Reviewerly/exHarmony huggingface dataset:
Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.
Estimating Sparse Covariance Matrix Under Differential Privacy via Thresholding

In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions. We propose a new method, called DP-Thresholding, to achieve a non-trivial <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm based error bound, which is significantly better than the existing ones from adding noise directly to the empirical covariance matrix. Experiments on the synthetic datasets show consistent results with our theoretical claims.
Geochemical signature of earthquake-induced surface flooding by mineralized groundwater over the buried Atlántida deposit, northern Chile

At the buried Atlantida deposit (Cu–Au–(Mo)) in the Atacama Desert of Chile, highly saline pockets of fine-grained material 10 cm–3 m in diameter were identified on the alluvial surface using remote sensing and detailed regolith mapping. The median salinity (NaCl dominant) of the saline pockets is 2.2% compared to background alluvial material with a median salinity of 0.01%. Their distribution along mapped fault structures and the highly saline nature of the material suggest they form as an expression of groundwater forced through fractures to the surface during seismic activity. A targeted geochemical survey, oriented parallel to the orientation of the structures (sample spacing 250 m along structural trend) specifically sampling saline pockets on relatively old surfaces, was performed over the deposit. Deionized water extraction of soluble salts and analysis by inductively coupled plasma mass spectrometry revealed strong correlations of increasing salinity and increasing concentrations of porphyry copper pathfinder elements. Elevated responses of Se, Mo, Re and Te normalized to a groundwater volume proxy are present directly over the Atlantida deposit. This suggests the rate of erosion and sedimentation is slow enough in the Atacama Desert to preserve surficial anomalies as saline pockets, formed by periodic seismically induced surface flooding of groundwater along faults extending to surface. Targeted sampling of saline pockets along structural trends using weak leach geochemistry in terrains dominated by transported cover can serve as a routine exploration method for the potential discovery of buried copper porphyries and other styles of mineralization in the Atacama Desert of Chile.
HapticPoints: The Extended PassPoints Graphical Password

The most common issue of alphanumeric passwords is users normally create weak passwords for the reason that strong passwords are difficult to recognise and memorise. Graphical password authentication system is one of the approaches to address the issues of alphanumeric passwords memorability. Wiedenbeck et al. propose PassPoints in which a password is a sequence of any 5 to 8 user-selected click points on a system-assigned image. Nevertheless, PassPoints still faces the problem of predictable click points and shoulder surfing attack. In this paper, we propose an alternative graphical password system on smartphones called HapticPoints. By adding haptic feedback to PassPoints as additional decoy click points, the aforementioned problems can be prevented without needing users to do any additional memory task. We also conduct a user study to evaluate and compare the usability of HapticPoints and PassPoints.
Research on massive information query and intelligent analysis method in a complex large-scale system

With the rapid growth of big data and network information, it is particularly important to perform information query and intelligent analysis on unstructured massive data in large-scale complex systems. The existing methods of directly collating, sorting, summarizing, and storing retrieval of documents cannot meet the needs of information management and rapid retrieval of massive data. This paper takes the standardized storage, effective extraction and standardized database construction of massive resume information in social large-scale complex systems as an example, and proposes a massive information query and intelligent analysis method. The method utilizes the semi-structured features of the resume document, constructs the extraction rule model of various resume data to extract the massive resume information. On the basis of HBase distributed storage, with the help of parallel computing technology to optimize the storage and query efficiency, which ensures the intelligent analysis and retrieval of massive resume information. The experimental results show that this method not only greatly improves the extraction accuracy and recall rate of resume information data, but also compared with the traditional methods, there are obvious improvements in the three aspects of massive information retrieval methods, query usage efficiency, and the intelligent analysis of complex systems.
Hybrid SVD For Document Representation Using Different Vectorization

Document Clustering is the process of segmenting a particular collection of text into subgroups. Nowadays all documents are in electronic form, because of the issue to retrieve relevant document from the large database. The goal is to transform text composed of daily language in a structured, database format. In this way, different documents are summarized and presented in a uniform manner. The challenging problem of document clustering are big volume, high dimensionality and complex semantics. The objective of this paper is mainly focused on clustering multi-sense word embeddings using three different algorithms(K-means, DBSCAN, CURE). Among these three algorithm CURE gives better accuracy and it can handle large databases efficiently.&lt;/p&gt;
Real-Time GF-4 Satellite Image Enhancement in Low Light Conditions

