methods typically adopt a one-shot strategy by indiscriminately applying the same filters to all local regions, which easily cause under-/over-dehazing across different regions by ignoring the inhomogeneity and asymmetry of illumination and detail distortions. In this paper, we propose a region adaptive two-shot network (RATNet) to address this issue. In the first shot, a lightweight subnetwork is utilized to conduct the regular global filtering, which could remove parts of haze but also distort some image details. In the second shot, a two-branch subnetwork is developed to restore the illumination and details of the initially renovated image respectively. The final dehazed image is obtained by fusing the outputs of the previous two branches, whose region-variant weights are adaptively learned by minimizing the difference between the haze-free image and our fused result. Experiments on four dehazing benchmark datasets show that our RATNet significantly outperforms many state-of-the-art dehazing approaches.
Modeling Word Formation in English–German Neural Machine Translation

This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology.Our linguistically sound segmentation is combined with a method for target-side inflection to accommodate modeling word formation.The best system variants employ source-side morphological analysis and model complex target-side words, improving over a standard system.
Deep Extreme Learning Machine-Based Optical Character Recognition System for Nastalique Urdu-Like Script Languages

Abstract Optical character recognition systems convert printed or handwritten scripts into digital text formats like ASCII or UNICODE. Urdu-like script languages like Urdu, Punjabi and Sindhi are widely spoken languages of the world, especially in Asia. An enormous amount of printed and handwritten text of such languages exist, which needs to be converted into computer-understandable formats for knowledge extraction. In this study, extreme learning machine’s (ELM’s) most recently proposed variant called deep extreme learning machine (DELM)-based optical character recognition (OCR) system is proposed to enhance Urdu-like script language’s character recognition rate. The proposed DELM-based character recognition model is optimizing the OCR process by reducing the overhead of Pre-processing, Segmentation and Feature Extraction Layer. The proposed system evaluations accomplished 98.75% training accuracy with 1.492 × 10−3 RMSE and 98.12% testing accuracy with 1.587 × 10−3 RMSE, with six DELM hidden layers. The results show that the proposed system has attained the foremost recognition rate as compared to any previously proposed Urdu-like script language OCR system. This technique is applicable for machine-printed text and fractionally useful for handwritten text as well. This study will aid in the advancement of more accurate Urdu-like script OCR’s software systems in the future.
Seasonal effects of atmospheric particulate matter on performance of different types of photovoltaic modules in sanliurfa, Turkey

In Turkey, Southeastern Anatolia region is the highest in terms of solar radiation level. However, the provinces in the region are subject to Particulate Matter (PM) coming from the Sahara desert, the Syrian Desert and the Arabian Desert by atmospheric transport. The daily limit of PM10 and PM2.5 set by WHO for health is often exceeded in Sanliurfa. PM10 and PM2.5 pollutants also accumulate on the Photovoltaic (PV) panels and cause loss of PV panel performance. In this study, the effects of atmospheric dust deposition on the performance of PV panel was determined for both monocrystalline and polycrystalline technologies under Sanliurfa atmospheric conditions. Two panels with the same characteristics were used for each PV panel group from 2 different PV technologies. One of the panels in the group was cleaned by washing with distilled water every Monday while the other was not cleaned. Thus, the effect of the dust accumulation on the PV panel was determined by comparison to the cleaned PV panel. PV panel power is measured with I–V meter. Panel surface temperature, solar radiation and other meteorological parameters are measured simultaneously. The measurements were done every Monday, Wednesday and Friday at 12:00 am from May 1 to December 31, 2019. It is observed that the dust accumulation reduces the PV power output up to 8% depending on the amount of radiation. During the summer months, the power loss on the average is 4.33% for monocrystalline and 4.57% for polycrystalline. In the autumn months, it is less than 1.77%.
Low-Latency Privacy-Preserving Outsourcing of Deep Neural Network Inference

