Reviewerly/exHarmony huggingface dataset:
One-Dimensional Deep Attention Convolution Network (ODACN) for Signals Classification

Handcraft features are commonly used for signal classification, which is a time-consuming feature engineering. In order to develop a general and robust feature learning method for radio signals, a novel One-dimensional Deep Attention Convolution Network (ODACN) is proposed to automatically extract discriminative features and classify various kinds of signals. First, one-dimensional (1-D) sparse filters are designed to learn hierarchical features of raw signals. Second, an attention layer is constructed to weight and assemble feature maps, to derive more context-relevant representation. By using simple 1-D filtering, ODACN is characteristic of less parameters and lower computation complexity than traditional Convolutional Neural Networks (CNNs). Moreover, feature attention can mimic a succession of partial glimpses of humans and focus on context parts of signals, thus helps in recognizing signals even at low Signal-to-Noise Ratio (SNR). Some experiments are taken to classify 31 kinds of signals with different modulation and channel coding types, and the results show that ODACN can achieve accurate classification of very similar signals, without any prior knowledge and manual operation.
Privacy Enhancement Scheme (PES) in a Blockchain-Edge Computing Environment

With the public key being the parameter users are mostly addressed by on blockchain network, an intruder can connect transactional patterns to the public key and make a probable revelation of the identity of the user. Due to the diversity in approaches in achieving privacy, integrating the principle of transparency in any blockchain - edge computing platform will present some structural security challenges. Thus, an attempt should be made to achieve confidentiality whilst not eliminating a key principle of blockchain - transparency. Based on elliptic curve cryptosystem (ECC), we present a privacy-aware scheme that preserves the privacy details of the user in a blockchain-edge computing environment based on a combination of randomly generated public keys and digital signatures. The resilience and practicality of our scheme were tested on AVISPA and NS2 respectively. The results indicated our scheme was robust against attacks, efficient and low on computation resources of edge devices.
VR Use in Online Learning for Higher Education in Indonesia

&lt;p&gt;This research leads to the object of learning in network-based learning programs application of the use of VR in online learning for students in Higher EducationThis research is located in the Education Technology study program, Universitas Negeri Jakarta, Indonesia. This study focuses on learning theories, learning methods and learning models used in technology to improve learning outcomes in class. This method used explanatory sequential mixed methods research. Field trials were conducted for students, with a total of 12 students and field trials would be conducted for students with a total of 30 people in 2018. Data collection through interviews of 15 students and 3 lecturers for 40 minutes. The results showed that using VR can foster motivation to learn to develop student skills in simulating learning models and learning can be efficient and effective. VR can also enhance students for teaching practices and train students to innovate learning models used in technology.&lt;/p&gt;
A Stroke-Based RNN for Writer-Independent Online Signature Verification

In the field of online handwritten signature verification, it is challenging to verify handwritten signature in a writer-independent scenario. In recent years, many researchers have been applying deep neural network methods to the signature verification task. However, these methods have not outperformed traditional methods, especially when the training samples are limited. In this paper, we propose a novel stroke-based bidirectional RNN architecture. The main idea is to split the signature into multiple patches using strokes. Concatenation of query and reference signature pairs are used as input. The proposed method uses two LSTM RNN networks to extract different features. The first one extracts the features of the strokes and the latter extracts the global features of the whole signatures. The results on the BiosecureID dataset demonstrate that our proposed method can reduce the EER by 33.05%, from 5.6% to 3.75% with fewer features and less training samples. Besides, we find that the proposed stroke based RNN network is 5x faster in training and testing time than Non stroke-based RNN network.
Using Adaboost and Stochastic gradient descent (SGD) Algorithms with R and Orange Software for Filtering E-mail Spam