GF-4 satellite images suffer from a general drawback of geostationary satellites that lose the efficacy on low light conditions such as gloaming and drawn. This significantly limits the GF-4 applications when disaster happens. In this paper, we investigate a real-time image enhancement technique for GF-4 satellites images on low light conditions and propose a reflectivity-constraint-entropy-maximization (RCEM) strategy to enhance the contrast of GF-4 images. Experiments show that the proposed RCEM outperforms the current state-of-the-art image enhancement techniques which will significantly extend the working time of GF-4 satellite.
Improve the Bayesian generalized latent variable models with non-linear variable and covariate of dichotomous data

In this paper, we develop generalized latent variable models with non-linear variable and covariate. Dichotomous variables and covariates are used in this research, and the Gibbs sampling method (Markov chain Monte-Carlo simulation) is applied for estimation. The deviance information Criterion (DIC) is used as a model comparison statistics. Truncated normal distribution is used to handle the problem of dichotomous data in variables and covariates. Statistical analyses, which include the estimation of parameters, standard deviations and their highest posterior density, are explained. The proposed method is discussed using dichotomous data with the findings extracted from the OpenBUGS software
Discriminative Features Reconstruction Network for Semantic Segmentation

Thanks to the development of convolutional neural networks (CNNs), researchers have proposed lots of effective semantic segmentation models. However, there are still two problems disturbing researchers, one of which is objects misidentification on the image level and another one is poor performance on details, especially the boundary of objects. To tackle these two problems, we propose Discriminative Features Reconstruction Network (DFR) containing two modules: Second-order Pyramid Features Reconstruction Module (SPFR) and Second-order Boundary Attention Module (SBA). Specifically, SPFR fuses different scales features to gain pyramid receptive field. Besides, SPFR extracts second-order statistics data to retrieve more discriminative features. Furthermore, we put forward SBA that is helpful to refine the segmentation results. On SBA, low-level features recover localization details under the high-level feature guidance. Our DFR achieves state-of-the-art performance on PASCAL VOC 2012 dataset with mIoU accuracy 81.1% without pre-training COCO dataset and post-processing.
Evolutionary Games for Audiovisual Works: Exploring the Demographic Prisoner’s Dilemma

This paper presents a minimalist audiovisual display of an evolutionary game known as the Demographic Prisoner's Dilemma, in which cooperation emerges as an evolutionary stable behaviour. Abiding by a dialogical approach foregrounding the dynamical negotiation of the author's aesthetic aspirational levels, the cross-space mapping between the formal model and the audiovisual work is explored, and the system undergoes several variations and modifications. Questions regarding computational measures of beauty are raised and discussed.
Using Deep-Q Network to Select Candidates from N-best Speech Recognition Hypotheses for Enhancing Dialogue State Tracking

Most state-of-the-art dialogue state tracking (DST) methods infer the dialogue state based on ground-truth transcriptions of utterances. In real-world situations, utterances are transcribed by automatic speech recognition (ASR) systems, which output the n-best candidate transcriptions (hypotheses). In certain noisy environments, the best transcription is often imperfect, severely influencing DST accuracy and possibly causing the dialogue system to stall or loop. The missed or misrecognized words can often be found in the runner-up candidate transcriptions from 2 to n, which could be used to improve accuracy of DST. However, looking beyond the top-ranked ASR results poses a dilemma: going too far may introduce noise, while not going far enough may not uncover any useful information. In this paper, we propose a novel approach to automatically determine the optimal time to stop reexamining runner-up ASR transcriptions based on deep reinforcement learning. Our method outperforms the baseline system, which uses only the top-1 ASR result, by 3.1%. Then, we select the dialogue rounds with the top-10 largest word error rate (WER), our method can improve DST accuracy by 15.4%, which is five times the overall improvement rate (3.1%). This improvement was expected because our proposed method is able to select informative ASR results at any rank.
Mobile use Style Mobile Phone use Style among College Students

The main purpose of present study was to find out mean difference certain personal mobile phone use style in college students the tata sample insisted of 200 students among which 100 of girls and 100 boys selected from different area of rural and urban of amreli city the research tool used for mobile phone use style by wikle (2003) It consists of 18 items to check in ‘t’ test and correlation of college students methods was used in mobile phone use style in gender and areas variables were showed not significant difference at 0.05 levels and correlation analysis showed that high positive in mobile phone use style gender and areas result.
Encryption of Text File Using a User Controlled Automatically-Generated Key

