Reviewerly/exHarmony huggingface dataset:
Enhancing Knowledge Graph Embedding with Relational Constraints

Knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translation-based models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translation-based embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which possesses certain robust guarantee. We evaluate our method on the task of knowledge graph completion. Extensive experiments show that KRC achieves substantial improvements against baselines.
An Overview of Artificial Intelligence based Autonomous Vehicle Robotics Simulators

In this paper diverse simulator available for autonomous automobile robotics simulators are reviewed. This paper offers quick description of simulators for autonomous car simulation, simulator for wise manage of quadrotor, open source hardware and software program platform for artificial intelligence and robotics simulations, and self reliant riding model simulator. This paper presents useful glimpse for researchers within the region of robotics and self sufficient cars. The diverse simulators and environments supplied in this paper are as compared and analyzed on the basis of numerous parameters.
Geoecological Maps of Surface Water Pollution Akmola Region of Kazakhstan Based on Hydrochemical Analyzes

Abstract Ecological and geographical map that characterizes the introduction and storage in the natural or built environment of physical, chemical, and biological substances in concentrations exceeding the natural (background) values and adversely affecting human health, fauna, flora, and other components of the environment. Maps may reflect contamination of the earth, atmosphere, surface water and groundwater, soils, vegetation, wildlife, and others. They show the sources of pollution (solid waste, air emissions of gases and aerosols, drains and diversion of utilities and others), pollution factors (impact of power plants, transportation, industrial production, animal husbandry, and so on), distribution of natural and man-made pollutants (mechanical, physical, chemical, biological), and others. The levels of pollution often characterize the performance of exceeding the maximum permissible concentration (MPC), the maximum permissible emissions (MPE), and other regulations.
EO Integration of Planar Ion Trap and Silicon Photonics for Optical Addressing in Quantum Computing

Feasibilities of Electro-Optical integration for ion-trapping in quantum computing are demonstrated. Photonics devices exhibit ~33% coupling efficiency and 1.12 dB/cm propagation loss. Wide positional tolerance of ± 10 pm is obtained for optical addressing of trapped ion.
Photonic Reservoir Computing enabled by Active Silicon Micro-Rings with Transparent Signal Injection

Photonic masking enables fully transparent silicon micro-ring-based reservoir computing for channel equalization. We have evaluated the performance of micro-ring-based reservoir computing estimation for a 28GBd SSB PAM-4 transmission setup over 100km SSMF with Kramers-Kronig reception.
Improved Frame Difference Algorithm Based on CNN for Moving Target Detection

In order to improve the shortcomings of frame difference method in moving target detection and improve the accuracy and robustness of moving target detection, an improved frame difference method based on CNN is proposed. Besides, in order to solve the problem of blind spot in autopilot and improve the safety of autopilot, SIFT algorithm is used to obtain the panoramic image of moving target. Firstly, frame difference method is adopted to obtain the foreground image, and then CNN is used to repair the foreground image. Finally, SIFT is used to splicing the restored foreground image to obtain the panoramic image of the foreground target. Experimental simulation results show that the proposed algorithm is effective, efficient and accurate in the extraction and repair of moving targets. With the proposed method, a complete panoramic image of moving targets can be obtained.
Online Receding Horizon Trajectory Planning Method for UUV Near Bottom Terrain Tracking

Seafloor terrain survey is an important appliance for unmanned underwater vehicle (UUV). In this paper, online receding horizon trajectory planning method is present to realize good effect of near bottom tracking when UUV performs the seafloor terrain survey task. Considering that the task process has the characteristics of real-time control and dynamic periodic planning, a three-loop feedback framework of planning, tracking and controlling is proposed. The framework has the advantage that the slow trajectory planning loop and the fast real-time tracking control loop are unified. Based on the framework, the online receding horizon planning strategy and algorithm are designed to realize dynamic trajectory planning when UUV tracks seafloor terrain. For outer loop of trajectory planning, the Bezier smoothing fitting method used to generate the online trajectory based on the real-time detection information of seafloor terrain. At last, the validity and feasibility of the proposed online trajectory planning framework and method are verified by the simulation results.
Dynamic Gesture Recognition Based on DS Evidence Theory

