Reviewerly/exHarmony huggingface dataset:
Attention Inspiring Receptive-Fields Network for Learning Invariant Representations

In this paper, we describe a simple and highly efficient module for image classification, which we term the "Attention Inspiring Receptive-fields" (Air) module. We effectively convert the spatial attention mechanism into a plug-in module. In addition, we reveal the relationship between the spatial attention mechanism and the receptive fields, indicating that the proper use of the spatial attention mechanism can effectively increase the receptive fields of the module, which is able to enhance translation invariance and scale invariance of the network. By integrating the Air module into advanced convolutional neural networks (such as ResNet and ResNeXt), we can construct AirNet architectures for learning invariant representations and gain significant improvements on challenging data sets. We present extensive experiments on CIFAR and ImageNet data sets to verify the effectiveness and feature invariance of the Air module and explore more concise and efficient designs of the proposed module. On ImageNet classification, our AirNet-50 and AirNet-101 (ResNet-50/101 with Air module) achieve 1.69% and 1.50% top-1 accuracy improvement with a small amount of extra computation and parameters compared with the original ResNet. We make models and code public available https://github.com/soeaver/AirNet-PyTorch. We further demonstrate that AirNet has a good ability for transfer learning and measure the performance on Microsoft Common Objects in Context object detection, instance segmentation, and pose estimation.
A robust D–S fusion algorithm for multi-target multi-sensor with higher reliability

This paper addresses the problem of track fusion for unordered distributed sensors with unknown measurement noise. A robust Dempster–Shafer (D–S) fusion algorithm is proposed, which includes three parts, namely, the local track estimation, the track association, and the state fusion. First, a labeling VB-PHD filter is derived to present target states with track labels and the unknown measurement noises of local sensors. Next, a heuristic D–S method is proposed to determine the relationship of local tracks and fused tracks, where the accumulated information is taken into account. Finally, a fusion method is given to show the state fusion results, which can fully utilize local state estimates and measurement noise information. Simulation results are provided to illustrate the high precision of tracking and good robustness, comparing with the traditional methods.
Toward Achieving Robust Low-Level and High-Level Scene Parsing

In this paper, we address the challenging task of scene segmentation. We first discuss and compare two widely used approaches to retain detailed spatial information from pretrained CNN - "dilation" and "skip". Then, we demonstrate that the parsing performance of "skip" network can be noticeably improved by modifying the parameterization of skip layers. Furthermore, we introduce a "dense skip" architecture to retain a rich set of low-level information from pre-trained CNN, which is essential to improve the low-level parsing performance. Meanwhile, we propose a convolutional context network (CCN) and place it on top of pre-trained CNNs, which is used to aggregate contexts for high-level feature maps so that robust high-level parsing can be achieved. We name our segmentation network enhanced fully convolutional network (EFCN) based on its significantly enhanced structure over FCN. Extensive experimental studies justify each contribution separately. Without bells and whistles, EFCN achieves state-of-the-arts on segmentation datasets of ADE20K, Pascal Context, SUN-RGBD and Pascal VOC 2012.
Automatic Signature Verification in the Mobile Cloud Scenario: Survey and Way Ahead

On-line signature verification is typically carried out with the use of digitizing tablets specifically designed for the aim.So far, stand-alone systems have been mainly inspected, but the current distributed/cloud scenario and the amount of mobile devices in everyday life is calling for a new challenge.Within this scenario, signatures are acquired around the world with different kinds of devices and processed on multiple platforms in order to be verified.Through the paper, the different phases of the signature verification process in the new scenario are presented and the most valuable results are discussed considering the following aspects: accessibility and usability, interoperability, security and performance.Achievements as well as weakness are focused to highlight promising directions for further research and technology development.
Developing a platform to evaluate and assess the security of wearable devices

Operating in a body area network around a smartphone user, wearables serve a variety of commercial, medical and personal uses. Depending on a certain smartphone application, a wearable can capture sensitive data about the user and provide critical, possibly life-or-death, functionality. When using wearables, security problems might occur on hardware/software of wearables, connected phone apps or web services devices, or Bluetooth channels used for communication. This paper develops an open source platform called SecuWear for identifying vulnerabilities in these areas and facilitating wearable security research to mitigate them. SecuWear supports the creation, evaluation, and analysis of security vulnerability tests on actual hardwares. Extending earlier results, this paper includes an empirical evaluation that demonstrates proof of concept attacks on commercial wearable devices and shows how SecuWear captures the information necessary for identifying such attacks. Also included is a process for releasing attack and mitigation information to the security community.
Fast approach for efficient vehicle counting

