Reviewerly/exHarmony huggingface dataset:
VeSenChain: Leveraging Consortium Blockchain for Secure and Efficient Vehicular Crowdsensing

Vehicular CrowdSensing (VCS) has become a promising paradigm to employ mobile vehicles for performing sensing tasks in supporting location-based services and applications. In traditional VCS, a central agency is highly depended on, from collecting task requests of requesters to employment and reward assignment to workers. However, the centralized manner causes critical problems, such as potential privacy leakage and unexpected free-riding and false-reporting behaviors due to the lack of recorded proofs. The challenging problems hinder the wide-spread deployment of VCS. In this paper, we utilize consomum blockchain to elaborately design a dedicated blockchain, called by VeSenChain, to provision VCS services in a decentralized, authentic and transparent manner. For the implementation of VeSenChain, we develop an interactive protocol to support smart contract based operations among different entities. Stackelberg game approach is further used to formulate and solve the sensing task scheduling problem between a requester and multiple workers, and thus enable optimal smart contract design. Numerical results demonstrate that VeSenChain is effective and efficient for promoting VCS in network security and efficiency.
Uni-and-Bi-Directional Video Prediction via Learning Object-Centric Transformation

Video prediction, including uni-directional prediction for future frames and bi-directional prediction for in-between frames, is a challenging task and a problem worth exploring in multimedia and computer vision fields. Existing practices usually make predictions by learning global motion information from the whole given image. However, humans often focus on key objects carrying vital motion information instead of the entire frame. Besides, different objects often show different movement and deformation, even in the same scene. In this connection, we build a novel model of object-centric video prediction, in which the motion signals of key objects are particularly learned. This model can predict new frames by repeatedly transforming objects into the original input images. To focus on these objects automatically, we create an attention module with substitutable strategies. Our method requires no annotated data, and we also use adversarial training to improve sharpness of the predictions. We evaluate our model through Moving MNIST, UCF101 and Penn Action datasets and achieve competitive results in both quantity and quality, compared to existing methods. The experiments demonstrate that our uni-and-bi-directional network can well predict motions for different objects and generate plausible future and in-between frames.
Design of high-resolution quantization scheme with exp-Golomb code applied to compression of special images

For the compression of special image, such as medical image and aerial image, the reconstructed image quality is of utmost importance in the performance analysis. In this paper, a high-resolution quantization scheme based on the exp-Golomb code is proposed, aiming at improving the reconstructed image quality and realizing high-resolution near-lossless compression. Rather than quantizing the whole image uniformly, which adopted by most popular quantization schemes, an adaptive quantizer with smaller distortion is designed. Both the quantization step size and the quantization proportion are determined adaptively. Compression algorithms based on our adaptive quantizer can provide better reconstructed image quality, and the high frequency information of the image, such as texture and edge, can be better preserved.
Listening to the user's voice: A temporal analysis of autism‐related questions on Quora

ABSTRACT Social question and answer (Q&amp;A) platforms offer a new way for identifying information needs of people with certain diseases. Taking Quora as an example, we examine which health topics are of interest to autistic people and how these topics evolve over time. Experimental results reveal increasingly heavy and diverse attention to the condition, from diagnosis and treatment of autism itself to extended issues like social challenges, parenting, and education issues. We find that users tend to post clinical concerns about autism on Quora although traditionally such social Q&amp;A platforms encourage more social and awareness‐level questions. New concerns have appeared recently about autism's relations to other diseases like attention deficit hyperactivity disorder (ADHD) and obsessive–compulsive disorder (OCD). This study is beneficial for tracking and responding to autistic patients' and caregivers' information needs.
Research on Path Planning of Plant Protection UAV Based on Grid Method and Improved Ant Colony Algorithm

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest controlling. The flight path planning is a key point of plant protection UAV, which directly affects the efficiency of spraying process. In this work, a novel method of planning spraying path for multiple irregular discontinuous working areas is proposed. Based on traditional grid method, the improved grid method is applied to reduce transitional working path for irregular working areas. Using the improved ant colony algorithm, a shortest total flight path between multiple discontinuous regions is designed. The results show that the optimized flight path based on this method is shortened about 4.46% and has broad prospects for application.
Restore the Incomplete Calligraphy Based on Style Transfer

