Reviewerly/exHarmony huggingface dataset:
Direct Torque Control of Synchronous Motors Using Artificial Neural Network

This paper introduces artificial neural network (ANN) controller for constant speed synchronous motors. The objective of the introduced ANN controller is directly control of the motor torque through three different control techniques namely, field control, armature control and field-armature control under load torque variations with minimizing the losses and consequently maximizing the efficiency of the motor. The idea behind the proposed controller is the injection of the appropriate field and armature voltages to directly control the motor torque with maximum efficiency. The presented neural controller structure is described by effortlessness and in practical by real time implementation. Results clearly demonstrate the adaptive property of the proposed scheme and ability of practical implementation. Moreover the ability of the three proposed control methods in direct torque control with the superiority of field-armature control method over both field control method and armature control methods.
Multi-View Capsule Network

Multi-view learning attempts to generate a model with a better performance by exploiting information among multi-view data. Most existing approaches only focus on either consistency or complementarity principle, and learn representations (or features) of the multi-view data. In this paper, to utilize both complementarity and consistency simultaneously, and explore the potential of deep learning in multi-view learning, we propose a novel supervised multi-view learning algorithm, called multi-view capsule network (MVCapsNet), which extracts a feature matrix of all views by a group of encoders, and obtains a classification matrix fusing common and special information of multiple views. Extensive experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method, and show its superiority over several state-of-the-art baseline methods.
Identity-based quantum signature based on Bell states

Based on the Bell states, an identity-based quantum signature scheme is proposed. In our scheme, the signer’s private key is generated by a trusted third party called private key generator(PKG), while the signer’s public key is his/her identity(such as his/her name or email address). The message to be signed is encoded into a Bell state sequence. To generate a quantum signature, the signer signs the Bell state sequence with his/her private key. The quantum signature can be verified by anyone with the signer’s identity. Our quantum signature scheme has the advantages of the classical identity-based signature scheme. It need not use long-term quantum memory. On the other hand, in our scheme, during the signature verification phase, the verifier need not perform any quantum swap test. What is more, in our scheme, PKG can arbitrate the disputation of losing quantum signature, which cannot be arbitrated in most of the quantum signature schemes. Our scheme also has the security properties of non-repudation, unforgeability, etc. Our signature is more secure, efficient and practicable than the similar schemes.
Enhancing the Performance of a Rainfall Measurement System Using Artificial Neural Networks Based Object Tracking Algorithms

With the recent development of optical sensing and digital image processing techniques, high-speed cameras have been applied to measure the microphysical properties of raindrops. However, the performance of such systems are significantly affected by object tracking algorithms. In order to improve the measurement accuracy of rainfall rate and accumulated rainfall, a novel object tracking algorithm based on artificial neural networks (ANN) is proposed in this paper. The ANN model takes the features of each raindrop in the two successive images as inputs including the center coordinates, area, canting angle, the lengths of long axis and minor axis of the equivalent ellipse. The output of the ANN model is the matched probabilities of each pair of raindrops between before and after images. Experimental data were collected during a real rainfall event. Performance comparisons between the traditional and ANN based object tracking algorithms are conducted based on the experimental data. Experimental results suggest the successful matching rate is significantly increased from 87.20% to 95.60% due to the usage of the ANN based algorithm. Hence, the improved disdrometer system is capable of producing more accurate and robust measurements of rainfall status.
A Survey on Existing Convolutional Neural Networks and Waste Management Techniques and an Approach to Solve Waste Classification Problem Using Neural Networks

In India, waste management has become one of the major crises with population explosion, coupled with improved lifestyle of people, results in increased generation of solid wastes in urban as well as rural areas of the country. It is well known that waste management policies, as they exist now, are not sustainable in the long term. Thus, waste management is undergoing drastic change to offer more options that are more sustainable. Most of the landfills are becoming full of waste in which most part is reusable and leading to spreading of disease damaging human body and leading to unpleasant air and only 5% of whole waste is actual waste. The government of Karnataka mandated system of 2 BIN 1 BAG to be adapted at every households in Bangalore, and 2 BIN 1 BAG is a color-coded system consisting of green bin which holds garden waste, and the wastes that are compostable, reject waste can be thrown in red bin, and finally a big category called as reusable bag which holds recyclable waste. Segregation of waste at source is best solution and should be done properly. Types of waste need to be remembered by members of home in order to put them to proper bins, and this may lead to human error. So our solution can answer this in good way, what if you just click picture of waste material and application says to which category it belongs. A convolutional neural network is trained with images of waste materials, and model can be inferred by giving waste-material image as input and get the perfect category of waste material in a second. This helps society in dealing with prime problem of segregating waste materials at source.
A GAN-based Transfer Learning Approach for Sentiment Analysis

