Reviewerly/exHarmony huggingface dataset:
Detecting Fake Points of Interest from Location Data

The pervasiveness of GPS-enabled mobile devices and the widespread use of location-based services have resulted in the generation of massive amounts of geo-tagged data. In recent times, the data analysis now has access to more sources, including reviews, news, and images, which also raises questions about the reliability of Point-of-Interest (POI) data sources. While previous research attempted to detect fake POI data through various security mechanisms, the current work attempts to capture the fake POI data in a much simpler way. The proposed work is focused on supervised learning methods and their capability to find hidden patterns in location-based data. The ground truth labels are obtained through real-world data, and the fake data is generated using an API, so we get a dataset with both the real and fake labels on the location data. The objective is to predict the truth about a POI using the Multi-Layer Perceptron (MLP) method. In the proposed work, MLP based on data classification technique is used to classify location data accurately. The proposed method is compared with traditional classification and robust and recent deep neural methods. The results show that the proposed method is better than the baseline methods.
Modeling the Gilbert Model for Communication Channels Based on Artificial Intellige

In this article, The Gilbert model of communication channel is modeled by using artificial intelligence methods. As well as, important aspects of the artificial intelligence to the Gilbert model are given. Furthermore, related research works are analyzed in this research work.
The use of mobile learning applications in listening classes among Indonesian EFL students across gender

Mobile learning takes the form of applications that can be accessed from the internet, making it possible for students to have more access to listening materials. However, research about mobile learning applications in listening courses at the university level is still limited. This study aims to identify EFL students’ perceptions and preferences regarding mobile learning applications in listening classes across gender. Open-ended questionnaires were distributed through Google Forms to 120 ELT students in a public university in Indonesia to gain relevant information. The results showed that the male students tended to use more than one mobile learning application than the female students based on their preferences of video-based language learning as a learning tool. The study also revealed that the male and female students positively perceived the use of mobile learning applications in listening classes.
Machine learning improves satellite imagery analysis of North Korean nuclear activity

Much of the available data on nuclear activities in North Korea comes from Earth observation satellites. However, with increased coverage and higher resolution satellite images, analysts face challenges in systematically analyzing large-scale image datasets. In this case study, we report on an automated analysis of over 800 satellite images that tracked vehicular traffic as a proxy for activity levels at the core North Korean nuclear facilities, as well as international trade at border crossings between China and North Korea. Geospatial artificial intelligence and automated image analysis improves the efficiency of collecting analytical metrics, yielding results more quickly and of higher quality.
Model Training

The chapter provides an approach and issues for model-training using Logistic Regression. (1) Regression—key model qualities plus i) options and settings, and ii) outputs to be expected/demanded. (2) Variable selection—i) criteria; ii) automation; iii) stepwise review; iv) constraining betas, where coefficients do not make sense; v) stepping by Gini, model pruning. (3) Correlation checks—i) multicollinearity—checks of variance inflation factors; ii) correlations—further checks to guard against the inclusion of highly correlated variables. (4) Blockwise variable selection—treatment in groups: i) variable reduction; ii) staged, or hierarchical regression; iii) embedded, model outputs as predictors; iv) ensemble, using outputs of other models. (5) Multi-model comparisons—Lorenz curves and strategy curves, should choices not be clear. (6) Calibration—i) simple adjustment by a constant; ii) piecewise, varying adjustments by the prediction; iii) score and points—adjusting the final score or constituent points; iv) MAPA, for more complex situations
Speech Recognition Using Data Augmentation

In this paper, a neural network is used for identifying emotions in speech with Toronto Emotional Speech Set(TESS), Surrey Audio-Visual Expressed Emotion(SAVEE), Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS) datasets. The main aim is to focus on recognizing the silent characteristic and the useful features of audio signals. Convolutional neural networks is used for extracting the important features and reduce the spectral variation that is in signals and one of the neural techniques as recurrent neural networks for learning the temporal relationships and finding the difference of alphabets. So, accuracies calculated by this model using the relu activation function have better impact on our datasets.
The 8th Annual International Conference (AIC) 2018 on Science and Engineering

