Reviewerly/exHarmony huggingface dataset:
Extended-Domain Golomb Code and Symmetry of Relative Redundancy

This paper presents extended-domain Golomb (XDG) code, an extension of Golomb code for sparse geometric sources as well as a generalization of extended-domain Golomb-Rice (XDGR) code, based on the idea of almost instantaneous fixed-to-variable length (AIFV) codes. Showing that the XDGR encoding can be interpreted as extended usage of the code proposed in the previous works, this paper discusses the following two facts: The proposed XDG code can be constructed as an AIFV code relating to Golomb code as XDGR code does to Rice code; XDG and Golomb codes are symmetric in the sense of relative redundancy. The proposed XDG code can be efficiently used for losslessly compressing geometric sources too sparse for the conventional Golomb and Rice codes. According to the symmetry, its relative redundancy is guaranteed to be as low as Golomb code compressing non-sparse geometric sources. Awing to this fact, the parameter of the proposed XDG code, which is more finely tunable than the conventional XDGR code, can be optimized for given inputs using the conventional techniques. Therefore, it is expected to be more useful for many coding applications that deal with geometric sources at low bit rates.
Improving the Responses Diversity of Persona-based Neural Conversation System: A Conditional Variational Autoencoders-based Approach

Given a conversational context based on persona information to a chatbot, how to generate diverse and relevant responses? Many existing works could effectively capture useful information from external knowledge, but they rarely consider the diversity of responses. In this paper, we consider both the persona information and the responses' diversity, upon which we develop a Seq2Seq-based model with hierarchical RNN context encoder, Conditional Variational Autoencoder (CVAE) and pointer generator. Hierarchical RNN allows those context utterances to be used more effectively. CVAE is used to capture the relationship between question and several appropriate answers. With pointer generator, an output token in a response could either be generated or copied from persona facts. The model is trained and tested on Persona-Chat dataset. Finally, automatic evaluation shows that compared with the baseline model, this developed model is able to better integrate the persona information and generate more diverse responses.
Adaptive Margin Based Liveness Detection for Face Recognition

Face recognition is currently is becoming the hotspot in the area of deep learning, pattern recognition, and computer vision where it has been broadly utilized in many fields. Facial feature extraction is a key link in the face recognition system. The texture features of human faces are highly discriminative, so extracting the texture features of face images can often get a good classification and recognition effect. Image texture feature extraction methods can generally be classified into four categories: statistical methods, model methods, structural methods, and signal processing methods. Recently, face recognition based person authentication systems have been popular among other biometrics. However, hacking methods are also developed with this methodology. In this paper, we present a margin based liveness detection method (MLDM) for the face recognition system based on texture feature analysis. The fake images captured from a video that has edges generated by differences among different face images of real and fake people images. Moreover, we exploit a convolutional neural network to extract these features and differentiate real and fake face images. Experimental results show that our model has higher accuracy and can efficiently classify real faces and spoofed faces compare with the existing model. The outcome shows that our approach is better than the existing work which is experimentally proven.
Multiscale Optimal Filtering on the Sphere

We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere. Filtering is performed in the wavelet domain given by the scale-discretized wavelet transform on the sphere. The proposed filter is optimal in the sense that it minimizes the mean square error between the filtered wavelet representation and wavelet representation of the noise-free signal. We also present a simplified formulation of the filter for the case when azimuthally symmetric wavelet functions are used. We demonstrate the use of the proposed optimal filter for denoising of an Earth topography map in the presence of additive, zero-mean, uncorrelated and white Gaussian noise, and show that the proposed filter performs better than the hard thresholding method and weighted spherical harmonic (weighted-SPHARM) signal estimation framework.
Keyword-Based Hadith Grouping Using Fuzzy C-Means Method

Most of the Islamic documents have been documented and published to Muslims to be studied and applied in daily activities. One of these documents is a Hadith. Along with the development of hadith technology not only recorded on sheets of paper but also stored on digital data storage media in the form of databases. Document grouping allows users to get better information than using documents in the same group. Fuzzy techniques allow different membership levels. The purpose of grouping documents is to get different groups. This is needed for better document search. The document used in this study is an Indonesian translation of the Sunnan An Nasa'i Hadith tradition with a grouping technique using a combination of Text Mining and Fuzzy C-Means algorithm as a method of grouping it. By comparing the results of manual calculations against the results of calculations using RStudio software, the accuracy obtained is 80%.
Using Blockchain for Smart Contracts

