Reviewerly/exHarmony huggingface dataset:
Label Propagated Nonnegative Matrix Factorization for Clustering

Semi-supervised learning (SSL) that utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples is a powerful learning paradigm with widely real-world applications such as information retrieval and document clustering. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, but it is fragile to bridge examples. Semi-supervised K-Means uses labeled examples to initialize clustering centers to separate different examples, however, semi-supervised K-Means fails in the situation of imbalanced issues, that is, the example size of each class varies significantly. This paper proposes a novel label propagated nonnegative matrix factorization method (LPNMF) to handle clean labeled but biased data and its extension LPNMF-E to handle noisy labeled data based on the framework of NMF. LPNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix. To propagate labels to unlabeled examples, LPNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. LPNMF absorbs the merits from both semi-supervised K-Means and label propagation to handle their respective shortages. Specifically, on the one hand, LPNMF learns representative clustering centers based on the distribution of the dataset, similar to semi-supervised K-means, and thus is robust to the bridge examples. On the other hand, LPNMF pushes labels according to the affinity between examples, similar to label propagation, and thus relieves the biased problem. Moreover, we introduce a LPNMF extension to handle the noisy label case. LPNMF-E relaxes the constraint of labeled examples. Since the label of each labeled example also obtains label information from the global distribution of the whole dataset and local manifold of its neighbors, LPNMF-E outputs reliable class indicators even if a portion of examples are incorrectly labeled. Theoretical analyses for the generalization ability of our proposed models are also provided. Experimental results on both clean and noisy labeled datasets confirm the effectiveness of LPNMF and LPNMF-E compared with both LP and the representative semi-supervised K-Means algorithms.
A Security Model and Fully Verified Implementation for the IETF QUIC Record Layer

Drawing on earlier protocol-verification work, we investigate the security of the QUIC record layer, as standardized by the IETF in draft version 30. This version features major differences compared to Google’s original protocol and early IETF drafts. It serves as a useful test case for our verification methodology and toolchain, while also, hopefully, drawing attention to a little studied yet crucially important emerging standard.We model QUIC packet and header encryption, which uses a custom construction for privacy. To capture its goals, we propose a security definition for authenticated encryption with semi-implicit nonces. We show that QUIC uses an instance of a generic construction parameterized by a standard AEAD-secure scheme and a PRF-secure cipher. We formalize and verify the security of this construction in F. The proof uncovers interesting limitations of nonce confidentiality, due to the malleability of short headers and the ability to choose the number of least significant bits included in the packet counter. We propose improvements that simplify the proof and increase robustness against strong attacker models. In addition to the verified security model, we also give a concrete functional specification for the record layer, and prove that it satisfies important functionality properties (such as the correct successful decryption of encrypted packets) after fixing more errors in the draft. We then provide a high-performance implementation of the record layer that we prove to be memory safe, correct with respect to our concrete specification (inheriting its functional correctness properties), and secure with respect to our verified model. To evaluate this component, we develop a provably-safe implementation of the rest of the QUIC protocol. Our record layer achieves nearly 2 GB/s throughput, and our QUIC implementation’s performance is within 21% of an unverified baseline.
Towards Design Methodology of Efficient Fast Algorithms for Accelerating Generative Adversarial Networks on FPGAs

Generative adversarial networks (GANs) have shown excellent performance in image and speech applications. GANs create impressive data primarily through a new type of operator called deconvolution (DeConv) or transposed convolution (Conv). To implement the DeConv layer in hardware, the state-of-the-art accelerator reduces the high computational complexity via the DeConv-to-Conv conversion and achieves the same results. However, there is a problem that the number of filters increases due to this conversion. Recently, Winograd minimal filtering has been recognized as an effective solution to improve the arithmetic complexity and resource efficiency of the Conv layer. In this paper, we propose an efficient Winograd DeConv accelerator that combines these two orthogonal approaches on FPGAs. Firstly, we introduce a new class of fast algorithm for DeConv layers using Winograd minimal filtering. Since there are regular sparse patterns in Winograd filters, we further amortize the computational complexity by skipping zero weights. Secondly, we propose a new dataflow to prevent resource underutilization by reorganizing the filter layout in the Winograd domain. Finally, we propose an efficient architecture for implementing Winograd DeConv by designing the line buffer and exploring the design space. Experimental results on various GANs show that our accelerator achieves up to 1.78×~8.38× speedup over the state-of-the-art DeConv accelerators.
Enhanced Decision Tree-J48 With SMOTE Machine Learning Algorithm for Effective Botnet Detection in Imbalance Dataset

