Reviewerly/exHarmony huggingface dataset:
Assessment of Buffer Overflow Based Attacks On an IoT Operating System

The vulnerability of electronic systems to attacks from hostile agents over the Internet is a matter of growing concern as the digital world becomes increasingly interconnected. Many countermeasures have been put in place to address these concerns, but the computational burden they impose may be excessive for low-power devices. In particular, the devices which comprise The Internet of Things are particularly vulnerable. This paper provides numerical assessments of their vulnerability to a class of attacks based on triggering buffer overflows. We focus on two such attacks, namely return-to-lib-c and code injection, for IoT devices using the FreeRTOS (V9.0.0) operating system. We show the deficiencies of the prevention measures provided in FreeRTOS to prevent these kinds of attacks, and compare them to the more robust mechanisms available in a mainstream OS (Ubuntu 16.04.1). The paper concludes with suggestions of how to mitigate these vulnerabilities in FreeRTOS - further investigation will be required to determine whether similar vulnerabilities exist in other IoT operating systems.
Stacked Wasserstein Autoencoder

Approximating distributions over complicated manifolds, such as natural images, are conceptually attractive. The deep latent variable model, trained using variational autoencoders and generative adversarial networks, is now a key technique for representation learning. However, it is difficult to unify these two models for exact latent-variable inference and parallelize both reconstruction and sampling, partly due to the regularization under the latent variables, to match a simple explicit prior distribution. These approaches are prone to be oversimplified, and can only characterize a few modes of the true distribution. Based on the recently proposed Wasserstein autoencoder (WAE) with a new regularization as an optimal transport. The paper proposes a stacked Wasserstein autoencoder (SWAE) to learn a deep latent variable model. SWAE is a hierarchical model, which relaxes the optimal transport constraints at two stages. At the first stage, the SWAE flexibly learns a representation distribution, i.e., the encoded prior; and at the second stage, the encoded representation distribution is approximated with a latent variable model under the regularization encouraging the latent distribution to match the explicit prior. This model allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce changes in the output space. Both quantitative and qualitative results demonstrate the superior performance of SWAE compared with the state-of-the-art approaches in terms of faithful reconstruction and generation quality.
Neural Task Planning With AND–OR Graph Representations

This paper focuses on semantic task planning, that is, predicting a sequence of actions toward accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The primary challenges are how to model the task-specific knowledge and how to integrate this knowledge into the learning procedure. In this paper, we propose training a recurrent long short-term memory (LSTM) network to address this problem, that is, taking a scene image (including prelocated objects) and the specified task as input and recurrently predicting action sequences. However, training such a network generally requires large numbers of annotated samples to cover the semantic space (e.g., diverse action decomposition and ordering). To overcome this issue, we introduce a knowledge and-or graph (AOG) for task description, which hierarchically represents a task as atomic actions. With this AOG representation, we can produce many valid samples (i.e., action sequences according to common sense) by training another auxiliary LSTM network with a small set of annotated samples. Furthermore, these generated samples (i.e., task-oriented action sequences) effectively facilitate training of the model for semantic task planning. In our experiments, we create a new dataset that contains diverse daily tasks and extensively evaluates the effectiveness of our approach.
Visual object tracking based on adaptive Siamese and motion estimation network

Recently, convolutional neural network (CNN) has attracted much attention in different areas of computer vision, due to its powerful abstract feature representation. Visual object tracking is one of the interesting and important areas in computer vision that achieves remarkable improvements in recent years. In this work, we aim to improve both the motion and observation models in visual object tracking by leveraging representation power of CNNs. To this end, a motion estimation network (named MEN) is utilized to seek the most likely locations of the target and prepare a further clue in addition to the previous target position. Hence the motion estimation would be enhanced by generating a small number of candidates near two plausible positions. The generated candidates are then fed into a trained Siamese network to detect the most probable candidate. Each candidate is compared to an adaptable buffer, which is updated under a predefined condition. To take into account the target appearance changes, a weighting CNN (called WCNN) adaptively assigns weights to the final similarity scores of the Siamese network using sequence-specific information. Evaluation results on well-known benchmark datasets (OTB100, OTB50 and OTB2013) prove that the proposed tracker outperforms the state-of-the-art competitors.
Discriminative Supervised Hashing for Cross-Modal Similarity Search

