Reviewerly/exHarmony huggingface dataset:
CS-R-FCN: Cross-Supervised Learning for Large-Scale Object Detection

Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories. In this paper, we present a novel cross-supervised learning pipeline for large-scale object detection, denoted as CS-R-FCN. First, we propose to utilize the data flow of image-level annotated images in the fully-supervised two-stage object detection framework, leading to cross-supervised learning combining bounding-box-level annotated data and image-level annotated data. Second, we introduce a semantic aggregation strategy utilizing the relationships among the cross-supervised categories to reduce the unreasonable mutual inhibition effects during the feature learning. Experimental results show that the proposed CS-R-FCN improves the mAP by a large margin compared to previous related works.
A Subjective Perceptual Quality Evaluation for Emerging Cloud VR Services

Cloud virtual reality (VR) is an emerging solution for VR applications. This technology can significantly reduce the requirement of the high computational capacity of the VR terminal and is conducive to the promotion of virtual reality services. Accurately measuring user's perceptual quality of cloud VR services is urgently demanded by VR service providers and network operators to further improve their quality of service. So far, there is still a lack of research about user's perceptual quality of cloud VR services. In this paper, we conducted a subjective experiment to investigate the impact of frame rate on the perceptual quality of the artificial scene and captured real scene in cloud VR services. Experimental results reveal that different frame rates have different influence on users' perceptual quality. Moreover, it is found that users' perceptual quality for the artificial scene and the captured real scene do not have significant difference.
Comparative study on electrocardiogram encryption using elliptic curves cryptography and data encryption standard for applications in Internet of medical things

Summary Electrocardiogram (ECG) monitoring systems are widely used in tele‐cardiology healthcare and applications of Internet of Medical Things. In order to guarantee the rapidity, stability, and accuracy of ECG signals in acquisition and transmission, the data delay, loss, and destruction must be considered in dynamic ECG monitoring system. Hence, data encryption as a key processing technique must carefully be selected in such applications. In this article, we comparatively studied the data encryption standard (DES) and elliptic curves cryptography (ECC) for applications in remote ECG monitoring. The study revealed that although both the encryption methods could effectively protect the integrity of data and ensure the operation of ECG monitoring system, the ECC method perform better than DES especially from the perspective of rapidity. In conclusion, the ECC is recommended as the candidate of encryption method in remote ECG monitoring systems.
Semantic Role Labeling for Generating Template of Indonesian News Sentences

Template-based approach is commonly used in automated journalism, including for Indonesian news article generation based on hand-crafted templates. Nowadays, there is still no research in automated template generation. This paper describes a method to generate templates automatically using semantic role labeling (SRL). Since there is no public dataset for Indonesian SRL, we also create the dataset for Indonesian SRL using PropBank annotation (Palmer et al., 2005). For the SRL model, we adapt the model that was introduced by He et al. (2017). Result from the model will be saved and compared to the templates that were manually defined by Indrayani and Khodra (2018). Our 2-layer BiLSTM model achieves 0.92 F1 on token level and 0.84 on sentence level. The automatically generated templates have similar quality to the ones that Indrayani and Khodra (2018) defined.
Design and Implementation of Visual Blockchain With Merkle Tree

Although our communities are paying extensive attention to the blockchain technology, it is still far away from realistic applications. Thus, this red-hot technology could be understood and employed for visual applications. Authors focus on online videos that collect sufficient user clicks owning to the high demand every day. When people watch the videos of TV drama episodes in an online website, they often need to exactly organise the playlists in ascending or descending order. However, video websites such as YouTube can't provide this service due to multiple reasons. This chapter creates a private blockchain for these video websites and applies Merkle tree to store the sorted videos in the chain. A sorted playlist has been created in the video website. Getting out of the box of video search so as to provide a quick video ranking solution is authors' main task. Sorting results are evaluated by using edit distance.
Understanding the Emergence and Importance of Blockchain-based Cyber-physical Social Machines: A Proposal

Cyber-physical Social Machines (CPSM) is a new socio-technical system, which has a dynamic network of various devices connected together through sensors and communicating devices embedded in them making humans interact using the networked digital technology through web applications. The main aim of the project is to understand the dynamics, emergence and importance of cyber-physical social machines (CPSMs) in terms of characterising emergent behaviours and analysing threats such as privacy, security, ethics, trust, reliability, usability, accessibility and acceptability by developing a decision-making framework. Though there are many advantages and benefits using CPSM there are several issues and challenges associated with them as well. We aim to identify all the threats, issues and challenges faced by the consumers in the CPSM and discuss the potential use of blockchain based solutions to overcome these threats, issues and challenges.
Voiceai Systems to NIST Sre19 Evaluation: Robust Speaker Recognition on Conversational Telephone Speech

