Reviewerly/exHarmony huggingface dataset:
NetCAT: Practical Cache Attacks from the Network

Increased peripheral performance is causing strain on the memory subsystem of modern processors. For example, available DRAM throughput can no longer sustain the traffic of a modern network card. Scrambling to deliver the promised performance, instead of transferring peripheral data to and from DRAM, modern Intel processors perform I/O operations directly on the Last Level Cache (LLC). While Direct Cache Access (DCA) instead of Direct Memory Access (DMA) is a sensible performance optimization, it is unfortunately implemented without care for security, as the LLC is now shared between the CPU and all the attached devices, including the network card.In this paper, we reverse engineer the behavior of DCA, widely referred to as Data-Direct I/O (DDIO), on recent Intel processors and present its first security analysis. Based on our analysis, we present NetCAT, the first Network-based PRIME+PROBE Cache Attack on the processor's LLC of a remote machine. We show that NetCAT not only enables attacks in cooperative settings where an attacker can build a covert channel between a network client and a sandboxed server process (without network), but more worryingly, in general adversarial settings. In such settings, NetCAT can enable disclosure of network timing-based sensitive information. As an example, we show a keystroke timing attack on a victim SSH connection belonging to another client on the target server. Our results should caution processor vendors against unsupervised sharing of (additional) microarchitectural components with peripherals exposed to malicious input.
Data-Driven Deep Learning for Signal Classification in Industrial Cognitive Radio Networks

With the proliferation of mobile access services and wireless devices, spectrum resources are increasingly becoming scarce. Industrial wireless sensor networks may have to share frequency bands with other systems and suffer from considerable interference. To address that, industrial cognitive radio networks (ICRNs) were developed for effective spectrum sharing, where signal classification is a fundamental and important technology, especially for industrial wireless devices, which need to identify suspicious transmissions. In this article, a novel framework of signal intelligent classification is proposed based on deep learning networks in ICRNs. In the proposed framework, wireless signals will be preprocessed first by Choi-Williams distribution time-frequency analysis and represented by two-dimensional time-frequency images. Then, features of wireless signals are extracted through stack hybrid autoencoders (SHAEs). To accommodate general cases, we design multiple signal classification methods, including unsupervised, semisupervised, and supervised methods, which are processed by Softmax function, semisupervised linear discriminant function, and Fisher discriminant function, respectively. Finally, simulation studies are conducted and the corresponding simulation results show that the proposed framework is able to learn hierarchical features accurately and achieve excellent signal classification performance. Moreover, it can effectively overcome the negative impact caused by feature parameters uncertainty.
Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.
Deep multiplex graph infomax: Attentive multiplex network embedding using global information

Network embedding has recently garnered attention due to the ubiquity of the networked data in the real-world. A network is useful for representing the relationships among objects, and these network include social network, publication network, and protein–protein interaction network. Most existing network embedding methods assume that only a single type of relation exists between nodes. However, we focus on the fact that two nodes in a network can be connected by multiple types of relations; such a network is called multi-view network or multiplex network. Although several existing work consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. In this work, we present an unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. Building on top of DGI, we devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing (1) the consensus regularization framework that minimizes the disagreements among the relation-type specific node embeddings, and (2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for filtering unnecessary relation types as a preprocessing step. We perform comprehensive experiments not only on unsupervised downstream tasks, such as clustering and similarity search, but also a supervised downstream task, i.e., node classification, and demonstrate that DMGI outperforms the state-of-the-art methods, even though DMGI is fully unsupervised. The source code is can be found here https://github.com/pcy1302/DMGI.
PIXEL: Photonic Neural Network Accelerator

Machine learning (ML) architectures such as Deep Neural Networks (DNNs) have achieved unprecedented accuracy on modern applications such as image classification and speech recognition. With power dissipation becoming a major concern in ML architectures, computer architects have focused on designing both energy-efficient hardware platforms as well as optimizing ML algorithms. To dramatically reduce power consumption and increase parallelism in neural network accelerators, disruptive technology such as silicon photonics has been proposed which can improve the performance-per-Watt when compared to electrical implementation. In this paper, we propose PIXEL - Photonic Neural Network Accelerator that efficiently implements the fundamental operation in neural computation, namely the multiply and accumulate (MAC) functionality using photonic components such as microring resonators (MRRs) and Mach-Zehnder interferometer (MZI). We design two versions of PIXEL - a hybrid version that multiplies optically and accumulates electrically and a fully optical version that multiplies and accumulates optically. We perform a detailed power, area and timing analysis of the different versions of photonic and electronic accelerators for different convolution neural networks (AlexNet, VGG16, and others). Our results indicate a significant improvement in the energy-delay product for both PIXEL designs over traditional electrical designs (48.4% for OE and 73.9% for OO) while minimizing latency, at the cost of increased area over electrical designs.
LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional codes is available only in the large block-length limit. Guided by such asymptotic analysis, code designs require large block lengths as well as latency to achieve the desired error rate. Tail-biting convolutional codes and other recent state-of-the-art short block codes, while promising reduced latency, are neither robust to channel-mismatch nor adaptive to varying channel conditions. When the codes designed for one channel (e.g., Additive White Gaussian Noise (AWGN) channel) are used for another (e.g., non-AWGN channels), heuristics are necessary to achieve non-trivial performance. In this paper, we first propose an end-to-end learned neural code, obtained by jointly designing a Recurrent Neural Network (RNN) based encoder and decoder. This code outperforms canonical convolutional code under block settings. We then leverage this experience to propose a new class of codes under low-latency constraints, which we call Low-latency Efficient Adaptive Robust Neural (LEARN) codes. These codes outperform state-of-the-art low-latency codes and exhibit robustness and adaptivity properties. LEARN codes show the potential to design new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.
Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering

Nowadays e-commerce has spread all over the world. The e-shops are not similar to the physical shops. The e-shops can have hundreds or thousands of items independent of physical boundaries. The information about all these products is available on the Internet. So now customer is overloaded with information. Recommendation System (RS) finds users interest by utilizing implicit or explicit user’s action on the e-commerce website and recommends items that best matches user’s preferences. In this way RS helps in alleviating information overload problem. The two mainstream recommendation systems are Content Based Filtering (CBF) and Collaborative Filtering (CF). The CBF recommends items that have similar characteristics as items used by the user in the past. The CF creates a group of similar users and recommends items to the target user which is preferred in his group. Another variant of collaborative filtering finds and recommends items similar to items rated by the user, it is known as Item based Collaborative Filtering. The CF usually creates a very big recommendation list but users like only small number of items as recommendations. The proposed approach has merged the content and user-item based collaborative filtering and created a single RS that generates relatively small number of recommendations. The main contribution of the paper is to curtail the size of the recommendation list and helps in placing good items in relatively smaller list of recommended items. The experimental results show that the proposed approach surpasses other traditional benchmark recommendation methods.
Fall Detection and Personnel Tracking System Using Infrared Array Sensors

Body falling is a major risk of the older people. This paper proposes a non-contact scheme for detecting human body state using infrared array sensors, which has the advantages of low cost, easy implementation and high detection accuracy. For achieving real-time monitoring, rather than simple motion analysis, the system identifies suspicious points of body falling by simple positioning, which attempts to achieve the real-time implementation and reduces the analysis of irrelevant data. In addition, considering the influence of the radiation temperature result of the human body, the falling characteristics are included, and the clustering algorithm is proposed to tackle the problem of inaccurate determination of the area caused by the Otsu area detection. Then, a two-layer threshold system model is proposed for excluding jogging movements that are similar to the falling feature. Finally, the features are engaged with the random forest classifier in order to improve the classification rate. Experiments have shown that the proposed fall detection scheme is highly feasible and behaves good fall detection accuracy.
ANTIQUE: A Non-factoid Question Answering Benchmark

Considering the widespread use of mobile and voice search, answer passage retrieval for non-factoid questions plays a critical role in modern information retrieval systems. Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments. In this paper, we develop and release a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. The dataset, called ANTIQUE, contains 34k manual relevance annotations. The questions were asked by real users in a community question answering service, i.e., Yahoo! Answers. Relevance judgments for all the answers to each question were collected through crowdsourcing. To facilitate further research, we also include a brief analysis of the data as well as baseline results on both classical and neural IR models.
Unsupervised and Semisupervised Projection With Graph Optimization

Graph-based technique is widely used in projection, clustering, and classification tasks. In this article, we propose a novel and solid framework, named unsupervised projection with graph optimization (UPGO), for both dimensionality reduction and clustering. Different from the existing algorithms which treat graph construction and projection learning as two separate steps, UPGO unifies graph construction and projection learning into a general framework. It learns the graph similarity matrix adaptively based on the relationships among the low-dimensional representations. A constraint is introduced to the Laplacian matrix to learn a structured graph which contains the clustering structure, from which the clustering results can be obtained directly without requiring any postprocessing. The structured graph achieves the ideal neighbors assignment, based on which an optimal low-dimensional subspace can be learned. Moreover, we generalize UPGO to tackle the semisupervised case, namely semisupervised projection with graph optimization (SPGO), a framework for both dimensionality reduction and classification. An efficient algorithm is derived to optimize the proposed frameworks. We provide theoretical analysis about convergence analysis, computational complexity, and parameter determination. Experimental results on real-world data sets show the effectiveness of the proposed frameworks compared with the state-of-the-art algorithms. Results also confirm the generality of the proposed frameworks.
Prime factorization algorithm based on parameter optimization of Ising model