Transfer learning is an important artificial intelligence approach which extracts knowledge from source domain to solve tasks in the target domain. As a research hot topic, Generative Adversarial Networks (GAN) provides a powerful framework in constructing unsupervised models. A GAN consists of two neural networks: a discriminator to distinguish natural and generated samples, and a generator to deceive the discriminator. Generally, sentiment analysis of text is a big challenge in the Natural Language Processing (NLP). In this paper, A GAN-based transfer learning approach for sentiment analysis of cross-domain texts is presented. Experiments are performed in cross-domain e-commerce reviews. The results compiled demonstrate the effectiveness of the proposed approach.
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10% of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models will be made publicly available.
Research of Elevator Security Data Mining Based on Hadoop

Hadoop data platform is studied to mine the massive elevator data from the elevator remote supervision system. The data mining and analysis platform is designed by Hadoop minimum cluster theory, which is mainly four modules composed of data transmission, data preprocessing, data mining and scheduling. Then both K-Means which is a cluster analysis algorithm and Apriori which is an association rule algorithm are improved, and parallelized with the computation framework of Hadoop's MapReduce. Finally, all modules are integrated through the scheduling module, and the practicability of the data mining and analysis platform is verified and tested, and the data mining of security status is realized for the elevator.
Face recognition based on improved residual neural network

For traditional face recognition algorithm based on convolution neural network in the insufficient light, side face of 45° environment, for large-scale face recognition accuracy is not high, and when the network layer increases to a certain layer, the performance of the network tends to saturation, continue to increase the network layer instead lead to performance problems. An improved neural network architecture combined with deep residual neural network and ArcFace Loss is proposed, namely, ArcFace Loss replaces the Loss function Softmax of deep residual neural network. Experimental results show that the improved residual neural network has an accuracy of 97.7% in face recognition on LFW data set, which is higher than the traditional and improved deep learning algorithm.
User Preferences of Voice Controlled Smart Light Systems

The use of smart home devices is widely spreading. Several ways exist to control such devices. Besides classical approaches such as buttons and remote controls, voice user interfaces (VUIs) are showcased as a primary input method in the context of smart home. Yet, few studies investigated the user's behaviour and experience with such newly offered input techniques. In this research, we carried out a field study to investigate the user preferences in using a VUIs for controlling a smart lighting system. The results indicate that the usage frequency drops after the first days and that users tend to use the system mainly once in the evening.
Improvement of Agile Software Development Process Based on Automotive SPICE: A Case Study

We describe an Automotive SPICE-based process improvement for in-vehicle software development using Scrum. First, we clarify issues in the application of Automotive SPICE to agile software development, considering research trends and features of agile methods and Automotive SPICE. Second, we describe the strategy and approach to resolve the identified issues and to promote process improvement. Third, we describe practical examples of implementing some aspects of Automotive SPICE considering the features of agile software development. Finally, we describe effects of the process improvement and future work.
Risky Host Detection with Bias Reduced Semi-Supervised Learning

To ensure the cyber security of an enterprise, a SIEM (Security Information and Event Management) system is in place to flag alerts and assign each of them a severity score based on some pre-determined rules. Analysts in the security operations center investigate the high severity alerts to decide if those alerts are truly malicious or not. However, generally the number of alerts is overwhelmingly large, far exceeding the SOC's capacity to handle them, and the majority of them are false positive. There is a great need for a machine learning system to accurately detect the risky hosts. Traditional supervised learning algorithms cannot be directly applied to this problem as very few risky hosts (positive labels) are identified and the positive labels are biased because the SOC analysts only investigate high severity alerts. In this paper, we propose a new distance-based PU learning approach, in which we use four different distances to measure similarity to the positive labels and a Gaussian Copula function to capture their correlation structure and ensemble four different distance measures into one joint probability density that we can directly use to infer new labels. The new approach has the advantage of significantly reducing the bias of the inferred labels while traditional supervised PU learning increases bias. To quantify bias, we also propose a new bias estimate method. We apply the new bias-reduction Positive Unlabeled (PU) learning system to detect host risk in cyber security. Results on real enterprise data indicate that the proposed PU learning is able to detect risky hosts effectively while at the same time greatly reducing the label bias. t-SNE 2-dimensional visualization also demonstrates that the labels from distance-based PU learning are more evenly distributed with higher Kozachenko-Leonenko entropy.
Heterogeneous Information Network Embedding with Meta-path Based Graph Attention Networks

