Reviewerly/exHarmony huggingface dataset:
Blockchain-Empowered Decentralized Storage in Air-to-Ground Industrial Networks

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service.
Shearing interferometry via geometric phase

We propose an approach based on geometric phase for performing several types of shearing interferometry through a robust, compact, common-path setup.The key elements are two identical parallel plates with spatially-varying birefringence distributions, which perform the shearing by writing opposite geometric phases on the two circular polarization components of the linearly polarized incident wavefront.This setup allows the independent control of the shearing magnitude and relative phase of the two wavefront replicas.The approach is first illustrated for the simplest case of lateral shearing, and then extended to other geometries where the magnitude and direction of the shear vary smoothly over the wavefront.
Decentralized attribute-based conjunctive keyword search scheme with online/offline encryption and outsource decryption for cloud computing

In recent years, the increasing popularity of cloud computing has led to a trend that data owners prefer to outsource their data to the clouds for the enjoyment of the on-demand storage and computing services. For security and privacy concerns, fine-grained access control and secure data retrieval for the outsourced data is of critical importance. Attribute-based keyword search (ABKS) scheme, as a cryptographic primitive which explores the notion of public key encryption with keyword search (PEKS) into the context of attribute-based encryption (ABE), can enable the data owner to flexibly share his data to a specified group of users satisfying the access policy and meanwhile, maintain the confidentiality and searchable properties of the sensitive data. However, in most of the previous ABKS schemes, the decryption service is not provided, and a fully trusted central authority is required, which is not practical in the scenario that the access policy is written over attributes or credentials issued across different trust domains and organizations. Moreover, the efficiency of storage and computation is also the bottleneck of implementation of ABKS scheme. In this paper, for the first time, we propose a decentralized ABKS scheme with conjunctive keyword search for the cloud storage system. Besides the multi-keyword search in the decentralized setting, our scheme outsources the undesirable costly operations of decryption to the cloud without degrading the user’s privacy. Furthermore, the encryption phase is also divided into two phases, an offline pre-computation phase which is independent with the plaintext message, access policy, and keyword set, and can be performed at any time when the data owner’s device is otherwise not in use, and an online encryption phase which only incurs very little computation costs. Security analysis indicates that our scheme is provably secure in the random oracle model. The asymptotic complexity comparison and simulation results also show that our scheme achieves high computation efficiency.
Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network

The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios.
An efficient spatiotemporal chaotic image cipher with an improved scrambling algorithm driven by dynamic diffusion phase

In recent years, with the rapid development of chaotic technology, many chaotic image cryptosystems based on pixel/bit-level techniques have been investigated. However, the drawbacks of a cryptosystem are as follows: (1) the security and efficiency are merely based on an investigation into standard test images and are not suitable for certain unique images in which the existence of “1s” or “0s” is significant; (2) the processing of bits requires more chaotic variables that elevate the computational complexity; (3) computational redundancy occurs when encrypting certain unique images. The first comprehensive analysis of different types of existing bit/pixel-level permutation techniques is carried out to show the inapplicability of such algorithms for popular cloud computing technology. To address the aforementioned security pitfalls of the state-of-the-art approaches and promote efficiency, an improved scrambling algorithm (ISA) and a reverse diffusion based on a dynamic keystream allocation strategy (DKAS) have been proposed. The present study adopts a spatiotemporal mixed linear-nonlinear coupling with the logistic-sine system (SMLNLC-LSS) for chaotic orbit generation. This system uses coveted cryptographic properties contrasted with the existing chaotic systems. The employed strategies such as ISA and reverse diffusion strongly enhance the relationship between the plaintext and encryption process to fortify the breaking of the proposed algorithm under known attacks. Because the ISA and DKAS mechanism share the same keystream, the efficiency of the proposed algorithm is effectively improved. Extensive security evaluation methods such as statistical, sensibility, visual-assessment, and efficiency analyses were imposed on the proposed methodology. The corresponding results accentuate the efficiency of the image cryptosystem while maintaining the superior security performance provided by the assurance of the desirable cryptographic properties.
A big-data oriented recommendation method based on multi-objective optimization

Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining. For traditional CF-based recommender systems, the accuracy of recommendation results can be guaranteed while the diversity will be lost. An ideal recommender system should be built with both accurate and diverse performance. Faced with accuracy–diversity dilemma, we propose a novel recommendation method based on MapReduce framework. In MapReduce framework, a block computational technique is used to shorten the operational time. And an improved collaborative filtering model is refined with a novel similarity computational process which considers many factors. By translating the procedure of generating personalized recommendation results into a multi-objective optimization problem, the multiple conflicts between accuracy and diversity are well handled. The experimental results demonstrate that our method outperforms other state-of-the-art methods.
Ranking of software developers based on expertise score for bug triaging