Abstract This paper provides a new (second) way, which is completely different from Shor’s algorithm, to show the optimistic potential of a D-Wave quantum computer for deciphering RSA and successfully factoring all integers within 10000. Our method significantly reduced the local field coefficient $$h$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>h</mml:mi></mml:math> and coupling term coefficient $$J$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>J</mml:mi></mml:math> by more than 33% and 26%, respectively, of those of Ising model, which can further improve the stability of qubit chains and improve the upper bound of integer factorization. In addition, our results obtained the best index (20-bit integer (1028171)) of quantum computing for deciphering RSA via the quantum computing software environment provided by D-Wave. Furthermore, Shor’s algorithm requires approximately 40 qubits to factor the integer 1028171, which is far beyond the capacity of universal quantum computers. Thus, post quantum cryptography should further consider the potential of the D-Wave quantum computer for deciphering the RSA cryptosystem in future.
Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.
A Framework for in-Network QoE Monitoring of Encrypted Video Streaming

With the amount of global network traffic steadily increasing, mainly due to video streaming services, network operators are faced with the challenge of efficiently managing their resources while meeting customer demands and expectations.A prerequisite for such Quality-of-Experience-driven (QoE) network traffic management is the monitoring and inference of application-level performance in terms of video Key Performance Indicators (KPIs) that directly influence end-user QoE.Given the persistent adoption of end-to-end encryption, operators lack direct insights into video quality metrics such as start-up delays, resolutions, or stalling events, which are needed to adequately estimate QoE and drive resource management decisions.Numerous solutions have been proposed to tackle this challenge on individual use-cases, most of them relying on machine learning (ML) for inferring KPIs from observable traffic patterns and statistics.In this paper, we summarize the key findings in state-of-the-art research on the topic.Going beyond previous work, we devise the concept of a generic framework for ML-based QoE/KPI monitoring of HTTP adaptive streaming (HAS) services, including model training, deployment, and re-evaluation.Components of the framework are designed in a generic way, independent of a particular streaming service and platform.The methodology for applying different framework components is discussed across various use-cases.In particular, we demonstrate framework applicability in a concrete use-case involving the YouTube service delivered to smartphones via the mobile YouTube app, as this presents one of the most prominent examples of accessing YouTube.We tackle both QoE/KPI estimation on a per-video-session level (utilizing the validated ITU-T P.1203 QoE model), as well as ''real-time'' KPI estimation over short time intervals.Obtained results provide important insights and challenges related to the deployment of a generic in-network QoE monitoring framework for encrypted video streams.
Secure and Efficient <i>k</i> NN Classification for Industrial Internet of Things

The k-nearest neighbors (kNN) classification has been widely used for defective product identification and anomaly detection in the Industrial Internet of Things (IIoT). In this article, we propose a secure and efficient distributed kNN classification algorithm (SEED-kNN) to prevent information and control flow exposure while supporting large-scale data classification on distributed servers. Specifically, we first design a secure and efficient vector homomorphic encryption (VHE) scheme by constructing a key-switching matrix and a noise matrix for data encryption. Based on the designed VHE, SEEDkNN is proposed to efficiently achieve the confidentiality of data flow, kNN query, and class label, while enabling homomorphic operations on the encrypted data. Moreover, by leveraging the Map/Reduce architecture, SEED-kNN enables the kNN classification over the large-scale encrypted data on distributed servers for industrial control systems. Finally, we demonstrate that SEEDkNN achieves semantic security and high classification accuracy, and is applicable in IIoT due to its high efficiency.
A systematic review of mobile learning in social studies

• The use of mobile learning in social studies promotes game-based learning, student engagement, and student collaboration. • Inclusion of mobile learning in social studies enhances historical thinking skills. • Mlearning in social studies supports learners in evaluation of primary and secondary sources and understanding causation. • Mlearning in social studies supports learners making connections over time, and contextualization of events. • Mlearning in social studies supports learners in making inferences and understanding multiple points of view.
A Polynomial Membership Function Approach for Stability Analysis of Fuzzy Systems

For the stability analysis of a polynomial fuzzy system, a new polynomial membership function approach is proposed to reduce conservatism. In this article, based on a state-feedback closed-loop system, a polynomials fitting method is utilized, and an improved membership function transformation technique is proposed to approximate the membership functions of the fuzzy system. Then, the membership-function-dependent polynomial-based stability conditions are derived. The obtained polynomial membership functions and approximation errors will be involved in the stability analysis process. Based on the sum-of-squares optimization technique, polynomial conditions can be directly solved. Finally, by several numerical and practical examples, conservatism reduction effects are shown by comparisons with existing methods.