Network embedding is an emerging research field which aims at projecting network elements into lower dimensional spaces. However, most network embedding algorithms focus on homogeneous networks, thus cannot be directly applied to the Heterogeneous Information Networks (HINs) which are prevalent in real world systems. Therefore, how to effectively preserve both the structural and semantic information, as well as higher-level proximity of large-scale HINs still remains an open problem. In this paper, we propose a novel heterogeneous network embedding model called Meta-path based Graph ATtention nEtwork (MetaGATE). To tackle the problem of preserving both structural and semantic information for HINs, we adopt a multiple meta-paths based random walk scheme with Skip-Gram for sampling and pre-training. In addition, the model involves graph attention networks to collect and aggregate heterogeneous information, which may reveal higher-level implicit semantics. Besides, to ensure stability, multi-head attention mechanism is employed in the graph attention model by concatenating the embedding of multiple independent self-attention processes. Experiments on two real-world bibliography networks are conducted. Compared to state-of-the-art baselines, our method achieves the best performance on the node classification task, which shows the effectiveness of the proposed model.
A generalization of the Mignotte’s scheme over Euclidean domains and applications to secret image sharing

Secret sharing scheme is an efficient method to hide secret key or secret image by partitioning it into parts such that some predetermined subsets of partitions can recover the secret but remaining subsets cannot. In 1979, the pioneer construction on this area was given by Shamir and Blakley independently. After these initial studies, Asmuth-Bloom and Mignotte have proposed a different $(k,n)$ threshold modular secret sharing scheme by using the Chinese remainder theorem. In this study, we explore the generalization of Mignotte's scheme to Euclidean domains for which we obtain some promising results. Next, we propose new algorithms to construct threshold secret image sharing schemes by using Mignotte's scheme over polynomial rings. Finally, we compare our proposed scheme to the existing ones and we show that this new method is more efficient and it has higher security.
The Impact of Fiber Random Birefringence in Polarization-Encoded Quantum Communications

We analyze the impact of fiber random birefringence in realistic quantum communication systems in which information is encoded in single-photon polarization qubits. We discuss an algorithm to dynamically compensate the polarization random rotations. We assess the algorithm performance considering both the polarization drift dynamics and the QBER threshold. We apply the polarization drift control algorithm to a realistic quantum key distribution system based on the BB84 and we assess its performance in terms of key generation rate for different values of polarization linewidth.
Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV

We empirically demonstrate that graphical models can be a valuable tool in the identification of mediating variables in causal pathways. We make use of graphical models to elucidate the causal pathway through which the treatment influences the levels of fatigue and weakness in people living with HIV (PLHIV) based on a secondary analysis of a categorical dataset collected in a behavioral clinical trial: is weakness a mediator for the treatment and fatigue, or is fatigue a mediator for the treatment and weakness? Causal mediation analysis could not offer any definite answers to these questions.
Named Entity Recognition in Document Summarization

Named entity recognition (NER) is a subtask of the information extraction. NER system reads the text and highlights the entities. NER will separate different entities according to the project. NER is the process of two steps. The steps are detection of names and classifications of them. The first step is further divided into the segmentation. The second step will consist to choose an ontology which will organize the things categorically. Document summarization is also called automatic summarization. It is a process in which the text document with the help of software will create a summary by selecting the important points of the original text. In this chapter, the authors explain how document summarization is performed using named entity recognition. They discuss about the different types of summarization techniques. They also discuss about how NER works and its applications. The libraries available for NER-based information extraction are explained. They finally explain how NER is applied into document summarization.
A dual-scanning white-light interferometer for exact thickness measurement of a large-thickness glass plate