Existing bug triage approaches for developer recommendation systems are mainly based on machine learning (ML) techniques. These approaches have shown low prediction accuracy and high bug tossing length (BTL). The objective of this paper is to develop a robust algorithm for reducing BTL based on the concept of developer expertise score (DES). None of the existing approaches to the best of our knowledge have utilized metrics to build developer expertise score. The novel strategy of DES is consisted of two stages: Stage-I consisted of an offline process for detecting the developers based on DES which computes the score using priority, versatility and average fix-time for his individual contributions. The online system process consisted of finding the capable developers using three kinds of similarity measures (feature-based, cosine-similarity and Jaccard). Stage-II of the online process consisted of simply ranking the developers. Hit-ratio and reassignment accuracy were used for performance evaluation. We compared our system against the ML-based bug triaging approaches using three types of classifiers: Navies Bayes, Support Vector Machine and C4.5 paradigms. By adapting the five open source databases, namely: Mozilla, Eclipse, Netbeans, Firefox, and Freedesktop, covering 41,622 bug reports, our novel DES system yielded a mean accuracy, precision, recall rate and F-score of 89.49%, 89.53%, 89.42% and 89.49%, respectively, reduced BTLs of up to 88.55%. This demonstrates an improvement of up to 20% over existing strategies. This work presented a novel developer recommendation algorithm to rank the developers based on a metric-based integrated score for bug triaging. This integrated score was based on the developer's expertise with an objective to improve (i) bug assignment and (ii) reduce the bug tossing length. Such architecture has an application in software bug triaging frameworks.
The recommender canvas: A model for developing and documenting recommender system design

The task of designing a recommender system is a complex process. Because of the many technological advancements that may be included in a recommender system, engineers are faced with a fast growing number of design related decisions to be taken. Unfortunately, there is no general approach yet for decision makers that can act as a framework guiding the design of a recommender system. The rich collection of literature on recommender systems, though, offers a great source to identify the key areas where these decisions need to be taken. In this paper, we survey existing literature with the aim of building a recommender system model inspired by Osterwalder's canvas theory. The result of our semi-structured synthesis is a novel design approach in the form of a canvas for designing recommender systems. This work provides a better understanding and can serve as a guide for decision making in recommender system design.
W!NCE

The ability to unobtrusively and continuously monitor one's facial expressions has implications for a variety of application domains ranging from affective computing to health-care and the entertainment industry. The standard Facial Action Coding System (FACS) along with camera based methods have been shown to provide objective indicators of facial expressions; however, these approaches can also be fairly limited for mobile applications due to privacy concerns and awkward positioning of the camera. To bridge this gap, W!NCE re-purposes a commercially available Electrooculography-based eyeglass (J!NS MEME) for continuously and unobtrusively sensing of upper facial action units with high fidelity. W!NCE detects facial gestures using a two-stage processing pipeline involving motion artifact removal and facial action detection. We validate our system's applicability through extensive evaluation on data from 17 users under stationary and ambulatory settings, a pilot study for continuous pain monitoring and several performance benchmarks. Our results are very encouraging, showing that we can detect five distinct facial action units with a mean F1 score of 0.88 in stationary and 0.82 in ambulatory settings, and that we can accurately detect facial gestures that due to pain.
Free-energy principle inspired visual quality assessment: An overview

The free energy principle was proposed several years ago as a unified justification for some brain theories. The process of human perception, cognition, action, and learning can be well explained using the free energy principle. The free energy principle suggests that the human perception and understanding of a given scene can be modeled as an active inference process, and the human brain tries to explain the scene using an internal generative model. The discrepancy between the given image or view and its best internal generative model explainable part is upper bounded by the free energy of the inference process. It was then conjectured that perceptual quality of the input image is closely related to free energy value of the process. Following this framework, dozens of visual quality assessment techniques have been proposed in the last few years and many have achieved state of the art performance. In this paper, we first give an overview of the free energy principle and then review the free energy principle inspired visual quality assessment metrics with a comparison in terms of algorithm design and performance.
Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

In the last decades, pervasive computing is generating a growing quantity of data.Data mining (DM) technology has become increasingly popular.However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern.Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM.In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM).We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature.Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms.Finally, we summarize some conclusions and come up with some future directions and challenges.
A new wavelet family for speckle noise reduction in medical ultrasound images

Ultrasonography is highly used as an image tool for medical diagnosis due to its non-invasive nature, low cost, adaptability and the possibility of real-time visualization. However, the noise present in these images makes medical analysis difficult. Therefore, noise reduction is essential for a more precise and accurate diagnosis. This work proposes a new orthonormal wavelet family designed to reduce noise in ultrasonic medical images, focusing on speckle noise, which is of particular concern in this scenario. A set of 110 real images is used to design the filters and to validate the results. The proposed wavelet family leads to better results than the ones obtained with the wavelets from the Daubechies, Symmlet, Coiflet and Biorthogonal families, in terms of both noise reduction and edge preservation. The results also compares favorably with those from the state-of-the-art BM3D denoising technique, when the noise intensity is not very high.
A Superpixel-Based Variational Model for Image Colorization

