Reviewerly/exHarmony huggingface dataset:
Attentional triplet neural networks for text-dependent speaker verification

Deep Neural Networks (DNNs) have been widely used in speech processing and show great performance on a range of tasks, such as speech recognition, machine translation, and speaker verification. In this paper we propose a new type of DNN model for text-dependent speaker verification. The frame-level features, being extracted by DNN, are usually equally weighted and aggregated (or averaged) to compute an utterance-level speaker representation. We combine Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to extract frame-level features, then provide every frame with different weights produced by attention mechanism, so that utterance-level speaker representation can be generated by weighted averaging frame-level features. We explore different generating methods on the attention weights. Besides, attention mechanism can also be used to align temporal information between enrollment and evaluation utterance. Triplet loss function is used to optimize our models, requiring inputs group in triplet style. Ultimately, results of experiment on RSR2015 database show that our attention-based model outperforms various baseline models.
Robot Path Planning Method Based on Level Set

Abstract For the problem of robot path planning under the action of external force field, the directionality of outward force can promote and hinder the movement of the robot. The continuous path planning method can break through the traditional robot path planning algorithm based on grid search. Limit, effectively utilize the external force field existing in the space, and improve the numerical implementation method to ensure the feasibility of the planned path in the strong external force field. Based on the Level Set method, the path planning problem is defined. The particle tracking of the robot is transformed into the numerical evolution of the curve. The optimal time path is obtained by solving the Hamilton-Jacques equation. The numerical calculation accuracy of the improved backward path tracking is guaranteed. The feasibility of planning the path. The simulation results show that the algorithm can effectively deal with a variety of complex external force fields and maintain the path in a strong external force field.
Research on Open Mining Intelligent Equipment Based on Internet

Abstract Based on the establishment of mines, the use of advanced industrial Internet technology, based on the solid model and block model, using 3DMine smart equipment software to study the intelligent collection equipment of open pit mines, through the intelligent research of equipment, research and development It has developed advanced quota intelligent equipment, including intelligent sorting system and intelligent collection device system, etc., and discussed the influence and application of different intelligent equipment in the state of open pit mine. The preliminary design provides a technical plan and decision basis for smart equipment.
Making Smart Buildings and Personal Systems Cooperate via Knowledge Base Overlays

Reactive IoT applications often have to deal with the data source Babel arising from their need to operate on context information originated from different data sources. Semantic knowledge bases can be fruitfully deployed to alleviate this problem: they provide a unified access point for context information including both long term (such as the structure of the environment) and transient (such as sensor readings) data thanks to their ability to host elements responding to different schemas within the same container. In previous works we introduced an architecture to create reactive IoT systems based on a semantic knowledge base that also hosts the definition of their behavior and on an accompanying reactive machinery. In this paper, we introduce the use of knowledge base overlays, i.e. containers providing a live, unified view over (parts of) different underlying knowledge bases, as a mechanism to enable interoperation between multiple IoT semantics-based systems. Specifically we explore the benefits of this approach in a case study in which a semantic IoT system governing a smart building interacts with the personal semantic systems of the people entering the building.
An Improved Hardware Architecture for modulo without Multiplication

This paper discusses a hardware implementation of modulo that does not require a multiplication. This implementation is accomplished through the use of an algorithm in which the new algorithm is presented to calculate the modulus of large values using shifting and adding. This allows our implementation to be equivalent in clock cycles to other implementations without the need for a multiplier's delay. This algorithm is compared with others, such as Barrett reduction and fast modular reduction. Our implementation of this modulo algorithm is shown to be faster in many cases. This paper proposes both a hardware implementation of this algorithm as well as synthesis results in soi12s0 45nm IBM Multi-threshold CMOS (MTCMOS) technology and ARM-based standard cells.
Study on Primary Harmonic Eliminator for Ferromagnetic Resonance in the Neutral Non-effective Grounding System

Abstract The ferromagnetic resonance overvoltage and overcurrent can often appear in the neutral non-effective grounding system, which is caused by the electromagnetic voltage transformer saturation under a certain condition. At present, two different types of primary harmonic eliminators are mainly used to solve the ferromagnetic resonance, which are voltage-sensitive resistor type and thermal resistor type. In this paper, the simulation model based on ATP-EMTP program is established and the results of different harmonic eliminators are compared. The results prove that the primary harmonic eliminator of thermal resistor type can more effectively suppress the ferromagnetic resonance.
AI for Health – Knowledge-Based Generation of Tailor-Made Exercise Plans