In this study, we present the VoiceAI (VAI) submissions to NIST SRE 2019 challenge on the task of speaker recognition using conversational telephone speech. Domain mismatching remains a challenging problem on SRE19. However, participants are unconstrained to use any public or proprietary data to mitigate this problem. Using larger scale and more diverse training data can effectively improve the performance of the front-end neural networks (NN). In our experiments, we focus on constructing robust systems using x-vectors. Different input acoustic features are also investigated. In addition, we propose hybrid neural architectures to utilizing the strength of different neural networks such as long short-term memory (LSTM), extended time delay neural network (ETDNN) and factorized TDNN (FTDNN). We also explore many back-end strategies to make full use of the development data and to relief the domain mismatching problem. Our best network topology, a FTDNN with two LSTMP layers, significantly outperforms the baseline on NIST SRE18 evaluation set (SRE18Eval). The final system, a fusion of five systems, yields EER 2.59%, minimum cost 0.153, and actual cost 0.155 on the progress data set, ranking second on the leaderboard among all participants.
Towards a Unified Theory of Learning and Information

In this paper, we introduce the notion of "learning capacity" for algorithms that learn from data, which is analogous to the Shannon channel capacity for communication systems. We show how "learning capacity" bridges the gap between statistical learning theory and information theory, and we will use it to derive generalization bounds for finite hypothesis spaces, differential privacy, and countable domains, among others. Moreover, we prove that under the Axiom of Choice, the existence of an empirical risk minimization (ERM) rule that has a vanishing learning capacity is equivalent to the assertion that the hypothesis space has a finite Vapnik-Chervonenkis (VC) dimension, thus establishing an equivalence relation between two of the most fundamental concepts in statistical learning theory and information theory. In addition, we show how the learning capacity of an algorithm provides important qualitative results, such as on the relation between generalization and algorithmic stability, information leakage, and data processing. Finally, we conclude by listing some open problems and suggesting future directions of research.
A Systematic Study for Learning-Based Software Defect Prediction

Abstract Software defect refers to the code error in the process of software development, which could cause execution fault under specific conditions, resulting in failure, collapse, and high cost of the target software. Traditional detection techniques for software defect contain static and dynamic analysis, both of which require a great deal of workforce and time. With the development of machine learning and deep learning, software defect prediction has opened a new avenue to circumvent the drawbacks of traditional analysis approaches. Although various learning-based techniques in the prediction field have been developed, there is a lack of systematic summary and classification from the technical point of view. This paper studies the problem from the three aspects: traditional machine learning, deep learning, and hybrid learning. Moreover, the predicted performance is discussed in detail, especially in cross-project and just-in-time, to understand current research status thoroughly. This paper also provides a useful guide for further research, particularly for the potential usage of deep learning in semantic defect prediction.
Improving Sample-Efficiency in Reinforcement Learning for Dialogue Systems by Using Trainable-Action-Mask

By interacting with human and learning from reward signals, reinforcement learning is an ideal way to build conversational AI. Concerning the expenses of real-users' responses, improving sample-efficiency has been the key issue when applying reinforcement learning in real-world spoken dialogue systems (SDS). Handcrafted action masks are commonly used to rule out impossible actions and accelerate the training process. However, the handcrafted action mask can barely be generalized to unseen domains. In this paper, we propose trainable-action-mask (TAM) which learns from data automatically without handcrafting complicated rules. In our experiments in Cambridge Restaurant domain, TAM requires only 30% of training data, compared with the baseline, to reach the 80% success rate and it also shows robustness to noisy environments.
Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising

The rise of machine learning in image processing has created a gap between trainable data-driven and classical model- driven approaches: While learning-based models often show superior performance, classical ones are often more transparent. To reduce this gap, we introduce a generic wavelet shrinkage function for denoising which is adaptive to both the wavelet scales as well as the noise standard deviation. It is inferred from trained results of a tightly parametrised function which is inherited from nonlinear diffusion. Our proposed shrinkage function is smooth and compact while only using two parameters. In contrast to many existing shrinkage functions, it is able to enhance image structures by amplifying wavelet coefficients. Experiments show that it outperforms classical shrinkage functions by a significant margin.
Identity Solutions for Verification using Blockchain Technology

Students and other individuals who want to create, share and send certificates that contain private or sensitive information, like academic transcripts or digital badges, can do so with an added layer of security. In this paper, we have proposed a system which is focused on verification of personal portfolio using the trending blockchain technology. This is a decentralized system which can be expanded to any professional domain where your personal identity attributes and certificates are mandatory. We aim to create a nation-wide coherent, simple, and universal atmosphere to provide a standardized platform for identity management in a permanent, transparent and secure way.
A Novel Framework For Sentiment Analysis Using Deep Learning