Systems for counting vehicles should be fast enough to be implemented in real-time situations. Most of the related work uses two stages for vehicle counting, vehicle detection and tracking, which increase the computational complexity. In this Letter, a fast and efficient approach for vehicle counting is proposed, where there is no need for the vehicle tracking step. A background model is created only for a narrow region, a line, in the video frames. The moving vehicles are detected as foreground objects while passing this narrow region. Morphological processes are applied to the extracted objects to enhance them and decrease the effects of vehicle occlusions. Finally, an efficient counting vehicles method is introduced employing only the extracted detection information. The experimental results performed on diverse videos show that the proposed method is fast and accurate. The average execution time per frame is 7.78 ms.
A Novel fuzzy frame selection based watermarking scheme for MPEG-4 videos using Bi-directional extreme learning machine

With the advancement of Internet Technology, a lot of multimedia content is shared over the Internet resulting in several concerns over multimedia content like copyright protection, content authentication, and ownership. In this paper, we have developed a novel watermarking scheme for Advanced Video Coding (MPEG-4 AVC) videos using Bi-directional Extreme Learning Machine (B-ELM). Relevant frames to be watermarked are identified by implementing a newly developed fuzzy algorithm based on threshold operation. A binary watermark, encrypted by transposition cipher, is used to ensure enhanced security. The proposed watermarking scheme has been tested on five benchmark videos (Akiyo, Hall_Monitor, Mother_Daughter, Pamphlet, and Silent). High average Peak Signal to Noise Ratio (PSNR) values of the signed video sequences indicate good visual quality post-embedding. Common video interferences such as scaling, noising, cropping, filtering, frame dropping, frame averaging, and frame swapping are used to evaluate the robustness of the watermark embedding scheme. The high values of Normalized Correlation (NC(W,W′)) and Bit-Error-Rate (BER(W,W′)) between the original watermark (W) and the recovered watermark (W′) establish the good robustness of the proposed scheme. While the established algorithms such as Fuzzy Inference System (FIS), Back Propagation Network (BPN), Meta-heuristic techniques take time of the order of several seconds to process a frame, the proposed algorithm consumes only a few milliseconds. Thus, it is well suited for the real-time watermarking of compressed videos.
Local contour symmetry facilitates scene categorization

People are able to rapidly categorize briefly flashed images of real-world environments, even when they are reduced to line drawings. This setting allows for the study of time-limited perceptual grouping processes in the human visual system that are applicable to line drawings. Previous work (Wilder, Dickinson, Jepson, & Walther, 2018) showed that standard local features of individual contours, or junctions between contours, do not account for this rapid classification ability but, rather, the relative placement of these contours appeared to be important. Here we provide strong support for this observation by demonstrating that local ribbon symmetry between neighboring pairs of contours facilitates the categorization of complex real-world environments. To this end, we introduce a novel computational approach, based on the medial axis transform, for measuring the degree of local ribbon symmetry in a line drawing. We use this measure to separate the contour pixels for a given scene into the most ribbon symmetric half and the least ribbon symmetric half. We then show human observers the resulting half-images in a rapid-categorization experiment. Our results demonstrate that local ribbon symmetry facilitates the categorization of complex real-world environments. This is the first study of the role of local symmetry in inter-contour grouping for human scene classification. We conclude that local ribbon symmetry appears to play an important role in jump-starting the grouping of image content into meaningful units, even in flashed presentations.
A novel ontology consistent with acknowledged standards in smart homes

With the development of Internet of Things, the Smart Home equipped with various sensors and devices has become a hot area attracting global attention and concern. In order to get a better understanding of ambient environments, adding semantics to sensor data plays a significant role. Researchers are attempting to build semantic models in order to satisfy their own requirements, which leads to little reusability between different models. This paper aims to provide a novel ontology which follows publicly acknowledged standards for achieving sensor data semantization in smart homes, including modeling sensors, context and activities with semantics. For keeping consistent with current accepted standards, the proposed ontology is based on the Semantic Sensor Network Ontology. In addition, we enrich the ontologies by incorporating spatiotemporal information and user profiles. The ontology is designed using Protégé and a use case is demonstrated to show the great potentiality in daily activity recognition in smart homes.
Two new steganography techniques based on quotient value differencing with addition-subtraction logic and PVD with modulus function

