Reviewerly/exHarmony huggingface dataset:
Fast Semi-Supervised Learning With Optimal Bipartite Graph

Recently, with the explosive increase in Internet data, the traditional Graph-based Semi-Supervised Learning (GSSL) model is not suitable to deal with large scale data as the high computation complexity. Besides, GSSL models perform classification on a fixed input data graph. The quality of initialized graph has a great effect on the classification result. To solve this problem, in this paper, we propose a novel approach, named optimal bipartite graph-based SSL (OBGSSL). Instead of fixing the input data graph, we learn a new bipartite graph to make the result more robust. Based on the learned bipartite graph, the labels of the original data and anchors can be calculated simultaneously, which solves co-classification problem in SSL. Then, we use the label of anchor to handle out-of-sample problem, which preserves well classification performance and saves much time. The computational complexity of OBGSSL is O(ndmt+nm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), which is a significant improvement compared with traditional GSSL methods that need O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> d+n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), where n, d, m and t are the number of samples, features anchors and iterations, respectively. Experimental results demonstrate the effectiveness and efficiency of our OBGSSL model.
A Multimodal Deep Learning-Based Distributed Network Latency Measurement System

Network latency plays an important role in the server-selection process as well as real-time applications. Depending on the network system size, network latency can be either explicitly measured or predicted. While for small-scale systems explicit delay measurements can be performed between any pair of network nodes, this method is not feasible for large-scale networks due to the tremendous traffic and processing overhead. As a result, networking companies as well as researchers use the estimation methods for round-trip time (RTT) in large-scale networks. In such methods, network latency estimation is based on performing a small set of actual RTT measurements and predicting the rest of latencies among all network nodes. However, they suffer from several drawbacks such as poor performance, long convergence duration, or lack of convergence. In this article, we present a novel method of large-scale network latency estimation using artificial intelligence (AI). Our system uses a multimodal deep learning algorithm for high accuracy and computing speed. The proposed AI-based system is trained and evaluated using the well-known KING data set derived from the measurements of a real large-scale network. Performance evaluations show that our proposed approach significantly outperform existing techniques, achieving the 90th percentile relative error of 0.25 and an average accuracy of 96.1%, and 76.4% of the measurements are within 20% estimation error.
A novel dataset-specific feature extractor for zero-shot learning

Most of the existing Zero-Shot Learning(ZSL) algorithms adopt pre-trained neural networks as their feature extractors. Since these pre-trained models are not specially designed for ZSL tasks, it is difficult to guarantee the stability and generalization ability of the ZSL algorithms due to the feature mismatch. To alleviate this problem, we propose a novel dataset-specific feature extractor for ZSL according to an attribute-based label tree. Specifically, an attribute-based label tree is firstly built via K-means clustering and then the information extracted from the label tree is used to fine-tune the parameters of the pre-trained models in order to make the extracted features more suitable for the current ZSL task. The experimental results on three typical ZSL datasets show that our approach can effectively improve the predictive accuracy of the existing ZSL algorithms and significantly accelerate their convergence rate. Additionally we explain the experimental phenomena from the perspective of feature visualization, which experimentally show that the features extracted by our method are much more separable than those of the original pre-trained models.
DNA Similarity Search With Access Control Over Encrypted Cloud Data

DNA similarity search has been widely applied in human genomic studies including DNA marking, genomic sequencing and genetic disease prediction. Meanwhile, with the explosive growth of data, users are increasingly inclining to store DNA data on the cloud for saving local cost. However, the high sensitivity of DNA data has forced the government to strictly control its acquisition and utilization. One potential solution is to encrypt DNA data before outsourcing them to the cloud. Nevertheless, private DNA similarity query has been an active research issue, state-of-the-art results are still defective in security, functionality, and efficiency. In this article, we propose EFSS, an efficient and fine-grained similarity search scheme over encrypted DNA data. In specific, first, we design an approximation algorithm to efficiently calculate the edit distances between two sequences. Second, we put forward a novel Boolean search strategy to achieve complicated logic queries such as mixed “AND” and “NO” operations on genes. Third, data access control is also supported in our EFSS through a variant of polynomial based design. Moreover, the K-means clustering algorithm is exploited to further improve the efficiency of execution. In the end, security analysis and extensive experiments demonstrate the high performance of EFSS compared with existing schemes.
GTM-CSec: Game theoretic model for cloud security based on IDS and honeypot