Today's era of globalization and digital transformation has produced many modern technologies that have influenced modern societies, blockchain being one. This chapter will set out definitions and criteria related to what blockchain is, its advantages and limitations, and its relation to the modern techniques used in the conclusion of smart contracts; and the impact of this technology on fighting administrative and financial corruption. Within this chapter, the central focus is on a new form of contracts founded as a result of the challenge of aligning the current system of the contract with the application of blockchain technology (i.e., to replace the idea of credit intermediation in dealing [notary, bank, management] with another thought based on a peer-to-peer system to increase contractual security and to establish the principle of self-implementation of the contract without the need to mediate with others).
A No-Reference Image Quality Measurement Approach for Mobile Video Calling Applications in Low Light Environment

The low light performance of mobile phone cameras has gained much improvement due to the rapid development of digital imaging technologies. Recently, many phone manufactures have advertised their remarkable night mode for taking photos in low light environment. At the same time, more and more mobile video applications are enhancing their low light performance. This work investigates image quality measurement for mobile video calling applications in low light environment. At first, a video database is constructed by capturing many videos under different lighting conditions. Then a no-reference quality measurement model, which contains spatial information, noise and motion blur metrics, is proposed and is fitted to human observers' mean opinion score (MOS). The experiment results show that this measurement method gives high correlation with subjective MOS. Using the proposed model, we can analyze how camera sensor parameters influence to image quality, which helps the development of more accurate exposure algorithms for video calling applications in low light environment.
Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Abstract In the field of civil engineering, nondestructive evaluation (NDE) can be understood as the process of a material or structural element assessment based on techniques that do not introduces damage to the assessed sample. NDE can be used to inspect the quality of materials or to find some damages, as cracks and voids. This way, ultrasonic data monitoring has become one of the most important tools for detecting local damages in civil engineering elements. Concerning to masonry application, this method consists in the measurement of the time that the ultrasonic wave takes to pass through a material and changes in the ultrasonic wave propagation can be used for identifying damages presence and anomaly. Despite statistical methods be widely used for processing large data sets, in the case of ultrasonic waves some issues need to be overcoming, as the analysis of the path traveled by the ultrasonic wave. Thus, the main aim of this work is to analyze the ultrasonic wave propagation behavior through a new approach of a statistical model. Using a Bayesian semiparametric method with Dirichlet process, a data‐driven hierarchical regression model is applied to simulated and experimental data sets. For simulated data sets, the sensibility of the model in clustering profiles in an unknown number of groups and also detecting outliers is observed. The application in experimental data shows the potential use of the model for real case applications due to its efficiency in identifying different behaviors of the wave propagation in materials. Therefore, considering the advanced of automation and Internet of Things, statistical methods based on clustering are important and useful tools for being explored in the masonry conditions monitoring.
A Text Image Super-Resolution Generation Network without Pre-training

Aiming at the problem of low recognition rate of optical character recognition for low-resolution text images, a non-training text image super-resolution generation network is designed. The network is mainly composed of a convolution layer, a Batch Normalization layer (BN layer), a LeakyReLU activiation layers, an upsampling layer, and a downsampling layer. The network does not need to use the dataset for training. It can transform the super-resolution reconstruction problem into an optimization problem and process the image directly. A new loss function for text image super-resolution to realize super-resolution reconstruction of text image is proposesing. The experimental results show that the network can achieve 73.30% OCR accuracy on the ICDAR 2015 TextSR dataset, which is 12.66% higher than the bicubic algorithm. The network also has better accuracy to the learning-based Adaptive Sparse Representation Selection algorithm.
Blockchain and Northbound Data Trusted Storage

The northbound data of the mobile communication network management system is an important performance indicator in mobile communication, which is of great significance for guiding network planning, construction, operation and maintenance. Since the northbound data comes from different units, data may be tampered with during the process of data collection, storage and usage. In order to solve the problem of data tampering and implement a credible data management method, in this paper, we will combine blockchain technology to propose a dual-chain storage structure, which stores core data and data operation logs on the chain, so as to realize the management and operation of data and ensure data security and credibility.
A rotation learning-based colliding bodies optimization algorithm