Botnet is one of the major security threats in the field of information technology today (IT). The increase in the rate of attack on industrial IT infrastructures, theft of personal data and attacks on financial information is becoming critical. Majority of available dataset for botnet detection are very old and may not be able to stand the present reality in this research area. One of the latest dataset from Canadian Institute of Cyber Security labeled “CICIDS2017” was noted as an imbalance data distribution ratio of 99% to 1%. This distribution represents majority to minority class ratio. This may pose a challenge of over-fitting in majority class to the research and create a bias in the analysis of results. This research work has adopted J48 decision tree machine learning algorithm with application of SMOTE technique in solving the problem of imbalance dataset, thereby leading to an improved detection of botnets. The accuracy of the highest scenario was 99.95%. This is a significant improvement in detection rate compare to the previous research work.
Real-Time Detection of Vehicle Speed Based on Video Image

With the continuous development and improvement of Intelligent Transportation System, more and more attention has been paid to the real-time and accurate speed information detection. Video vehicle speed detection based on machine vision has attracted the attention of the researchers due to its practical convenience and other advantages. In this paper, we propose a target tracking method based on video image feature matching vehicle to simplify the algorithm complexity and obtain high accuracy. The background subtraction method based on KNN algorithm is used to identify vehicle targets and obtain good initial vehicle characteristics. Experimental results show that the method has high real-time and the relative error of vehicle speed detection can be controlled at about 5%.
Design for EM Side-Channel Security through Quantitative Assessment of RTL Implementations

Electromagnetic (EM) side-channel attacks aim at extracting secret information from cryptographic hardware implementations. Countermeasures have been proposed at device level, register-transfer level (RTL) and layout level, though efficient, there are still requirements for quantitative assessment of the hardware implementations' resistance against EM side-channel attacks. In this paper, we propose a design for EM side-channel security evaluation and optimization framework based on the t-test evaluation results derived from RTL hardware implementations. Different implementations of the same cryptographic algorithm are evaluated under different hypothesis leakage models considering the driven capabilities of logic components, and the evaluation results are validated with side-channel attacks on FPGA platform. Experimental results prove the feasibility of the proposed side-channel leakage evaluation method at pre-silicon stage. The remedies and suggested security design rules are also discussed.
Concurrent Monitoring of Operational Health in Neural Networks Through Balanced Output Partitions

The abundant usage of deep neural networks in safety-critical domains such as autonomous driving raises concerns regarding the impact of hardware-level faults on deep neural network computations. As a failure can prove to be disastrous, low-cost safety mechanisms are needed to check the integrity of the deep neural network computations. We embed safety checksums into deep neural networks by introducing a custom regularization term in the network training. We partition the outputs of each network layer into two groups and guide the network to balance the summation of these groups through an additional penalty term in the cost function. The proposed approach delivers twin benefits. While the embedded checksums deliver low-cost detection of computation errors upon violations of the trained equilibrium during network inference, the regularization term enables the network to generalize better during training by preventing overfitting, thus leading to significantly higher network accuracy.
RSA and redactable blockchains

A blockchain is redactable if a private key holder (e.g. a central authority) can change any single block without violating integrity of the whole blockchain, but no other party can do that. In this paper, we offer a simple method of constructing redactable blockchains inspired by the ideas underlying the well-known RSA encryption scheme. Notably, our method can be used in conjunction with any reasonable hash function that is used to build a blockchain. Public immutability of a blockchain in our construction is based on the computational hardness of the RSA problem and not on properties of the underlying hash function. Corruption resistance is based on the computational hardness of the discrete logarithm problem.
Tomography of time-bin quantum states using time-resolved detection

We present a method for measuring quantum states encoded in the temporal modes of photons. The basis for the multilevel quantum states is defined by the use of modes propagating in a dispersive medium, which is a fiber in this case. The propagation and time-resolved single-photon detection allow us to define a positive-operator valued measure (POVM). The POVM depends on the amount of dispersion and the characteristics of a detector. This framework is numerically tested by performing quantum state tomography on a large number of states for a set of realistic experimental settings. Finally, the average fidelity between the expected and reconstructed states is computed for qubits, qutrits, and entangled qubits.
A Verifiable and Practical Lattice-Based Decryption Mix Net with External Auditing