Efficiently supporting inference tasks of deep neural network (DNN) on the resource-constrained Internet-of-Things (IoT) devices has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, one prevalent solution is to outsource DNN inference tasks to the public cloud. However, this type of “cloud-backed” solutions can cause privacy breach since the outsourced data may contain sensitive information. For privacy protection, the research community has resorted to advanced cryptographic primitives to support DNN inference over encrypted data. Nevertheless, these attempts are limited by the real-time performance due to the heavy IoT computational overhead brought by cryptographic primitives. In this article, we proposed an edge computing-assisted framework to boost the efficiency of DNN inference tasks on IoT devices, which also protects the privacy of IoT data to be outsourced. In our framework, the most time-consuming DNN layers are outsourced to edge computing devices. The IoT device only processes compute-efficient layers and fast encryption/decryption. Thorough security analysis and numerical analysis are carried out to show the security and efficiency of the proposed framework. Our analysis results indicate a 99%+ outsourcing rate of DNN operations for IoT devices. Experiments on AlexNet show that our scheme can speed up DNN inference for 40.6× with a 96.2% energy saving for IoT devices.
Low-Resolution Overhead Thermal Tripwire for Occupancy Estimation

Smart buildings use occupancy sensing for various tasks ranging from energy-efficient HVAC and lighting to space-utilization analysis and emergency response. We propose a people counting system which uses a low-resolution thermal sensor. Unlike previous people-counting systems based on thermal sensors, we use an overhead tripwire configuration at entryways to detect and track transient entries or exits. We develop two distinct people counting algorithms for this configuration. To evaluate our algorithms, we have collected and labeled a low-resolution thermal video dataset using the proposed system. The dataset, the first of its kind, is public and available for download <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We also propose new evaluation metrics that are more suitable for systems that are subject to drift and jitter.
Utility-aware Exponential Mechanism for Personalized Differential Privacy

Personalized Differential Privacy (PDP) was proposed to satisfy users' different privacy requirements. However, most of the existing PDP mechanisms may significantly destroy the utility of released statistical results. Differentially private statistical results with poor utility may mislead the data analysts, thus it may even decrease the acceptability of the technique used to protect data privacy. Therefore, in this paper, our goal is to pursue higher data utility while satisfying personalized differential privacy. To achieve this goal, we propose the Utility-aware Personalized Exponential Mechanism (UPEM) to effectively achieve PDP while pursuing better utility. UPEM distinguishes the different possible results with the same personalized score, which is used in Personalized Exponential Mechanism (PEM) [1]. PEM considers the personalized privacy budgets of changing elements to achieve PDP. Based on PEM, our UPEM further considers the quantitative changes of these changing tuples to enhance the utility. We confirm the effectiveness and efficiency of UPEM through extensive experiments.
Entanglement concentration service for the quantum Internet

Abstract Here, we define the entanglement concentration service for the quantum Internet. The aim of the entanglement concentration service is to provide reliable, high-quality entanglement for a dedicated set of strongly connected quantum nodes in the quantum Internet. The objectives of the service are to simultaneously maximize the entanglement throughput of all entangled connections and to minimize the hop distance between the high-priority quantum nodes. We propose a method for the resolution of the entanglement concentration problem and provide a performance analysis.
Multilevel particle filters for the non-linear filtering problem in continuous time

In the following article we consider the numerical approximation of the non-linear filter in continuous-time, where the observations and signal follow diffusion processes. Given access to high-frequency, but discrete-time observations, we resort to a first order time discretization of the non-linear filter, followed by an Euler discretization of the signal dynamics. In order to approximate the associated discretized non-linear filter, one can use a particle filter. Under assumptions, this can achieve a mean square error of $$\mathcal {O}(\epsilon ^2)$$ , for $$\epsilon >0$$ arbitrary, such that the associated cost is $$\mathcal {O}(\epsilon ^{-4})$$ . We prove, under assumptions, that the multilevel particle filter of Jasra et al. (SIAM J Numer Anal 55:3068–3096, 2017) can achieve a mean square error of $$\mathcal {O}(\epsilon ^2)$$ , for cost $$\mathcal {O}(\epsilon ^{-3})$$ . This is supported by numerical simulations in several examples.
Classification of malicious emails

An inherent part of everyday life and work on a computer is ownership and use of an email address. The main aim of this paper is to analyze existing approaches to classification of malicious emails. We have implemented a system, which is able to distinguish between legitimate and malicious emails. Subsequently, malicious emails are classified into three subcategories: spam, scam, and phishing. We prepared a labeled dataset. We extracted several features from emails contained in the dataset. Within the system, we have implemented four supervised machine learning methods (Random Forest, Decision Tree, Support Vector Machines, k-Nearest Neighbors) and evaluated them. According to our results, the Random Forest is the most suitable approach for email classification.
Channel Estimation in C-V2X using Deep Learning