Abstract In this paper, we propose an enhancement algorithm of colliding bodies optimization based on rotation learning, named by RBL-CBO. Firstly, we use the rotation-based learning to search for any point in the rotating space by adjusting the rotation angle, thereby improving the ability of the proposed algorithm jump out of the local optimum. Next, we leverage the sinusoid-based nonlinear adjustment strategy to modify the control parameters to improve the calculation accuracy of the proposed algorithm. Finally, we process the cross-boundary object by the mirroring strategy. We conduct extensive experiments to test the performance of the proposed algorithm. In simulation-based experiments, 23 benchmark functions are used to compare RBL-CBO algorithm with the CBO, DE, BBO, PSO and GSA algorithm. The experimental results demonstrate that the proposed RBL-CBO algorithm is superior to the other comparison algorithms, while the RBL-CBO algorithm is at least 20% higher than the CBO algorithm in terms of the accuracy of solving function optimization problem.
A study of spot recommendation personalization by considering personality traits

In recent years, the number of foreign visitors to Japan has been increasing. Along with that, information about famous sightseeing spots can easily be obtained from various websites, etc., and sightseeing spot recommendation systems using AI have also increased. The mainstream is a system that recommends spots based on the user's hobbies and preference information. However, such existing recommendation systems tend to recommend spots based on users with similar hobbies and preferences, resulting in a single recommendation for the user. In this research, we would like to recommend sightseeing spots by a new approach and construct a system that recommends based on personality characteristics information using personality insights [1] (hereinafter PI) as described later. In this paper, we first verify that there is a tendency in the personality characteristics of the user for each spot, which is a prerequisite for constructing the system.
Analysis of Two-variable Recurrence Relations with Application to Parameterized Approximations

In this paper we introduce randomized branching as a tool for parameterized approximation and develop the mathematical machinery for its analysis. Our algorithms substantially improve the best known running times of parameterized approximation algorithms for Vertex Cover and 3-Hitting Set for a wide range of approximation ratios. The running times of our algorithms are derived from an asymptotic analysis of a broad class of two-variable recurrence relations. Our main theorem gives a simple formula for this asymptotics. The formula can be efficiently calculated by solving a simple numerical optimization problem, and provides the mathematical insight required for the algorithm design. To this end, we show an equivalence between the recurrence and a stochastic process. We analyze this process using the method of types, by introducing an adaptation of Sanov's theorem to our setting. We believe our novel analysis of recurrence relations which is of independent interest is a main contribution of this paper.
Performance-Based Analogising of Needleman Wunsch Algorithm to Align DNA Sequences Using GPU and FPGA

This paper presents a performance-based approach to the Needleman-Wunsch algorithm of global sequence alignment. Sequence comparison lies at the heart of the bioinformatics, which describes the way of arrangement of DNA, RNA or protein sequences, to identify the regions of similarity among them. We compare the execution time of sequential CPU-based implementation with parallel GPU-based implementation using CUDA-C and hardware-based implementation using FPGA. Both the approaches were far more improved over the sequential CPU-based implementation.
XQM: Interactive Learning on Mobile Phones

There is an increasing need for intelligent interaction with media collections, and mobile phones are gaining significant traction as the device of choice for many users. In this paper, we present XQM, a mobile approach for intelligent interaction with the user’s media on the phone, tackling the inherent challenges of the highly dynamic nature of mobile media collections and limited computational resources of the mobile device. We employ interactive learning, a method that conducts interaction rounds with the user, each consisting of the system suggesting relevant images based on its current model, the user providing relevance labels, the system’s model retraining itself based on these labels, and the system obtaining a new set of suggestions for the next round. This method is suitable for the dynamic nature of mobile media collections and the limited computational resources. We show that XQM, a full-fledged app implemented for Android, operates on 10K image collections in interactive time (less than 1.4 s per interaction round), and evaluate user experience in a user study that confirms XQM’s effectiveness.
Evaluation of financial credit risk on pharmaceutical supply chain based on support vector machine

Abstract This paper selects the financial data of 80 listed pharmaceutical small and medium-sized enterprises (SMEs) to build a credit risk evaluation system, and compares the risk evaluation effect of Logistic model and support vector machine (SVM) model on the basis of factor analysis. The results show that the overall prediction accuracy of SVM model is 3.7% higher than that of Logistic model, and the first type of error rate is lower at 12.3%, which indicates the superiority and effectiveness of SVM model applied to the credit risk evaluation of the pharmaceutical industry under the supply chain finance (SCF) mode.
Interest Point Recommendation based on Multi Scenario Information Fusion