Dynamic gesture recognition is gradually playing an increasingly important role in non-contact human-computer interaction. It is important to improve the accuracy of the recognition while reducing the computational cost. In this paper, we consider the dynamic gesture recognition based on multiple feature extraction and Dempster-Shafer (DS) evidence theory. First, to improve the accuracy of the gesture recognition, HOG <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> &HOG-LBP features are extracted and used for the training of Support Vector Machines (SVM). Then, DS evidence theory is used to optimize the classification results of SVMs. Experimental results on the Cambridge Hand Gesture Dataset demonstrate the effectiveness of the proposed method.
Utility Requirement Description for Utility-Preserving and Privacy-Respecting Data Pseudonymization

Many of the existing pseudonymization techniques aim at preserving the use-case specific utility of the data. However, retracing under which condition a utility is present in pseudonymized data is hard. Therefore, specifying and applying pseudonymization techniques adequately becomes challenging to non-experts. There is the need for a solution that enables non-experts to generate pseudonymizations that can be utilized in their specific use case without deep understanding of pseudonymization techniques. To address this, we introduce a methodology for describing the utility that should be kept after pseudonymizing data together with privacy restrictions that must be respected while processing the pseudonymized data. We present Util, a description language for defining utility policies, i.e. policies that document utility requirements together with privacy requirements. Using Util does not require deeper knowledge of pseudonymization function and their parametrization. We sketch rules that can be used to translate from a utility policy to an appropriate data pseudonymization. For that, we introduce pseudonymization with utility tags.
Selection of Countermeasures against Harmful Information based on the Assessment of Semantic Content of Information Objects in the Conditions of Uncertainty

The paper suggests models, an algorithm and a common technique for selection of countermeasures against harmful information based on the assessment of semantic content of information objects in the conditions of uncertainty. The methods of processing of incomplete, conflicting and fuzzy knowledge are used. A version of the common algorithm for eliminating the uncertainties of assessment and categorization of information objects' semantic content while detecting harmful information is analysed. The results of operation of the technique to determine the list of available countermeasures considering the responsibility areas are discussed.
An Improved RE Framewrok for IoT-Oriented Smart Applications Using Inetgrated Approach

Requirement engineering (RE) plays a vital part for developing proficient systems and a major cause of system failure is due to the flaws in the RE. Understanding requirements for developing a system is an essential task and requires domain expertise. Internet of things (IoT) is the emerging field consists of smart devices and sensors connected to the Internet where they communicate with each other in order to exchange data and information. RE community gives very less attention towards IoT domain. This research proposes an improved RE framework for IoT-based smart applications using integrated approach. The proposed framework addresses the challenges faced in the requirement development of smart applications using existing RE methodologies. The proposed framework is an integrated approach which is equipped with the emerging RE techniques in the context of IoT-based smart applications. The proposed framework is comprised of five different steps of RE which are embedded with set of verified RE techniques for efficient requirement management. The proposed framework is validated with a real-time healthcare IoT-based case study to verify and realize the applicability of proposed framework. It is revealed that the projected framework provides precious impending into the requirement management of IoT based smart applications.
Image Processing based on Adaptive Morphological Techniques

Morphological techniques are clean to value and works on effective base of set philosophy. The equitable of applying morphological techniques is to discard the defect in the design of the picture. Utmost of the technique utilized here are a sequence of two mechanisms, dilation and erosion. The technique needs a short matrix architecture called as a grouping component. The model and range of the grouping component have a powerful impact on the ultimate result. In the present paper an experiment is built to grasp the main morphological techniques by applying them on any standard picture. MATLAB program is worn as the tool for examining the morphological techniques.
One-step direct measurement of the entangled W states with cross-Kerr nonlinearity

We propose a scheme to directly measure the entangled W states with cross-Kerr nonlinearity mediums. This scheme can measure three-photon, four-photon, and even N-photon W states in one step. Only PBSs and cross-Kerr nonlinearity mediums are used in this scheme, which is feasible for experiments. We describe the three-photon W states measurement process and extend it to four-photon and N-photon W states.
Perfectly Secure Message Transmission Against Rational Adversaries

