Reviewerly/exHarmony huggingface dataset:
New Approach to Set Representation and Practical Private Set-Intersection Protocols

Private set intersection (PSI) is a fundamental cryptographic protocol, which has many important applications, such as personal properties matching, data sharing, or data mining. PSI has been widely studied in the literature, and many PSI protocols have been presented; however, today, many real-world applications do not use a secure PSI protocol, mainly because current PSI protocols have two issues: the insufficient efficiency and not considering the secure storage of users' datasets. Without using the PSI protocols, users of the real-world applications will sacrifice their privacy. In this paper, we propose a new approach for sets representation, which denotes sets by bit vectors and naturally hides the cardinality of a set. The new approach is particularly suitable to cloud computing environments. Then, we present two practical PSI and PSI cardinality protocols based on an additive homomorphic public-key cryptosystem (PKC). The new protocols enjoy two main advantages: 1) they are more efficient than other related protocols, especially when the set size is less than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> and 2) the approach used in our protocols provides a good solution to securely store users' datasets, and the encrypted datasets could be used as protocols' messages directly without any additional computations. Finally, we implement our PSI and PSI cardinality protocols with Paillier PKC and ElGamal PKC in Java.
The Discussion on Interior Design Mode Based on 3D Virtual Vision Technology

Three dimensional virtual vision technologies improves the realistic effect of interior design, improves the rationality of interior design, and guides interior decoration and graphic design. In this paper, MultiGen Creator 3D modeling technology is used to reconstruct the indoor landscape and form the plane image of interior design. At the same time, the edge matching method is used to divide and decompose the features of the interior design image, and the 3D vision of the interior design is reconstructed with the key points of the interior design. In addition, through the adaptive tracking and rendering technology, the fidelity and space utilization efficiency of the room plane design are improved. The simulation results show that 3D virtual vision technology has better guidance for interior design, visual effect and landscape color fusion.
Improved Probability Prediction Method Research for Photovoltaic Power Output

Due to solar radiation and other meteorological factors, photovoltaic (PV) output is intermittent and random. Accurate and reliable photovoltaic power prediction can improve the stability and safety of grid operation. Compared to solar power point prediction, probabilistic prediction methods can provide more information about potential uncertainty. Therefore, this paper first proposes two kinds of photovoltaic output probability prediction models, which are improved sparse Gaussian process regression model (IMSPGP), and improved least squares support vector machine error prediction model (IMLSSVM). In order to make full use of the advantages of the different models, this paper proposes a combined forecasting method with divided-interval and variable weights, which divides one day into four intervals. The models are combined by the optimal combination method in each interval. The simulation results show that IMSPGP and IMLSSVM have better prediction accuracy than the original models, and the combination model obtained by the combination method proposed in this paper further improves the prediction performance.
Naïve Bayes Approach for the Crime Prediction in Data Mining

Prediction analysis is the analysis in which future trends and outcomes are predicted on the basis of assumption.It is the analysis in which future trends and outcomes are predicted on the basis of assumption.Machine learning techniques and regression techniques are the two approaches that have been utilized in order to conduct predictive analytics.In the conducting predictive analytics, machine learning techniques are widely utilized and become popular as large scale datasets handled by it is effective manner and provide high performance.It provides the results with uniform characteristics and noisy data.The KNN is the popular technique which is applied in the prediction analysis.To improve accuracy of crime prediction technique of Naïve Bayes is applied in this research work.It is evaluated that Naïve Bayes give higher accuracy as compared to KNN for the crime prediction.
STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection

This paper focuses on the detection of non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations, and disasters. Comparing to existing approaches, it considers the spatial and temporal propagation of traffic anomalies from one road to other neighbor roads by proposing an STLP-OD framework. The experimental results on a real data set show that the proposed approach can improve the accuracy of traffic outlier detection baselines significantly.
Benchmarking Hierarchical Script Knowledge

Yonatan Bisk, Jan Buys, Karl Pichotta, Yejin Choi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Privacy preserving linkage using multiple dynamic match keys