In order to solve the above problems, this paper proposes a method of interest points recommendation based on location-based social network fusion of multiple situation information based on the user data of check-in on location-based social network. The social network is transformed into a friend set, and the user interest information model is established based on the user and interest point backward lookup table method, which reduces the running time of interest point recommendation and improves the recommendation efficiency. LDA model is used to model the user's emotion, and the topic probability distribution of user's comments is calculated, and the similarity of user's emotional tendency is mined out. The geographic impact information is analyzed, the geographic impact information model is established, and the access probability of geographic information is calculated. The results show that the proposed method can improve the accuracy of recommendation, reduce the impact of data sparsity, and improve the efficiency of recommendation.
An Improved Exponential-Time Approximation Algorithm for Fully-Alternating Games Against Nature

“Games against Nature” [1] are two-player games of perfect information, in which one player's moves are made randomly (here, uniformly); the final payoff to the non-random player is given by some bounded-value function of the move history. Estimating the value of such games under optimal play, and computing near-optimal strategies, is an important goal in the study of decision-making under uncertainty, and has seen significant research in AI and allied areas [2], with only experimental evaluation of most algorithms' performance. The problem's PSPACE-completeness does not rule out nontrivial algorithms. Improved algorithms with theoretical guarantees are known in various cases where the payoff function F has special structure, and Littman, Majercik, and Pitassi [3] give a sampling-based improved algorithm for general F, for turn-orders which restrict the number of non-random player strategies. We study the case of general F for which the players strictly alternate with binary moves—for which the approach of [3] does not improve over brute force. We give a randomized algorithm to approximate the value of such games under optimal play, and to execute near-optimal strategies. Our algorithm, while exponential-time, achieves exponential savings over brute-force, and certifies a lower bound on the game value with exponentially small additive expected error. (On the downside, the constant of improvement in the base of the runtime exponent is tiny, and the algorithm uses exponential space.) Our algorithm is recursive, and bootstraps a “base case” algorithm for fixed-size inputs. The method of recursive composition used, the specific base-case guarantees needed, and the steps to establish these guarantees are interesting and, we feel, likely to find uses beyond the present work.
Projected Dynamic Conditional Correlations

We propose a novel specification of the Dynamic Conditional Correlation (DCC) model based on an alternative normalization of the pseudo-correlation matrix called Projected DCC (Pro-DCC). Our modification consists in projecting, rather than re-scaling, the pseudo-correlation matrix onto the set of correlation matrices in order to obtain a well defined conditional correlation matrix. A simulation study shows that projecting performs better than re-scaling when the dimensionality of the correlation matrix is large. An empirical application to the constituents of the S&P 100 shows that the proposed methodology performs favorably to the standard DCC in an out-of-sample asset allocation exercise.
NASIL: Neural Network Architecture Searching for Incremental Learning in Image Classification

“Catastrophic forgetting” and scalability of tasks are two major challenges of incremental learning. Both of these issues were related to the insufficient capacity of machine learning model and the insufficiently trained weights as the increasing of tasks. In this paper, we try to figure out the impact of the neural network architecture to the performance of incremental learning in the case of image classification. During the increasing of tasks, we propose to use neural network architecture searching (NAS) to find a structure that fits the new tasks collection better. We build a NAS environment with reinforcement learning as the searching strategy and Long Short-Term Memory network as the controller network. Computation operation and connecting previous nodes are selected for each layer in the search phase. For each time a new group of tasks is added, the neural network architecture is searched and reorganized according to the training data set. To speed up the searching, we design a parameter sharing mechanism, in which the same building blocks in each layer share a group of parameters. We also introduce the quantified-parameter building blocks into the NAS, to identify the best candidate during each round of searching. We test our solution in cifar100 data set, the average accuracy outperforms the current representative solutions (LwEMC, iCaRL, GANIL) by 24.92%, 5.62%, and 3.6%, respectively, the more tasks added, the better our solution performs.
Side-channel Analysis using Deep Learning on Hardware Trojans

Abstract A power side-channel analysis is proposed to nd the correct key in hardware trojan affected AES by evaluating correlation between every sub-key guesses. A deep learning method for side-channel analysis (SCA) is proposed, which consists of two phases-characterization and attack. Multilayer perceptron (MLP) and convolutional neural networks(CNN) are used as the models to determine the SCA and their performances are evaluated. Two kinds of desynchronization were used, where maximum possible value with which the trace can be shifted was 0 in rst and 100 in the second. SCA is performed in these desynchronized traces also.