There is an increasing awareness of the essential impact of physical exercising on health. Properly planning such activities requires knowledge about prerequisites and impact of different exercises and selecting those that reflect all relevant physical properties of the individual and contextual conditions. Otherwise, exercising may not only be ineffective, but even harmful to health. We developed an app for the generation of tailor-made exercise plans using techniques from knowledge-based configuration and consistency-based diagnosis. The paper focuses on presenting the systematic and formal representation of the domain knowledge and its exploitation by a generic configuration algorithm.
Scotopic Vision Image Enhancement Algorithm Based on Retinex Model

Scotopic vision image enhancement has problems of over-enhancement and halo artifacts, based on retinex theory, this article proposes illumination map estimate method to enhancement image to solve those problems. Specifically, we extracted the image of Max-RGB and Y channel as global and local initial illumination map respectively. Then, we improved weighted guided filter to reduce halo artifacts. Next, we refined the initial illumination maps with weighted average, improved weighted guided filtering and gamma correction, as the final illumination map. Finally, the enhanced result is obtained by the corrected illumination map according to retinex theory. Experiments shown that the method can obtain results with less lightness distortion and reduce halo artifacts compared with that of several state-of-the-art methods.
On Configurable Defense against Adversarial Example Attacks

Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications. Recently, however, DNNs are shown to be vulnerable to adversarial example attacks with slight perturbations on the inputs. Existing defense mechanisms against such attacks try to improve the overall robustness of the system, but they do not differentiate different targeted attacks even though the corresponding impacts may vary significantly. To tackle this problem, we propose a novel configurable defense mechanism in this work, wherein we are able to flexibly tune the robustness of the system against different targeted attacks to satisfy application requirements. This is achieved by refining the DNN loss function with an attack sensitive matrix to represent the impacts of different targeted attacks. Experimental results on CIFAR-10 data set demonstrate the efficacy of the proposed solution.
Fitness Landscape Analysis Metrics based on Sobol Indices and Fitness- and State-Distributions

This paper proposes new Fitness Landscape Analysis (FLA) metrics as measures for problem difficulty in heuristic search. The sensitivity and variable interaction metrics are based on Sobol indices, a common technique in sensitivity analysis. The fitness- and state-variance and the fitness- and state-skewness metrics are based on the second and third central statistical moments. We compute metric values for around 550 continuous test functions in 2, 5 and 10 dimensions and compare it to well-established metrics (Fitness Distance Correlation, Autocorrelation, Information Content, Density-Basin Information and Partial Information Content). By conducting two-sample statistical tests (T-Test, F-Test, Kolmogorov-Smirnov Test, and Rank-Sum Test) for all combinations of FLA metrics, we demonstrate that our proposed metrics result in significantly different distributions. Thus, we can conclude that they reveal fitness landscape characteristics not captured by the existing metrics that were considered.
Dynamic Dispatching for FOUP Cleaning

In a semiconductor manufacturing facility (FAB), wafers spend the majority of their manufacturing life in Front Opening Unified Pods (FOUPs), and it is essential that the environment be as clean as, or cleaner than, that of the manufacturing floor. While manufacturing equipment are usually constructed to actively purge and exchange internal environment to fab atmosphere, the FOUP is a relatively static environment that can only equalize its environment with the ambient area of storage. Hence periodic cleaning of FOUPs is essential to ensure the wafers produced meet requisite quality. However, selection of the number of FOUPs for cleaning provides challenges to the WIP flow strategy, scheduling and dispatching decisions. In this poster, we will cover the generic options for FOUP cleaning, metrology and dispatching challenges. Subsequently we will discuss the various strategies considered and employed to achieve appropriate trade-offs between various factors with respect to dispatching.
Bay Number Recognition Based on Deep Convolutional Recurrent Neural Network

The recognition of image text sequences has been the subject of long-term research in computer vision. The recognition problem of the shell number image sequence studied in this paper is based on the deep convolutional neural network architecture, and an image recognition algorithm that integrates feature extraction, sequence modeling, and transcription into a unified framework is adopted. The algorithm used has four characteristics: (1) Compared with most algorithms that require separate training and coordination, the method adopted in this paper has end-to-end characteristics. (2) It can handle indefinite length image sequences without involving character segmentation or horizontal-scale normalization. (3) The algorithm is not limited to any predefined vocabulary, and has achieved remarkable performance in both no-lexicon and dictionary-based scene text recognition tasks. (4) The algorithm produces an efficient and much smaller model, which is more practical for real-world scenarios. Using this algorithm for bay number recognition can assist in scene location and provide smart services and application through AI technology.
Pseudo-normalized Hecke eigenform and its application to extremal 2-modular lattices