Large amount of un-structured data is present online in the form of opinions and reviews. The most important task of NLP is to Extract useful information from unstructured data by first converting into structured form. Many customers write down reviews online but do not give rating to them. The main concern of this paper is to perform sentiment analysis by predicting two main types of polarities from reviews available online i-e positive and negative. Neural networks models fail to capture the contextual meaning of words and also fails to save long sequences of words and thus results in reducing performance. To overcome this issue a novel Hybrid model (RNN-LSTM-BiLSTM-CNN) using majority voting, word2vec and pre-trained Glove embedding (100d) is proposed to predict sentiment polarity against each review. Loss function used is Binary cross entropy. The proposed model is tested on different state-of-the-art datasets like SST-1, SST-2 and MR Movie review dataset. Results proved that our proposed model results in improved accuracy.
Efficient task pruning mechanism to improve robustness of heterogeneous computing systems

In heterogeneous distributed computing (HC) systems, diversity can exist in both computational resources and arriving tasks. In an inconsistently heterogeneous computing system, task types have different execution times on heterogeneous machines. A method is required to map arriving tasks to machines based on machine availability and performance, maximizing the number of tasks meeting deadlines (defined as robustness). For tasks with hard deadlines (e.g., those in live video streaming), tasks that miss their deadlines are dropped. The problem investigated in this research is maximizing the robustness of an oversubscribed HC system. A way to maximize this robustness is to prune (i.e., defer or drop) tasks with low probability of meeting their deadlines to increase the probability of other tasks meeting their deadlines. In this paper, we first provide a mathematical model to estimate a task’s probability of meeting its deadline in the presence of task dropping. We then investigate methods for engaging probabilistic dropping. We propose methods to dynamically determine task dropping and deferring threshold probabilities. Next, we develop a pruning system and a pruning-aware mapping heuristic, which we extend to engender fairness across various task types. We present the pruning mechanism as an independent component that can be applied to any mapping heuristic to improve the system robustness. To reduce overhead of the pruning mechanism, we propose approximation methods that remarkably reduce the number of mathematical calculations and improve the practicality of deploying the mechanism in heterogeneous or even homogeneous computing systems. We show the cost and energy gains of the pruning mechanism. Simulation results, harnessing a selection of mapping heuristics, show efficacy of the pruning mechanism in improving robustness (on average by ≃22%) and cost in an oversubscribed HC system by up to ≃33%.
Mixture Factorized Auto-Encoder for Unsupervised Hierarchical Deep Factorization of Speech Signal

Speech signal is constituted and contributed by various informative factors, such as linguistic content and speaker characteristic. There have been notable recent studies attempting to factorize speech signal into these individual factors without requiring any annotation. These studies typically assume continuous representation for linguistic content, which is not in accordance with general linguistic knowledge and may make the extraction of speaker information less successful. This paper proposes the mixture factorized auto-encoder (mFAE) for unsupervised deep factorization. The encoder part of mFAE comprises a frame tokenizer and an utterance embedder. The frame tokenizer models linguistic content of input speech with a discrete categorical distribution. It performs frame clustering by assigning each frame a soft mixture label. The utterance embedder generates an utterance-level vector representation. A frame decoder serves to reconstruct speech features from the encoders' outputs. The mFAE is evaluated on speaker verification (SV) task and unsupervised subword modeling (USM) task. The SV experiments on VoxCeleb 1 show that the utterance embedder is capable of extracting speaker-discriminative embeddings with performance comparable to a x-vector baseline. The USM experiments on ZeroSpeech 2017 dataset verify that the frame tokenizer is able to capture linguistic content and the utterance embedder can acquire speaker-related information.
A Rank Attack Against Extension Field Cancellation

Extension Field Cancellation (EFC) is a multivariate-based primitive for encryption proposed by Szepieniec, Ding and Preneel in 2016. They claim to provide 80 bits of security for all the proposed variants and parameters. In this paper, we develop a rigorous security analysis and show that none of the proposed variants archive the claimed security levels. While the Joux-Vitse algorithm can perform message recovery on the variants EFC$$_{p}^{-}(2,83,10)$$ and EFC$$_{pt^{2}}^{-}(2,83,8)$$ in less than $$2^{80}$$ bit operations, we offer a new key recovery technique based on MinRank that can break the last proposed variant EFC$$^{-}_{p}(3,59,6)$$ with complexity $$2^{73}$$. We also introduce a new technique based on a spectral decomposition with respect to a subfield to recover the first half of the isomorphism of polynomials in EFC$$^{-}_{p}(q,n,a)$$, when $$a = 0,1$$. This technique is of independent interest.
An Alternative Signature Design Using L1 Principal Components for Spread-Spectrum Steganography

