Reviewerly/exHarmony huggingface dataset:
A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models.
Skin detection and lightweight encryption for privacy protection in real-time surveillance applications

An individual's privacy is a significant concern in surveillance videos. Existing research work into the location of individuals on the basis of detecting their skin is focused either on different techniques for detecting human skin on protecting individuals from the consequences of applying such techniques. This paper considers both lines of research and proposes a hybrid scheme for human skin detection and subsequent privacy protection by utilizing color information in dynamically varying illumination and environmental conditions. For those purposes, dynamic and explicit skin-detection approaches are implemented, simultaneously considering multiple color-spaces, i.e. RGB, perceptual (HSV) and orthogonal (YCbCr) color-spaces, and then detecting the human skin by the proposed Combined Threshold Rule (CTR)-based segmentation. Comparative qualitative and quantitative detection results with an average 93.73% accuracy, imply that the proposed scheme achieves considerable accuracy without incurring a training cost. Once skin detection has been performed, the detected skin pixels (including false positives) are encrypted, when standard AES-CFB encryption of skin pixels is shown to be preferable compared to selective encryption of a whole video frame. The scheme preserves the behavior of the subjects within the video. Hence, subsequent image processing and behavior analysis, if required, can be performed by an authorized user. The experimental results are encouraging, as they show that the average encryption time is 8.268 s and the Encryption Space Ratio (ESR) is an average 7.25% for a high definition video (1280 × 720 pixels/frame). A performance comparison in terms of Correct Detection Rate (CDR) showed an average 91.5% for CTB-based segmentation compared to using only one color space for segmentation, such as using RGB with 85.86%, HSV with 80.93% and YCbCr with an average 84.8%, which implies that the proposed method of combining color-space skin identifications has a higher ability to detect skin accurately. Security analysis confirmed that the proposed scheme could be a suitable choice for real-time surveillance applications operating on resource-constrained devices.
A novel sampling moiré method and its application for distortion calibration in scanning electron microscope

High-resolution scanning electron microscope (SEM) is widely used for microscale material and structure characterization. However, the distortion in SEM imaging causes serious error for the quantitative measurement of optical methods. In previous work, the distortion calibration requires to set up a mathematic model of SEM imaging and then to determine the model parameters by calculation. The distortion filed is hard to directly extract from the SEM image by classic methods. In order to tackle this problem, we developed a direct sampling moiré method (DSM) for simple and fast analysis of grating distortion. In DSM, the distortion fields can be directly presented visually through processing a single grating image. The measurement accuracy of DSM was verified by numerical simulation to be better than the classic sampling moiré. In the experiment, a standard grating with pitch of 6.5 μm was used in DSM to calibrate the distortion fields at different magnifications (50 × , 100 × , 150 × , 300 × ) in SEM. The fitting results of the calibrated distortion field agreed well with the existing distortion model, which demonstrated the feasibility of DSM for distortion calibration in SEM.
Scale-Recursive Network with point supervision for crowd scene analysis

Crowd scene analysis, and in particular its density estimation, is a challenging task due to the lack of spatial information, scale variation, and the large amount of supervised-learning parameters. In order to address these challenges, we propose a Scale-Recursive encoder–decoder Network with Point Supervision (SRN+PS). On the one hand, an encoder–decoder recurrent structure uses features between adjacent scales to tackle scale variation, and a novel loss function, called the row vector-based counting loss, is proposed to focus on the crowd counting accuracy. On the other hand, we employ an additional point segmentation task in training and combine features learned from the two tasks above. The Euclidean loss, row vector-based counting loss, and two-label focal loss are integrated by a joint training scheme, which improves both the quality of density map estimation and the performance of crowd counting. Finally, we propose a weakly supervised framework based on the SRN structure and the Convolutional Winner-Take-All(CWTA) module. In this framework, most parameters are obtained by unsupervised learning with the exception of a few which are tuned by supervised learning in model training. As a result, our multi-scale structure can obtain salient object sparse spatial features from unsupervised learning. Experiments on the ShanghaiTech, UCF_CC_50 and UCSD datasets demonstrate the effectiveness of our proposed method.
Hierarchically Localizing Software Faults Using DNN

