Reviewerly/exHarmony huggingface dataset:
Incremental Response Generation Using Prefix-to-Prefix Model for Dialogue System

A spoken dialogue system that is currently deployed in many devices cannot respond to a user with a natural switching pause. One of the reasons is that the conventional system generates the response with the pipe-line of several processes, such as speech recognition, response generation, and speech synthesis. The dialogue system should process the user's utterance and generate the response incrementally to achieve natural turn-taking as human-being. In this paper, we examined an incremental response generation method based on a Prefix-to-Prefix model, which is proposed for simultaneous machine translation. This model has a similar structure with the Sequence-to-Sequence model, which is successfully applied to the response generation. We conducted several experiments to confirm the effectiveness of the Prefix-to-Prefix model for incremental response generation.
A feature selection algorithm for multilayer perceptron based on simultaneous two-sample representation

Classification is one of the hot topics of machine learning domains, its main task is to learn a classification model from training data and predict the labels of unknown samples. To date, many classification models have been proposed and are widely used in various realworld applications, e.g., naive Bayes (NB), logistic regression (LR), support vector machine (SVM) have been successfully employed in spam recognition, bank loan credit scoring and network rumor recognition, respectively. Imbalance learning is an important branch of classification task in machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning. To alleviate the issues of data explosion and feature selection for a multilayer perceptron with simultaneous two-sample representation, in this paper, we propose a novel feature selection method based on the pairwise samples distance constraint, which considers the class labels of paired samples, select the features which push two similar samples closer together and pull two different samples farther apart. Finally, we conduct experiments on four high-dimensional DNA microarray datasets. The experimental results demonstrate that our proposed algorithms outperform some state-of-the-art algorithms in terms of F-measure and G-mean.
Development of a Web Browsing Support System Using Eye Movements

We attempt to develop a web-browsing support system using a contactless and low-cost gaze-input device. This system automatically derives search queries that can help to find related or additional information just by looking at the screen. Therefore, it is possible to support the user's web browsing by gaze information. This is a basic study to consider an interactive system that can be used with a low-cost gaze-input device for consumers. we believe that this study will contribute to the development of gaze interaction systems that utilize gaze interfaces in the future.
Lifting propositional proof compression algorithms to first-order logic

Abstract Proofs are a key feature of modern propositional and first-order theorem provers. Proofs generated by such tools serve as explanations for unsatisfiability of statements. However, these explanations are complicated by proofs which are not necessarily as concise as possible. There are a wide variety of compression techniques for propositional resolution proofs but fewer compression techniques for first-order resolution proofs generated by automated theorem provers. This paper describes an approach to compressing first-order logic proofs based on lifting proof compression ideas used in propositional logic to first-order logic. The first approach lifted from propositional logic delays resolution with unit clauses, which are clauses that have a single literal. The second approach is partial regularization, which removes an inference $\eta $ when it is redundant in the sense that its pivot literal already occurs as the pivot of another inference in every path from $\eta $ to the root of the proof. This paper describes the generalization of the algorithms LowerUnits and RecyclePivotsWithIntersection (Fontaine et al.. Compression of propositional resolution proofs via partial regularization. In Automated Deduction—CADE-23—23rd International Conference on Automated Deduction, Wroclaw, Poland, July 31–August 5, 2011, p. 237--251. Springer, 2011) from propositional logic to first-order logic. The generalized algorithms compresses resolution proofs containing resolution and factoring inferences with unification. An empirical evaluation of these approaches is included.
Unsupervised Anomaly Detection in Daily WAN Traffic Patterns

Growth in large-scale experiments using high capacity reliable networking as part of their design is creating a need for better monitoring and analysis of observed traffic. Network providers need intelligent solutions that can help quickly identify and understand anomalous behaviors at the network edge, allowing reactions to unexpected traffic or attacks on facilities and their peerings. However, due to lack of labeled data in network traffic analysis and user diversity, we introduce novel methods that process very large network datasets quickly for outlier identification. In this paper, we leverage artificial intelligence (AI), network research, and edge computing to collect and train unsupervised classification algorithms using streaming data pipelines from multiple months of network flow records. Once trained, individual classifiers quickly observe and flag alerts in hourly behaviors. Our work describes building the data pipeline as well as addressing issues of false positives and workflow integration.
Efficiency-enhanced Progressive Sampling Method on One-shot Person Re-Identification