This paper proposes two steganography techniques, (i) quotient value differencing (QVD), and (ii) pixel value differencing with modulus function (MFPVD). The QVD technique operates on 3×3 size non-overlapped pixel blocks. In every pixel of a block LSB substitution is applied on 2 LSBs and the remaining 6 bits forms a quotient block. In the quotient block, QVD embedding approach is applied by exploiting the edges in eight directions. The MFPVD technique uses 2×3 size non-overlapped pixel blocks to exploit the edges in five directions. One of the pixels is termed as the central pixel. By considering this central pixel as a reference value, five difference values with five neighboring pixels are calculated. Based on the average of these five difference values, the hiding capacity in all the five directions is decided. As compared to the existing techniques, the proposed QVD technique possesses higher hiding capacity and the proposed MFPVD technique possesses higher peak signal-to-noise ratio (PSNR). Furthermore, the proposed techniques are undetectable by RS analysis and pixel difference histogram (PDH) analysis.
<i>FallSense</i>: An Automatic Fall Detection and Alarm Generation System in IoT-Enabled Environment

In this paper, we elaborate the design, implementation, and testing of a system-FallSense, which is able to detect accidental falls of human beings in a precise way. The distinguishing feature of FallSense is its endeavors beyond the scope of accelerometer, which is a component of traditional body sensor network. Along with this acceleration measuring unit, FallSense uses the benefits of an Internet-of-Things-enabled environment, which consists of a number of infrared transmitter-receiver pairs and ultrasonic sensors. Employing a fuzzy inference system, FallSense fuses the data from multiple sensors and becomes over sure before inferring that a fall has occurred. Depending on the inputs from multiple sensors, FallSense generates a value between 0 and 1, which signifies the chance of fall. Results show that multi-sensor-based FallSense achieves overall 16% improvement in comparison with the existing approaches, on an average. Beyond the theoretical modeling, this paper also practically implemented the same with the help of the real-sensing units.
Local activity-driven structural-preserving filtering for noise removal and image smoothing

In this paper, a local activity measurement of the clipped and normalized variance or standard deviation is proposed to drive anisotropic diffusion and relative total variation (RTV) to work better for structural preservation. Firstly, two novel edge-stop functions are introduced for our local activity-driven anisotropic diffusion (LAD-AD) to efficiently remove severe artifacts and preserve the fine geometry structures in HEVC-compressed depth images. Secondly, we propose a simple yet effective local activity-driven RTV (LAD-RTV) with the way of the product between gradient and the local activity measurement for image smoothing and scale representation. Meanwhile, both color-sharing information and each-channel discriminative information are considered, which are significant to color image edge-preserving but not included in the RTV model. Besides, LAD-RTV leverages the form of the division of gradient and the local activity measurement to resolve the problem of general image de-noising by regarding the noises as the duplicate texture elements. Experimental results have validated that the proposed LAD-AD can greatly improve the precision of the HEVC-compressed depth image and the quality of its synthesized image. Additionally, large numbers of results have shown our LAD-RTV is superior to several existing methods.
Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.
Ni-Co enrichment and High-Tech metals geochemistry in the Wingellina Ni-Co oxide-type laterite deposit (Western Australia)