Calligraphy is an important humanistic symbol of Chinese civilization. However, most of the calligraphy is incomplete, which has only a small number of Chinese characters circulating in the world. How to use these samples to efficiently restore the remaining calligraphy characters has always been considered as a difficult task. In this work, we propose Densenet-pix2pix model based style transfer method to solve this problem. By training some samples to learn the rules of transferring the printed font images to the calligraphy characters images, Densenet-pix2pix can predict the remaining calligraphy characters. Our method modify the generation network and optimization strategies of style transfer, which improves the generation quality of the calligraphy characters and the stability of the model. In addition, we use pre-trained feature extraction models to extract content information and style information, and scientifically evaluate the quality of our generated calligraphy characters from these two aspects. We compared our method with several other baseline methods. The experimental results show that our method can effectively restore the remaining calligraphy characters, and the generated Chinese characters are more delicate.
Higher Weight Spectra of Veronese Codes

We study q-ary linear codes C obtained from Veronese surfaces over finite fields. We show how one can find the higher weight spectra of these codes, or equivalently, the weight distribution of all extension codes of C over all field extensions of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . Our methods will be a study of the Stanley-Reisner rings of a series of matroids associated to each code C.
Fuzzy Probabilistic Ontology Approach

In spite of the undeniable success of the ontologies, where they have been widely applied successfully to represent the knowledge in lots of real-world problems, they cannot represent and reason with uncertain knowledge which inherently appears in most domains. To cope with this issue, this article presents a new approach for dealing with rich-uncertainty domains. In fact, it is mainly based on integrating hybrid models which combine both fuzzy logic and Bayesian networks. On the other hand, the Fuzzy multi-entity Bayesian network (FzMEBN) proposed as a hybrid model which enhances the classical multi-entity Bayesian network using fuzzy logic, it can be used to represent and reason with probabilistic and vague knowledge simultaneously. Thus, as a language belongs to the proposed approach, this study proposes a promising solution to overcome the weakness of the Probabilistic Ontology Web Language (PR-OWL) based on FzMEBN to allow dealing with vague and probabilistic knowledge in ontologies. The proposed extension is evaluated with a case study in the medical field (diabetes diseases).
Two-Stream CNN Architecture for Anomalous Event Detection in Real World Scenarios

Anomalous event detection in any surveillance system has become an important area of research to make the surveillance effective and real time. In recent years, deep learning schemes are predominant to improve the detection accuracy. However, due to high computational complexity associated in deep learning architectures, it becomes a challenge to implement them in real-time scenarios. In this paper we propose a scheme to detect anomalous event in real time surveillance video. A database pre-processing algorithm has been proposed to capture the spatial and temporal frames in every second, which is subsequently utilized in two-stream 2D-CNN architecture for feature extraction and classification. A standard dataset, UCF-crime has been used to validate the proposed method. Finally, a comparative analysis has been made and it is observed that the classification accuracy and area under curve (AUC) of the suggested scheme is superior as compared to the recently proposed competent schemes.
Compensated closed-loop Kalman filtering for nonlinear systems

In this paper, we introduce new state estimation algorithm for nonlinear systems with the measurement loss at output channels. The goal of proposed methodology is to compensate for the output state-measurement losses through an advanced scheme that relies on the nonlinear prediction (NLP) and Kalman filter. Using NLP subsystem, based on Exponential Autoregressive (EXPAR) model, a compensated observation signal is first regenerated and then it is delivered to nonlinear discrete filtering or linearized Kalman filter in the form of closed-loop. Two proposed scheme versions (EXPAR-Substitution and EXPAR-Elimination) are presented in this paper. A two-phase permanent magnet synchronous motor (PMSM) case study is exposed to loss of measurements and then the loss is reconstructed using EXPAR-based schemes to demonstrate some capable results of the proposed algorithm. Simulation results of the proposed algorithm are found to be superior compared to other algorithms in the literature.
Mind the gap when implementing technologies intended to reduce or shift energy consumption in blocks-of-buildings