Label estimation on unlabeled samples has become a typical method in one-shot Person Re-Identification. Many existing methods take this form of data augmentation to create the conditions for fully supervised learning. The state-of-theart method uses a cross-iterative mode and achieves huge performance improvement. However, the learning efficiency is very low in this mode. In this paper, we first propose an exponential sampling curve to further explore the relations between sampling number and the performance of the model in each iteration. We found that expanding a proper amount of pseudo-label samples for training can accelerate the growth of performance at the risk of losing final performance. To avoid performance loss, an efficiency-enhanced progressive sampling method is proposed subsequently to expanding sampling number in each iteration by amplification factor and increment factor. Our method not only makes full use of more pseudo-label samples, but also avoids adding too many mislabeled samples at the beginning. Our method is validated on DukeMTMCVideoReID dataset, with the results that our method has a performance comparable to the state-of-the-art method and reduces the number of training iterations by 30% to obtain the best performance.
Comprehensive Modelling of ANN

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.
Bandwidth-Aware Rescheduling Mechanism in SDN-based Data Center Networks

Recently, all kinds of cloud computing applications are popular since the network bandwidth raises. The company needs to set up the server to maintain the data in the earlier year. Nowadays, the company just rents the virtual server from the cloud service provider, and then it can flexibly adjust the server level according to its requirement. Moreover, this scheme can save the deployment and maintenance costs of the company. However, it calls for an efficient mechanism to shorten the task completion time when the amount of data is waiting to process in data center networks. Unfortunately, it is an NP-complete problem in the Hadoop system for minimum task completion time. Recently, many studies propose improvement schemes, but these schemes still can be enhanced. Therefore, in this paper, we proposed a bandwidth-aware rescheduling (BARE) mechanism in the software-defined network (SDN)-based data center networks. BARE uses the feature of the SDN to collect the global information, sorts out the locality data process, performs the task split, and executes the rescheduling mechanism for reducing the task completion time. Finally, we use the simulation to prove our scheme. The performance results show that our scheme outperforms other existing schemes.
SVD Based Digital Watermarking Algorithm for Print-And-San Process

Analysing the effect of scanning process on the image. This paper proposes a watermarking algorithm based on DWT and SVD. With the features of printing and scanning, the algorithm firstly decomposes a image by discrete wavelet transform for first order, then carries on singular value decomposition in the low-frequency subgraph, and then embeds watermark sequence in the singular value, and corrects distortion before watermarking extraction. Experiments show that this algorithm is robust for printing and scanning and determine the second copy.
Attack Isolation via an Improvement Cubature Kalman Filter Algorithm for BD2/SINS Integrated Navigation Systems

Motivated by the necessity to provide secure navigation information in this context, especially against potential data injection attacks, the paper proposes an improvement cubature Kalman filter (CKF) algorithm by introducing attack detection and isolation ideas combined with residual chi-square test. The CKF is used to estimate state through the measurements. Then the residual chi-square test is incorporated through the proposed CKF framework. By calculating the value of residual chi-square test and comparing with the detection threshold, we can detect the attacks. Finally, an isolation strategy is proposed to avoid attack measurements being injected into the system. To be concrete, the BeiDou-2 (BD2)/ Strap-down Inertial Navigation System (SINS) integrated navigation system is simulated to evaluate the performance of the proposed algorithm, which contains two types of measurement attacks ranging from a constant bias attack to a time varying bias attack.
PenalViz: A Web-Based Visualization Tool for the Indonesian Penal Code

Criminal law is a set of legal regulations made by the state. In Indonesia, criminal law was formulated in The Indonesian Penal Code or Kitab Undang-Undang Hukum Pidana (KUHP). The Indonesian Penal Code consisted of three books: general provisions, crimes, and misdemeanors. On the contrary, people remain to face difficulty in understanding the code. Most of the public law data are documented in a rigid format like pdf. Therefore, we developed PenalViz, a web-based tool to visualize in user-friendly graphs. This study aims to help the public read and understand the information about the Indonesian Penal Code. PenalViz has several main features: search, visualization, synonym, and description. We used the User Experience Questionnaire (UEQ) to measure the user experience of our tool. We found that our tool has levels of user experiences: excellent (novelty), above average (attractiveness, perspicuity, efficiency), and good (stimulation). In conclusion, it shows that our tool had good results in user experiences in most categories, especially in novelty.
Retrieving curvature’s equation of a radially symmetric concave surface using Fizeau ring fringes