With the advantages of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search. As multiple modal data reflect similar semantic content, many works aim at learning unified binary codes. However, discriminative hashing features learned by these methods are not adequate. This results in lower accuracy and robustness. We propose a novel hashing learning framework which jointly performs classifier learning, subspace learning, and matrix factorization to preserve class-specific semantic content, termed Discriminative Supervised Hashing (DSH), to learn the discriminative unified binary codes for multi-modal data. Besides, reducing the loss of information and preserving the non-linear structure of data, DSH non-linearly projects different modalities into the common space in which the similarity among heterogeneous data points can be measured. Extensive experiments conducted on the three publicly available datasets demonstrate that the framework proposed in this paper outperforms several state-of-the-art methods.
Automatic layout generation for graphical design magazines

We propose a system that automatically generates layouts for magazines that require graphical design. In this system, when images or texts are input as the content to be placed in layouts, an appropriate layout is automatically generated in consideration of content and design. The layout generation process is performed by randomized processing in accordance with a rule set of minimum conditions that must be satisfied for layouts (minimum condition rule set), where a large number of candidates are generated. The appearance, style, design, and composition of the work are evaluated by a learning-to-rank estimator, top scores are returned to the user. Users can greatly improve the efficiency of layout creation/editing by selecting from among automatically generated candidate layouts.
A new simple and efficient density estimator that enables fast systematic search

This paper introduces a simple and efficient density estimator that enables fast systematic search. To show its advantage over commonly used kernel density estimator, we apply it to outlying aspects mining. Outlying aspects mining discovers feature subsets (or subspaces) that describe how a query stand out from a given dataset. The task demands a systematic search of subspaces. We identify that existing outlying aspects miners are restricted to datasets with small data size and dimensions because they employ kernel density estimator, which is computationally expensive, for subspace assessments. We show that a recent outlying aspects miner can run orders of magnitude faster by simply replacing its density estimator with the proposed density estimator, enabling it to deal with large datasets with thousands of dimensions that would otherwise be impossible.
Learning Image-to-Image Translation Using Paired and Unpaired Training Samples

Image-to-image translation is a general name for a task where an image from one domain is converted to a corresponding image in another domain, given sufficient training data. Traditionally different approaches have been proposed depending on whether aligned image pairs or two sets of (unaligned) examples from both domains are available for training. While paired training samples might be difficult to obtain, the unpaired setup leads to a highly under-constrained problem and inferior results. In this paper, we propose a new general purpose image-to-image translation model that is able to utilize both paired and unpaired training data simultaneously. We compare our method with two strong baselines and obtain both qualitatively and quantitatively improved results. Our model outperforms the baselines also in the case of purely paired and unpaired training data. To our knowledge, this is the first work to consider such hybrid setup in image-to-image translation.
Resource Management in Cloud Data Centers: A Survey

Cloud computing faces many challenges which constitute research problems. The key challenge is to achieve good resource management that will result in minimum energy consumption. This problem has been the subject of extensive research. In this paper, we report major solutions to the existing work proposed for the infrastructure cloud. We categorize these solutions based on approach optimization, used methods, and objective models. The latter are introduced by mathematical formulations that may be classified as mono-objective and multi-objective formulations. Typically, the single objective takes into account energy. Whereas, the multi-objective considers others, like resource utilization. Extensive simulation, using cloudsim, shows that the multi-objective formulations provide significant improvement than the mono-objective ones. Indeed, the results show an improvement of as much as 12% in the average energy consumption and 15% in the average resource utilization.
Kernel cross-view collaborative representation based classification for person re-identification

Person re-identification aims at the maintenance of a global identity as a person moves among non-overlapping surveillance cameras. It is a hard task due to different illumination conditions, viewpoints and the small number of annotated individuals from each pair of cameras (small-sample-size problem). Common subspace learning methods have been proposed to handle the camera transition problems. However, after learning the low-dimensional representation, these methods usually compute distances using a simple cosine or Mahalanobis distance. Therefore, an still open question is how to better match probe and gallery images in the learned common subspace considering reduced number of training samples and the nonlinear behavior of the data. Collaborative Representation based Classification (CRC) has been employed successfully to address the small-sample-size problem in computer vision. However, the original CRC formulation is not well-suited for person re-identification since it does not consider that probe and gallery samples are from different cameras. Furthermore, it is a linear model, while appearance changes caused by different camera conditions indicate a strong nonlinear transition between cameras. To overcome such limitations, we propose the Kernel Cross-View Collaborative Representation based Classification (Kernel X-CRC), method that represents probe and gallery images by balancing representativeness and similarity nonlinearly. According to experimental results, we achieve state-of-the-art for rank-1 matching rates in three person re-identification datasets (CUHK03, PRID450S and GRID) and the second best results on VIPeR and CUHK01 datasets. Furthermore, we present outperforming results on Market-1501 dataset demonstrating that the Kernel X-CRC is suitable to a large-scale and multiple cameras scenario.
Multi-User Cooperative Mobile Video Streaming: Performance Analysis and Online Mechanism Design