It is shown that extremal 2-modular lattices of ranks 32 and 48 are generated by their vectors of minimal norm. In the proof, we use certain properties of the difference of normalized Hecke eigenforms. We refer to them as the pseudo-normalized Hecke eigenform, the concept of which is introduced in this paper.
Anomaly Detection of Predicted Frames Based on U-Net Feature Vector Reconstruction

Abstract Anomaly detection in surveillance video scenes is one of the current research hotspots. Due to the small sample collection of anomalous events, the lack of negative sample labeling data training in anomaly detection research adds a lot of difficulties. Therefore, we adopt the method of unsupervised training and improve the method of anomaly detection based on the reconstruction of the potential features of the predicted frame and ground truth based on u-net. We reduce the reconstruction error between the potential features of u-net in the predicted frame and the potential features of the real frame. Then through other constraints, the reconstruction error of the entire predicted frame is minimized according to the generative adversarial training. Due to the use of normal behavior sample training, when the abnormal behavior is detected, the reconstruction error value exceeds the set threshold to judge whether abnormal behavior occurs in the surveillance video. Experiments prove that our improved method is effective and accurate.
Prototype of Data Link Based on Random Modulation Hopping and Automatic Modulation Recognition

Abstract A model of data link system based on modulation hopping and Automatic Modulation Recognition (AMR) is proposed, and the prototype of the pro-posed system is developed with GNU radio platform and the universal software radio peripheral (USRP). At the transmitting end of the prototype, the random sequence is generated to control modulation hopping pattern. At the receiver part, an AMR Network module recognizes the modulation mode and sends the information to the demodulation module to obtain the data flow. In the experiment, the speed of modulation recognition and the tracking ability of the modulation hopping under typical working environments are verified and analysed. The experimental results and the analysis of the corresponding parameters verify the rationality and availability of the system design.
An Image Deblurring Method Based on Improved Dark Channel Prior

Abstract The traditional dark channel priori has been successfully applied to the single image deblurring problem. According to the characteristics of the dark channel priori, clear images are recovered. However, when the fuzzy image has significant noise pollution, the dark channel prior can not play a role in the fuzzy kernel estimation. The new image denoising method based on the rational order differential inherits the advantages of the total variation denoising method which greatly improves the high frequency part of the image and the fractional order differential denoising method which can well retain the texture details of the image. In this paper, the theory of rational order differential calculation is combined with the dark channel priori of fuzzy image, and an image deblurring method based on the improved dark channel priori is proposed. The specific work is as follows: combining the maximum posterior estimation algorithm and the rational order dark channel prior, a fuzzy image model is constructed; furthermore, the model is solved by using the semi quadratic splitting method. Finally, the multi-scale iterative framework is used to estimate the fuzzy kernel of the accurate image, and then the new non blind image deblurring algorithm can be used to solve the clear image. Experimental results show that the method is effective.
Penerapan model pembelajaran kooperatif tipe think pair share untuk meningkatkan pemahaman konsep kegiatan ekonomi dan jenis-jenis usaha di indonesia pada peserta didik kelas v sekolah dasar

The purpose of this thorough to improving the conceptual understanding of economic activities and kind of business in Indonesia for fifth grade students of SD N 03 Karanganyar in the 2018/2019 academic year using cooperative learning type Think Pair Share. This classroom action research consist of two cycles, each cycle includes two meeting. Subject and sorce data in this thorough were teacher and students of V SD N 03 Karanganyar. The thorough uses collection and teachnique through observation, interviews, test, and documentation. The data validity used in content validation. The data analysis teachnique used is an interactive analysis model. The conclusion in this research are cooperative learning type TPS that can improve the concept understanding of economic activities and types of business in Indonesiafor fifth grade students of SD N 03 Karanganyar in the academic year 2018/2019.
Blockchain-based Agricultural Supply Chain Management System in Malaysia

Blockchain is a revolutionary immutable, shared ledger technology for recording transactions between parties in a secure, chronological chain. By distributing Blockchain databases between multiple linked-nodes, it essentially does not require legal tender nor abiding legal framework by central bank regulation thus, removing intermediaries in transaction. This decentralized approach effectively made the transaction transparent to all connected nodes in the network. In this paper, a preliminary study on the implementation of smart contract in adoption of Blockchain based system will be discussed focusing on agricultural sector in supply chain system, to prevent fraud, track and to verify that by applying smart contract, the state of an event in the supply chain transaction can be autonomously execute when specific rule are meet. A proposed solution is to embed Blockchain technology into agricultural supply chain management system with digital authentication and tagging function to track product’s record of ownership and location tracking in Malaysia perspective.
Research on Laser Distance Measurement Technology for Remaining Volume of Large Open-pit Material Yard

