Reviewerly/exHarmony huggingface dataset:
Adjusting Packet Size to Mitigate TCP Incast in Data Center Networks with COTS Switches

In data center networks, a large number of concurrent TCP connections suffer the TCP Incast throughput collapse due to packet drops in shallow-buffered Ethernet switches. In this work, we first reveal theoretically and empirically that controlling the IP packet size is much more effective in avoiding Incast than cutting congestion window under severe congestion. We further design a general supporting scheme Packet Slicing, which adjusts the IP packet on widely used commodity switches. The design uses standard ICMP signaling, which makes no modification on TCP protocols and can be transparently utilized by various TCP protocols. To alleviate the impact of micro-burst caused by high flow concurrency, we utilize the TCP Pacing scheme to disperse packets over the round trip time, helping Packet Slicing to support more concurrent TCP flows. We integrate Packet Slicing with three state-of-the-art data center TCP protocols on NS2 simulation and a physical testbed. The experimental results show that Packet Slicing broadly improves the goodput of different data center TCP protocols by average 26x, while having almost no effect on the I/O performance of switches and end hosts.
Identification of Wearable Devices with Bluetooth

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5%, 98.3% precision and recall for identifying wearables using the Bluetooth classic protocol.
Deep learning of smartphone sensor data for personal health assistance

This paper strives to construct the relevance between smartphone sensor data and personal health via a deep learning method. Firstly, the data captured by smartphone sensors is categorized into groups by deep stacked autoencoders (SAE), which is a traditional deep learning architecture with multi-layer sparse autoencoders for feature extraction and a softmax layer for classification. For instance, accelerometer data from smartphones can be classified in accordance with different user motion states such as sitting, standing, walking, and running. Secondly, the quantitative correlation between divided sensor data and individual health is built. For example, walking and running for a suitable amount of time are beneficial to human health. Finally, a simulation experiment is devised to verify the performance of the proposed method. A 0.15 percent gain on the overall classification accuracy for automatic feature extraction on the human action recognition (HAR) dataset has been achieved compared with the state of the art methods and 0.5 percent gain for manual feature extraction. It is also demonstrated by the experimental results that smartphone sensor data can reveal the health status of the user to some degree and smartphone can serve as an assistant for human health.
An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels

Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these methods actually neglect the tail labels - labels that are infrequently assigned to instances. In this paper, we propose an embedding-based method that non-linearly embeds the label vectors using a stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method has excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. Experiments on real-world datasets show that our method outperforms state-of-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time. Our implementation of the proposed method is available online at:https://github.com/Akbarnejad/ESMC_ Implementation.
Filtering impulse noise in medical images using information sets

An efficient filtering algorithm is required to remove noise and simultaneously protect fine details and important features in the medical images. In this paper, a noise adaptive information set based switching median (NAISM) filter is proposed for the removal of impulse noise. NAISM filter is inspired from fuzzy switching median filter and works on the concept of information sets. Information sets are derived from fuzzy sets to deal with the uncertainty. It works in two phases; first phase identifies noisy pixels and second applies filtering based on an adaptive switching criterion. It is by virtue of this switching criterion and the local effective information surrounding the noisy pixel, the best calculated value replaces the noisy pixel in the selected window. The proposed information set based filter is capable of removing both low and high noise densities and can preserve image details better than the fuzzy filter. The applicability of the proposed filter is demonstrated on different datasets including Berkeley Segmentation Dataset (BSD), medical and real images. The qualitative and quantitative results demonstrate the effectiveness of the proposed approach in suppressing noise over the existing approaches.
A Comparative Study of Ontology Matching Systems via Inferential Statistics