In this article, we propose a hierarchical fault localization technique using a deep neural network (DNN). First, we prioritize the functions of a program based on their suspiciousness score. Subsequently, the fault is localized to specific statements within the top k suspected functions, where the value of k is determined heuristically. We use two function-level features to train a DNN for fault localization at the function level. Subsequently, the invocation information of the statements of the top-k functions is used to train another neural network to localize the faulty statement. We also report an extension to our approach for localizing multiple faults. This involves partitioning the failed test cases into clusters such that they target different faults. Our empirical evaluation indicates that our proposed approach requires examining 30.05 to 50.74% less code on an average, as compared to related fault localization techniques.
Gabor-CNN for object detection based on small samples

Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect.
Autonomous Trash Collector Based on Object Detection Using Deep Neural Network

Non-biodegradable product usage and ignorance towards proper disposal are creating the problem of ever-growing trash stacks. An autonomous mobile trash collector which collects trash lying on ground in a trash-container attached to it can be a feasible solution to the problem. In this procedure trash detection is done via deep learning algorithm. An ultrasonic sonar sensor on the robot detects object along the path and a camera module sends pictures of the object to raspberry pi for classification into trash or not trash. The prototype robot is of low-cost and can detect a wide range of trash with high accuracy. Therefore has good environmental as well as economic impact.
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Linear complementary dual codes were defined by Massey in 1992, and were used to give an optimum linear coding solution for the two user binary adder channel. In this paper, we define the analog of LCD codes over fields in the ambient space with mixed binary and quaternary alphabets. These codes are additive, in the sense that they are additive subgroups, rather than linear as they are not vector spaces over some finite field. We study the structure of these codes and we use the canonical Gray map from this space to the Hamming space to construct binary LCD codes in certain cases. We give examples of such binary LCD codes which are distance-optimal, i.e., they have the largest minimum distance among all binary LCD codes with the same length and dimension.
Implementing a Vision-Based Collision Avoidance Algorithm on a UR3 Robot

Collaborative robotics implies the possibility for robots and humans to cooperate to perform a common task. A fundamental element to make collaboration possible is to ensure the safety of the human operators. In this paper a collision avoidance algorithm designed for a robotic redundant task is presented. This algorithm permits the robot to avoid collisions with obstacles (e.g. human operators) and in the same time to achieve the planned task when possible. The algorithm was tested with a UR3 robot of Universal Robots company, considering only tool centre point (TCP) position in order to obtain kinematic redundancy. The robot is controlled by an external PC that communicates with the controller of the robot via Ethernet network. The communication and control algorithms are written in the Matlab environment. The PC-UR communication functions permit to send joint velocities commands from the PC to the controller of the robot and feedback data from the UR to the PC. Control functions estimate the distances between an obstacle (whose position was acquired by a system of motion capture) and the robot and calculate the needed joints velocities to avoid any collision. The functions developed in Matlab will be explained in detail. Results of the experimental tests conducted to verify the effectiveness of the collision avoidance system are reported.
Cube-based incremental outlier detection for streaming computing

Outlier detection is one of the most critical and challenging tasks of data mining. It aims to find patterns in data that do not conform to expected behavior. Data streams in streaming computing are huge in nature and arrive continuously with changing distribution, which imposes new challenges for outlier detection algorithms in time and space efficiency. Incremental local outlier factor (ILOF) detection dynamically updates the profiles of data points, while the arrival of consecutive and massive volume data points in a streaming manner causes high local data density and leads to expensive time and space overhead. Our work is motivated by its deficiencies, and in this paper we propose a cube-based outlier detection algorithm (CB-ILOF). The data space of streaming data is divided into multiple cubes, then the outlier detection of data points is transferred into the outlier detection of cubes, which significantly reduces time and memory overhead. We also present a performance evaluation on 5 datasets. Experimental results show the superiority of the CB-ILOF over the ILOF on accuracy, memory usage, and execution time.
HAFLoop: An architecture for supporting Highly Adaptive Feedback Loops in self-adaptive systems