If the designers of technologies intended to reduce or shift energy consumption are not sensitive to how people live and work in buildings, a gap occurs between the expected and actual performance of those technologies. This paper explores this problem using the concepts of ‘design logic’ (designers’ ideas, values, intentions and user representations) and the ‘user logic’ (related in this case to how building occupants currently live and work in a building). The research presented unpacks the ‘design logic’ embedded in DR approaches planned for implementation at four blocks of buildings in a Horizon 2020 funded project, called “Demand Response in Blocks of Buildings” (DR-BoB). It discusses how the ‘user logic’ may differ from the ‘design logic’ and the potential impact of this on the performance of the technologies being implemented to reduce or shift energy consumption. The data analysed includes technical working documents describing the implementation scenarios of DR at four pilot sites, interviews and workshops conducted with the project team and building occupants during the first phases of the project. The analysis presented identifies how expectations about building occupants and their behaviours are built into the DR scenarios (to be tested during the project demonstrations). Initial findings suggest that building occupants’ energy use practices and routines may be different from those expectations. The paper illustrates how the concepts of ‘design logic’ and ‘user logic’ can be used to identify mismatches before technologies are implemented. The paper concludes with recommendations for improving the design and implementation of DR.
Detecting and Reporting Object-Relational Mapping Problems: An Industrial Report

Background: Object-Relational Mapping (ORM) frameworks are regarded as key tools in the software engineer arsenal. However, developers often face ORM problems, and the solution to these problems are not always clear. To mitigate these problems, we created a framework that detects and reports a family of ORM problems. Aims: The aim of this work is to assess how practitioners perceive our framework, the problems, they face, and the eventual points for improvements. Method: We first report an observational study in which we curated 12 ORM-related problems, which are implemented in our framework. We then conducted a developer experience (DX) study with 13 developers (10 well-experienced and 3 students) to assess their experience with our framework to implement six ORM-related tasks. Results: All participants agreed that our framework helped them to finish the programming tasks. The participants perceived that our framework eases the ORM modeling, has precise error messages, and employs ORM best practices. As a shortcoming, however, one participant mentioned that some custom annotations are not very intuitive. Conclusions: Our findings indicate that developers are willing to use frameworks that catch ORM problems, which create opportunities for new research and tools.
Deep Features for Kinship Verification from Facial Images

The kinship verification through facial images is an active research topic due to its potential applications. In this paper, we propose an approach which takes two images as an input then give kinship result (kinship / No-kinship) as an output. The approach contains five steps which are: (1) face preprocessing, (2) deep features extraction, (3) pair features representation and normalization, (4) features selection, (5) kinship verification. Experiments are conducted on five public databases (Cornell KinFace, UB Kin database, Familly, KinFace-I, and KinFace-II). The experimental results shows that our approach is comparable with existed approaches.
Group Tracking Method with Adaptive Gate for Multiple Extended Objects Tracking

The group tracking method always apply to situations that a target can generate one measurement at most and multiple targets move in the same style. However, extended object appears as high-resolution sensors become available. This paper adopts the concept of group targets and proposes a group tracking method with adaptive gate in multiple extended object. As constant ellipsoid gate causes changing volume, this paper proposes the idea of making the volume changeable to calculate the ellipsoid gate for accepting into the group. The proposed method is compared with the LJPDA algorithm for multiple extended objects tracking. Tracking results demonstrate that two methods have equivalent performance while group tracking algorithm cost One-third of the time in tracking.
Exploring NEURAghe: A Customizable Template for APSoC-Based CNN Inference at the Edge

The NEURAghe architecture has proved to be a powerful accelerator for deep convolutional neural networks running on heterogeneous architectures based on Xilinx Zynq-7000 all programmable system-on-chips. NEURAghe exploits the processing system and the programmable logic available in these devices to improve performance through parallelism, and to widen the scope of use-cases that can be supported. In this letter, we extend the NEURAghe template-based architecture to guarantee design-time scalability to multiprocessor SoCs with vastly different cost, size, and power envelope, such as Xilinx's Z-7007s, Z-7020, and Z-7045. The proposed architecture achieves state-of-the-art performance and cost effectiveness in all the analyzed configurations, reaching up to 335 GOps/s on the Z-7045.
Investigating Performance and Cost in Function-as-a-Service Platforms