Cloud Computing has been adopted by many leading organizations for storage, processing, sharing and to provide other services. It faces several security challenges from its surroundings in terms of regular and sophisticated attacks. These challenges become worse when the Internet of Things (IoT) devices are connected to it. This is because of their low-security characteristics. These threats are generally handled using techniques such as Intrusion Detection System (IDS), Honeypots, Firewall, etc. To identify the best defense strategy in these techniques, further research is needed. Hence in this paper, a game-theoretic model GTM-CSec has been proposed. The proposed model intelligently selects the most suitable module out of the signature, anomaly, and honeypot based detection to detect the attack. The selection of a particular detection module instead of using all in parallel not only leads to the reduction of energy consumption but also increases the overall efficiency of the defender system. The strategies for both the defender and attacker have been evaluated and the best one has been delineated with Nash Equilibrium (NE). The simulation results prove that with the introduction of the proposed model, the defender system is very effective against the attacker.
Theory of relaxation and pumping of quantum oscillator non-resonantly coupled with the other oscillator

The paper shows mechanisms of both the pumping and energy decay of an 'isolated' oscillator. The oscillator is only coupled non-resonantly with the adjacent oscillator which resonantly interacts with the thermal bath environment. Under these conditions the 'isolated' oscillator begins interacting with the thermal bath environment of the adjacent oscillator. The conclusion is based on the kinetic equation derived relative to anti-rotating terms of the initial Hamiltonian. The original model is reduced to two 'independent' open systems, with each containing original oscillators connected with their effective independent thermal baths, whose parameters are determined. The paper also gives a description of the obtained model in terms of quantum stochastic differential equations.
SliceNDice: Mining Suspicious Multi-Attribute Entity Groups with Multi-View Graphs

Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity. This has spurred research in numerous suspicious behavior detection tasks, including detection of sybil accounts, false information, and payment scams/fraud. In this paper, we draw the insight that many such initiatives can be tackled in a common framework by posing a detection task which seeks to find groups of entities which share too many properties with one another across multiple attributes (sybil accounts created at the same time and location, propaganda spreaders broadcasting articles with the same rhetoric and with similar reshares, etc.) Our work makes four core contributions: Firstly, we posit a novel formulation of this task as a multi-view graph mining problem, in which distinct views reflect distinct attribute similarities across entities, and contextual similarity and attribute importance are respected. Secondly, we propose a novel suspiciousness metric for scoring entity groups given the abnormality of their synchronicity across multiple views, which obeys intuitive desiderata that existing metrics do not. Finally, we propose the SliceNDice algorithm which enables efficient extraction of highly suspicious entity groups, and demonstrate its practicality in production, in terms of strong detection performance and discoveries on Snapchat's large advertiser ecosystem (89% precision and numerous discoveries of real fraud rings), marked outperformance of baselines (over 97% precision/recall in simulated settings) and linear scalability.
Gaussian Process Regression for Data Fulfilling Linear Differential Equations with Localized Sources

Specialized Gaussian process regression is presented for data that are known to fulfill a given linear differential equation with vanishing or localized sources. The method allows estimation of system parameters as well as strength and location of point sources. It is applicable to a wide range of data from measurement and simulation. The underlying principle is the well-known invariance of the Gaussian probability distribution under linear operators, in particular differentiation. In contrast to approaches with a generic covariance function/kernel, we restrict the Gaussian process to generate only solutions of the homogeneous part of the differential equation. This requires specialized kernels with a direct correspondence of certain kernel hyperparameters to parameters in the underlying equation and leads to more reliable regression results with less training data. Inhomogeneous contributions from linear superposition of point sources are treated via a linear model over fundamental solutions. Maximum likelihood estimates for hyperparameters and source positions are obtained by nonlinear optimization. For differential equations representing laws of physics the present approach generates only physically possible solutions, and estimated hyperparameters represent physical properties. After a general derivation, modeling of source-free data and parameter estimation is demonstrated for Laplace's equation and the heat/diffusion equation. Finally, the Helmholtz equation with point sources is treated, representing scalar wave data such as acoustic pressure in the frequency domain.
RePAD: Real-Time Proactive Anomaly Detection for Time Series