Ontology matching systems are typically compared by comparing their average performances over multiple datasets. However, this paper examines the alignment systems using statistical inference since averaging is statistically unsafe and inappropriate. The statistical tests for comparison of two or multiple alignment systems are theoretically and empirically reviewed. For comparison of two systems, the Wilcoxon signed-rank and McNemar's mid-p and asymptotic tests are recommended due to their robustness and statistical safety in different circumstances. The Friedman and Quade tests with their corresponding post-hoc procedures are studied for comparison of multiple systems, and their [dis]advantages are discussed. The statistical methods are then applied to benchmark and multifarm tracks from the ontology matching evaluation initiative (OAEI) 2015 and their results are reported and visualized by critical difference diagrams.
MILP-Based Differential Attack on Round-Reduced GIFT

At Asiacrypt 2014, Sun et al. proposed a MILP model [20] to search for differential characteristics of bit-oriented block ciphers. In this paper, we improve this model to search for differential characteristics of GIFT [2], a new lightweight block cipher proposed at CHES 2017. GIFT has two versions, namely GIFT-64 and GIFT-128. For GIFT-64, we find the best 12-round differential characteristic and a number of iterative 4-round differential characteristics with our MILP-based model. We give a key-recovery attack on 19-round GIFT-64. For GIFT-128, we find a 18-round differential characteristic and give the first attack on 23-round GIFT-128.
Long Activity Video Understanding Using Functional Object-Oriented Network

Video understanding is one of the most challenging topics in computer vision. In this paper, a four-stage video understanding pipeline is presented to simultaneously recognize all atomic actions and the single ongoing activity in a video. This pipeline uses objects and motions from the video and a graph-based knowledge representation network as prior reference. Two deep networks are trained to identify objects and motions in each video sequence associated with an action and low level image features are used to identify objects of interest in the video sequence. Confidence scores are assigned to objects of interest to represent their involvement in the action and to motion classes based on results from a deep neural network that classifies an ongoing action in video into motion classes. Confidence scores are computed for each candidate functional unit to associate them with an action using a knowledge representation network, object confidences, and motion confidences. Each action, therefore, is associated with a functional unit, and the sequence of actions is evaluated to identify the sole activity occurring in the video. The knowledge representation used in the pipeline is called the functional object-oriented network, which is a graph-based network useful for encoding knowledge about manipulation tasks. Experiments are performed on a dataset of cooking videos to test the proposed algorithm with action inference and activity classification. Experiments show that using a functional object-oriented network improves video understanding significantly.
Narrating the future: A distentive capability approach to strategic foresight

Abstract Strategic foresight encompasses thinking and organizing differently within the contingencies of the present in ways that is mindful of the unpredictable future. Building on the Ricoeurian notion of distentio- ‘the stretching of consciousness through simultaneous attention to memory and expectation’, we develop the concept of distentive capability as a spatio-temporal process of ‘way-finding’ that sustains the creative emergence of strategic foresight across multiple time horizons. Emphasizing narratives as the site of strategic foresight, we draw on tropes taken from three of the most popular speeches delivered by Steve Jobs, the American information technology entrepreneur, inventor and founder of Apple Inc. to illustrate how distentive capability as played out in the reassembling of the past, the present and the future, stretched out in time, may contribute to the identification of potentialities and limits for strategic action. Implication for the theory and practice of strategic foresight are presented.
A Game-Based Price Bidding Algorithm for Multi-Attribute Cloud Resource Provision

The pricing mechanism of cloud-computing resources is an essential issue for both cloud customers and service providers, especially from the point of multi-provider competition. Although various mechanisms for resource provision are proposed, few studies have focused on multi-attribute resource provision with the objective of improving benefits of both cloud customers and service providers. To address the issue, we propose a price bidding mechanism for multi-attribute cloud-computing resource provision from the perspective of a non-cooperative game, in which the information of each player (customers and providers) is incomplete to others and each player wishes to maximize his/her own benefit. More specifically, considering the fairness pricing competition, we propose a novel and incentive resource provision model referring to the Quality-of-Service (QoS) and the bidding price. Then, combining with the resource provision model, the problem of price bidding is formulated as a game to find a proper price for each cloud provider. We demonstrate the existence of Nash equilibrium solution set for the formulated game model by assuming that the quantity function of provided resources from every provider is continuous. To find a Nash equilibrium solution, we propose an Equilibrium Solution Iterative (ESI) algorithm, which is proved to converge to a Nash equilibrium. Finally, a Near-equalization Price Bidding (NPB) algorithm is proposed to modify the obtained Nash equilibrium solution. Extensive simulated experiments results and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.
Adversarial Deep Tracking