Image colorization refers to a computer-assisted process that adds colors to grayscale images. It is a challenging task since there is usually no one-to-one correspondence between color and local texture. In this paper, we tackle this issue by exploiting weighted nonlocal self-similarity and local consistency constraints at the resolution of superpixels. Given a grayscale target image, we first select a color source image containing similar segments to target image and extract multi-level features of each superpixel in both images after superpixel segmentation. Then a set of color candidates for each target superpixel is selected by adopting a top-down feature matching scheme with confidence assignment. Finally, we propose a variational approach to determine the most appropriate color for each target superpixel from color candidates. Experiments demonstrate the effectiveness of the proposed method and show its superiority to other state-of-the-art methods. Furthermore, our method can be easily extended to color transfer between two color images.
Head mouse control system for people with disabilities

Abstract In this paper, a human–machine interface for disabled people with spinal cord injuries is proposed. The designed human–machine interface is an assistive system that uses head movements and blinking for mouse control. In the proposed system, by moving one's head, the user moves the mouse pointer to the required coordinates and then blinks to send commands. The considered head mouse control is based on image processing including facial recognition, in particular, the recognition of the eyes, mouth, and nose. The proposed recognition system is based on the convolutional neural network, which uses the low‐quality images that are captured by a computer's camera. The convolutional neural network (CNN) includes convolutional layers, a pooling layer, and a fully connected network. The CNN transforms the head movements to the actual coordinates of the mouse. The designed system allows people with disabilities to control a mouse pointer with head movements and to control mouse buttons with blinks. The results of the experiments demonstrate that this system is robust and accurate. This invention allows people with disabilities to freely control mouse cursors and mouse buttons without wearing any equipment.
Dense Convolutional Networks for Semantic Segmentation

Recent studies have greatly promoted the development of semantic segmentation.Most stateof-the-art methods adopt fully convolutional networks (FCNs) to accomplish this task, in which the fully connected layer is replaced with the convolution layer for dense prediction.However, standard convolution has limited ability in maintaining continuity between predicted labels as well as forcing local smooth.In this paper, we propose the dense convolution unit (DCU), which is more suitable for pixel-wise classification.The DCU adopts dense prediction instead of the center-prediction manner used in current convolution layers.The semantic label for every pixel is inferred from those overlapped center/off-center predictions from the perspective of probability.It helps to aggregate contexts and embeds connections between predictions, thus successfully generating accurate segmentation maps.DCU serves as the classification layer and is a better option than standard convolution in FCNs.This technique is applicable and beneficial to FCN-based state-of-the-art methods and works well in generating segmentation results.Ablation experiments on benchmark datasets validate the effectiveness and generalization ability of the proposed approach in semantic segmentation tasks.
EDCAR: A knowledge representation framework to enhance automatic video surveillance

The main purpose of video-based event recognition is to interpret activities or behaviors within video sequences, in order to detect and isolate specific events, which have to be readily recognized and prompted to the people responsible for their monitoring. In this paper, we present a knowledge representation framework and a system for automatic video surveillance, which analyzes record scenes in order to detect the occurrence of specific events defined as targets. The framework, named Elements and Descriptors of Context and Action Representations (EDCAR), enables the representation of relevant elements, general descriptors of the context, and actions that have to be captured, including the definition of action compositions and sequences, in order to monitor and recognize abnormal situations. EDCAR and the associated system also support video summarization of relevant scenes, providing an inference engine to handle complex queries. They have been used experimentally on several video surveillance scenarios, which enabled us to prove their effectiveness with respect to similar solutions described in the literature.
Discrete Curvature Representations for Noise Robust Image Corner Detection

Image corner detection is very important in the fields of image analysis and computer vision. Curvature calculation techniques are used in many contour-based corner detectors. We identify that existing calculation of curvature is sensitive to local variation and noise in the discrete domain and does not perform well when corners are closely located. In this paper, discrete curvature representations of single and double corner models are investigated and obtained. A number of model properties have been discovered, which help us detect corners on contours. It is shown that the proposed method has a high corner resolution (the ability to accurately detect neighboring corners), and a corresponding corner resolution constant is also derived. Meanwhile, this method is less sensitive to any local variations and noise on the contour; and false corner detection is less likely to occur. The proposed detector is compared with seven state-of-the-art detectors. Three test images with ground truths are used to assess the detection capability and localization accuracy of these methods in cases with noise-free and different noise levels; 24 images with various scenes without ground truths are used to evaluate their repeatability under affine transformation, JPEG compression, and noise degradations. The experimental results show that our proposed detector attains a better overall performance.
Aggregated Visualization of Playtesting Data

Playtesting is a key component in the game development process aimed at improving the quality of games through the collection of gameplay data and identification of design issues. Visualization techniques are currently being employed to help integrate quantitative and qualitative data. Despite that, two existing challenges are to determine the level of detail to be presented to developers based on their needs and to effectively communicate the collected data so that informed design changes can be reached. In this paper, we first propose an aggregated visualization technique that makes use of clustering, territory tessellation, and trajectory aggregation to simultaneously display mixed playtesting data. Secondly, to assess the usefulness of our technique we evaluate it through interviews with professional game developers and compare it to a non-aggregated visualization. The results of this study also provide an important contribution towards identifying areas of improvement in the portrayal of gameplay data.