Available and practical methods for privacy preserving linkage have shortcomings: methods utilising anonymous linkage codes provide limited accuracy while methods based on Bloom filters have proven vulnerable to frequency-based attacks.In this paper, we present and evaluate a novel protocol that aims to meld both the accuracy of the Bloom filter method with the privacy achievable through the anonymous linkage code methodology.The protocol involves creating multiple match-keys for each record, with the composition of each match-key depending on attributes of the underlying datasets being compared. The protocol was evaluated through de-duplication of four administrative datasets and two synthetic datasets; the 'answers' outlining which records belonged to the same individual were known for each dataset. The results were compared against results achieved with un-encoded linkage and other privacy preserving techniques on the same datasets.The multiple match-key protocol presented here achieved high quality across all datasets, performing better than record-level Bloom filters and the SLK, but worse than field-level Bloom filters.The presented method provides high linkage quality while avoiding the frequency based attacks that have been demonstrated against the Bloom filter approach. The method appears promising for real world use.
Deep Fictitious Play for Games with Continuous Action Spaces

Fictitious play has been a classic algorithm to solve two-player adversarial games with discrete action spaces. In this work we develop an approximate extension of fictitious play to two-player games with high-dimensional continuous action spaces. We use generative neural networks to approximate players' best responses while also learning a differentiable approximate model to the players' rewards given their actions. Both these networks are trained jointly with gradient-based optimization to emulate fictitious play. We explore our approach in zero-sum games, non zero-sum games and security game domains.
THE ROLE OF SOCIAL MEDIA ENGAGEMENT IN INFLUENCING CUSTOMER LOYALTY IN SAUDI BANKING INDUSTRY

Social media has gained significant popularity in the modern era, as people are spending an increasing amount of time over platforms like Facebook, Twitter and Instagram. This has instigated the organizations to amend their marketing communication techniques and to invest in the development of activities which may engage the customers. The aim of this research paper is to determine the role of social media engagement in influencing the customer loyalty from the perspective of the banking sector of Saudi Arabia. In order to ascertain this role, a combination of quantitative and qualitative methods has been employed in order to obtain relevant data and to come up with the conclusions. The regression analysis and the in-depth analysis of the qualitative data have established that the use of social media engagement has influenced the customers' loyalty in the banking sector. Therefore, banks should further focus on the social media in enhancing the customers' loyalty. Moreover, qualitative analysis suggests that customer loyalty gives strength to the financial position of the banks.Keywords: Social media; customers' loyalty; Banking sectorJEL Classifications: G21, L82DOI: https://doi.org/10.32479/irmm.8060
Image compression and encryption based on wavelet transform and chaos

With the rapid development of network technology, more and more digital images are transmitted on the network, and gradually become one important means for people to access the information. The security problem of the image information data increasingly highlights and has become one problem to be attended. The current image encryption algorithm basically focuses on the simple encryption in the frequency domain or airspace domain, and related methods also have some shortcomings. Based on the characteristics of wavelet transform, this paper puts forward the image compression and encryption based on the wavelet transform and chaos by combining the advantages of chaotic mapping. This method introduces the chaos and wavelet transform into the digital image encryption algorithm, and transforms the image from the spatial domain to the frequency domain of wavelet transform, and adds the hybrid noise to the high frequency part of the wavelet transform, thus achieving the purpose of the image degradation and improving the encryption security by combining the encryption approaches in the spatial domain and frequency domain based on the chaotic sequence and the excellent characteristics of wavelet transform. Testing experiments show that such algorithm reduces the memory consumption and implements the complexity, not only can decrease the key spending and compress the time spending, but also can improve the quality of decoded and reconstructed image, thus showing good encryption features with better encryption effect.
Experimental 4-intensity decoy-state quantum key distribution with asymmetric basis-detector efficiency

