Reviewerly/exHarmony huggingface dataset:
Detection and Classification of Vehicles based on Deep Semantic Features of Faster R-CNN

In the current era, automation of vehicle detection and classification is considered as one of the challenging tasks which leads to the safety of roads and Transportation system. With the development of Computer vision and Image processing techniques, many researchers overcome the obstacles and achieved the aim. However, they lack accuracy and to handle this issue deep learning technology received a lot of attention from researchers. Deep learning-based Convolution Neural Network Specifically Faster Region (CNN) has been very successful in object detection and Image classification. In the proposed system we have used the latest techniques of deep learning to achieve state of the art results. Proposed system uses Faster R-CNN for both detection and classification purposes, it not only saves the time but also has superiority over other machine learning techniques in sense of accuracy and robustness. Firstly, Region Proposal Network detects the vehicle then CNN classifies the vehicle by its type. We have achieved 97.3% accuracy on dataset generated by ourselves. Achieving such an accuracy on locally generated dataset shows the novelty.
An Anonymous Blockchain-Based Logging System for Cloud Computing

Cloud computing has been in increasing concentration these years. Nevertheless, recently, it suffers the ambiguity in discerning responsibilities for unexpected incapable services. At this time, people refer to the logs. However, since logs may judge the fault, they are susceptible to malicious modification. Thus, their integrity is vital. Existing solutions leverage blockchain technology to tackle this problem, but few of them take the pseudo-anonymous nature of blockchain into considerations. This risks users’ privacy when they anchor their logs onto the blockchain. Some solutions try to be anonymous, but they are not general enough to be practical. In this paper, we propose a blockchain-based anonymous logging system for cloud environment. Our system introduces zero-knowledge proof to realize anonymous authentication, which can ensure undeniability in auditing while doing no harm to anonymity. Through experimental evaluation, we evaluate the feasibility and anonymity of the proposed system.
Evaluating the Coefficient of Thermal Expansion of Electronic Board Using the Virtual Fields Method

This paper proposes a method for identifying the coefficient of thermal expansion of dissimilar materials. Dissimilar materials are simulating on an electronic packaging. Displacement data used for inverse analysis are obtained by digital image correlation which is a method for measuring displacement in the full field of view without contact. The virtual fields method based on the principle of virtual work is employed as a method for inverse analysis. Each coefficient of thermal expansion that is unknown parameters is determined by preparing virtual displacements as many as the number of unknowns. The effectiveness of the inverse analysis method is demonstrated by identifying the coefficients of thermal expansion of dissimilar materials. Results show that the coefficient of thermal expansion can be obtained by the proposed method.
No-Reference Stereoscopic Image Quality Assessment by Both Complex Contourlet Domain with Spatial Domain Features

Stereoscopic image quality assessment (SIQA) is an essential and tricky part of image processing. Recently, many scholars have conducted image quality assessment in either spatial or frequency domain respectively. In this paper, we propose a no-reference stereoscopic image quality evaluation method by considering both complex contourlet and spatial features. Firstly, the original views are converted to the Lab color space. Then the luminance channel of Lab color space and the synthetic cyclopean image are calculated from the original stereo pairs. Next, the perceptual features of these types of images are extracted in the complex contour domain and the spatial domain respectively. Finally, all pre-extracted features are sent to the regression model for training and predicting image quality scores. Our experimental results show that the proposed algorithm achieves high consistency with human subjective perception, and compete with state-of-the-art algorithms.
New Sentiment Analysis Model Using LDA for Arabic Tweets

Due to the lack of sentiment analysis for Arabic social platforms (like twitter and Facebook), this paper presents a new approach to extract lexical features based on the semantically aspects (topics) which has been extracted using Latent Dirichlet Allocation (LDA) for Arabic tweets. In proposed approach we extracted lexical features based on the semantically aspects called topics which are relatedness value for using the most related terms which could be considered as a semantic class. It will group all words which are usually appearing in the same "context". The proposed model shows better results compared to different algorithms such as SVM, NB which making it a better approach for sentiment analysis for the described data.
Customs-Based Distributed Risk Assessment Method