Preface the 8 th AIC 2018 The 8th AIC 2018 International Conference is is hosted by Syiah Kuala University (recognizably abbreviated as UNSYIAH), the largest and the oldest national university in Aceh. The AIC is an annual international conference carried out regularly by Syiah Kuala University since 2011. The 8th AIC 2018 International Conference held on September 12-14, 2018, in Banda Aceh, Aceh Province, Indonesia. Under the theme of the conference “Connecting the World through innovation and suistainable development”, this conference features a rich program, including Syiah Kuala University Innovation Expo 2018, a keynote speech delivered by Prof. Dr. Ahmad Fauzi Ismail, Universitas Teknologi Malaysia, Malaysia, and two invited speeches by Prof. Dr. Azman Bin Abd Ghani, University of Malaya, and Prof. Marwan, Syiah Kuala University. The 8th AIC on Science and Engineering 2018 offers a special opportunity to bring together professors, researchers and scholars around the globe, and serves as a platform to deliver innovative research results and latest trends and development in the fields of sciences and engineering. The scientific papers published in these proceeding have been revised and approved by the scientific committee of the 8th AIC on Science and Engineering 2018. All the papers exhibit clear, concise, and precise expositions that appeal to a broad international readership interested in Science and Engineering. To all members of the organizing committees, the international scientific committee, the reviewers, and the collaboration partners, we would like to thank all of them for their tremendous efforts to organize this conference successfully. We want to thank the organizing committee for having a successful conference, the UNSYIAH and Sponsors supporting the conference and we hope that all the attendees benefit from this conference. Best Regards, Dr. Heru Fahlevi General Chair of AIC-SE 2018
Global Correlation and Local Geometric Information Coupled Channel Contrast Learning for Thyroid Nodule Risk Stratification

Thyroid nodule risk stratification based on ultra-sound images is vital for follow-up clinical treatment. Due to the complexity of the inter-risk stratification difference, experienced physicians are required to comprehensively analyze all ultrasound signs of the thyroid nodule to diagnose the corresponding risk stratification the thyroid nodule should belong to, however, which is often labor-intensive, subjective, and unstable. To this end, we propose a global correlation and local geometric information coupled channel contrast learning network for thyroid nodule risk stratification based on ultrasound images. Specifically, a channel contrast learning module by combining the contrastive learning with a novel cross-class interaction strategy is proposed to obtain the discriminative feature for different risk stratification levels. Furthermore, multiple ultrasound signs may be observed simultaneously in the same risk stratification level. To introduce the correlation among different ultrasound signs into the feature, a global correlation learning module is proposed by the spectral decomposition of the channel correlation matrix. Additionally, some ultrasound signs used as the basis for judging risk stratification are local characteristics. Therefore, a local geometry learning module is proposed using gradient matrix to model the local information of ultrasound signs to further strengthen the feature. Extensive experiments and comprehensive evaluations confirm that the proposed method achieves superior performance and presents to be promising in thyroid nodule risk stratification.
Improvement of Online Journal System (OJS) Information to Optimize Journal Accreditation Score

Scientific journal is an indicator of the progress of an educational institution. Publication in Scientific Journals is a tool to build the reputation of lecturers, researchers, teachers, and universities. However, the management of journals in Indonesia still does not consider the importance of indexing as a way of global dissemination. This is evident from the data showing that of the total journals registered in Indonesia, only 5.17% have been accredited. Department of Environmental Engineering, Faculty of Mineral Technology, UPN "Veteran" Yogyakarta has a Scientific Journal called Jurnal Ilmiah Lingkungan Kebumian (JILK). JILK is a forum for writing, scientific work, and research related to the environmental management of the mining industry, environmental management of petroleum and geothermal industry, regional development and disaster mitigation and relevant fields of environmental science and technology studies. Journal accreditation can expand JILK connectivity to improve the quality of publications. The main objectives of this research are to optimize and improve the management of JILK to get maximum accreditation score. There are several steps to conduct this research including identification of the requirement for journal accreditation, preliminary assessment, evaluation of journal management, workshop and benchmarking, implementation of evaluation results, and submit the journal accreditation. The methodology that used for this research are discussion with journal expert and benchmarking with another scientific journal. From the implementation of preliminary assessment from internal editorial team and evaluation from external journal expert, JILK can increase the accreditation score from 59 to 67.
Access Control System based on Secret Sharing Scheme with Secure Web Database and SHA-3 Password Authentication

An access control system based on a secret sharing scheme that uses fingerprint authentication requires increased security for the database and its connections. Attacks that often occur on RFID storage and web database systems are card copying, data theft and leaks, and man-in-the-middle attacks. The AES-256 algorithm with an SHA-3 password key is implemented in web database encryption. The SSL VPN is also applied for securing the connection by internet lines. It is experimentally proven that the access control system can overcome the man-in-the-middle attack and the data theft. The success rate for login is up to 65&#x0025;. The average time required for the enrollment process is 29.64 seconds while for the login process is 30.79 seconds.
Early Warning Radar intelligence quality evaluation with Fuzzy neural network