In this work, we attempt to precisely find out the equation describing the shape of a radially symmetric concave surface. An experimental interference pattern of Fizeau ring fringes in case of transmission is obtained using the semi-reflecting concave surface (under study) placed on a glass plate. Theoretically, we showed that concentric ring fringes can be obtained by a concave surface which is a part of an ellipsoid or a paraboloid in addition to a sphere by simulating the ring fringes in each case. The simulated interferograms were obtained considering the traced optical paths of the interfered rays which guarantee accurate calculations. From a comparison between the experimental and the estimated interference patterns, we knew which equation was adapted to represent the concave surface used in our experiment. The best surface’s equation is that whose simulated pattern satisfied the minimum percent error with the experimental pattern. Interestingly, the surface’s equation was retrieved from a single experimental interferogram which was produced by a simple well-known optical setup. The proposed method is fast, simple and accurate which make it suitable for quality control of concave surfaces manufacturing.
Numerical Simulation of Room Transfer Function Extrapolation using the Equivalent Source Method

Many methods of interpolation for the spatial impulse response have been proposed to improve the measurement efficiency. In this paper, we propose a method of extrapolating the room transfer functions including higher-order reflections using a small number of microphones. The direct sound and reflected sounds are represented by the sparse equivalent source method and the image source method. Experiments using a finite-difference time-domain (FDTD) simulation show that the proposed method has smaller errors than linear interpolation at low frequencies.
Machine Learning Algorithms Based on Hidden Markov Models in Low-Speed Speech Codecs for Assessing Speech Quality

A machine learning algorithm for a low-speed primary coding algorithm based on automatic recognition of speech signal fragments (SS) using hidden Markov models (HMM) has been developed. When assessing the quality of SS at transmitter output, spectral dynamics algorithm is used, as well as complex algorithms for assessing speech quality. Reduction of redundancy in SS is provided by using speech recognition and synthesis algorithms. Training with HMM to solve speech recognition problem included the definition of such parameters as: matrix of probabilities of transitions between different states of the model. To solve these problems two iterative algorithms were used: forward-backward algorithm and Baum-Welsh algorithm. As an experiment, using Hidden Markov Model Toolkit (HTK) software model, an independent monophonic speech model of Russian language was built. The model was based on the recognition from a list of phonemes consisting of 52 phonetic units and Russian language dictionary including 500000 words. Well-known algorithms to assess speech quality at low-speed codec output are investigated in the interests of machine learning. It is shown that the use of these algorithms does not provide necessary correlation of objective and subjective algorithms to assess speech quality under the action of acoustic interference. Algorithms for objective quality assessment that ensure the correlation of objective and subjective assessments of speech quality with the accuracy of 0.5 points in accordance with GOST R 50840-95 are proposed. The research has shown that the use of these algorithms in the encoder allows increasing speech quality at the output of low-speed codec with its machine learning by 0.5 ... 1 point in accordance with GOST R 50840-95.
Optimization of time error in large scale synchronization network via quantum annealing method

optimal combination problem for time error in synchronization network is proposed, formulated to quadratic binary models and solved via quantum annealing which shows better experimental results of optimal performance and time comparing with simulated annealing.
Power System Real-Time Operation Situation Assessment Based on Random Matrix Theory

The operation situation assessment is an important task in power systems, result of which can offer support on the safety analysis and control decision. With the deployments of phasor measurement units, massive amount of synchrophasor data is collected, which makes it possible for the real-time situational awareness of the entire system. In this paper, based on random matrix theory, a data-driven approach is proposed for real-time operation situation assessment in power systems. First, spatiotemporal data set is formulated by arranging high-dimensional synchrophasor measurements in chronological order. Based on the Ring Law in RMT for the empirical spectral analysis of `signal+noise' matrix, the mean spectral radius is introduced to indicate the system situation in macroscopic. The proposed approach is sensitive to the variation of the system situation and robust against random fluctuations and measurement errors. Cases on the synthetic data generated from IEEE 118-bus test system validate the effectiveness of the approach.
Split and Attentive-Aggregated Learnable Shift Module for Video Action Recognition