The aim of this study is to evaluate the Ni-Co enrichment and the high-tech metals (REE and Sc) geochemistry in the Wingellina Ni-Co oxide-type laterite deposit (Western Australia). The study has been carried out on two mineralized cores (WPDD0012 and WPDD0019 drillcores) originating from two areas of the deposit. The geochemical assessments have been integrated with the analysis of laterite facies and sample mineralogy. In both the studied laterite profiles Ni enrichment was mostly controlled by the paragenetic evolution of the laterite itself, whereas Co enrichment was related to the formation of Mn-oxy-hydroxide-rich horizons in the limonitic ore-body. Significant REEs concentrations (up to 890 ppm) have been observed within the limonite zones. The correlation between REEs and the major oxides shows a decoupling between Ce and the other REEs, likely reflecting either a variable enrichment process among the different REEs, or a heterogeneous mineralogy of the REEs-bearing phases. Differently from other Ni-Co laterite deposits, Sc is only in limited part correlated with Fe2O3, having been most commonly detected in Ni- and SiO2-rich zones of the saprolitic parts of the profiles. All the geochemical features, the facies characteristics and the Ni-Co, REEs and Sc grades observed in the two studied profiles seems to be the results of the interactions between two main controlling factors: distinct parent rock lithology and diverse degree of serpentinization.
Survey on secure search over encrypted data on the cloud

Summary Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, the potential of cloud services has not been fully realized due to users concerns about data privacy and security. User‐side encryption techniques can be employed to mitigate the security concerns, but once the data is encrypted, no processing (eg, searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are encrypted. These techniques enable various types of search on the encrypted data and offer different levels of security. While these techniques enable different search types and vary in details, they share similarities in their components and architectures. In this paper, we provide a comprehensive survey on different secure search techniques, a high‐level architecture for these systems, and an analysis of their performance and security level.
Quality Inspection of Remote Radio Units Using Depth-Free Image-Based Visual Servo With Acceleration Command

In this paper, the problem of quality inspection of remote radio units (RRUs) is approached using the image-based visual servo control. A novel computer vision pipeline is designed that recognizes the power port of RRU and tracks it from the stream of images. For the control part, a new depth-independent interaction matrix is designed that relates the depth information with the area of the region of interest surrounding the power port. Based on this, an acceleration command is generated to drive the robot's trajectories. Furthermore, a propotional-derivative (PD)-type controller is designed based on the idea of sliding surface in variable structure control. This reduces the number of design parameters to a single parameter. The designed controllers are proven to be stable using the Lyapunov stability analysis. Simulation results and experimental validations are provided to support the research arguments.
Dynamic topology and relevance learning SOM-based algorithm for image clustering tasks

In this paper, the task of unsupervised visual object categorization (UVOC) is addressed. We utilize a variant of Self-organizing Map (SOM) to cluster images in two different scenarios: disjoint (images from Caltech256) and non-disjoint (images from MSRC2) sets. First, we ran several tests to evaluate different image representation techniques: features obtained by a deep convolutional network were compared with those obtained by handcrafted methods, such as SIFT combined with a set of interest point detectors. As expected, we found that deep convolutional network features significantly outperformed its handcrafted counterparts. After choosing the best image representation technique, we compared the state-of-the-art image clustering algorithms with a SOM-based subspace clustering method that identifies automatically the relevant features in the high-dimensional image representations. The results have shown that our method achieves substantially lower clustering error than all competitors in several challenging testing settings.
On the solvability of bipolar max-product fuzzy relation equations with the product negation

This paper studies the solvability of the max-product fuzzy relation equations in which a negation operator is considered. Specifically, the residuated negation of the product t-norm has been introduced in these equations in order to increase the flexibility of the standard fuzzy relation equations introduced by Sanchez in 1976. The solvability and the set of solutions of these bipolar equations have been studied in different scenarios, depending on the considered number of variables and equations.
Efficiency investigation from shallow to deep neural network techniques in human activity recognition

In the last years, several researchers measured different recognition rates with different artificial neural network (ANN) techniques on public data sets in the human activity recognition (HAR) problem. However an overall investigation does not exist in the literature and the efficiency of complex and deeper ANNs over shallow networks is not clear. The purpose of this paper is to investigate the recognition rate and time requirement of different kinds of ANN approaches in HAR. This work examines the performance of shallow ANN architectures with different hyper-parameters, ANN ensembles, binary ANN classifier groups, and convolutional neural networks on two public databases. Although the popularity of binary classifiers, classifier ensembles and deep learning have been significantly increasing, this study shows that shallow ANNs with appropriate hyper-parameters in combination with extracted features can reach similar or higher recognition rate in less time than other artificial neural network methods in HAR. With a well-tuned ANN we outperformed all previous results on two public databases. Consequently, instead of the more complex ANN techniques, the usage of simple ANN with two or three layers can be an appropriate choice for activity recognition.