Customs administration oversees the important processes of facilitating trade and protecting local societies and economies: the former by minimising shipment processing times and the latter by ensuring the lawfulness of trade. One of the main processes that affect the facilitation and protection of trade is the risk of the shipment assessment process. This assessment is performed by analysing available information about shipments to determine whether or not they require physical inspection. When a shipment is identified as suspicious, a physical inspection that can take hours is performed to identify and (dis)confirm the risk factors. In this process, changing trading behaviour by increasing the volume of expected shipments can be a source of pressure. This work proposes a secondary distributed risk assessment method that provides customs administration with an online risk assessment capabilities. The proposed method complements the risk assessments performed at customs administration by providing feedback from the early stage of risk analysis. The results show that the proposed method can provide classification that is 83% accurate on average.
An Application of a Wiener Filtering Based on Short Time Spectrum Estimation for High Frequency Seismic Signal Processing

This paper introduces a Wiener filtering method based on short time spectrum estimation which is implemented in frequency domain and applied to process seismic signals of high frequency. Since many seismic signal filtering methods could preserve the valid information of middle and low frequency well but could not preserve the valid information of high frequency well, so it needs to adopt a method which could preserve both low and high frequency of seismic signal. The Wiener filtering is the optimal filtering in the sense of minimum mean square error, and the way of frequency domain implementation is simpler and more effective than time domain implementation. So, it adopts Wiener filtering of frequency domain in this paper to obtain better results in seismic signal processing than conventional filtering method of time domain such as Kalman filtering, especially for high frequency seismic signals.
Matrix factorization with heterogeneous multiclass preference context

The spreading use of the Internet and big data technology has spawned the need for recommendation systems. However, to alleviate public anxiety about privacy, this paper advocates making recommendation with internal context, which refers to the implicit context hidden beneath the rating matrix only. Inspired by a recent work that embeds neighborhood information among users (as represented by multiclass preference context, MPC) into a factorization-based method, we extend it to both user-oriented and item-oriented, and put forward both user-oriented MPC and item-oriented MPC into a generic factorization framework called matrix factorization with heterogeneous MPC (MF-HMPC). In particular, we derive two specific recommendation methods from MF-HMPC with different structures, including MF with dual MPC (MF-DMPC) and MF with pipelined MPC (MF-PMPC), which are corresponding to concurrent structure and sequential structure, respectively. Extensive empirical studies on four public datasets show that our two forms of MF-HMPC outperform the referenced state-of-the-art MF methods as well as two representative deep learning methods. Moreover, we also discover some interesting facts about how these two kinds of internal contextual information complement each other. The main advantage of our methods is that they manage to strike a good balance between user-oriented neighborhood information and item-oriented neighborhood information.
Generating diverse conversation responses by creating and ranking multiple candidates

This paper introduces our systems built for Track 2 of Dialog System Technology Challenge 7 (DSTC7). This challenge track aimed to evaluate the response generation methods using fully data-driven conversation models in a knowledge-grounded setting, where textual facts were provided as the knowledge for each context-response pair. The sequence-to-sequence models have achieved impressive results in machine translation and have also been widely used for end-to-end generative conversation modelling. However, they tended to output dull and repeated responses in previous studies. Our work aims to promote the diversity of end-to-end conversation response generation by adopting a two-stage pipeline. 1) Create multiple responses for an input context together with its textual facts. At this stage, two different models are designed, i.e., a variational generative (VariGen) model and a retrieval-based (Retrieval) model. 2) Rank and return the most relevant response by training a topic coherence discrimination (TCD) model for calculating ranking scores. In our experiments, we demonstrated the effectiveness of the response ranking strategy and the external textual knowledge for generating better responses. According to the official evaluation results, our Retrieval and VariGen systems ranked first and second respectively among all participant systems on Entropy metrics which measured the objective diversity of generated responses. Besides, the VariGen system ranked second on NIST and METEOR metrics which measured the objective quality of generated responses.
Scalable, On-Demand Secure Multiparty Computation for Privacy-Aware Blockchains

In private, permissioned blockchains, organizations desire to transact with one another in a privacy-aware manner. For instance, when Alice sends X crypto-tokens to Bob at time t, it is desirable for Alice and Bob to perform double-spending check without revealing each other’s token balance. This also illustrates the fact that some input data from individual party is needed for secure computation in order to produce result data forming transaction details. In this paper, we consider secure computations in a blockchain involving multiple parties: Whenever a party has sensitive data to be computed with other parties, there is a need to exercise secure multiparty data sharing and computation (SMPC) among the parties (where parties may be malicious) to yield the result. Conventional SMPC is not scalable for a blockchain that has thousands of parties (blockchain nodes), and where secure computations may not always involve all blockchain nodes all the time, and the practical need for secure computation may range from sporadic to frequent. In this paper, we address these issues by designing a scheme that allows SMPC to be conveniently launched on-demand by any number of k-clique subsets of blockchain nodes. We show that our scheme is secure against any input data leakage and output leakage before, during, and after SMPC.
Out-of-Core GPU-Accelerated Causal Structure Learning