During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historical data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.
Learning rules for Sugeno ANFIS with parametric conjunction operations

The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.
TH-GAN: Generative Adversarial Network Based Transfer Learning for Historical Chinese Character Recognition

Historical Chinese character recognition faces problems including low image quality and lack of labeled training samples. We propose a generative adversarial network (GAN) based transfer learning method to ease these problems. The proposed TH-GAN architecture includes a discriminator and a generator. The network structure of the discriminator is based on a convolutional neural network (CNN). Inspired by Wasserstein GAN, the loss function of the discriminator aims to measure the probabilistic distribution distance of the generated images and the target images. The network structure of the generator is a CNN based encoder-decoder. The loss function of the generator aims to minimize the distribution distance between the real samples and the generated samples. In order to preserve the complex glyph structure of a historical Chinese character, a weighted mean squared error (MSE) criterion by incorporating both the edge and the skeleton information in the ground truth image is proposed as the weighted pixel loss in the generator. These loss functions are used for joint training of the discriminator and the generator. Experiments are conducted on two tasks to evaluate the performance of the proposed TH-GAN. The first task is carried out on style transfer mapping for multi-font printed traditional Chinese character samples. The second task is carried out on transfer learning for historical Chinese character samples by adding samples generated by TH-GAN. Experimental results show that the proposed TH-GAN is effective.
Segmentation-Free Bangla Offline Handwriting Recognition using Sequential Detection of Characters and Diacritics with a Faster R-CNN

This paper presents an offline handwriting recognition system for Bangla script using sequential detection of characters and diacritics with a Faster R-CNN. This is an entirely segmentation-free approach where the characters and associated diacritics are detected separately with different networks named C-Net and D-Net. Both of these networks were prepared with transfer learning from VGG-16. The essay scripts from the Boise State Bangla Handwriting Dataset along with standard data augmentation techniques were used for training and testing. The F1 scores for the C-Net and D-Net networks are 89.6% and 93.2% respectively. Afterwards, both of these detection modules were fused into a word recognition unit with CER (Character Error Rate) of 11.2% and WER (Word Error Rate) of 24.4%. A spell checker further minimized the errors to 8.9% and 21.5% respectively. This same method is likely to be equally effective on several other Abugida scripts similar to Bangla.
MHCOOS: An Offline-Online Certificateless Signature Scheme for M-Health Devices

Current trends of mobile technology have seen a tremendous growth in its application in smart healthcare. This has resulted in the adoption and implementation of mobile health (m-health) systems by providing health assistance to the aging population. Despite its advantageous benefits, its computational complexities cannot be overlooked. M-health devices are portable processing tiny equipment with limited computational capabilities thereby making them complex for the implementation of public key cryptosystems. In spite of this, an Offline-Online signature scheme called the MHCOOS has been proposed to solve the difficulties in the computational ability. The scheme enjoys the following benefits by splitting the signing part into both offline and online phases. The offline phase performs heavy computations when a message is absent, whereas lighter computations are performed at the online stage when a message is present. Secondly, the online computations are extremely fast due to the already computed offline signature value and lighter pairings involved. Our performance analysis demonstrates how the proposed scheme outperforms other schemes. Finally, the hardness of the scheme is proven under the Bilinear Diffie–Hellman (BDH) and Computational Diffie–Hellman (CDH) problem in the random oracle model.
Idiomatic Patterns and Aesthetic Influence in Computer Music Languages