As methods for detecting hidden data evolve, there exits an ever increasing need to develop new steganographic solutions. This paper introduces novel spread spectrum (SS) and improved spread spectrum (ISS) multimedia data embedding techniques using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> principal component signatures. The design presented performs well in terms of bit error rate and the structural similarity index metric.
Slice-feature based deep hashing algorithm for remote sensing image retrieval

Remote sensing image retrieval is aimed at obtaining a target image from a typically large number of remote sensing images. Different from natural scene images, remote sensing images usually includes more channels such as near-infrared reflectances (NIR), which will result in higher data dimensionality. However, high dimensionality of image features may lead to information redundancy and high computational costs. Recently, deep hashing methods to reduce image feature dimensionality have been proposed. However, compared with natural images, remote sensing images have smaller interclass distances, which hinder the application of deep hashing methods for image retrieval. To overcome this problem, in this paper, we develop a slice-feature deep hashing (SFDH) method for remote sensing image retrieval. The major contribution of this work is proposing an image correlation reduction strategy by separating the features in the fully connected layers into many slices. The SFDH architecture is based on a pre-trained Inception V3 model; the proposed model not only extracts image features from remote sensing images, but also introduces the slice-feature strategy to improve the fully connected structure of the previously proposed metric learning based deep hashing network (MiLaN). Besides, triplet loss is utilized to further enlarge differences between classes. Our experimental results demonstrate that the proposed slice-feature strategy outperforms state-of-the-art remote sensing image retrieval methods.
REV-AE: A Learned Frame Set for Image Reconstruction

Reversible residual network naturally extends the linear lifting scheme with no theoretic guarantee. In this paper, we propose a reversible autoencoder (Rev-AE) with this extended non-linear lifting scheme to improve image reconstruction. Nonlinear prediction and update operators are designed based on shallow convolutional neural networks to model multilayer non-linearities. Different from existing autoencoders, Rev-AE support efficient image reconstruction with parameters reusable for the symmetric encoder and decoder. Rev-AE forms a set of related frames to guarantee perfect reconstruction with the non-linear extension of classic lifting scheme. Lower and upper bounds are developed for the set of frames to relate with the singular values for each non-linear operator. Furthermore, we employ Rev-AE into lossy image compression to evaluate its effectiveness on image reconstruction. Experimental results show that Rev-AE achieves competitive performance in comparison to the state-of-the-art.
POSTER

Website fingerprinting (WFP) is a special type of traffic analysis, which aims to infer the websites visited by a user. Recent studies have shown that WFP targeting Tor users is notably more effective than previously expected. Concurrently, state-of-the-art defenses have been proven to be less effective. In response, we present a novel WFP defense that splits traffic over multiple entry nodes to limit the data a single malicious entry can use. Here, we explore several traffic-splitting strategies to distribute user traffic. We establish that our weighted random strategy dramatically reduces the accuracy from nearly 95% to less than 35% for four state-of-the-art WFP attacks without adding any artificial delays or dummy traffic.
Using Multi-Feature Selection with machine learning for De novo Acute Myeloid Leukemia in Egypt

De novo Acute Myeloid Leukemia is one of the diseases from which many people die each year. It is the most common type of all types of cancer and causes death of people all over the world. Classification methods are an efficient means to separate data. Especially in the field of medicine, where these methods are widely used in diagnosis and analysis for decision-making. In this paper, we consider group feature selection in a multiclass classification of other ways. The performance will be compared between different machine learning algorithms: Random Forest classifier (RF), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB) on AML dataset National Cancer Institute (NCI), Cairo University. The main objective is to evaluate the correction in the classification of the data concerning the efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity, and specificity. Experimental results determine that LR gives the enormous accuracy (92.30%) with the lowest error rate. All experiments are affected within a simulation environment and manipulated in Python 3.7 data mining tool.
Randomized sketches for kernel CCA

Kernel canonical correlation analysis (KCCA) is a popular tool as a nonlinear extension of canonical correlation analysis. Consistency and optimal convergence rate have been established in the literature. However, the time complexity of KCCA scales as O(n3) and is thus prohibitive when n is large. We propose an m-dimensional randomized sketches approach for KCCA with m<<n, based on the recent work on randomized sketches for kernel ridge regression (KRR). Technically we establish our theoretical results relying on an interesting connection between KCCA and KRR by utilizing a novel "duality tracking" device that alternates between the infinite-dimensional operator-theory-based view of KCCA and the finite-dimensional kernel-matrix-based view.