Most of the current self-adaptive systems (SASs) rely on static feedback loops such as the IBM's MAPE-K loop for managing their adaptation process. Static loops do not allow SASs to react to runtime events such as changing adaptation requirements or MAPE-K elements' faults. In order to address this issue, some solutions have emerged for manually or automatically perform changes on SASs' feedback loops. However, from the software engineering perspective, most of the proposals cannot be reused or extended by other SASs. In this paper, we present HAFLoop (Highly Adaptive Feedback control Loop), a generic architectural proposal that aims at easing and fastening the design and implementation of adaptive feedback loops in modern SASs. Our solution enables both structural and parameter adaptation of the loop elements. Moreover, it provides a highly modular design that allows SASs' owners to support a variety of feedback loop settings from centralized to fully decentralized. In this work, HAFLoop has been implemented as a framework for Java-based systems and evaluated in two emerging software application domains: self-driving vehicles and IoT networks. Results demonstrate that our proposal easies and accelerates the development of adaptive feedback loops as well as how it could help to address some of the most relevant challenges of self-driving vehicles and IoT applications. Concretely, HAFLoop has demonstrated to improve SASs' feedback loops' runtime availability and operation.
Mineral processing plant site selection using integrated fuzzy cognitive map and fuzzy analytical hierarchy process approach: A case study of gilsonite mines in Iran

Selecting the most suitable location for a mineral processing plant is of vital importance for achieving the highest throughput and lowering overall costs. The aim of the research is to select the best location for a gilsonite processing plant (Kermanshah Province, Iran). First, laboratory scale concentration experiments were conducted to determine the most suitable concentration technique for the material from all gilsonite mines nearby. It was concluded that, one production line is enough to concentrate of the material from all mines by flotation method. Afterwards, the effective criteria for locating the processing plant were identified as processing, facilities and infrastructure, mining, environmental, geographical, social and cultural, legal, geological and finally technical and technological factors. Considering the multiplicity of effective factors and interactions between them, fuzzy cognitive map (FCM) method is used for ranking of the factors. A survey for the evaluation of each factor was conducted through interviewing a number of experts in various mining engineering fields. Then, the FCM method was used to determine the weights of each factor. The results show that distribution of mines is the most important factor. In order to determine the appropriate site for installation of the plant, 4 sites were selected in the vicinity of larger mines by investigating the geological maps, using Geological Information System (GIS), aerial photos and objective visits. In order to obtain the global weights of alternatives, the local weights obtained from FCM and FAHP methods were combined. According to the results, site No.4, located at longitude 34˚ 18′28″ and latitude 45˚ 41′ 49″ was proposed as the best location.
Unsupervised learning of optical flow with patch consistency and occlusion estimation

Recent works have shown that deep networks can be trained for optical flow estimation without supervision. Based on the photometric constancy assumption, most of these methods adopt the reconstruction loss as the supervision by point-based backward warping. Inspired by the traditional patch matching based approaches, we propose a patch-based consistency to improve the vanilla unsupervised learning method Ren et al. [1]. Instead of only comparing the corresponding pixel intensity, we locate the correspondence by using the image patches with census transform, which is more robust for the illumination variation and occlusion. Moreover, a novel parallel branch is devised to estimate a soft occlusion mask jointly in an unsupervised way. The mask is adopted to weight our patch-based consistency loss to alleviate the influence of the occlusion. The plenty of experiments have been implemented on Flying Chairs, KITTI and MPI-Sintel benchmarks. The results show that our method is efficient and outperforms the peer unsupervised learning methods that are using the FlowNet-liked network.
CoDAG: An Efficient and Compacted DAG-Based Blockchain Protocol