The function-as-a-service (FaaS) service model has been gaining adoption at a fast pace. In this service model, cloud applications are structured as self-contained code modules called functions that are instantiated on-demand, and billing is based on the number of function invocations and on function execution time. Developers are attracted to FaaS because it promises to remove two drawbacks of the traditional IaaS and PaaS service models, the need to provision and manage infrastructure, and the need to pay for unused resources. In practice, however, things are a little less rosy: developers still have to choose the amount of memory allocated to functions, and costs are less predictable, especially because they are tied to function performance. This work investigates performance and cost variations within and across FaaS providers. Our results show that performance and cost can be significantly affected by the choice of memory allocation, FaaS provider, and programming language: we observed differences of up to $$8.5\times $$ in performance and 67 $$\times $$ in cost between providers (with the same language and memory size), and $$16.8\times $$ in performance and $$67.2\times $$ in cost between programming languages (with the same provider and memory).
Toward recursion aware complexity metrics

Context: Software developers spend a significant amount of time on reading, comprehending, and debugging of source code. Numerous software metrics can give us awareness of incomprehensible functions or of flaws in their collaboration. Invocation chains, especially recursive ones, affect solution complexity, readability, and understandability. Even though decomposed and recursive solutions are characterized as short and clear in comparison with iterative ones, they hide the complexity of the observed problem and solution. As the collaboration between functions can strongly depend on context, difficulties are usually detected in debugging, testing or by static analysis, while metrics support is still very weak. Objective: We introduce a new complexity metric, called Overall Path Complexity (OPC), which is aware of (recursive) call chains in the observed source code. As invocations are basic collaboration mechanism and recursions are broadly accepted, the OPC metric is intended to be applicable independently on programming language and paradigm. Method: We propose four different versions of the OPC calculation algorithm and explore and discuss their suitability. We have validated proposed metrics based on a Framework specially designed for evaluation and validation of software complexity metrics and accordingly performed theoretical, empirical and practical validation. Practical validation was performed on toy examples and industrial cases (47012 LOCs, 2899 functions, and 758 recursive paths) written in Erlang. Result: Based on our analysis we selected the most suitable (of 4 proposed) OPC calculation formula, and showed that the new metric expresses advanced properties of the software in comparison with other available metrics that was confirmed by low correlation. Conclusion: We introduced the OPC metric calculated on the Overall Control Flow Graph as an extension of Cyclomatic Complexity by adding awareness of (recursive) invocations. The values of the new metric can lead us to find the problematic fragments of the code or of the execution paths.
Methods for a similarity measure for clinical attributes based on survival data analysis

Case-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials.Our approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute.We define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute "applied therapy" are not applicable. This is often the case for data from clinical trials containing randomized arms.In silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909-0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657-0.831) and Discretized Value Difference Metric (0.535-0.671). The weight calculation method showed a more than six times (6.59-6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.
Image Denoising Based on Wavelet Transform and BM3D Algorithm

Image denoising as a key method is innovating continuously. Since the Block matching and 3D (BM3D) algorithm is superior to other methods in suppressing Gaussian noise, it has become the current state-of-the-art of denoising. Nevertheless, the image detail information will be partially lost during eliminating image additive noise, there is still room for improvement. Aiming at the existing problems of BM3D algorithm, an improved BM3D algorithm is designed by a combination of wavelet transform and BM3D algorithm. The method applies the principle that wavelet denoising preserves fine edge information to compensate the missing edge details caused by BM3D algorithm. The wavelet threshold denoising runs in parallel with the BM3D algorithm. Firstly, the wavelet threshold denoising method is used to obtain the preprocessed image. Meanwhile, the BM3D algorithm is applied to the corrupted image, including the basic estimate and the final estimate operation, to get another denoising preprocessed image. Finally, the final result comes out from pixel-level averaging of the two preprocessed images. Experimental analysis on three images of Lena, Barbara and Cameraman illustrate that denoising, using the proposed algorithm, can provide largest value of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The qualitative and quantitative research results illuminate that the improved algorithm is effective and robust.