With the increasing usage of electronic emails, the ratio of spam is increasing day by day. Thus, spam emails have become a major threat that lowers the usage of electronic emails as a way for communication. There are several machine learning techniques that provide email spam filtering methods, such as Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Decision tree (DT). This paper considers different machine learning techniques to filter spam emails, specifically Adaboost and Stochastic Gradient Descent (SGD). R tool was used for the pre-processing stage. Adaboost and SGD were implemented in Orange software for building the classifiers. Using Orange tool, the experimental results showed that the algorithms Adaboost and stochastic gradient descent (SGD) provided true positive value of 100 % and 98.1% respectively and false positive rates of 0.0% and 1.9% respectively. The good accuracy of these algorithms and the favorable results put them among the best choices of spam filtering methods.
ASBKS: Towards attribute set based keyword search over encrypted personal health records

With the growth of public demand for online access to health services, many efforts have been devoted to personal health records (PHR) in cloud computing. It enables patients to manage their personal health information (PHI) in cloud servers, which greatly facilitates the collection, access and sharing of PHI. Since cloud servers are not fully trusted, it is desirable that the PHI can be encrypted for privacy protection before uploaded to the cloud. Besides the privacy of PHI, fine-grained and flexible search control is also strongly desired for a secure PHR system. In this article, we first present attribute set based keyword search (ASBKS) which can realize fine-grained keyword search of encrypted PHR. Compared with the existing searchable encryption with access control, the proposed ASBKS can achieve more flexibility in user attributes organization and more efficiency in specifying policies. Furthermore, we present a hierarchical ASBKS scheme to improve scalability by extending ASBKS with a hierarchical structure of users. We implement our ASBKS scheme and the experimental results demonstrate that it is both efficient and flexible for encrypted PHR in cloud computing.
Pulsars detection by machine learning with very few features

ABSTRACT It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve the detection performance, input features into an ML model should be investigated specifically. In the existing pulsar detection researches based on ML methods, there are mainly two kinds of feature designs: the empirical features and statistical features. Due to the combinational effects from multiple features, however, there exist some redundancies and even irrelevant components in the available features, which can reduce the accuracy of a pulsar detection model. Therefore, it is essential to select a subset of relevant features from a set of available candidate features and known as feature selection. In this work, two feature selection algorithms –Grid Search (GS) and Recursive Feature Elimination (RFE) – are proposed to improve the detection performance by removing the redundant and irrelevant features. The algorithms were evaluated on the Southern High Time Resolution University survey (HTRU-S) with five pulsar detection models. The experimental results verify the effectiveness and efficiency of our proposed feature selection algorithms. By the GS, a model with only two features reach a recall rate as high as 99 per cent and a false positive rate (FPR) as low as 0.65 per cent; by the RFE, another model with only three features achieves a recall rate of 99 per cent and an FPR of 0.16 per cent in pulsar candidates classification. Furthermore, this work investigated the number of features required as well as the misclassified pulsars by our models.
Adversarial Learning for Joint Optimization of Depth and Ego-Motion

In recent years, supervised deep learning methods have shown a great promise in dense depth estimation. However, massive high-quality training data are expensive and impractical to acquire. Alternatively, self-supervised learning-based depth estimators can learn the latent transformation from monocular or binocular video sequences by minimizing the photometric warp error between consecutive frames, but they suffer from the scale ambiguity problem or have difficulty in estimating precise pose changes between frames. In this paper, we propose a joint self-supervised deep learning pipeline for depth and ego-motion estimation by employing the advantages of adversarial learning and joint optimization with spatial-temporal geometrical constraints. The stereo reconstruction error provides the spatial geometric constraint to estimate the absolute scale depth. Meanwhile, the depth map with an absolute scale and a pre-trained pose network serves as a good starting point for direct visual odometry (DVO). DVO optimization based on spatial geometric constraints can result in a fine-grained ego-motion estimation with the additional backpropagation signals provided to the depth estimation network. Finally, the spatial and temporal domain-based reconstructed views are concatenated, and the iterative coupling optimization process is implemented in combination with the adversarial learning for accurate depth and precise ego-motion estimation. The experimental results show superior performance compared with state-of-the-art methods for monocular depth and ego-motion estimation on the KITTI dataset and a great generalization ability of the proposed approach.
DGI: Recognition of Textual Entailment via dynamic gate Matching

