Reviewerly/exHarmony huggingface dataset:
Multi-layer Attention Aggregation in Deep Neural Network

Convolutional neural networks have achieved significant successes in image classification recently due to its high capacity in learning discriminative features. In this work, we propose Multi-layer attention aggregation(MAA) model, a convolutional architecture using attention mechanism and global aggregation module iteratively. By merging attention-aware features from every convolutional stage, MAA improves the performance of image classification. Specifically, the proposed MAA model can be applied to state-of-the-art convolutional architectures, such as ResNet, and improve its performance by increasing 30% computational cost. Furthermore, we also employ ArcFace loss in the training process to improve the performance of image classification. Applying the proposed method on ResNet, our MAA model achieves higher image classification performance including on standard benchmarks of Google-Landmarks dataset, CIFAR-10 and CIFAR-100 dataset. Note that, our method achieves 0.68% top-1 accuracy improvement on Google-Landmarks dataset, 2.27% top-1 accuracy improvement on CIFAR-100 and 1.14% top-1 accuracy improvement on CIFAR-10.
Sample Projection Watermarking Improvement Using the Increasing of Samples in Wavelet Domain

In this paper, a watermarking technique is presented for digital images based on discrete wavelet transform (DWT) and genetic algorithm (GA). In this technique, in order to achieve greater capacity, the watermark is embedded in two points of each block. The angles of code lines and the selection of samples are determined using the GA algorithm in order to balance the imperceptibility and watermark stability against attacks. In similar methods with the proposed model, the angle value for embedding is considered a constant value. In fact, achieving the high resistance and capacity along with very good image quality is the main goal of this paper. The proposed method is performed on different images in grayscale and system performance is evaluated by the peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM), bit error rate (BER), watermark-to-noise ratio (WNR) and signal-to-noise ratio (SNR). The results show the better performance than different similar approaches.
A Novel NLP Application to Automatically Generate Text Extraction Concepts from Textual Descriptions

Text summarization has become a sophisticated approach for the quick searching, automatic sorting, abstract generating etc., to the large amount of data. The involvement of complete study of passage and extra time is needed to generate the essence of any content. Subsequently, Natural Language Processing is an information extraction approach to automatically extract the artifacts from the textual descriptions. Moreover, NLP is often applied to generate the various element of concerns like essential terms, class models, test cases from the initial Textual descriptions. However, it is usually required to study complete passage to extract relevant information from textual content that makes this process time consuming. This research article proposed a novel and fully automatic NLP methodology to generate crux from content. As a part of research, a tool Efficient Text Summary from Text (ETST) is developed. Research authentication is achieved through the implementation of two state-of-the-art case studies. The experimental outcome proved that our suggested Natural Language Processing methodology is novel and fully automatic and is also useful for the future researchers of this domain.
Exploration of the Big Data Applications Under the Background of “Internet+”

With the rapid development of the Internet technologies, the “Internet+” has become an important driving force for the transformation and upgrading of various industries. Driven by the new situations of the “Internet+”, the big data has been rapidly infiltrated and applied to many fields and industries, playing a huge role in improving the working efficiency and creating the business values for enterprises. This article first describes the characteristics of the “Internet+” and the big data, and then explores the application of the big data in several main industries. On this basis, it explores the innovation of the Internet + Financial mode based on the big data, and the big data provides the environment and space for the financial model innovation. Finally, the paper discusses the innovation strategies for the financial model under the background of the big data.
Using Personas in the Design Process. Towards the Development of Green Product Personality for In-Car User Interfaces

Abstract The desire to combine advanced user-friendly interfaces with a product personality communicating environmental friendliness to customers poses new challenges for car interior designers, as little research has been carried out in this field to date. In this paper, the creation of three personas aimed at defining key German car users with pro-environmental behaviour is presented. After collecting ethnographic data of potential drivers through literature review, information about generation and Euro car segment led to the definition of three key user groups. The resulting personas were applied to determine the most important interaction points in car interior. Finally, present design cues of eco-friendly product personality developed in the field of automotive design were explored. Our work presents three strategic directions for the design development of future in-car user interfaces named as a) foster multimodal mobility; b) emphasize the interlinkage economy - sustainable driving; and c) highlight new technological developments. The presented results are meant as an impulse for developers to fit the needs of green customers and drivers when designing user-friendly HMI components.
Development and Usage of a Large-Scale Model to Learn Structural and Functional Aspects for Steel Bridges