This paper presents a novel approach based on fuzzy BP neural network for evaluating the early warning radar(EWR) intelligence quality. The evaluation indexes system of the EWR intelligence quality is established firstly after analyzing and refining the main production process, the main influencing factors and the characteristics of EWR intelligence. And the factor set, comment set and membership matrix are then structured according to the indexes system. Finally, a Fuzzy BP neural network is designed to evaluate the EWR intelligence quality. The experiment shows that the accuracy and stability of the evaluation can be improved by using fuzzy BP neural network to evaluate EWR intelligence quality.
Wide High-resolution Projection System Using High-speed Gaze Point Estimation

Although high-resolution and wide-area display holds promise for high immersion feelings, conventional projection methods have difficulty to manage both of them. We propose gaze oriented projection system using high-speed gaze point estimation and high-resolution tracking projection. Experimental results showed millisecond-order speed, sufficient accuracy, and wide-area of the gaze point estimation and low latency of the proposed system. We also showed two demonstrations: pseudo gaze-oriented view and user-demo without fixing the eye camera.
Monitoring physical parameters from wearable sensors for detection of cognitive decline in routine care

Abstract Background Wearable sensors offer the chance to measure activity and vital signs continuously and unobtrusively in elderly people living in domesticity or stationary care. To derive meaningful features from raw data and transform sensor data into clinically relevant information it is necessary to develop efficient algorithms and validate the results, e.g. with synchronized observational data. Only then sensor data bear the potential to detect functional changes or specific behaviors that are linked to Alzheimer’s disease and allow the complementation of the diagnostic process with sensor data. Method International data point to a potential role of serious games applications to detect early signs of cognitive decline. Along this line a study of the DZNE uses a comprehensive memory paradigm examining age sensitive decline in a citizen science study performed with digital platforms. Complementary approaches include non‐intrusive assessment of everyday behaviour. We performed several studies to examine the feasibility and potential of mobile sensors in detection of challenging behaviors, spatial disorientation and gait changes to classify people with MCI or dementia and detect situations with need for help. Result In a study with dementia‐patients that were wearing mobile sensors continuously over several weeks, we could show that multimodal sensors can be applied long‐term in the setting of PwD living in stationary care. Accelerometry can be used to detect behavioral patterns in dementia patients. We extended the set of sensor data with vital signs (e.g. heart rate, electrodermal activity) and indoor tracking to detect spatial disorientation in an ongoing wayfinding study to classify MCI /dementia based on sensor data. Conclusion Mobile sensors will be omnipresent in the following years. With complex algorithms we were able to derive features that allow information about crucial personal conditions with respect to mental health. Digital markers including sensor data won’t be a stand‐alone diagnostic tool and need to be embedded in an innovative diagnostic pathway.
Torque control of new energy four wheel hub motor based on distribution algorithm

Due to the nonlinear, strong coupling and uncertain parameters of the new energy four-wheel hub motor, it is more difficult to control the torque of the motor. In order to solve this problem, a torque control method of the new energy four-wheel hub motor based on the distribution algorithm is proposed. The dynamic model of the new energy four-wheel hub motor is established, and the unmeasurable flux, electric power and other state variables in the motor model are derived according to the degree of freedom of the body. The whole four-wheel hub motor is taken as the research object, and the optimal efficiency of the drive system is taken as the goal, and the distribution algorithm is used to control the electromagnetic torque of the motor. The simulation results show that after the torque control of new energy four wheel hub motor, the driving range of the vehicle is longer, the amplitude of stator flux changes little, the stator current changes and the stability of motor speed are good, and the torque control effect is better.
Cross-Modal Retrieval Based on Full-Modal Autoencoder with Generative Adversarial Mechanism

<p indent="0mm">Existing cross-modal retrieval methods based on generative adversarial networks can’t fully explore the inter-modality invariance. Aiming to solve the problem, a novel cross-modal retrieval method based on full-modal autoencoder with generative adversarial mechanism is proposed. Two parallel full- modal autoencoders are introduced to embed samples of different modalities into a common space. Each full-modal autoencoder not only reconstructs the feature representation of its own modality, but also reconstructs the feature representation of the other modality. A classifier is designed to predict the categories of the embedding features in the common space, which aims to preserve the semantic discriminative information of samples. Three discriminators are designed to determine the modal categories of the input features, respectively, and these three discriminators work cooperatively to fully explore the inter-modality invariance. The mean average precision (mAP) is used to evaluate the accuracy of cross-modal retrieval and extensive experiments are conducted on three public datasets which are Pascal Sentence, Wikipedia and NUS-WIDE-10k. Compared to ten state-of-the-art cross-modal retrieval methods including traditional methods and deep learning methods, the mAP of the proposed method on the three datasets improves at least 4.8%, 1.4% and 1.1% on the three datasets respectively. The experimental results prove the effectiveness of the proposed method.
Building a Framework for the Future of the Record of Science