The decoy-state method has been developed rapidly in quantum key distribution (QKD) since it is immune to photon-number splitting attacks. We examine two-basis-detector-efficiency asymmetry, which exists in realistic scenarios. By using the recent 4-intensity decoy-state optimization protocol, we report an implementation of high-rate QKD with asymmetric basis-detector efficiency, demonstrating a 1.9 to 33.2 times higher key rate than previous protocols in the situation of large basis-detector-efficiency asymmetry. The results ruled out an implicit assumption in QKD that the efficiency of $Z$ basis and $X$ basis are restricted to be the same. This work paves the way towards a morepractical QKD setting.
Analysis of Non Hierarchical Bomb for Collection of Community Health Degrees in Jambi and Muaro Jambi City

This research purpose to investigate the community health degree in Jambi and Muaro Jambi City using a non-hierarchical cluster. This method based on community health building indicators with 22 sub-districts, eleven in Jambi City and 11 in Muaro Jambi district. The results was basically 22 sub-districts in Jambi City and District. Muaro Jambi already has a fairly good IPKM value, such as South Jambi sub-district, Red Paal, Jambi Market and Teluk Danau based on analysis of hierarchical bomb.
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical

Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Planning coordinated motions for tethered planar mobile robots

This paper considers the motion planning problem for multiple tethered planar mobile robots. Each robot is attached to a fixed base by a flexible cable. Since the robots share a common workspace, the interactions amongst the robots, cables, and obstacles pose significant difficulties for planning. Previous works have studied the problem of detecting whether a target cable configuration is intersecting (or entangled). Here, we are interested in the motion planning problem: how to plan and coordinate the robot motions to realize a given non-intersecting target cable configuration. We identify four possible modes of motion, depending on whether (i) the robots move in straight lines or following their cable lines; (ii) the robots move sequentially or concurrently. We present an in-depth analysis of Straight/Concurrent, which is the most practically-interesting mode of motion. In particular, we propose algorithms that (a) detect whether a given target cable configuration is realizable by a Straight/Concurrent motion, and (b) return a valid coordinated motion plan. The algorithms are analyzed in detail and validated in simulations and in a hardware experiment.
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines

In recent years neural language models (LMs) have set state-of-the-art performance for several benchmarking datasets.While the reasons for their success and their computational demand are well-documented, a comparison between neural models and more recent developments in -gram models is neglected.In this paper, we examine the recent progress in -gram literature, running experiments on 50 languages covering all morphological language families.Experimental results illustrate that a simple extension of Modified Kneser-Ney outperforms an LSTM language model on 42 languages while a word-level Bayesiangram LM (Shareghi et al., 2017) outperforms the character-aware neural model (Kim et al., 2016) on average across all languages, and its extension which explicitly injects linguistic knowledge (Gerz et al., 2018a) on 8 languages.Further experiments on larger Europarl datasets for 3 languages indicate that neural architectures are able to outperform computationally much cheaper -gram models:-gram training is up to 15, 000× quicker.Our experiments illustrate that standalone -gram models lend themselves as natural choices for resource-lean or morphologically rich languages, while the recent progress has significantly improved their accuracy.
An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition

Speech is the natural mode of communication between humans.Human-to-machine interaction is gaining importance in the past few decades which demands the machine to be able to analyze, respond and perform tasks at the same speed as performed by human.This task is achieved by Automatic Speech Recognition (ASR) system which is typically a speech-to-text converter.In order to recognize the areas of further research in ASR, one must be aware of the current approaches, challenges faced by each and issues that needs to be addressed.Therefore, in this paper human speech production mechanism is discussed.The various speech recognition techniques and models are addressed in detail.The performance parameters that measure the accuracy of the system in recognizing the speech signal are described.
Toward a Quality Predictor for Stereoscopic Images via Analysis of Human Binocular Visual Perception