To provide opportunities to learn about the structural and functional aspects of civil engineering structures, the “Infrastructure Museum” has been developed by the Center for Infrastructure Asset Management Technology and Research (CIAM) in Gifu University. The “Infrastructure Museum” consists of four large scale models. The models can be utilized for maintenance engineer education and also extend for usage of verification of bridge inspection technologies such as non-destructive testing and newly developing robotic technologies. This paper presents the concepts and expected usage of the “Steel Bridge Model” which constructed as one of the large-scale models of the museum.
On the adaptability of ensemble methods for distributed classification systems: A comparative analysis

In this work, a two-stage architecture is used to analyze the information collected from several sensors. The first stage makes classifications from partial information of the entire target (i.e. from different points of view or from different kind of measures) using a simple artificial neural network as a classifier. In addition, the second stage aggregates all the estimations given by the ensemble in order to obtain the final classification. Four different ensembles methods are compared in the second stage: artificial neural network, plurality majority, basic weighted majority, and stochastic weighted majority. However, not only reliability is an important factor but also adaptation is critical when the ensemble is working in changing environments. Therefore, the artificial neural network and the plurality majority algorithm are compared against our two proposed adaptive algorithms. Unlike artificial neural network, majority methods do not require previous training. The effects of improving the first stage and how the system behaves when different perturbations are presented have been measured. Results have been obtained from two applications: a realistic one and another simpler one, with more training examples for a more accurate comparison. These results show that artificial neural network is the most accurate proposal, whereas the most innovative proposed stochastic weighted voting is the most adaptive one.
Comparison of multimedia communications QoE models by Bayesian networks and Bayesian statistics: a case study

This paper presents a comparison of Bayesian Network (BN) and Bayesian Statistics (BS) modeling for QoE (Quality of Experience) estimation and prediction in multimedia communications, with special attention to prediction. As an example of the comparison, we employ a haptic-audiovisual interactive communication system with guaranteed bandwidth. The QoE measure adopted here is subjects’ overall satisfaction (average score) of performing an interactive task under conditions specified by combinations of the video guaranteed bandwidth, video encoding bit rate, receiver’s playout buffering time and gender of each subject. For BN modeling, we utilize an R package bnlearn and create a discrete BN model of a directed acyclic graph with four nodes corresponding to the four parameters. For BS modeling, we build (1) a Bayesian hierarchical regression model with covariates of the four parameters and random effect terms reflecting users’ individualities and gender, and (2) a Bayesian regression model without the random effect terms. The two BS models are analyzed by Markov chain Monte Carlo (MCMC) simulation with the software OpenBUGS. We then find that the BN and BS models provide approximately the same estimates of the QoE measure. Regarding the prediction, however, the BS model with random effect terms outperforms the BN model and BS model without random effect terms. We thus learn that the random effect terms enhance the ability of Bayesian approaches in QoE prediction.
Action Recognition Using Temporal Partitioning of Motion Information

In this paper, a temporal representation method for video action recognition is proposed. Since the intrinsic property of the video stream is its temporal variation, the optical flow images are calculated to show the short-term motion. In order to avoid training a complex network from scratch, a pre-trained network is utilized for frame-level feature extraction. For video level representation, pyramidal pooled time series is considered since the short-term variation can be captured in order to represent fixed-size long-term features. In addition, to solve the information missing problem through long videos, a simple video level representation using temporal partitioning is proposed too. The experimental results of the proposed method illustrates an acceptable performance both in first and third-person action recognition.
Direct Solution of Multiscale Compressed Block Decomposition with Fast Adaptive Cross Sampling- Based Recompression Technology

In this paper, a new fast recompression technology is proposed based on the fast adaptive cross sampling method (FACS). Then this technology is applied with the multiscale compressed block decomposition (MSCBD) method to accelerate the direct solutions of electrically large problems. This technology is adaptive and purely algebraic. What's more, it can be applied into many other methods. The simulation results show the efficiency and accuracy of our method.
Path Planning for Swarm AUV Visiting Communication Node

This paper proposes a method for path planning of an underwater robot swarm. The method is based on biological inspired neural network to plan path between robots and communication nodes. The robot swarm is used to search wild sea area. To solve the long distance communication problem, we deploy some communication nodes ahead, forming a communication network under the water. The robots visit the nodes to communicate. With this method, robots can also avoid obstacles in real time. Firstly, put the landscape into grid map. Then build biologically inspired neural network based on the grid map. The node attracts the robots and the obstacles reject the robots through neural activity. At last, robots plan their path by the activity with a steepest gradient descent rule. Simulation result shows the method may lose in local optimum, so we improve the method to avoid repetitive path. The results show that the improvement effective for path planning.
Review of Data Protection Technique

