Reviewerly/exHarmony huggingface dataset:
Trusted Execution Environments: Properties, Applications, and Challenges

Software attacks on modern computer systems have been a persisting challenge for several decades, leading to a continuous arms race between attacks and defenses. As a first line of defense, operating system kernels enforce process isolation to limit potential attacks to only the code containing the vulnerabilities. However, vulnerabilities in the kernel itself (for example, various vulnerabilities found by Google Project Zero), side-channel attacks,<sup>1</sup> or even physical attacks<sup>2</sup> can be used to undermine process isolation.
B4SDC: A Blockchain System for Security Data Collection in MANETs

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). A detection node (i.e., collector) should discover available routes to a collection node for data collection and collect security-related data during route discovery for determining reliable routes. However, few studies provide incentives for security-related data collection in MANETs. In this article, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of Route REQuest (RREQ) forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and Route REPlies, in short RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.
PoPPL: Pedestrian Trajectory Prediction by LSTM With Automatic Route Class Clustering

Pedestrian path prediction is a very challenging problem because scenes are often crowded or contain obstacles. Existing state-of-the-art long short-term memory (LSTM)-based prediction methods have been mainly focused on analyzing the influence of other people in the neighborhood of each pedestrian while neglecting the role of potential destinations in determining a walking path. In this article, we propose classifying pedestrian trajectories into a number of route classes (RCs) and using them to describe the pedestrian movement patterns. Based on the RCs obtained from trajectory clustering, our algorithm, which we name the prediction of pedestrian paths by LSTM (PoPPL), predicts the destination regions through a bidirectional LSTM classification network in the first stage and then generates trajectories corresponding to the predicted destination regions through one of the three proposed LSTM-based architectures in the second stage. Our algorithm also outputs probabilities of multiple predicted trajectories that head toward the destination regions. We have evaluated PoPPL against other state-of-the-art methods on two public data sets. The results show that our algorithm outperforms other methods and incorporating potential destination prediction improves the trajectory prediction accuracy.
The Microfoundations of State Cybersecurity: Cyber Risk Perceptions and the Mass Public

Abstract Cybersecurity represents a unique national security challenge for states: data breaches with the potential for national, macro-level consequences are most likely to occur at the micro-level, originating through the security errors of individual computer users. Thus, aspects of national cybersecurity can often critically depend on the personal attitudes and behavior of average citizens connecting online. However, to date, theories of state cybersecurity have almost exclusively focused on the macro-level, and very little is known about how the mass public reacts to—and protects themselves from—cybersecurity threats. This study addresses this gap, drawing on psychological theories of risk perception to explain why the public simultaneously reports great concern about cybersecurity, yet does little to protect their personal safety online. Using a novel survey experiment, we examine how exposure to different types of data breaches impacts citizens’ cyber risk assessments, personal online behavior, and support for various national cybersecurity policies. We find that baseline concerns about cybersecurity and knowledge about safe online practices are very low. However, exposure to a personally relevant data breach heightens risk perception and increases willingness to engage in safer online practices. But these effects are circumscribed—actual online behavior is more resistant to change. These results have important implications for the design of effective state cybersecurity policy.
Autoencoder Constrained Clustering With Adaptive Neighbors

The conventional subspace clustering method obtains explicit data representation that captures the global structure of data and clusters via the associated subspace. However, due to the limitation of intrinsic linearity and fixed structure, the advantages of prior structure are limited. To address this problem, in this brief, we embed the structured graph learning with adaptive neighbors into the deep autoencoder networks such that an adaptive deep clustering approach, namely, autoencoder constrained clustering with adaptive neighbors (ACC_AN), is developed. The proposed method not only can adaptively investigate the nonlinear structure of data via a parameter-free graph built upon deep features but also can iteratively strengthen the correlations among the deep representations in the learning process. In addition, the local structure of raw data is preserved by minimizing the reconstruction error. Compared to the state-of-the-art works, ACC_AN is the first deep clustering method embedded with the adaptive structured graph learning to update the latent representation of data and structured deep graph simultaneously.
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera

We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB sensors using simple cameras. The approach proceeds along two stages. In the first, a real-time 2D pose detector is run to determine the precise pixel location of important keypoints of the human body. A two-stream deep neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second stage, the Efficient Neural Architecture Search (ENAS) algorithm is deployed to find an optimal network architecture that is used for modeling the spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, MSR Action3D and SBU Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that the method requires a low computational budget for training and inference. In particular, the experimental results show that by using a monocular RGB sensor, we can develop a 3D pose estimation and human action recognition approach that reaches the performance of RGB-depth sensors. This opens up many opportunities for leveraging RGB cameras (which are much cheaper than depth cameras and extensively deployed in private and public places) to build intelligent recognition systems.
Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches

The purpose of this study is to investigate the possibility of applying multilayer perceptron neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored lithofacies. These techniques are carried out to predict missing lithofacies intervals from the reservoir of Trias Argileux Grèseux Inférieur in the Sif Fatima oil field (Berkine basin- Southern Algeria). The performances of the suggested models were evaluated using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R). The MLPNN model was developed using four input variables of nuclear well logging data including: Gamma-Ray, Density, Potassium and Thorium. MLPNN model shows lower RMSE and MAE with 0.39 and 0.23 respectively, together with strong R values (training = 0.87; validation = 0.78; test = 0.92). The cluster analysis model displays lower performances (R = 0.68, RMSE = 1.04 and MAE = 0.54). This quantitative comparison between real and predicted electrofacies using the two methods indicates that MLPNN model is more recommended in rebuilding non-cored lithofacies than cluster analysis. The MLPNN model makes possible to estimate lithofacies in 333 m of non-cored lithofacies allowing an important economic benefit.
A study of deep neural networks for human activity recognition

Abstract Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, and the latter for its recent and excellent performance achievements in different domains of application such as image and speech recognition. In this article, an extensive analysis among the most suited deep learning architectures for activity recognition is conducted to compare its performance in terms of accuracy, speed, and memory requirements. In particular, convolutional neural networks (CNN), long short‐term memory networks (LSTM), bidirectional LSTM (biLSTM), gated recurrent unit networks (GRU), and deep belief networks (DBN) have been tested on a total of 10 publicly available datasets, with different sensors, sets of activities, and sampling rates. All tests have been designed under a multimodal approach to take advantage of synchronized raw sensor' signals. Results show that CNNs are efficient at capturing local temporal dependencies of activity signals, as well as at identifying correlations among sensors. Their performance in activity classification is comparable with, and in most cases better than, the performance of recurrent models. Their faster response and lower memory footprint make them the architecture of choice for wearable and IoT devices.
Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries

Code search methods, especially those that allow programmers to raise queries in a natural language, plays an important role in software development. It helps to improve programmers' productivity by returning sample code snippets from the Internet and/or source-code repositories for their natural-language queries. Meanwhile, there are many code search methods in the literature that support natural-language queries. Difficulties exist in recognizing the strengths and weaknesses of each method and choosing the right one for different usage scenarios, because (1) the implementations of those methods and the datasets for evaluating them are usually not publicly available, and (2) some methods leverage different training datasets or auxiliary data sources and thus their effectiveness cannot be fairly measured and may be negatively affected in practical uses. To build a common ground for measuring code search methods, this paper builds CosBench, a dataset that consists of 1000 projects, 52 code-independent natural-language queries with ground truths, and a set of scripts for calculating four metrics on code research results. We have evaluated four IR (Information Retrieval)-based and two DL (Deep Learning)-based code search methods on CosBench. The empirical evaluation results clearly show the usefulness of the CosBench dataset and various strengths of each code search method. We found that DL-based methods are more suitable for queries on reusing code, and IR-based ones for queries on resolving bugs and learning API uses.
A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.
SpecCFI: Mitigating Spectre Attacks using CFI Informed Speculation

Spectre attacks and their many subsequent variants are a new vulnerability class affecting modern CPUs.The attacks rely on the ability to misguide speculative execution, generally by exploiting the branch prediction structures, to execute a vulnerable code sequence speculatively.In this paper, we propose to use Control-Flow Integrity (CFI), a security technique used to stop control-flow hijacking attacks, on the committed path, to prevent speculative control-flow from being hijacked to launch the most dangerous variants of the Spectre attacks (Spectre-BTB and Spectre-RSB).Specifically, CFI attempts to constrain the possible targets of an indirect branch to a set of legal targets defined by a pre-calculated control-flow graph (CFG).As CFI is being adopted by commodity software (e.g., Windows and Android) and commodity hardware (e.g., Intel's CET and ARM's BTI), the CFI information becomes readily available through the hardware CFI extensions.With the CFI information, we apply CFI principles to also constrain illegal control-flow during speculative execution.Specifically, our proposed defense, SPECCFI, ensures that control flow instructions target legal destinations to constrain dangerous speculation on forward control-flow paths (indirect calls and branches).We augment this protection with a precise speculation-aware hardware stack to constrain speculation on backward control-flow edges (returns).We combine this solution with existing solutions against branch target predictor attacks (Spectre-PHT) to close all known nonvendor-specific Spectre vulnerabilities.We show that SPECCFI results in small overheads both in terms of performance and additional hardware complexity.
Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR

Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. However, in these models, the decisions to generate tokens are delayed compared to the actual acoustic boundaries since their unidirectional encoders lack future information. This leads to an inevitable latency during inference. To alleviate this issue and reduce latency, we propose several strategies during training by leveraging external hard alignments extracted from the hybrid model. We investigate to utilize the alignments in both the encoder and the decoder. On the encoder side, (1) multi-task learning and (2) pre-training with the framewise classification task are studied. On the decoder side, we (3) remove inappropriate alignment paths beyond an acceptable latency during the alignment marginalization, and (4) directly min-imize the differentiable expected latency loss. Experiments on the Cortana voice search task demonstrate that our proposed methods can significantly reduce the latency, and even improve the recognition accuracy in certain cases on the decoder side. We also present some analysis to understand the behaviors of streaming S2S models.
Multi-Keyword ranked search based on mapping set matching in cloud ciphertext storage system