There are many different approaches that can be taken in order to generate automatically secret keys, however, for high security applications, end users may require control over the key generation process rather than leaving the process to third parties. In this paper, both the decryption and the encryption keys are obtained using the evolutionary computing tool Eureqa to model pseudo-random input data. The secret keys found using this technique when applied to encryption and decryption data files are validated using a range of tests, encryption and encryption time, throughput, entropy analyses and key sensitivity. The results obtained from the experiments show that the proposed encryption and decryption approaches are both reliable and secure when applied to text files and have the potential to be applied in high-security text communication applications.
INVESTIGATING THE LEVELS OF READING SKILLS AMONG GRADE THREE PUPILS IN NAIROBI COUNTY, KENYA

Reading skills are vital to lower primary school pupils because it is the foundation for learning. However, studies worldwide indicate low levels of reading skills in schools and little has been done to find out how different instructional strategies used in lower grade schools influence learners’ acquisition of reading skills. The purpose of this study was to establish levels of reading skills among the Grade Three Pupils The research was guided by Holdaway’s Theory of Literacy Development and supported by Instructional Theory by Robert Gagne. The study established that 50% of the pupils were below average in reading of letter and letter sound recognition, 57.1% in sentence and paragraph reading, 53.6% in story reading and a mere 60.7% in comprehension skills. Article visualizations:
서비스 경영이 소비자 구매의사결정과정에 미치는 영향

This study analyzes the effects of service management on the consumer purchase decision making processes. With the era of the fourth industrial revolution where artificial intelligence is applied to many areas in the business world, it is only natural to reflect on this change of paradigm and delve into the changes of consumer interaction with the businesses as well. Thus, we have investigated the interrelationship between service management and consumer purchase behavior, interrelationship between service management and customer relationship, and interrelationship between customer relationship and purchasing behavior triggered by service management. The results from this study show that service management of companies can solve the shortage of consumers'' time, lack of attention and lack of trust. In addition, it could be seen that when a company is able to eliminate these deficiencies on the consumers time, lack of attention and trust through service management, it maintains a positive internal work atmosphere, improves its image and customer satisfaction levels, not to mention it creates a positive word of mouth. All of these factors leads to an increase in business transactions and the influx of new customers, which shows that this phenomenon is a positive and important factor in the consumers'' decision making processes.
Error Bounds for Spectral Clustering over Samples from Spherical Gaussian Mixture Models

Spectral clustering has been one of the most popular methods for clustering multivariate data and has been widely used in image processing and data mining. Despite its considerable empirical success, the theoretical properties of spectral clustering are not yet fully developed. In this paper, we derive upper bounds for the clustering error of spectral clustering for data samples generated from spherical Gaussian mixture models. In our analysis, first, the graph Laplacian calculated from samples is approximated by a reference graph Laplacian which has good spectral properties. Second, we use the Davis-Kahan perturbation theorem to provide an upper bound for the sum of squared distances between each projected data point and its cluster center. Finally, we leverage theoretical results of MeilIa's to prove an upper bound for the clustering error from the upper bound for the sum of squared distances.
Ontology-Based Database for Chemical Experiments: Design and Implementation

Virtual laboratory teaching helps develop and acquire skills. Chemistry ontologies address chemical elements and their interaction. We seek to implement ChemOnto, ontology that makes it easy to develop chemical virtual laboratories that will help learners get acquainted with hands-on tools and procedures. We will study existing ontologies on chemical elements by searching, prioritizing, and linking key terms related to experimentation and safety rules. We will implement an ontology describing chemical experiments with the inception of an interface for database usage, safety rules, and skills involved. We will feed our practicum and safety rule database. Anticipated results are: Safety rules are known and skills involved are acquired. For each practicum, our ontology proposes a set of skills to be acquired over time along with various actions to complete them.
Audio Watermarking Art of Hiding Copyright Information for Piracy Control

Background &amp; Objective: Revolution in digital multimedia is a boon to music industries, music creators for decades as digital music can be created, stored replicated and transferred easily and efficiently. But digitalization of multimedia becomes a curse when the multimedia content is illegally and freely distributed, shared across the network online or offline. Countering the illegal copying and distribution of digital media is the driving force behind the evolution of copyright protection and digital watermarking techniques. Methods: This paper presents the problem of piracy and the overview of the evolution of different digital audio watermarking techniques in time, transformed and compression domains to counter the problem of music piracy. The limitations of the audio watermarking techniques and the future scope for improvement are also presented. Results &amp; Conclusion: This paper summarizes the evolution of audio watermarking techniques and reviews the existing watermarking techniques applied on audios. The limitations of the audio watermarking techniques and the future scope for improvement are also proposed. Additionally, the preliminaries for audio and brief of the properties which are exploited for watermarking of audio are presented.
An Iterative Time Domain Denoising Method