Channel estimation forms one of the central component in current Orthogonal Frequency Division Multiplexing (OFDM) systems that aims to eliminate the inter-symbol interference by calculating the Channel State Information (CSI) using the pilot symbols and interpolating them across the entire time-frequency grid. It is also one of the most researched field in the Physical Layer (PHY) with Least-Squares (LS) and Minimum Mean Squared Error (MMSE) being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on Convolutional Neural Networks (CNNs) for channel estimation in vehicular environments used in 3GPP Rel.14 Cellular-Vehicle-to-Everything (C-V2X) technology. To this end, we compare the performance of the proposed Deep Learning (DL) architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy C-V2X channel estimation methods especially at high mobile speeds.
Biprediction-Based Video Quality Enhancement via Learning

Convolutional neural networks (CNNs)-based video quality enhancement generally employs optical flow for pixelwise motion estimation and compensation, followed by utilizing motion-compensated frames and jointly exploring the spatiotemporal correlation across frames to facilitate the enhancement. This method, called the optical-flow-based method (OPT), usually achieves high accuracy at the expense of high computational complexity. In this article, we develop a new framework, referred to as biprediction-based multiframe video enhancement (PMVE), to achieve a one-pass enhancement procedure. PMVE designs two networks, that is, the prediction network (Pred-net) and the frame-fusion network (FF-net), to implement the two steps of synthesization and fusion, respectively. Specifically, the Pred-net leverages frame pairs to synthesize the so-called virtual frames (VFs) for those low-quality frames (LFs) through biprediction. Afterward, the slowly fused FF-net takes the VFs as the input to extract the correlation across the VFs and the related LFs, to obtain an enhanced version of those LFs. Such a framework allows PMVE to leverage the cross-correlation between successive frames for enhancement, hence capable of achieving high accuracy performance. Meanwhile, PMVE effectively avoids the explicit operations of motion estimation and compensation, hence greatly reducing the complexity compared to OPT. The experimental results demonstrate that the peak signal-to-noise ratio (PSNR) performance of PMVE is fully on par with that of OPT while its computational complexity is only 1% of OPT. Compared with other state-of-the-art methods in the literature, PMVE is also confirmed to achieve superior performance in both objective quality and visual quality at a reasonable complexity level. For instance, PMVE can surpass its best counterpart method by up to 0.42 dB in PSNR.
A particle filter via constrained sampling for nonlinear dynamic systems

Summary This article is concentrated on the particle filtering problem for nonlinear systems with nonlinear equality constraints. Considering the constraint information incorporated into filters can improve the state estimation accuracy, we propose an adaptive constrained particle filter via constrained sampling. First, in order to obtain particles drawn from the constrained important density function, we construct and solve a general optimization function theoretically fusing equality constraints and the importance density function. Furthermore, to reduce the computation time caused by the number of particles, the constrained Kullback‐Leiler distance sampling method is given to online adapt the number of particles needed for state estimation. A simulation study in the context of road‐confined vehicle tracking demonstrates that the proposed filter outperforms the typical constrained ones for equality constrained dynamic systems.
Collaborative Mission Planning for Long-Term Operation Considering Energy Limitations

Mobile robotics research and deployment is highly challenged by energy limitations, particularly in marine robotics applications. This challenge can be addressed by autonomous transfer and sharing of energy in addition to effective mission planning. Specifically, it is possible to overcome energy limitations in robotic missions using an optimization approach that can generate trajectories for both working robots and mobile chargers while adapting to environmental changes. Such a method must simultaneously optimize all trajectories in the robotic network to be able to maximize overall system efficiency. This letter presents a Genetic Algorithm based approach that is capable of solving this problem at a variety of scales, both in terms of the size of the mission area and the number of robots. The algorithm is capable of re-planning during operation, allowing for the mission to adapt to changing conditions and disturbances. The proposed approach has been validated in multiple simulation scenarios. Field experiments using an autonomous underwater vehicle and a surface vehicle verify feasibility of the generated trajectories. The simulation and experimental validation show that the approach efficiently generates feasible trajectories to minimize energy use when operating multi-robot networks.
Mobile Application Outdoor Navigation Using Location-Based Augmented Reality (AR)

Abstract In order to formalize and co-create the Industrial Revolution 4.0. Augmented Reality is indispensable to facilitate human life. As a result, the difficulty is experienced by the user to know the exact location. As such, these developed applications can greatly assist mobile application users in making it easier to decide where to use their smartphones in specific location. Using Augmented Reality GPS location-based is well suited to a phone’s ability to have GPS sensors. Therefore, the place the user is searching or choosing is at the end of the user’s finger and it is in the form of virtual environment real-time.
Effect of Dataset Size on Efficiency of Collaborative Filtering Recommender Systems with Multi-clustering as a Neighbourhood Identification Strategy