Learning the causal structures in high-dimensional datasets enables deriving advanced insights from observational data. For example, the construction of gene regulatory networks inferred from gene expression data supports solving biological and biomedical problems, such as, in drug design or diagnostics. With the adoption of Graphics Processing Units (GPUs) the runtime of constraint-based causal structure learning algorithms on multivariate normal distributed data is significantly reduced. For extremely high-dimensional datasets, e.g., provided by The Cancer Genome Atlas (TCGA), state-of-the-art GPU-accelerated algorithms hit the device memory limit of single GPUs and consequently, execution fails. In order to overcome this limitation, we propose an out-of-core algorithm for GPU-accelerated constraint-based causal structure learning on multivariate normal distributed data. We experimentally validate the scalability of our algorithm, beyond GPU device memory capacities and compare our implementation to a baseline using Unified Memory (UM). In recent GPU generations, UM overcomes the device memory limit, by utilizing the GPU page migration engine. On a real-world gene expression dataset from the TCGA, our approach outperforms the baseline by a factor of 95 and is faster than a parallel Central Processing Unit (CPU)-based version by a factor of 236.
Hybrid Utility Function for Unexpected Recommendations

Unexpectedness constitutes an important factor for recommender system to improve user satisfaction and avoid filter bubble issues. In this proposal, we propose to provide unexpected recommendations using the hybrid utility function as a mixture of estimated ratings, unexpectedness, relevance and annoyance. We plan to conduct extensive experiments to validate the superiority of the proposed method.
Intelligible Machine Learning and Knowledge Discovery Boosted by Visual Means

Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity, link prediction, community detection, crowd-generated data, and others. The role of the interpretable method in web search and mining activities is also very significant to enhance clustering, classification, data summarization, knowledge acquisition, opinion and sentiment mining, web traffic analysis, and web recommender systems. Deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts motivated the surge of efforts to make Machine Learning (ML) models more intelligible and understandable. The prominence of visual methods in getting appealing explanations of ML models motivated the growth of deep visualization, and visual knowledge discovery. This tutorial covers the state-of-the-art research, development, and applications in the area of Intelligible Knowledge Discovery, and Machine Learning boosted by Visual Means.
A Preliminary Study for The Risk of Probabilistic Blockchain Protocols

Blockchain as an emergent technology is quickly gaining ground as a decentralised distributed trustless database in the form of a linked-list data structure. This paper discusses the non-deterministic nature of public blockchain consensus protocols and argues that the probabilistic validity of a new block does not provide adequate guarantees of accuracy and integrity which in the case of cryptocurrencies could result in quite substantial asset losses. The contribution this paper adds is to spark a new line of thinking in regards to the validity of data in blockchains.
Government Data Sharing Framework based on DIKW Hierarchy Model

A government holds vast amounts of public sector data that it collects from everyday work delivering services to citizens. These data have significant potential to inform policy development, evaluate projects, contribute to economic growth, and support government plans, for the benefit of all people. Technology of “Big Data” analytics is relatively new for public administration, it can be analyzed for insights that lead to better decisions and strategic government services moves. This paper presents the framework for data sharing among government agencies that are secure and reliable. Additionally, the data-sharing model based on the DIKW hierarchy model is proposed to data classification belong to the level of data usage requirement of the user group to protect data with the right users for the right purpose.
Long term predictions of coal fired power plant data using evolved recurrent neural networks