The increase use of data in the businesses have proved to be profitable but also provides serious threats in form of data leakage, protection and infringement. The company provides sensitive data to the employees but the data is most likely to be leaked. Data protection must be provided to all forms of data. Data protection should be provided in such a way that it strikes balance between data allowance and data rights to the user as well as owner. The level or extent of protection varies with methods used. In this review paper, numerous methods for protecting data from leakage, unauthorized access, unfair use etc. are compared.
SCSE: Boosting Symbolic Execution via State Concretization

Symbolic execution is capable of automatically generating tests that achieve high coverage. However, its practical use is limited by the scalability problem. To mitigate it, this paper proposes State Concretization based Symbolic Execution (SCSE). SCSE speeds up symbolic execution via state concretization. Specifically, by introducing a concrete store, our approach avoids invoking the constraint solver to check path feasibility at conditional instructions. Intuitively, there is no need to check the feasibility of a path along a concrete execution since it is always feasible. With state concretization, the number of solver queries greatly decreases, thus improving the efficiency of symbolic execution. Through experimental evaluation on real programs, we show that state concretization helps to speed up symbolic execution significantly.
Information aggregation of Qin Huai coloured-lanterns

Abstract The scattered information concerning Qin Huai coloured-lanterns was integrated from literature resources through information digging, organization and association. Firstly, The topic types about Qin Huai coloured-lanterns from literature resources were clarified on the software Cite Space and through card sorting. Then associations and semantic relation networks were constructed together with the semantic relations among topics. A literature resource information base on Qin Huai coloured-lanterns was established with the help of Ontopia topic maps. This study offers some convenient research methods for coloured-lantern researches and fans, improves the utilization rate of coloured-lantern resources, and promotes the transmission and sharing of coloured-lantern information. Finally, the usability of coloured-lantern information base was confirmed by designing a case study. The research methods and flowchart used here can be adopted by researchers from other fields.
Rainfall Scenario of West Nusa Tenggara in 2040 Based on CCAM RCP 4.5

Abstract Climate change is one of the global issues with a broad impact to society. Because of its importance, there is a need to build a prediction system that can support mitigation, adaptation or prevention of climate change. West Nusa Tenggara (NTB) is an area where most of the population relies on agriculture for their livelihood so that the availability of rainwater becomes very important for daily survival. In this research, rainfall in NTB was predicted by CCAM ( Conformal Cubic Atmospheric Model ) model based on RCP ( Recent Concentration Pathways ) 4.5 scenario. Based on the verification results, the CCAM model was proven to perform fairly well in estimating rainfall in NTB in 2014-2016. This is evidenced by the high correlation coefficient between CCAM and observed rainfall data in several locations such as Kuripan, Kumbe, and Gunung Sari which reached 0.57, 0.51, and 0.51 respectively. In January 2040 places such as Bima, Sumbawa Besar and parts of Lombok Island are predicted to have a decrease in rainfall when compared to that in January 2018. The decrease ranges from 40-120 mm in one month. While in Kumbe and Kuripan, rainfall is predicted to increase, with an increase of about 0-40 mm in one month. In August 2040 rainfall is predicted to generally rise in NTB with an increase of up to 40 mm in one month.
Application of Physical Interactive Mixed Reality System Based on MLAT in the Field of Stage Performance

At present, in some real-time occasion like as stage performances, mixed reality effects are often generated by a mechanical means, or only between physical objects and virtual objects. This will lead to many uncontrollable shortcomings. in order to overcome the above disadvantage, the authors developed a mixed reality system for physical interaction. The experiment unfolds between the experiencer and the controllable Four-axis unmanned aerial vehicle with some performance props. The first step of the study is to build the stereoscopic scene through the camera calibration, the target coordinates in image are obtained by infrared sensors deployed on the experimenter and the aircraft respectively by Camshift algorithm and it can be transformed into the spatial coordinates by projection transformation. And the second step is based on different preset working modes and the coordinate relationship expressions between them that have been configured, with the action of the experimenter, Ant Colony Algorithm is used to move the Four-axis UAV to a certain position, thereby realizing this kind of precisely controllable interaction between entities. The final experiment in this paper has proven that our work provided a great mixed reality effect for occasions such as stage performances.
Tutorial: Are You My Neighbor?

Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions have been found by carefully partitioning or filtering the search space in a way that avoids most unnecessary comparisons.
Information-theoretical approach to the many-particle hierarchy problem

We use the maximum entropy principle to circumvent the many-particle hierarchy problem that arises in conventional equation of motion techniques. Our efficient approach enables us to numerically determine the full density matrix of driven-dissipative quantum many-particle systems and gain access to all relevant expectation values and the full statistics and not only moments and correlation functions. We compare the maximum entropy method results with the numerically exact solution of the von Neumann-Lindblad equation for a four-level system resonantly coupled to one cavity mode and demonstrate excellent agreement in terms of entropy, mean photon number, autocorrelation function, and photon statistics. Moreover, we show that our approach can be used as a tool for learning about the relevant processes of quantum systems.
Embedding the Digital Watermarks into FPGA-Projects Containing the Adaptive Logic Modules

The given paper deals with the problem of embedding the digital watermarks into program code of the FPGA chips. A digital watermark gives the possibility to place the extra information hidden from an outside surveillance in the program code FPGA. This can provide the integrity monitoring and monitoring of usage of FPGA program code processes. The matter is that the existing methods of the digital watermark embedding are focused on FPGA, in which basic logic elements contain only a programmable calculating unit LUT and programmable flip-flop. The adaptation of method of the digital watermark embedding to FPGA chips, which have dedicated hard full adders in the composition of basic logic elements, is offered. The analysis of the proposed method implementation has been carried out. It turns out that the usage of the offered method increases the embedded digital watermark size.
Effective vision- and SoC-based fall detection for the elderly

In this paper, we propose a novel Vision- and system on chip (SoC)- based fall detection method for the elderly. Once, a fall event is detected, an alarm signal is immediately sent out to query first aid to the elderly. Our novel fall detection method consists of five effective steps: checking whether the light condition has been stabilized, GMM-based background and foreground estimation, a new strategy to solve the foreground lag problem, solving the false fall detection problem when light comes from a neighboring room, as well as the fall detection determination and the general-purpose input/output based warning mechanism. Based on the test videos, the experiments have been carried out demonstrate that our proposed fall detection method can meet the real-time, low cost, and high accuracy demands.
Information Extraction Model for Afan Oromo News Text

Information Extraction (IE) concerned with the automatic extraction of facts from text and stores them in a database for easy use and management of the data. As the first research work on IE from Afan Oromo text, we designed a model that deals with Infrastructure news domains in the Oromo language. The proposed model has document preprocessing, learning and extraction and post processing as its main components. In this work recall, precision and F-measure are used as evaluation metrics for Afan Oromo Text Information Extraction (AOTIE). Being trained and tested for the dataset of size 3169 tokens, AOTIE performed 79.5% precision, 80.5% recall and 80% F-measure. These results are used as a baseline to experiment on AOTIE. We set up two main experimentation scenarios to experiment on AOTIE. The first scenario is conducted by developing a gazetteer. The second scenario is aimed at observing the influence of Afan Oromo grammatical structure. Both scenarios showed that, the performance of AOTIE is mostly dependent on grammatical structure of Afan Oromo.
<i>Research Notes</i> — Software Process Selection Based Upon Abstract Machines for Slow Intelligence Systems

The abstract-machine model was developed by Chang to formalize the decision cycles of slow intelligence systems. It turns out that the selection of software process can also be regarded as a slow intelligence system. In this paper, we formalize abstract-machine prototypes for different software process models (SPMs) such as waterfall model, incremental model, spiral model, extreme programming model and scrum model. A Software Process Generator (SPG) was implemented to generate SPMs based upon design considerations. Initial evaluation by undergraduate students using SPG to learn software processes suggests further improvements to make it a useful learning tool.
SVR-based collaborative filtering algorithm: From dense to sparse

Recommender systems have been very important components to prevent people from dwelling in the overwhelming information. In this paper we analyze the difference between item-based recommendation algorithms and SVR-based collaborative filtering algorithms, and it can be found that item-based method performs much better while the data is not sparse significantly, and SVR-based method performs better while the data is dense and small. On this premise we propose a method that can combine the advantages of these two methods by predicting a small part of ratings using SVR method firstly and then predicting the rest of ratings using the item-based algorithm, which can solve the problem of data sparsity to certain extend. Finally, we evaluate our results compared with the benchmark on different datasets and prove our method’s advantages.