Mix nets are often used to provide privacy in modern security protocols, through shuffling. Some of the most important applications, such as secure electronic voting, require mix nets that are verifiable. In the literature, numerous techniques have been proposed to make mix nets verifiable. Some of them have also been employed for securing real political elections. With the looming possibility of quantum computers and their threat to cryptosystems based on classical hardness assumptions, there is significant pressure to migrate mix nets to post-quantum alternatives. At present, no verifiable and practical post-quantum mix net with external auditing is available as a drop-in replacement of existing constructions. In this paper, we give the first such construction. We propose a verifiable decryption mix net which solely employs practical lattice-based primitives. We formally prove that our mix net provides a high level of verifiability, and even accountability which guarantees that misbehaving mix servers can also be identified. Verification is executed by a (temporarily trusted) public auditor whose role can easily be distributed. To demonstrate practicality for real-world systems, we provide detailed performance benchmarks on our stand-alone implementation based only on the most conservative lattice hardness assumptions.
Fano effect in resonant Raman spectrum of CdTe

In this work, we show a line-shape analysis of the resonant Raman scattering spectrum of un-doped CdTe single crystal. We found that the main peak correspondent to the longitudinal-optic (LO) phonon mode is asymmetrically broadened toward the high-frequency side. We attributed this asymmetry to an electron-phonon coupling effect, which was demonstrated using a Fano-type function to describe the spectrum line-shape. In our fitting we have additionally considered the weak contribution (in the low-frequency region) of two added phonons, whose presence is attributed to the rupture of the symmetry due to the surface. Results show an excellent agreement between the experimental data and the fitting model, where Fano asymmetry parameter was found to be q=4.5. The results obtained, are a tool that contributes to the understanding of the origin of LO phonon line-shape asymmetry of the Raman spectrum; and its use will be helpful to investigate the strength of electron-phonon coupling in semiconductors.
Balancing computational complexity and generalization ability: A novel design for ELM

Learning paradigms that use random basis functions provide effective tools to deal with large datasets, as they combine efficient training algorithms with remarkable generalization performances. The paper first considers the affinity between the paradigm of learning with similarity functions and the Extreme Learning Machine (ELM) model, and reformulates the mapping scheme of ELMs. A mapping scheme that better balances generalization ability and network size is a novelty point of the proposed approach, and represent a major advantage when targeting implementation on resource-constrained devices. A computationally efficient heuristic supports the training procedure, and suitably applies the theory of learning with similarity functions to the availability of consistent amounts of data. Experimental results on standard datasets confirm the effectiveness of the proposed approach.
Background subtraction by probabilistic modeling of patch features learned by deep autoencoders

Video sequence analysis systems must be able to operate for long periods of time and they must attempt that the different events that can affect the quality of the input data do not diminish the performance of the system to an excessive extent. In this work a method called Probabilistic Mixture of Deeply Autoencoded Patch Features (PMDAPF) is proposed. A Deep Autoencoder is the cornerstone of the methodology for robust background modeling and foreground detection that is presented in this document. Its purpose is to obtain a reduced set of significant features from each patch belonging to one of the several shifted tilings of the video frames. Then, a probabilistic model is responsible for determining whether the whole patch belongs to the background or not. Foreground pixel detection takes into account the information of all patches in which the pixel is included. The robustness of the proposal, as well as its suitability to the uninterrupted analysis and processing of visual information, is reflected in the experiments, in which the performance of the proposed system is affected slightly whereas those of the classic methods are degraded drastically.
Investigating class rarity in big data

Abstract In Machine Learning , if one class has a significantly larger number of instances (majority) than the other (minority), this condition is defined as class imbalance. With regard to datasets, class imbalance can bias the predictive capabilities of Machine Learning algorithms towards the majority (negative) class, and in situations where false negatives incur a greater penalty than false positives, this imbalance may lead to adverse consequences. Our paper incorporates two case studies, each utilizing a unique approach of three learners (gradient-boosted trees, logistic regression, random forest) and three performance metrics ( Area Under the Receiver Operating Characteristic Curve , Area Under the Precision-Recall Curve , Geometric Mean ) to investigate class rarity in big data. Class rarity, a notably extreme degree of class imbalance, was effected in our experiments by randomly removing minority (positive) instances to artificially generate eight subsets of gradually decreasing positive class instances. All model evaluations were performed through Cross-Validation. In the first case study, which uses a Medicare Part B dataset, performance scores for the learners generally improve with the Area Under the Receiver Operating Characteristic Curve metric as the rarity level decreases, while corresponding scores with the Area Under the Precision-Recall Curve and Geometric Mean metrics show no improvement. In the second case study, which uses a dataset built from Distributed Denial of Service attack attack data (POSTSlowloris Combined), the Area Under the Receiver Operating Characteristic Curve metric produces very high-performance scores for the learners, with all subsets of positive class instances. For the second study, scores for the learners generally improve with the Area Under the Precision-Recall Curve and Geometric Mean metrics as the rarity level decreases. Overall, with regard to both case studies, the Gradient-Boosted Trees (GBT) learner performs the best.
Live Video Streaming with Joint User Association and Caching Placement in Mobile Edge Computing