Conventional scanning white-light interferometers (SWLIs) cannot measure exactly a shape of glass plate with a large thickness because of great dispersion effect. In this paper, a novel dual SWLI is proposed where both thicknesses of an object and a compensation glass plate can be measured exactly in four-step measurements by decreasing the dispersion effect with each other in the two arms of the dual SWLI. The compensation glass plate is the same material with a similar thickness compared to the object. The peak positions of the interference signals in SWLI can be exactly measured by using a piezoelectric transducer stage and an additional interference signal which detects the time-varying optical path difference in SWLI. Experimental results show that the measurement error is less than 60 nm for 1 mm-thickness glass plate.
Computing Feedback Plans from Dynamical System Composition

Computing plans for systems with differential constraints is a fundamental component in numerous robotic applications. Most previous approaches are based on creating motion plans between an initial and a goal location. However, a more robust approach is to compute feedback plans over the entire configuration space to account for uncertainty in the robot's motions. In this paper, we therefore propose a new method that constructs a feedback plan by incrementally composing the long-term behavior of the robot's motions for a set of actions. Our method takes advantage of dynamical system analysis techniques and efficient combinatorial algorithms. We implement our method in simulations considering a robot under a simple bouncing behavior. A feedback plan for the robot to reach the goal region starting from any location of an environment is successfully constructed using the implementation of our method. Our method is also applicable to non-linear systems with uncertainty.
Iris Center Localization Using Geodesic Distance and CNN

In this paper, we propose a new eye iris center localization method for remote tracking scenarios. The method combines the geodesic distance with CNN-based classification. Firstly, the geodesic distance is used for fast preliminary localization of the regions possibly containing the iris. Then a convolutional neural network is used to carry out the final decision and to refine the final position of the iris center. In the first step, the areas that do not appear to contain the eyeball are quickly filtered out, which makes the whole algorithm fast even on less powerful computers. The proposed method is evaluated and compared with the state-of-the-art methods on two publicly available datasets focused to the remote tracking scenarios (namely BioID [9], GI4E [15]).
About Perceptual Quality Estimation for Image Compression

The article is devoted to the problem of perceptual quality estimation for the task of still image and video compression. LPIPS is described as a reference method for approximation of human perception of visual distortions. To consider only lossy image compression artifacts a low complexity modification of LPIPS is proposed.
Multi-Head Self-Attention Networks for Language Identification

Self-attention networks are being popularly employed in sequence classification and sequence summarization tasks. State-of-the-art models use sequential models to capture the high-level information, but these models are sensitive to length of utterance and do not equally generalize over variable length utterances. This work explores to study the efficiency of recent advancements in self-attentive networks for improving the performance of the LID system. In self-attentive network, variable length input sequence is converted to fixed dimensional vector which represents the whole sequence. The weighted mean of input sequence is considered as utterance level representation. Along with the mean, a standard deviation is employed to represent the whole input sequence. Experiments are performed using AP17-OLR database. Use of mean with standard deviation has reduced the equal error rate (EER) with an 8% relative improvement. A multi-head attention mechanism is introduced in self-attention networks with an assumption that each head captures the distinct information to discriminate languages. Use of multi-head self-attention has further reduced the EER with a 13% relative improvement. Best performance is achieved with multi-head self-attention network with residual connections. Shifted delta cepstral features (SDC) and stacked SDC features are used for developing LID systems.
Case Study on Model Free Determination of Optimal Trajectories in Highly Automated Driving

Developments regarding driver assistant systems, in the scope of highly automated driving, has gained significant attention during the last years. As a result, planning the movement of the vehicle considering all road conditions and driver/passenger comfort became a crucial issue in that sense. In this paper, a trajectory planning concept that is based on the analytical solution of a given optimization problem is presented where the cost function includes jerk and acceleration of the longitudinal and lateral movements in order to increase the comfort of the passengers. The error of the output trajectory is evaluated over a reference curve that is determined using polynomial fitting approaches. Since the structure of the optimal trajectory is fixed, it is only required to determine the coefficient terms. Therefore, the presented approach is also beneficial from the computational complexity point of view. Real-time experiments have been carried out with our test vehicle on realistic scenarios. The experimental results demonstrated the capabilities and effectiveness of the proposed trajectory planning framework.