Recognizing Textual Entailment (RTE) is an integral part of intelligent machines which is able to understand and reason with natural languages. Some special embedding methods such as the attention mechanism exploit the semantic information without considering the features of sentence interaction, which also affect the word-level attention weight when a word appears at multiple positions of a sentence. In this study, we propose a Dynamic Gate Inference model (DGI) to fulfill the RTE task. In the DGI model, different aspects of semantic information are extracted from a premise sentence and a hypothesis sentence by a proposed dynamic gate Matching LSTM structure (gMatch), which combines the word-level fine-grained reasoning mechanism with the sentence-level gating structure to capture the global semantics. The textual relationship between the premise and the hypothesis is inferred by the three categories of attention including direct concatenation, similarity and difference. Extensive experiments were conducted to evaluate the performance of the proposed DGI model in two popular corpus by the metric of accuracy, and the results demonstrate that our approach outperforms the state-of-the-art baseline models in textual entailment in an effective manner.
Deep Multi-Layer Perceptron-Based Obstacle Classification Method From Partial Visual Information: Application to the Assistance of Visually Impaired People

How to navigate safely, recognize encountered obstacles, and move independently from one location to another in unknown environments are some of the challenges that face visually impaired people.By proposing a solution towards overcoming these challenges, this work will be of most importance to visually impaired people.In this work, we propose a consistent, reliable and robust smartphone-based method to classify obstacles in unknown environments from partial visual information based on computer vision and machine learning techniques.Our proposed method handles high levels of noise and bad resolution in frames captured from a phone camera.In addition, our proposed method offers maximum flexibility to users and use the least expensive equipment possible.Moreover, by leveraging on deep-learning techniques, the proposed method enables semantic categorization in order to classify obstacles and increase the awareness of the explored environment.The efficiency of the work has been experimentally measured on a variety of experiments studies on different complex scenes.It records high accuracy of [90.2 % ].
Modeling-based mineral system approach to prospectivity mapping of stratabound hydrothermal deposits: A case study of MVT Pb-Zn deposits in the Huayuan area, northwestern Hunan Province, China

An in-depth analysis of the modelled mineral system is prerequisite for producing mineral prospectivity maps. Critical to this is the accurate analysis of key ore-controlling processes and their translation into mappable targeting criteria and their spatial proxies. A case study illustrating and implementing the mineral prospectivity based on the theory of mineral system for stratabound hydrothermal deposits in the Huayuan area, northwestern (NW) Hunan Province, China helps analyze and summary the important mineral system components and promote the subsequent mineral prospectivity mapping. In the light of a local Pb-Zn metallogenic model and regional metallogeny, the ore-bearing strata were deposited in a platform edge shoal-reef facies, which not only facilitated the formation of a fluid reservoir, but also provided organic matter for the thermochemical sulfate reduction and the space for the ore accumulation. The left-lateral strike-slip movement of the Huayuan-Zhangjiajie fault forming the NE-trending secondary tensional fractures provides channels for ore-fluid migration. Based on the aforementioned, the NE-trending syn-ore fractures and thick ore-bearing strata, are crucial mappable targeting criteria for the delineation of prospective area. By detail analysis of the Huayuan-Zhangjiajie fault movement mode and the ore-bearing stratoisohypse mapping, three prospective areas were delineated. Our study from the perspective of mineral system emphasizes the importance of syn-ore fault analyzing, lithofacies paleogeographic and stratoisohypse mapping in the prospecting of MVT deposits in this case study. The method procedure for mineral prospectivity adopted in this contribution is also suitable for the prediction of other stratabound hydrothermal deposits.
An End-to-End Video Text Detector with Online Tracking

Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods attempt to enhance the performance of video text detection by cooperating with video text tracking, but treat these two tasks separately. In this work, we propose an end-to-end video text detection model with online tracking to address these two challenges. Specifically, in the detection branch, we adopt ConvLSTM to capture spatial structure information and motion memory. In the tracking branch, we convert the tracking problem to text instance association, and an appearance-geometry descriptor with memory mechanism is proposed to generate robust representation of text instances. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Experiments on existing video text benchmarks including ICDAR2013 Video, Minetto and YVT demonstrate that the proposed method significantly outperforms state-of-the-art methods. Our method improves F-score by about 2% on all datasets and it can run realtime with 24.36 fps on TITAN Xp.
Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.
DeepHSV: User-Independent Offline Signature Verification Using Two-Channel CNN

Off-line handwritten signature verification has practical significance in financial, administrative and judicial field. Traditional methods rely on handcrafted local and global feature descriptors. Recently, researchers start to apply convolutional neural network (CNN) to learn representative features towards this task. However, most of these methods suffer from overfitting. In this paper we propose a novel two-channel CNN network, namely 2-Channel-2-Logit (2C2L), to address this issue. The input to the network is the concatenation of reference and query signatures. The output of convolutional layers are two logits that measure the similarity between reference and query signatures. We explicitly add dropout layers and a 2-Logit layer to make the network less prone to overfitting issues. The proposed method uses only one reference signature for testing and the training system is independent from the queried user. Experiments on the latest GPDS-Synthetic database demonstrate that the proposed method can significantly improve verification accuracy. We improve the performance by a large margin, from 22.24% in state-of-the-art to 9.95% in terms of equal error rate (EER).
RecSplit: Minimal Perfect Hashing via Recursive Splitting

A minimal perfect hash function bijectively maps a key set S out of a universe U into the first |S| natural numbers. Minimal perfect hash functions are used, for example, to map irregularly-shaped keys, such as strings, in a compact space so that metadata can then be simply stored in an array. While it is known that just 1.44 bits per key are necessary to store a minimal perfect hash function, no published technique can go below 2 bits per key in practice. We propose a new technique for storing minimal perfect hash functions with expected linear construction time and expected constant lookup time that makes it possible to build for the first time, for example, structures which need 1.56 bits per key, that is, within 8.3% of the lower bound, in less than 2 ms per key. We show that instances of our construction are able to simultaneously beat the construction time, space usage and lookup time of the state-of-the-art data structure reaching 2 bits per key. Moreover, we provide parameter choices giving structures which are competitive with alternative, larger-size data structures in terms of space and lookup time. The construction of our data structures can be easily parallelized or mapped on distributed computational units (e.g., within the MapReduce framework), and structures larger than the available RAM can be directly built in mass storage.
Facing the Artificial: Understanding Affinity, Trustworthiness, and Preference for More Realistic Digital Humans

In recent years, companies have been developing more realistic looking human faces for digital, virtual agents controlled by artificial intelligence (AI).But how do users feel about interacting with such virtual agents?We used a controlled lab experiment to examine users' perceived trustworthiness, affinity, and preference towards a real human travel agent appearing via video (i.e., Skype) as well as in the form of a very human-realistic avatar; half of the participants were (deceptively) told the avatar was a virtual agent controlled by AI while the other half were told the avatar was controlled by the same human travel agent.Results show that participants rated the video human agent more trustworthy, had more affinity for him, and preferred him to both avatar versions.Users who believed the avatar was a virtual agent controlled by AI reported the same level of affinity, trustworthiness, and preferences towards the agent as those who believed it was controlled by a human.Thus, use of a realistic digital avatar lowered affinity, trustworthiness, and preferences, but how the avatar was controlled (by human or machine) had no effect.The conclusion is that improved visual fidelity alone makes a significant positive difference and that users are not averse to advanced AI simulating human presence, some may even be anticipating such an advanced technology.
AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.