It is widely accepted that acoustic and digital musical instruments shape the cognitive processes of the performer on both embodied and conceptual levels, ultimately influencing the structure and aesthetics of the resulting performance. In this article we examine the ways in which computer music languages might similarly influence the aesthetic decisions of the digital music practitioner, even when those languages are designed for generality and theoretically capable of implementing any sound-producing process. We examine the basis for querying the non-neutrality of tools with a particular focus on the concept of idiomaticity: patterns of instruments or languages which are particularly easy or natural to execute in comparison to others. We then present correspondence with the developers of several major music programming languages and a survey of digital musical instrument creators examining the relationship between idiomatic patterns of the language and the characteristics of the resulting instruments and pieces. In an open-ended creative domain, asserting causal relationships is difficult and potentially inappropriate, but we find a complex interplay between language, instrument, piece and performance that suggests that the creator of the music programming language should be considered one party to a creative conversation that occurs each time a new instrument is designed.
Emotion Detection from Tweets using AIT-2018 Dataset

People show emotions for everyday communication. Emotions are identified by facial expressions, behavior, writing, speaking, gestures and physical actions. Emotion plays a vital role in the interaction between two people. The detection of emotions through text is a challenge for researchers. Emotion detection from the text can be useful for real-world application. Automatic emotion detection in the original text aims to recognize emotions in any digital medium by using natural language processing techniques and different approaches. Enabling machines with the ability to recognize emotions in a particular kind of text such as twitter's tweet has important applications in sentiment analysis and affective computing. We have worked on the newly published gold dataset (AIT-2018) and propose a model consisting of lexical-based using Word Net-Affect and EmoSenticNet with supervised classifiers for detecting emotions in a tweet text.
Perceptual Image Hashing Based on Color Structure and Intensity Gradient

In order to improve classification performance of the hash algorithm for color images, this paper presents an hash algorithm that based on three-dimensional color structure features and luminance gradient feature. Firstly, the algorithm preprocesses the input image to produce a secondary image, and extracts the color opponent component from the secondary image. Secondly, three-dimensional external structural features are constructed by using the peak curve, mean curve, and valley curve of the color opponent component. Thirdly, the overlapping points of the peak points in different visual angles and the valley points in different visual angles are selected as the salient points. The position information of the salient points are used to construct internal features. The gradient feature is constructed by using pixel changes in four directions of Y component of luminance image in YCbCr color space. Finally, the three-dimensional color structure features and luminance gradient feature are combined and disturbed to obtain the final hash sequence. Experiment results show that the proposed scheme has preferable robustness and discrimination. Compared with some existing schemes, the proposed scheme has better classification performance, shorter hash length and less average time of hash generation, and it has good detection performance in image copy detection and image tampered detection.
Towards Optimal Separations between Quantum and Randomized Query Complexities

The query model offers a concrete setting where quantum algorithms are provably superior to randomized algorithms. Beautiful results by Bernstein-Vazirani, Simon, Aaronson, and others presented partial Boolean functions that can be computed by quantum algorithms making much fewer queries compared to their randomized analogs. To date, separations of O(1) vs. √N between quantum and randomized query complexities remain the state-of-the-art (where N is the input length), leaving open the question of whether O(1) vs. N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2+Ω(1)</sup> separations are possible? We answer this question in the affirmative. Our separating problem is a variant of the Aaronson-Ambainis k-fold Forrelation problem. We show that our variant: 1)Can be solved by a quantum algorithm making 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(k)</sup> queries to the inputs. 2)Requires at least ~Ω(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(k-1)/(3k-1)</sup> ) queries for any randomized algorithm. For any constant , this gives a O(1) vs. N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2-ε</sup> separation between the quantum and randomized query complexities of partial Boolean functions. Our proof is Fourier analytical and uses new bounds on the Fourier spectrum of classical decision trees, which could be of independent interest. Looking forward, we conjecture that the Fourier bounds could be further improved in a precise manner, and show that such conjectured bounds imply optimal O(1) vs. N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-ε</sup> separations between the quantum and randomized query complexities of partial Boolean functions.