Determination of accurate neighbourhood of an active user (a user to whom recommendations are generated) is one of the essential problems that collaborative filtering based recommender systems encounter. Properly adjusted neighbourhood leads to more accurate recommendation generated by a recommender system. In classical collaborative filtering technique, the neighbourhood is modelled by kNN algorithm, but this approach has poor scalability. Clustering techniques, although improved time efficiency of recommender systems, can negatively affect the quality (precision or accuracy) of recommendations. This article presents a new approach to collaborative filtering recommender systems that focuses on the problem of an active user's neighbourhood modelling. Instead of one clustering scheme, it works on a set of partitions, therefore it selects the most appropriate one that models the neighbourhood precisely. This article presents the results of the experiments validating the advantage of multi-clustering approach, $$M-CCF$$ , over the traditional methods based on single-scheme clustering. The experiments particularly focus on the effect of great size of datasets concerning overall recommendation performance including accuracy and coverage.
Spatio-Temporal Deep Residual Network with Hierarchical Attentions for Video Event Recognition

Event recognition in surveillance video has gained extensive attention from the computer vision community. This process still faces enormous challenges due to the tiny inter-class variations that are caused by various facets, such as severe occlusion, cluttered backgrounds, and so forth. To address these issues, we propose a spatio-temporal deep residual network with hierarchical attentions (STDRN-HA) for video event recognition. In the first attention layer, the ResNet fully connected feature guides the Faster R-CNN feature to generate object-based attention (O-attention) for target objects. In the second attention layer, the O-attention further guides the ResNet convolutional feature to yield the holistic attention (H-attention) in order to perceive more details of the occluded objects and the global background. In the third attention layer, the attention maps use the deep features to obtain the attention-enhanced features. Then, the attention-enhanced features are input into a deep residual recurrent network, which is used to mine more event clues from videos. Furthermore, an optimized loss function named softmax-RC is designed, which embeds the residual block regularization and center loss to solve the vanishing gradient in a deep network and enlarge the distance between inter-classes. We also build a temporal branch to exploit the long- and short-term motion information. The final results are obtained by fusing the outputs of the spatial and temporal streams. Experiments on the four realistic video datasets, CCV, VIRAT 1.0, VIRAT 2.0, and HMDB51, demonstrate that the proposed method has good performance and achieves state-of-the-art results.
Design of Lightweight Aerial Manipulator with a CoG Compensation Mechanism

This paper discusses the design and validation processes involved in the development of a lightweight aerial manipulator. The structure consists of 3D printed links with a counterweight on one end and a gripper on the other, its task is a pick and place operation. The manipulator is inspired from pantograph mechanism, which acts as a center of gravity (CoG) compensation active mechanism. This mechanism is intended to be attached beneath a drone or quadrotor to solve the CoG shifting problem during manipulator operation. An experimental test bench is developed to simulate real flying operations. The mechanism base tilting pitch angle during movement is obtained and compared both with and without the using of proposed aerial mechanism to validate the performance and stability of the quadrotor while used for pick and place applications.
Runtime revision of sanctions in normative multi-agent systems

Abstract To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement of the system-level objectives in every operating context. In this paper, we propose a runtime mechanism for the automated revision of norms by altering their sanctions. We use a Bayesian Network to learn, from system execution data, the relationship between the obedience/violation of the norms and the achievement of the system-level objectives. By combining the knowledge acquired at runtime with an estimation of the preferences of rational agents, we devise heuristic strategies that automatically revise the sanctions of the enforced norms. We evaluate our heuristics using a traffic simulator and we show that our mechanism is able to quickly identify optimal revisions of the initially enforced norms.
Vision-based posture-consistent teleoperation of robotic arm using multi-stage deep neural network

This paper proposes a visual teleoperation with human–robot posture-consistent based on deep neural network. A multi-stage structure of visual teleoperation network, in which the angles of robotic joints are obtained from human, is deduced. Furthermore, a novel human–robot posture-consistent mapping method is developed to generate dataset of the visual teleoperation network by solving constrained nonlinear matrix functions. Based on the designed framework, the data generator and a well trained multi-stage visual teleoperation network are presented. Finally teleoperation experiments are implemented to demonstrate that the proposed method is effectiveness and reliable.