The APE Conferences aim to provide a better understanding of scholarly communication and the role of information in science, education and society. They encourage debate about the future of value-added scientific publishing, information dissemination and access to scientific results and offer an independent forum for open discourse.
An Embarrassingly Simple Approach to Discrete Supervised Hashing

Prior hashing works typically learn a projection function from high-dimensional visual feature space to low-dimensional latent space. However, such a projection function remains several crucial bottlenecks: 1) information loss and coding redundancy are inevitable; 2) the available information of semantic labels is not well-explored; 3) the learned latent embedding lacks explicit semantic meaning. To overcome these limitations, we propose a novel supervised Discrete Auto-Encoder Hashing (DAEH) framework, in which a linear auto-encoder can effectively project the semantic labels of images into a latent representation space. Instead of using the visual feature projection, the proposed DAEH framework skillfully explores the semantic information of supervised labels to refine the latent feature embedding and further optimizes hashing function. Meanwhile, we reformulate the objective and relax the discrete constraints for the binary optimization problem. Extensive experiments on Caltech-256, CIFAR-10, and MNIST datasets demonstrate that our method can outperform the state-of-the-art hashing baselines.
A High Energy Efficiency and Low Resource Consumption FPGA Accelerator for Convolutional Neural Network

With the rapid development of convolutional neural networks (CNN), the design of hardware accelerators for CNN calculations has become a major focus of current research. However, power consumption and resource consumption are two main limits for the application of CNN hardware accelerators. In response to these problems, a FPGA-based implementation of a CNN hardware accelerator with high energy efficiency and low resource consumption is proposed. The implementation includes zero padding, convolution, batch normalization, ReLU6, and MaxPooling. The computing performance of the network is improved by using pipeline design and layer fusion. The LeNet-5 network is to test the implementation on the Xilinx Virtex VC-707 FPGA board. The experimental result shows that when inferring the MNIST test set, the energy efficiency is 843.91 times and 228.35 times that of the CPU and GPU, respectively.
Cavity Protected Multifrequency Polaritons in a Cold Atomic System

We generate multifrequency polaritons in a cold atom optical cavity interface by modulating the emitters frequency. The polaritons coherence time is protected by the cavity which prevents the mixing with dark states.
NON-CLASSICAL SUBJECT OF VISION. PART II

The article is a continuation of the previous article Non-Classical Subject of Vision. Part I and is devoted to the analysis of the eventivity of a non-classical subject. The analysis of non-classical subjectivity in the article is based on the three-part mechanism of the power of distance, power of gaze and power of memory proposed by W. Benjamin. The concept of the image as a mediator through which the subject regains the lost distance with the world is discussed. The article deals with the concepts of the non-classical subject of visuality by J.-P. Sartre and G. Didi-Huberman as different types of transformation of the power of gaze and the role of memory in non-classical vision. Important elements of the concept of «scanty image» by J.-P. Sartre are analyzed: criticism of the naive understanding of the immanence of consciousness and the world, criticism of images as a weak copy of the object of observation, the development of a specific givenness of a thing in an image through its distant present absence. It is shown that the theory of «scanty image» breaks the unreal objects of visual consciousness and the sensually perceived world into two poles that are not connected with each other. Therefore, in Sartre’s concept, the relationship with the world — both in visual and sensory comprehension of reality, turns out to be problematic. In the theory of G. Didi-Huberman, built on the reorganization of the understanding of the aura in technically reproducible art, a deeper understanding of the image is given. The presence as well as the absence of things of the world do not appear as separate from each other, but turn out to be the dialectical unity of the game of near and far (Fort-Da). The article discusses this dialectical understanding of the relationship between the man and the world, which acts as an incessant rhythm of approaching and removing a visible object. In this eventful space of a vision (D. Joselit) turns a thing into a hybrid object, and the person appears as a flickering subject of vision.
Financial policy analysis based on TVP-FAVAR numerical simulation model under the background of blockchain technology application

Under the background of blockchain, using TVP-FAVAR mode to analyze the coordination and linkage between China’s monetary policy, macro-prudential policy and fiscal policy in recent years. The research results show that: (1) Both monetary policy and macro-prudential policy have maintained a positive and stable state for a long time. The implementation of macro-prudential policy and tight monetary policy are easier to implement together, and there is a transmission lag effect with loose monetary policy. (2) The implementation of macro-prudential policy and the tightening of monetary policy are easier to implement, and there is a transmission lag effect with loose monetary policy. (3) There is a relatively stable linkage effect between fiscal policy and the “Double-Pillar” policy, but compared with monetary policy, the transmission of macro-prudential policies is more obvious.