Secure Message Transmission (SMT) is a two-party cryptographic protocol by which the sender can securely and reliably transmit messages to the receiver using multiple channels. An adversary can corrupt a subset of the channels and commit eavesdropping and tampering attacks over the channels. In this work, we introduce a game-theoretic security model for SMT in which adversaries have some preferences for protocol execution. We define rational “timid” adversaries who prefer to violate security requirements but do not prefer the tampering to be detected. First, we consider the basic setting where a single adversary attacks the protocol. We construct perfect SMT protocols against any rational adversary corrupting all but one of the channels. Since minority corruption is required in the traditional setting, our results demonstrate a way of circumventing the cryptographic impossibility results by a game-theoretic approach. Next, we study the setting in which all the channels can be corrupted by multiple adversaries who do not cooperate. Since we cannot hope for any security if a single adversary corrupts all the channels or multiple adversaries cooperate maliciously, the scenario can arise from a game-theoretic model. We also study the scenario in which both malicious and rational adversaries exist.
Unanticipated Scenarios

Presents the introductory editorial for this issue of the publication.
Secure and flexible algorithm for outsourcing of bilinear pairings effectively resisting conspiracy

With the development of Internet of Things (IoT) and 5G network, IoT devices become more and more important to our life. They contain smart speaker, web camera and smart home products. No doubt they bring a lot of convenience for our daily life and work affairs, but will cause some secure problems. The resource constrained IoT devices may not have enough computational ability and energy to run complicated cryptosystem like bilinear pairing. Currently, most of the architecture of distribution strategy for outsourcing bilinear pairing are fixed. The number of servers will be one or two without conspiracy. Nevertheless, in 5G and IoT environment, they support a large amount of assistant nodes to speed up calculation and data processing, such as edge computing. In this paper, we propose a flexible and secure architecture for outsourcing bilinear pairings with few assistant nodes which can be semi-trusted or untrusted. We can adjust our architecture to meet the different requirements according to the environment. Even if the most servers conspire each other, our proposed scheme also can detect the flaw of cheating with high probability.
A Real-Time Recommender System Design Based on Spark Streaming

In the big data environment, the personalized recommender system based on offline batch processing has the advantages of accurate calculation and high fault tolerance. However, due to its large amount of calculation, it cannot satisfy the real-time requirement. At present, the real-time recommender system architecture based on the combination of offline layer and online layer also has some disadvantages in summarizing results and maintaining updates, and the recommended result depends on the offline layer. If the user behavior changes in a short time and the result update of the offline layer is lagging, the final recommendation will not reflect the user’s interest change very well. Aiming at these problems, this paper makes use of the Spark Streaming micro-batch data stream processing function to implement a real-time recommendation system. The system realized a real-time recommendation composed entirely of online stream computing based on the consistency of the user positive and negative evaluation of the item .Considering the problem of unstable data flow in the actual application scenario, the message queue Kafka is introduced to ensure the smooth and stable real-time data inflow speed. Finally, the performance of the entire system and the effectiveness of the recommendation are verified by Movielens data set.
Adaptive Command Filtered Backstepping Control for Uncertain Nonlinear Systems and Its Application to Wind Turbine

In this paper, an adaptive control scheme based on command-filtered backstepping technique is developed for a class of uncertain multiple-input-multiple-output (MIMO) strict-feedback nonlinear systems. Within this scheme, extreme learning machine (ELM) with random hidden nodes is used in the controller to approximate unknown functions, and a smooth projection algorithm is adopted to adjust online estimated parameters in the controller such that the boundedness of the parameter estimates can be ensured. Furthermore, some stale command filters are designed to produce virtual control signals and their derivations such that the analytic calculation of the partial derivatives of virtual control signals is removed. Also, some other stable filters are proposed to generate compensating signals of above-mentioned filtered errors to compute compensated tracking errors. It is proved that the proposed control scheme can guarantee the boundedness of all signals in the closed-loop system. Finally, the proposed control scheme is applied to control wind turbine and the simulation studies illustrate the theoretic results obtained.
Analysis on FFT and DWT Transformations in Image Processing

An Image processing is a coming out research which needs heed in bio-medical field. There are many image processing methods which are useful to bring out information for analysis, memory space and conserves computing time. Transformation is one of image processing techniques. Some of the transformation techniques are wavelet transform, Hilbert transform, Radon Transform, Fourier transform etc. Transformation techniques are chosen based on their applications, advantages and disadvantages. The wavelet transformation be a technique which equates time domain and frequency domain. It is exactly as popular as the time-frequency representation of a non -stationary signal. Where Fourier transform breaks down the image into real and imaginary components, which is nothing but representation of image in frequency domain. In this paper, discrete wavelet transform using Haar and fast Fourier transform are applied on images using MATLAB 2018a.
Modeling and Enforcing Integrity Constraints on Graph Databases