Existing approaches for video action recognition using convolutional neural network (CNN) usually suffer from the trade-off between accuracy and complexity. On the one hand, the 2D CNNs have difficulty in modeling the long-term temporal dependencies though they are computationally cheap. On the other hand, 3D CNNs have the ability to perceive temporal cues however lead to a high computational cost. In this paper, we propose a generic building block named Split and Attentive-aggregated Learnable Shift Module (SALSM) which has capacity of modeling spatiotemporal representations while maintain the complexity of the 2D CNN. Specifically, we split the input tensor into multiple groups, and conduct adaptive shift operations by applying the learnable shift kernels for different channels of each group along time dimension, so that the spatiotemporal information from neighboring frames can be mingled with 2D convolutions. The output feature maps of each group are integrated together with attention mechanism. With SALSM plugged in, the 2D CNN is enhanced to handle temporal information and become a highly efficient spatiotemporal feature extractor with little parameters and computational cost. We conduct ablation experiments to verify the effectiveness of our method, and our proposed SALSM achieves competitive or even better results over the state-of-the-art methods on several benchmark datasets.
Incorporating Noise into Adaptive Sampling

Adaptive sampling is important in robotic environmental monitoring, allowing a robot to intelligently select sampling locations to build an informative model of a phenomenon of interest. Most adaptive sampling techniques assume the localization noise does not vary with location, or that this variation is negligible, and thus do not model this behavior. In practice, the noise will vary greatly depending on the robot’s trajectory and location. Additionally, prior surveys collected by other means, e.g., satellite or drone imagery, may use different state estimators or parameters. If these are used to drive sampling, this dependence may be significant. We provide a unified framework for adaptively collecting and modeling samples when heteroskedastic noise is present. Our framework is agnostic to the distribution of the noise. Our method outperforms others which do not take into account localization noise, validated by simulated trials and noise from a real state estimator.
A Triangulation-Based Backward Adaptive Motion Field Subsampling Scheme

Optical flow procedures are used to generate dense motion fields which approximate true motion. Such fields contain a large amount of data and if we need to transmit such a field, the raw data usually exceeds the raw data of the two images it was computed from. In many scenarios, however, it is of interest to transmit a dense motion field efficiently. Most prominently this is the case in inter prediction for video coding. In this paper we propose a transmission scheme based on subsampling the motion field. Since a field which was subsampled with a regularly spaced pattern usually yields suboptimal results, we propose an adaptive subsampling algorithm that preferably samples vectors at positions where changes in motion occur. The subsampling pattern is fully reconstructable without the need for signaling of position information. We show an average gain of 2.95 dB in average end point error compared to regular subsampling. Furthermore we show that an additional prediction stage can improve the results by an additional 0.43 dB, gaining 3.38 dB in total.
On the Development of Tools for Modelling Dynamic Beliefs Based on Past Data

In order to develop effective ubiquitous computing systems, we often need to predict an agent's behaviour based on past data. One way to do this is to maintain a model of what the agent believes at any point in time, as well as a mechanism for changing the beliefs to incorporate new information. In the knowledge representation community, this process is captured through formal belief revision operators. In this paper, we assume that we are monitoring the behaviour of an agent that uses a belief revision operator to incorporate new information; but we do not know exactly which operator is being used. Given past data about the beliefs of the agent, we propose two approaches for predicting future changes in belief. In the first approach, we simply search for all revision operators consistent with the data. In the second approach, we use machine learning to predict if a certain formula will be believed based on past data. We describe work in progress on prototype software to experiment with both approaches, and discuss when each is appropriate. We argue that modelling the dynamic beliefs of an agent in this way can be a useful component of a software system tasked with predicting behaviour when new information is received.
A BLOCKCHAIN BASED FRAMEWORK TO MONITOR THE USE AND HANDLING OF THE CONTROLLED SUBSTANCES

A controlled substance is a drug which is not legitimate, and which has damaging effect on mankind. National and state administration and governments take utmost care to regulate these controlled substances. But production or usage of these substances are not always illegal. Since these drugs are sometimes prescribed by medical practitioners for various genuine treatment purposes and are also available through pharmacies. All entities who manufacture, procure, distribute and sell the controlled substances have to send detailed reports to the Drug Enforcement Agencies (DEA) at regular intervals. Instead of different parties in the supply chain sending individual reports to the DEA, these movement of the controlled substances should be tracked in a Blockchain environment so that without the manual reporting from different sources a complete tamper proof history can be maintained. DEA would be able to get the full traceability of the controlled substances from the point of manufacturing to the point of consumption. This product lifecycle would be transparent to all the stakeholders in the supply chain to get better visibility about the source and end consumer of the product.