Perceptual stereoscopic image quality assessment (SIQA) has become a challenge research problem due to the poor understanding of human binocular visual characteristics. For the task of SIQA, an intuitive idea is to develop effective models on the basis of the image content and depth perception. In this paper, we propose a full-reference objective quality evaluator for stereoscopic images by simulating binocular behaviors of the human visual system (HVS): Binocular interaction and depth perception. This model is based on a cyclopean image from a novel binocular combination model as image content quality description and a depth binocular combination model from a depth synthesized procedure as depth perception description. The final quality score of the distorted stereoscopic images is calculated by integrating the above two perception indicators. The experimental results on two stereoscopic image quality databases demonstrate that our proposed metric works efficiently for both symmetric and asymmetric distortions and achieves high consistent alignment with subjective observations.
Secure Data Provenance in Internet of Things based Networks by Outsourcing Attribute based Signatures and using Bloom Filters

With the dawn of autonomous organization and network and service management, the integration of existing networks with Internet of Things (IoT) based networks is becoming a reality.With minimal human interaction, the security of IoT data moving through the network becomes prone to attacks.IoT networks require a secure provenance mechanism, which is efficient and lightweight because of the scarce computing and storage resources at the IoT nodes.In this paper, we have proposed a secure mechanism to sign and authenticate provenance messages using Ciphertext-Policy Attribute Based Encryption (CP-ABE) based signatures.The proposed technique uses Bloom filters to reduce storage requirements and an outsourced ABE mechanism to use lessen the computational requirements at the IoT devices.The proposed technique helps in reducing the storage requirements and computation time in IoT devices.The performance of the proposed mechanism is evaluated and the results show that the proposed solution is best suited for resourced constrained IoT network.
An Improved Word Representation for Deep Learning Based NER in Indian Languages

Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions were proposed for NER, it is still an unsolved problem. Traditional approaches to Named Entity Recognition were based on the application of hand-crafted features to classical machine learning techniques such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so forth. But the introduction of deep learning techniques to the NER problem changed the scenario, where the state of art results have been achieved using deep learning architectures. In this paper, we address the problem of effective word representation for NER in Indian languages by capturing the syntactic, semantic and morphological information. We propose a deep learning based entity extraction system for Indian languages using a novel combined word representation, including character-level, word-level and affix-level embeddings. We have used ‘ARNEKT-IECSIL 2018’ shared data for training and testing. Our results highlight the improvement that we obtained over the existing pre-trained word representations.
Towards fast and kernelized orthogonal discriminant analysis on person re-identification

Recognizing a person across different non-overlapping camera views, is the task of person re-identification. For achieving the task, an effective way is to learn a discriminative metric by minimizing the within-class variance and maximizing the between-class variance simultaneously. However, the dimension of sample feature vector is usually greater than the number of training samples, as a result, the within-class scatter matrix is singular and the metric cannot be learned. In this paper, we propose to solve the singularity problem by employing the pseudo-inverse of the within-class scatter matrix and learning an orthogonal transformation for the metric. The proposed method can be effectively solved with a closed-form solution and no parameters required to tune. In addition, we develop a kernel version against non-linearity in person re-identification, and a fast version for more efficient solution. In experiments, we prove the validity and advantage of the proposed method for solving the singularity problem in person re-identification, and analyze the effectiveness of both kernel version and fast version. Extensively comparative experiments on VIPeR, PRID2011, CUHK01 and CUHK03 person re-identification benchmark datasets, show the state-of-the-art results of the proposed method.
Semantic consistency cross-modal dictionary learning with rank constraint

Cross-modal retrieval develops rapidly due to the growth and widespread applications of multimodal data. How to reduce the heterogeneous gap and impose effective constraints on different modalities are two basic problems. In this paper, we propose a novel Semantic Consistency cross-modal Dictionary learning algorithm with rank Constraint (SCDC) to solve these aforementioned problems. An orthogonal space learned by spectral regression is introduced, in which different modalities can be measured directly. Specifically, images and texts are encoded by their dictionaries to obtain corresponding reconstruction coefficients. A l21-norm term is imposed on these coefficients in order to select discriminative features and avoid over-fitting simultaneously. In the meantime, a rank constraint is imposed on the transformed features so as to improve the correlation of different modalities. Experimental results on three popular datasets demonstrate that SCDC is significantly superior to several state-of-the-art methods.
Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.