Adaptive bitrate streaming enables video users to <i>adapt</i> their playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user based bitrate adaptation methods may fail to provide the desirable QoE, due to lack of consideration of multi-user interactions (such as the multi-user interferences and network congestion). In this work, we propose a novel user cooperation framework based on <i>user-provided networking</i> for multi-user mobile video streaming over wireless cellular networks. The framework enables nearby mobile video users to crowdsource their cellular links and resources for cooperative video streaming. We first analyze the social welfare performance bound of the proposed cooperative streaming system by introducing a virtual time-slotted system. Then, we design a low complexity Lyapunov-based online algorithm, which can be implemented in an online and distributed manner without the complete future and global network information. Numerical results show that the proposed online algorithm achieves an average 97 percent of the theoretical maximum social welfare. We further conduct experiments with real data traces, to compare our proposed online algorithm with the existing online algorithms in the literature. Experiment results show that our algorithm outperforms the existing algorithms in terms of both the achievable bitrate (with an average gain of 20 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 30 percent) and social welfare (with an average gain of 10 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 50 percent).
Latency-aware failover strategies for containerized web applications in distributed clouds

Despite advances in Cloud computing, ensuring high availability (HA) remains a challenge due to varying loads and the potential for Cloud outages. Deploying applications in distributed Clouds can help overcome this challenge by geo-replicating applications across multiple Cloud data centers (DCs). However, this distributed deployment can be a performance bottleneck due to network latencies between users and DCs as well as inter-DC latencies incurred during the geo-replication process. For most web applications, both HA and Performance (HAP) are essential and need to meet pre-agreed Service Level Objectives (SLOs). Efficiently placing and managing primary and backup replicas of applications in distributed Clouds to achieve HAP is a challenging task. Existing solutions consider either HA or performance but not both. In this paper we propose an approach for automating the process of providing a latency-aware failover strategy through a server placement algorithm leveraging genetic algorithms that factor in the proximity of users and inter-DC latencies. To facilitate the distributed deployment of applications and avoid the overheads of Clouds, we utilize container technologies. To evaluate our proposed approach, we conduct experiments on the Australia-wide National eResearch Collaboration Tools and Resources (NeCTAR - www.nectar.org.au) Research Cloud. Our results show at least a 23.3% and 22.6% improvement in response times under normal and failover conditions respectively compared to traditional, latency-unaware approaches. Also, the 95th percentile of response times in our approach are at most1.5 ms above the SLO compared to 11–32 ms using other approaches.
How to Make Sure My Cryptokitties Are Here Forever? The Complementary Roles of Blockchain and the Law to Bring Trust

Under the phrase “code is law” and based on its “trustless trust”, blockchain has emerged as a disrupting technology considered by some as an alternative to the law. Based on a study of real-life blockchain-based decentralised applications (Dapps), this article takes blockchain developers at their word and adopts the point of view of users: can blockchain live up to its promise and enable them to transact with each other without the need for the trust granted by the law? The article particularly highlights that users need to be able to ascertain that a self-advertised Dapp indeed qualifies as one. Blockchain technology may make it possible to do away with trust in third parties, but this is not enough. Users also need to trust that an alleged Dapp genuinely is one, and blockchain alone cannot provide this. Beyond Dapps, it is argued that blockchain needs the complementary role of the law to deliver its promises and especially to authenticate blockchain “virtues”. The EU certification mark is identified as a promising form of co-regulation for that purpose.
Automatically Generating Documentation for Lambda Expressions in Java

When lambda expressions were introduced to the Java programming language as part of the release of Java 8 in 2014, they were the language's first step into functional programming. Since lambda expressions are still relatively new, not all developers use or understand them. In this paper, we first present the results of an empirical study to determine how frequently developers of GitHub repositories make use of lambda expressions and how they are documented. We find that 11% of Java GitHub repositories use lambda expressions, and that only 6% of the lambda expressions are accompanied by source code comments. We then present a tool called LambdaDoc which can automatically detect lambda expressions in a Java repository and generate natural language documentation for them. Our evaluation of LambdaDoc with 23 professional developers shows that they perceive the generated documentation to be complete, concise, and expressive, while the majority of the documentation produced by our participants without tool support was inadequate. Our contribution builds an important step towards automatically generating documentation for functional programming constructs in an object-oriented language.
Multi-level adaptive coupled method for industrial control networks safety based on machine learning