The enormous volume and high variety of information that is constantly produced by computing systems requires storage technologies able to provide high processing velocity and data quality. The suitability for modeling complex data and for delivering performance are characteristics that are making graph databases become very popular. However, existing limitations still prevent database management systems that adopt the graph model to fully ensure data consistency, given that the means for ensuring data consistency are usually nonexistent or at most very simple. This work intends to overcome this limitation by extending the support for defining and enforcing integrity constraints on graph databases, in order to prevent the graph to reach an inconsistent state and compromise the correctness of applications. The proposed integrity constraints are implemented on OrientDB. Experimental results show that the prototype implementation can improve the performance in comparison to verification of constraints on a client application.
A Lightweight VLSI Architecture for RECTANGLE Cipher and its Implementation on an FPGA

Block ciphers are one of the most fundamental building blocks for information and network security. In recent years, the need for lightweight ciphers has dramatically been increased due to their wide use in low-cost cryptosystems, wireless networks and resource-constrained embedded devices including RFIDs, sensor nodes, smart cards etc. In this paper, an efficient lightweight architecture for RECTANGLE block cipher has been proposed. The architecture is suitable for extremely hardware-constrained environments and multiple platforms due to its support of bit-slice technique. The proposed architecture has been synthesized and implemented on Xilinx Virtex-5 xc5vlx110t-1ff1136 field programmable gate array (FPGA) device. Implementation results have been presented and compared with the existing architectures and have shown commensurable performance. Also, an application-specific integrated circuit (ASIC) implementation of the architecture is done on SCL 180 nm CMOS technology where it consumes 2362 gate equivalent (GE).
Attention-based Sampling Distribution for Motion Planning in Autonomous Driving

Sampling-based motion planning(SMPs) approach has been very popular for its ability of computing collision-free and asymptotically optimal path without explicit formulation of the configuration space. SMPs use sampling to generate a discrete representation of the problem and then run graph searching algorithm on this representation. Which means the representation itself is at least as important as graph searching algorithm. In general this is enabled by uniformly sampling the configuration space. This paper proposes a machine learning based biased sampling approach for autonomous driving. The sampling distribution was learned from previous demonstrations using conditional variational encoder(CVAE) with attention mechanism. Combined with a sampling-based algorithm called rapidly-exploring random tree*(RRT*), we proposed Attention-RRT*. This approach was proved to be effective in several driving scenarios.
Reliable Potential Friends Identification Based on Trust Circuit for Social Recommendation

Direct trust links among users may be unreliable due to noise. Simple use of these direct trust links may lead to inferior recommend effects, and most of the existed methods don’t consider the difference in trust strength. We propose a novel model called TrustE which combines the trust relationships and users similarity. Specifically, we design a new method called Trust Circuit in TrustE to model trust relationships which calculates trust values by taking into account the asymmetry, transitivity, attenuation, and multiplicity-paths of trusts. Then we calculate user similarity through meta-paths guided embedded representation learning in the heterogeneous information network. Finally, we combine trust value and users similarity to get the personalized numbers of reliable potential friends for each user and make recommendation for target user according to his friends’ preferences. The experimental results on Epinions and Douban datasets verify that TrustE is superior to other existing recommendation methods and it also has high accuracy for cold-start users’ recommendation.
Dioxin Emission Concentration Soft Measurement Model of MSWI Process Based on Unmarked Samples and Improved Deep Belief Network

Municipal solid waste incineration (MSWI) is a widely used domestic waste resource treatment technology. However, this process products pollution dioxins (DXN). It has high toxicity and durability. This is one of the main reasons of "not in my backyard" effect when constructing MSWI plant. As the long period and high cost of off-line DXN detection method, it is difficult to realize the real-time monitoring of DXN emission concentration. The massive unlabeled samples in the industrial field contain the generation mechanism of DXN, which are not fully utilized. Aim at the above problems, this paper proposes an unmarked samples and improved deep belief network (DBN) based method to construct DXN soft measurement model. Firstly, a large number of unlabeled samples are added to the training input sample set to improve the learning ability of soft measurement model at pre training phase. Then, an energy function is derived as the activation function of restricted Boltzmann machine (RBM). Finally, for the whole DBN, dropout algorithm is used to improve the robustness of the model, and the adaptive learning rate error back propagation algorithm is used to fine tune the weight iteratively at the fine tuning phase. The validity and rationality of this method are validated by DXN data set.