Mobile live video streaming is expected to become mainstream in the fifth-generation (5G) mobile networks. To boost the Quality of Experience (QoE) of live video streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability, reliability, and the low-latency transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into live video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision, and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we develop a two-step approach from a Lagrangian optimization under the dual pricing specification. The proposed algorithm has an explicit analytic solution and can be applied from the supply-demand pricing perspective. The simulation results show that the service quality of live video streaming in the MEC-enabled cellular system can be significantly improved through the proposed system.
Is Developer Sentiment Related to Software Bugs: An Exploratory Study on GitHub Commits

The outcome of software products primarily depends on the developers, including their emotion or sentiment in a software development environment. Developer emotions have been observed to be correlated to several patterns, for instance, task resolution time, developer turnover, etc. by conducting sentiment analysis on software collaborative artifacts like Commits. This study aims to quantify the impact of those patterns by finding a relation between developer sentiment and software bugs. To do so, Fix-Inducing Changes — changes that introduce bugs to the system — are detected, along with changes that precede or fix those bugs. Sentiment of these changes are determined from their Commit messages using Senti4SD. It is statistically observed that Commits that introduce, precede or fix bugs are significantly more negative than regular Commits, with a higher proportion of emotional (non-neutral) messages. It is also found that a distinction between buggy and correct fixes exists based on the message's neutrality.
A Parametric Optimization Oriented, AFSA Based Random Forest Algorithm: Application to the Detection of Cervical Epithelial Cells

Cervical cancer is one of the most common cancers among women in the world, over 570,000 patients are affected annually.Pathological examination for patients using Pap Smear becomes the mainstream of cervical cancer diagnoses.Accurate diagnoses and analyses largely rely on 3 factors: cell segmentation, feature extraction and selection as well as classification.Firstly, a 2-layer segmentation algorithm based on block Maximum Between-Class Variance (Otsu) and Gradient Vector Flow (GVF) Snake model is applied to obtain regions of interest (ROI).Then the features of chroma, shape and texture are extracted and selected for a better classification performance.The random forest algorithm based on Artificial Fish Swarms Algorithm (AFSA) is used to recognize and classify cervical epithelial cells.The proposed methods were tested on 200 cervical Pap Smear images.Experimental results show that cervical cells can be segmented with an effective segmentation result.The proposed feature selection method achieved an accuracy of 81.31% with the minimum feature number.The improved random forest algorithm with 2 and 7 classification under fivefold cross-validation reaches the highest classification accuracy (96.86%).Experimental results showed that the proposed method has obvious recognition advantages, and thus provides a practical classification frame for the diagnoses of cervical cancer.
Recognizing actions in images by fusing multiple body structure cues

Although Convolutional Neural Networks (CNNs) have made substantial improvements in many computer vision tasks, there remains room for improvements in image-based action recognition due to the limited capability to exploit the body structure information.In this work, we propose a unified deep model to explicitly explore body structure information and fuse multiple body structure cues for robust action recognition in images.In order to fully explore the body structure information, we design the Body Structure Exploration sub-network.It generates two novel body structure cues, Structural Body Parts and Limb Angle Descriptor, which capture structure information of human bodies from the global and local perspectives respectively. And then, we design the Action Classification sub-network to fuse the predictions from multiple body structure cues to obtain precise results. Moreover, we integrate the two sub-networks into a unified model by sharing the bottom convolutional layers, which improves the computational efficiency in both training and testing stages. We comprehensively evaluate our network on the challenging image-based human action datasets, Pascal VOC 2012 Action and Stanford40. Our approach achieves 93.5% and 93.8% mAP respectively, which outperforms all recent approaches in this field.