Blockchain is seen as a promising technology to provide reliable and secure services due to its decentralized characteristic. However, because of the limited throughput, current blockchain platforms can not meet the transaction demand in practical use. Though researchers proposed many new solutions, they suffered either decentralization or security issues. In this paper, using Directed Acyclic Graph (DAG) structure, we improve the linear structure of traditional blockchain protocol. In the new structure, blocks are organized in levels and width, which will generate into a compacted DAG structure (CoDAG). To make CoDAG more efficient and secure, we design algorithms and protocols to place the new-generated blocks appropriately. Compared with traditional blockchain protocols, CoDAG improves the security and transaction verification time, and enjoys the consistency and liveness properties of blockchain. Taking adversary parties into consideration, two possible attack strategies are presented in this paper, and we further prove that CoDAG is a secure and robust protocol to resist them. The experimental results show that CoDAG can achieve 394 transactions per second, which is 56 times of Bitcoin's throughput and 26 times of Ethereum's.
Multi-view clustering via clusterwise weights learning

It is known that the performance of multi-view clustering could be improved by assigning weights to the views, since different views play different roles in the final clustering results. Nevertheless, we observe that weights could be further refined, since in reality different clusters also have different impacts on finding the correct results. We propose a multi-view clustering algorithm with clusterwise weights (MCW), which assigns a weight on each cluster within each view. The objective function of MCW consists of three parts: (1) intra-view clustering: clustering each view by using non-negative matrix factorization; (2) inter-view relationship learning: learning the consensus clustering results by a weighted combination of each view; (3) clusterwise weight learning: learning the weight of a cluster by making the weight be proportional to the average distance between the cluster and other clusters. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results on several benchmark datasets demonstrate the superiority of the proposed algorithm over existing multi-view clustering methods.
An intelligent cloud service discovery framework

Due to the heterogeneity nature and the huge number of published cloud services, as well as, the continuous increase of the users’ complexity demands, the discovery process of cloud services remains an open issue. According to our conducted comprehensive survey study, the existing cloud service discovery solutions suffer from a set of drawbacks; missing a standardized and comprehensive specification of cloud services, not considering the lack of knowledge about cloud concepts, and not considering the continuous increase of the complex cloud services published to meet the complexity of the users’ demands. In this paper, a comprehensive, standardized, flexible, and intelligent cloud service discovery framework is proposed to overcome these drawbacks. According to this framework, a comprehensive ontology has been developed to provide a standardized semantic specification of cloud services based on their functional features and non-functional features. For exploiting the high performance of the relational databases in managing the large amounts of data and improving the effectiveness of the proposed framework, instances of the non- functional features’ ontological concepts are separated from the developed ontology to be stored in a relational database, where Ontop OBDA platform is used to translate the complex queries over the ontology to SQL queries that are understood by the underlying relational database source. Also, our proposed framework provides a user-centric interface that enables users with low knowledge about cloud concepts to compose complex queries using their natural English language. The effectiveness evaluation shows that the proposed framework provides better results than other solutions. According to the implementation results, the average amount of error expected to identify a service by using the proposed framework is 11% compared to 31% by using the Cloudle service discovery solution. Also, the framework achieves entirely the cloud service discovery evaluation criteria compared to 66% of these criteria for the current cloud service discovery solutions. For efficiency, the proposed framework achieves 88% F-Score for mapping cloud services into suitable functional features and 80% for discovering services that match the users’ queries.
Vehicle Detection in Aerial Images Using Modified YOLO

In recent years, object detectors have been developed significantly, many general object detectors have sprung up. However, these object detectors are design for general object, and do not directly apply well to detecting vehicles in aerial image. Vehicles in aerial image occupy smaller pixels comparing to general object in general data set, and have special angle. General object detectors are prone to miss detection and Confuse the roof of the car with the roof of the house in aerial image. Therefor we improved YOLOv3 in order to resolve the task of vehicles detecting. Increasing the depth of network is applied for enhancing network fitting, top-level feature maps are called to provide more detail information for getting better detection effect. Through a series of network structure changes, our algorithm performance excels State-of-the-art aerial vehicles detection algorithms.