Abstract This work presents an investigation into the ability of recurrent neural networks (RNNs) to provide long term predictions of time series data generated by coal fired power plants. While there are numerous studies which have used artificial neural networks (ANNs) to predict coal plant parameters, to the authors’ knowledge these have almost entirely been restricted to predicting values at the next time step, and not farther into the future. Using a novel neuro-evolution strategy called Evolutionary eXploration of Augmenting Memory Models (EXAMM), we evolved RNNs with advanced memory cells to predict per-minute plant parameters and per-hour boiler parameters up to 8 hours into the future. These data sets were challenging prediction tasks as they involve spiking behavior in the parameters being predicted. While the evolved RNNs were able to successfully predict the spikes in the hourly data they did not perform very well in accurately predicting their severity. The per-minute data proved even more challenging as medium range predictions miscalculated the beginning and ending of spikes, and longer range predictions reverted to long term trends and ignored the spikes entirely. We hope this initial study will motivate further study into this highly challenging prediction problem. The use of fuel properties data generated by a new Coal Tracker Optimization (CTO) program was also investigated and this work shows that their use improved predictive ability of the evolved RNNs.
A competency question-oriented approach for the transformation of semi-structured bioinformatics data into linked open data

Bioinformatics data obtained using different molecular biology techniques must be processed through different analysis tools to discover new biological knowledge. Since plain processed data have no explicit semantic value, the extraction of additional knowledge through data exploration would benefit from the transformation of bioinformatics data into Linked Open Data (LOD). Different approaches have been proposed to support the transformation of different types of biomedical data into LOD. However, these approaches are not flexible enough so they can be easily adapted for the transformation of semi-structured bioinformatics data into LOD. Thus, this paper proposes a novel approach to support such transformation. According to this approach, a set of competency questions drive not only the definition of transformation rules, but also the data transformation and exploration afterwards. The paper also presents a support toolset and describes the successful application of the proposed approach in the functional genomics domain.
Implementation of the sugeno fuzzy logic method in identifying the quality of coffee beans

Abstract Identifying the quality of coffee beans is quite important to be followed up better because it involves the products to be processed or traded. Because the management is quite a lot and complicated, it is proposed to design an application for identifying the quality of coffee beans. With these demands, it is necessary to measure the quality of coffee beans by using Sugeno’s Fuzzy Logic method. The identification of the quality of coffee beans using Fuzzy Sugeno by using the Sugeno Fuzzy model of the zero order and using the rule as much as 36, which is in accordance with the need to support the conclusions of quality from the identification results.
Grammar Inference with Multiparameter Genetic Model

The problem of regular inference is of interest in the formal language theory and its conjugates. In recent years, models have been proposed that solve this problem using machine learning methods. In this paper, we present a brand new model for regular inference which is based on principles of genetic algorithms along with inner special measures for evaluating and controlling the model performance. We present the results of testing of developed model on formal grammars of various complexity.
Path Planning and Simulation of Helicopter Power Patrolling

The line patrolling work of power grids at all levels is one of the important parts to ensure the safety of the power grid. The construction, operation, maintenance and support services of the power line of different voltage levels are carried out by helicopters. By analyzing the core workflow of the navigable power line, the constraint relationship between the maximum number of patrol miles in the single day and the distance of the patrol point is clarified. On this basis, the model of the navigation line for the single airport is constructed, and the model solution based on the ant colony algorithm and the maximum and minimum ant system algorithm is given. The experimental results based on the actual operational data of the general aviation show that the proposed model can solve the planning problem of helicopter patrolling line better, and the optimal solution provides decision support for the business department.
Lattice Based Multi-replica Remote Data Integrity Checking for Data Storage on Cloud

With the development and popularity of cloud computing, it is of crucial importance to guarantee cloud security and privacy. Remote data integrity checking (RDIC) makes cloud server capable of proving to users that their data store in the cloud is intact. To ensure the availability and reliability of critical data, users may generate multiple replicas for those data and deploy those replicas on the cloud. However, it is a problem how to check all replicas’ integrity of data saved in cloud. In previous works, some PDP schemes were proposed to solve the auditing problem of multi-replica data’s integrity on cloud servers. In this paper, we proposed a novel lattice based certificateless RDIC scheme to public auditing of outsourced data with multiple replicas. This scheme can eliminate certificate management issue and burden in PKI (Public Key Infrastructure) using users own identity to support the whole verification. Finally, our analysis demonstrates our scheme is efficient and secure.
Proof pearl: Braun trees

Braun trees are functional data structures for implementing extensible arrays and priority queues (and sorting functions based on the latter) efficiently. Some well-known functions on Braun trees have not yet been verified, including especially Okasaki’s linear time conversion from lists to Braun trees. We supply the missing proofs and verify all of these algorithms in Isabelle, including non-obvious time complexity claims. In particular we provide the first linear-time conversion from Braun trees to lists. We also state and verify a new characterization of Braun trees as the trees t whose index set is the interval {1, …, size of t}.