This paper focuses on the classical additive noise signal restoration problem. The proposed time domain denoising method iteratively removes outliers. The proposed denoising filter incorporates a threshold operation to determine which sample values are outliers. This method is compared with wavelet soft/hard thresholding and empirical mode decomposition interval thresholding. The proposed method is shown to be a promising method to denoise signals where a frequency decomposition may not be a robust representation of the noise free signal. The paper provides a discussion on threshold selection and proposes future work to include applying noise estimation to automatically determine thresholds, possible stopping (convergence) criteria, and possible inclusion of an weighted mean computation to improve denoising performance.
Some Remarks on Self-Dual Codes Invariant Under Almost Simple Permutation Groups

We present some recent advances on the question of enumeration and classification of self-dual codes invariant under finite non-solvable permutation groups. In particular, we examine the question for almost simple groups of sporadic or rank 3 types. As a by-product we determine all doubly even and extremal codes of length n ≤ 4095 which admit an almost simple group of sporadic or rank 3 type as a permutation group of automorphisms acting transitively on the code’s coordinate positions.
R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;: Information or Noise? Textual Analysis Based on SSE E-Interaction

The question whether R 2 represents information or noise is still a fundamental question in the study of stock price synchronicity.There are two main difficulties.Firstly, the trait information of a company is hard to measure; Secondly, the investors' sophistication is ignored when we discuss the effectiveness of market.Through the study of Chinese SSE E-interaction platform and the measurement by textual analysis method, this article argues that the improvement of investors' sophistication has negative association with stock price synchronicity.This result is more salient in companies with lower opacity or with higher corporate governance.This paper contributes to the deepen understanding of stock price synchronicity and new method of measuring trait information of companies.
A Method for Solving Generalized Implicit Factorization Problem

The problem of factoring RSA moduli with the implicit hint was firstly proposed by May and Ritzenhofen at PKC’09 where unknown prime factors of several RSA moduli shared some number of least significant bits (LSBs), and was later considered by Faugère et al. where some most significant bits (MSBs) were shared between the primes. Recently, Nitaj and Ariffin proposed a generalization of the implicit factorization problem. Let $$ {\text{N}}_{1} = {\text{p}}_{1} {\rm{q}}_{1} $$ and $$ {\text{N}}_{2} = {\text{p}}_{2} {\rm{q}}_{2} $$ be two distinct RSA moduli, Nitaj and Ariffin showed that when $$ {\text{a}}_{1} {\rm{p}}_{1} $$ and $$ {\text{a}}_{2} {\rm{p}}_{2} $$ share enough bits, $$ {\text{N}}_{1} , {\rm{N}}_{2} $$ can be factored in polynomial time, where $$ {\text{a}}_{1} $$ and $$ {\text{a}}_{2} $$ are some unknown positive integers. They also extended their work to the case of $$ k\left( { \ge 3} \right) $$ moduli. In this paper, we revisit Nitaj-Ariffin’s work and transform the problem into solving small roots of a modular equation. Then by utilizing Coppersmith’s method, for the case of two moduli we improve Nitaj-Ariffin’s result when the unknowns $$ {\text{a}}_{1} ,{\rm{a}}_{2} $$ are relatively small, and our result is always better than Nitaj-Ariffin’s result for the case of $$ k\left( { \ge 3} \right) $$ moduli.
Implementation feature selection method for detection packet data on <i>Fasilkom Unsri</i> computer network

Network traffic generates data traffic accessed by users. These data sometimes have nothing to do with the work area of an office or institution. A lot of data traffic as an example at a university is caused by diverse and numerous user populations. They access various data. For this reason, it is necessary to monitor data traffic in order to control and make decisions for network administrators. To get data packages that are often accessed and the most dominant, it is necessary to build a monitoring system on the network. In building this system, stages are made using the feature selection method. The results of this method to get the detection of data packet engines and applications. Furthermore, these results can be displayed visually in the form of graphs. This result is a dashboard for network monitoring systems. This dashboard can be used to help a network administrator to consider network monitoring decisions.
Inductive Conformal Predictor for Sparse Coding Classifiers: Applications to Image Classification

Conformal prediction uses the degree of strangeness (nonconformity) of new data instances to determine the confidence values of new predictions. We propose an inductive conformal predictor for sparse coding classifiers, referred to as ICP-SCC. Our contribution is twofold: first, we present two nonconformity measures that produce reliable confidence values; second, we propose a batch mode active learning algorithm within the conformal prediction framework to improve classification performance by selecting training instances based on two criteria, informativeness and diversity. Experiments conducted on face and object recognition databases demonstrate that ICP-SCC improves the classification accuracy of state-of-the-art dictionary learning algorithms while producing reliable confidence values.