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WHISPER: A Tool for Run-Time Detection of Side-Channel Attacks

High resolution and stealthy attacks and their variants such as Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown have completely exposed the vulnerabilities in Intel's computing architecture over the past few years. Mitigation techniques against such attacks are not very effective for two reasons: 1) Most mitigation techniques protect against a specific vulnerability and do not take a system-wide approach, and 2) they either completely remove or greatly reduce the performance benefits of resource sharing. In this work, we argue in favor of detection-based protection, which would help apply mitigation only after successful detection of the attack at runtime. As such, detection would serve as the first line of defense against such attacks. However, for a detection based protection strategy to be effective, detection needs to be highly accurate, to incur minimum system overhead at runtime, should cover a large set of attacks and be capable of early stage detection, i.e., at the very least before the attack is completed. We propose a machine learning based side-channel attack (SCA) detection tool, called WHISPER that satisfies the above mentioned design constraints. WHISPER uses multiple machine learning models in an Ensemble fashion to detect SCAs at runtime using behavioral data of concurrent processes, that are collected through hardware performance counters (HPCs). Through extensive experiments with different variants of state-of-the-art attacks, we demonstrate that the proposed tool is capable of detecting a large set of known attacks that target both computational and storage parts in computing systems. We present experimental evaluation of WHISPER against Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown attacks. The results are provided under variable system load conditions and stringent evaluation metrics comprising detection accuracy, speed, system-wide performance overhead and distribution of error (i.e., False Positives & False Negatives). Our experiments show that WHISPER can detect a large and diverse attack vector with more than 99% accuracy at a reasonably low performance overhead.
Learning in Big Data: Introduction to Machine Learning

Machine learning (ML) is a subset of artificial intelligence that develops dynamic algorithms capable of data-driven decisions, in contrast to models that follow static programming instructions. ML is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Astronomy and geosciences are two areas where the application of ML can be very fruitful. While the adoption of ML methods in astronomy and geosciences has been slow, there are several published studies using ML in these disciplines. This chapter introduces and evaluates several ML techniques. Special attention is given to inductive learning, which is among the most mature of the ML approaches currently available. The supervised, unsupervised, semisupervised and reinforcement learning types are described. ML algorithms are programs of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Current trends and recent developments in ML algorithms are discussed. Scalable ML algorithms and frameworks are also described. Selected case study applications in which ML techniques have been successfully deployed in astronomy and geosciences are described. The chapter concludes with a summary of some of the key research issues in ML related to astronomy and geosciences, with emphasis on the scope for the application of ML algorithms to the rapidly increasing volumes of astronomical and remotely sensed geophysical data for geological mapping and other problems.
Crowd counting by using multi-level density-based spatial information: A Multi-scale CNN framework

Crowd counting is an extremely challenging task due to occlusions, scale variations of people’s heads, and non-uniform distributions of people. In this paper, we propose a scale-aware convolutional neural network (CNN), named MMNet, to generate density maps for crowd counting. In comparison with most extant scale-aware works, the proposed MMNet not only captures multi-scale features generated by various sizes of filters, but also integrates multi-scale features generated by different stages to handle scale variations of people’s heads. Considering that crowd density distribution information contains critical information with respect to people’s head sizes, multi-level density-based spatial information is employed to supervise the fusion of multi-scale features in our proposed network. Specifically, two kinds of effective spatial distribution prior representation are introduced by using estimated density maps generated from intermediate stages for integrating two kinds of multi-scale features, respectively. Experimental results demonstrate the effectiveness of our proposed MMNet in comparison to state-of-the-art methods on four benchmark datasets and a real-world application.
Video anomaly detection and localization using motion-field shape description and homogeneity testing

Detection and localization of abnormal behaviors in surveillance videos of crowded scenes is challenging, where high-density people and various objects performing highly unpredictable motions lead to severe occlusions, making object segmentation and tracking extremely difficult. We associate the optical flows between multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor to describe such short-term trajectories, which reflects faithfully the motion trend and details in local patches. Furthermore, we propose a method to detect anomalies over time and space by judging whether the similarities between the testing sample and the retrieved K-NN samples follow the pattern distribution of homogeneous intra-class similarities, which is unsupervised one-class learning requiring no clustering nor prior assumption. Such a scheme can adapt to the whole scene, since the probability is used to judge and the calculation of probability is not affected by motion distortions arising from perspective distortion, which gains advantage over the existing solutions. We conduct experiments on real-world surveillance videos, and the results demonstrate that the proposed method can reliably detect and locate the abnormal events in video sequences, outperforming the state-of-the-art approaches.