In response to the problem of low detection rate on different types of attacks in industrial control networks safety by traditional single detection algorithm models, a multi-level adaptive coupled method combining white list technology and machine learning was proposed. The white list was used to filter the communication behaviors that could not match with the rules at first level, then machine learning model were used to anomaly detect the abnormal communication behaviors at second level. Firstly, In the process of machine learning, the original dataset was preprocessed by Principal Component Analysis (PCA). Then the off-line data training was carried out by adaptive coupled algorithm, and the classifier model was constructed secondly. Finally, on-line anomaly detection was realized. The experimental results show that the proposed method was improved the detection rate than other algorithm significantly.
Anomaly detection in the presence of missing values for weather data quality control

Accurate weather data is important for improving agricultural productivity in developing countries. Unfortunately, weather sensors can fail for a wide variety of reasons. One approach to detecting failed sensors is to identify statistical anomalies in the joint distribution of sensor readings. This powerful method can break down if some of the sensor readings are missing. This paper evaluates five strategies for handling missing values in anomaly detection: (a) mean imputation, (b) MAP imputation, (c) reduction (reduced-dimension anomaly detectors via feature bagging), (d) marginalization (for density estimators only), and (e) proportional distribution (for tree-based methods only). Our analysis suggests that MAP imputation and proportional distribution should give better results than mean imputation, reduction, and marginalization. These hypotheses are largely confirmed by experimental studies on synthetic data and on anomaly detection benchmark data sets using the Isolation Forest (IF), LODA, and EGMM anomaly detection algorithms. However, marginalization worked surprisingly well for EGMM, and there are exceptions where reduction works well on some benchmark problems. We recommend proportional distribution for IF, MAP imputation for LODA, and marginalization for EGMM.
Performance, Energy, and Scalability Analysis and Improvement of Parallel Cancer Deep Learning CANDLE Benchmarks

Training scientific deep learning models requires the significant compute power of high-performance computing systems. In this paper, we analyze the performance characteristics of the benchmarks from the exploratory research project CANDLE (Cancer Distributed Learning Environment) with a focus on the hyperparameters epochs, batch sizes, and learning rates. We present the parallel methodology that uses the distributed deep learning framework Horovod to parallelize the CANDLE benchmarks. We then use scaling strategies for both epochs and batch size with linear learning rate scaling to investigate how they impact the execution time and accuracy as well as the power, energy, and scalability of the parallel CANDLE benchmarks under conditions of strong scaling and weak scaling on the IBM Power9 heterogeneous system Summit at Oak Ridge National Laboratory and the Cray XC40 Theta at Argonne National Laboratory. This study provides insights into how to set the proper numbers of epochs, batch sizes, and compute resources for these benchmarks to preserve the high accuracy and to reduce the execution time of the benchmarks. We identify the data-loading performance bottleneck and then improve the performance and energy for better scalability. Results with the modified benchmarks on Summit indicate up to 78.25% in performance improvement and up to 78% in energy saving under strong scaling on up to 384 GPUs, and up to 79.5% in performance improvement and up to 77.11% in energy saving under weak scaling on up to 3,072 GPUs. On Theta, we achieve up to 45.22% performance improvement and up to 41.78% in energy saving under strong scaling on up to 384 nodes. Moreover, the modification dramatically reduces the broadcast overhead.
Scalable and compact 3D action recognition with approximated RBF kernel machines

Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition. DL has brought the use of large datasets and this is typically a problem for kernel approaches, which are not scaling up efficiently due to kernel Gram matrices. Nevertheless, kernel methods are still attractive and more generally applicable since they can equally manage different sizes of the datasets, also in cases where DL techniques show some limitations. This work investigates these issues by proposing an explicit approximated representation that, together with a linear model, is an equivalent, yet scalable, implementation of a kernel machine. Our approximation is directly inspired by the exact feature map that is induced by an RBF Gaussian kernel but, unlike the latter, it is finite dimensional and very compact. We justify the soundness of our idea with a theoretical analysis which proves the unbiasedness of the approximation, and provides a vanishing bound for its variance, which is shown to decrease much rapidly than in alternative methods in the literature. In a broad experimental validation, we assess the superiority of our approximation in terms of (1) ease and speed of training, (2) compactness of the model, and (3) improvements with respect to the state-of-the-art performance.