Abstract In view of the problems of low volume calculation accuracy, long data acquisition period, and inability to uniformly measure and calculate the storage yard type when measuring the storage of large yards using volume and density conversion, a high-speed laser scanner, distance sensor, angle sensor, and pitch sensor are proposed Integrated technology, quickly obtain the three-dimensional coordinate data of the yard, and derive a unified three-dimensional coordinate solution algorithm, establish a three-dimensional coordinate system of the yard space to solve the three-dimensional coordinates of the yard, and accurately calculate the storage volume of the yard. At the same time, the system construction architecture, composition, data processing methods and issues to be considered are proposed. This method effectively solves the problems faced by the existing measurements in large yards, and improves the measurement efficiency, accuracy and safety. Under the test conditions, the absolute accuracy reaches 0.4%. Several project examples show that the method is simple, efficient and accurate. The ordinary measurement time of a 200m stockyard does not exceed 20mIn, and the repeated measurement error of the measurement volume is less than 0.7%. It has good application prospects and popularization value.
Neuronal Networks as a Way to introduce System Knowledge in Numerical Computations

The calculation of eigenvalues of a given set of linear equations is necessary for many numerical algorithms. If these calculations are implemented on personal computers or by high performance microcontrollers, powerful algorithms are available. However, most on-chip implementations especially in the field of low-cost smart sensors possess only limited computational performance. Thus, especially algorithms, which are based on an eigenvalue calculation, are mostly not implementable by low-end systems on a chip. In contrast, in this paper a neuronal network is used for the estimation of the eigenvalues, which leads to a simplification of the implementation of numerical methods. The neuronal network calculates the eigenvalue problems by using a priori knowledge regarding the specific application. Thus, the presented approach enables the implementation of complex numerical algorithms by low-end hardware.
AN ANALYTIC METHOD TO ESTIMATE THE RESPONSE SPECTRUM OF THE SYNTHETIC RECORDS WITH EVOLUTIONARY SPECTRUM

In this paper, an analytical method have been proposed to find the median and other fractiles of the response spectrum of the records generated using Priestley’s evolutionary model. The response spect
Classification models for data fusion in human activity recognition

In this paper, we present alternative deep learning architectures that perform data fusion, more specifically, feature-level fusion in the context of human activity recognition. The proposed architectures combine statistical features from the time-domain and features extracted automatically with stacked convolutional layers. The power of these architectures relies on the fact that they can be fed by various sources of data (e.g., time series, images). Additionally, we exploited the publicly available Mobile Health dataset to assess the performances of the proposed architectures. The results obtained show that the architectures are suitable for future use.
Pictorial usability inventory (PUI)

The Pictorial Usability Inventory (PUI) is an image-based instrument that was designed for the assessment of perceived usability of smartphone apps. The aim was to provide users with an easy and intuitive access to the evaluation of perceived usability. PUI was developed in several iterations using a user-centred approach. The pictorial items were based on selected items of various standardized usability questionnaires. A pilot study with 83 participants was conducted online to test the psychometric properties of the newly developed instrument. The factor structure, reliability, convergent, divergent and criterion-related validity, and sensitivity of the instrument were evaluated. Furthermore, respondent motivation, questionnaire preference, and questionnaire completion time were assessed. Participants interacted first with a prototype of the app in which the product-inherent usability was manipulated on two levels. The manipulation was implemented using a between-subjects design. Afterwards, participants completed the PUI and further verbal instruments to evaluate usability, as well as further questionnaires to determine the different forms of validity. First results indicate good psychometric qualities of the scale and an increased motivation among participants when completing the questionnaire. Nevertheless, 60% of the participants preferred the verbal scales. In addition, questionnaire completion time was longer for verbal scales. Some ideas for the further development of the PUI are discussed.
Optimizing Hyper-Phrase Queries

A hyper-phrase query (HPQ) consists of a sequence of phrase sets. Such queries naturally arise when attempting to spot knowledge graph (KG) facts or sets of KG facts in large document collections to establish their provenance. Our approach addresses this challenge by proposing query operators to detect text regions in documents that correspond to the HPQ as combinations of n-grams and skip-grams. The optimization lies in identifying the most cost-efficient order of query operators that can be executed to identify the text regions containing the HPQ. We show the efficiency of our optimizations on spotting facts from Wikidata in document collections amounting to more than thirty million documents.