A number of visual tracking methods achieve the state-of-the-art performance based on deep learning recently. However, most of these trackers utilize the deep neural network in regression task or classification task separately. In this paper, we propose an adversarial deep tracking framework. The framework is composed of a fully convolutional Siamese neural network (regression network) and a discriminative classification network. Then, we jointly optimize the regression network and the classification network by adversarial learning. In the uniform framework, the regression network and classification network can be trained end-to-end as a whole using large amounts of video training data sets. During the testing phase, the regression network generates a response map which reflects the location and the size of the target within each candidate search patch, and the classification network discriminates which response map is the best in terms of the corresponding template patch and candidate search patch. In addition, we propose an attention visualization algorithm for our tracker, and it reflects the area that attracts the attention of our tracker during tracking. The experimental results on three large-scale visual tracking benchmarks (OTB-100, TC-128, and VOT2016) demonstrate the effectiveness of the proposed tracking algorithm and show that our tracker performs comparably against the state-of-the-art trackers.
Multi-level attention model for person re-identification

Person re-identification (re-ID) is an important research topic in computer vision. Due to its important applications in video surveillance, it has been receiving increasing attention. The key challenge of this task is how to capture appearance variations under different camera views. Current state-of-the-art methods employ deep CNN feature and part-level representations to generate robust representation for pedestrians. However, when human parts are not well located and aligned, discriminative information is difficult to be captured. To address this issue, we propose a feature attention block for person re-ID task. The proposed block learns part-level attention on different local regions, and the weighted part-level features are pooled into a global representation. The proposed attention block can be extended to multi-level situation and generates more robust representation. The proposed feature attention block can be seamlessly integrated into existing CNN structures (e.g., ResNet and DenseNet), and is trained only with identity loss. We conduct extensive experiments on three popular person re-ID benchmarks including Market-1501, DukeMTMC-reID, and CUHK03. The proposed framework achieves promising results compared with current state-of-the-arts.
An Approach for the Prediction of Number of Software Faults Based on the Dynamic Selection of Learning Techniques

Determining the most appropriate learning technique(s) is vital for the accurate and effective software fault prediction (SFP). Earlier techniques used for SFP have reported varying performance for different software projects and none of them has always reported the best performance across different projects. The problem of varying performance can be solved by using an approach, which partitions the fault dataset into different module subsets, trains learning techniques for each subset, and integrates the outcomes of all the learning techniques. This paper presents an approach that dynamically selects learning techniques to predict the number of software faults. For a given testing module, the presented approach first locates its neighbor module subset that contained modules similar to testing module using a distance function and then chooses the best learning technique in the region of that module subset to make the prediction for testing module. The learning technique is selected based on its past performance in the region of module subset. We have performed an evaluation of the proposed approach using fault datasets garnered from the PROMISE data repository and Eclipse bug data repository. Experimental results showed that the proposed approach led to an improved performance when predicting the number of faults in software systems.
Supervised feature selection by self-paced learning regression

Feature selection is an effective method to reduce the feature dimension and improve the efficiency of data mining algorithms, it can be used to solve the "curse of dimensionality" problem. Most existing feature selection methods do not adequately consider the effects of noise and outliers, thereby reducing the performance of regression. The paper proposed a new feature selection model and supervised learning method, using self-paced regularizer (SP-regularizer) terms and ℓ2, 1-norm to constrain the model. Specifically, the feature selection model is trained firstly using the most high-confidence samples base on the self-paced learning theory, and then adds the more high-confidence training samples in the remaining samples to increase the generalization ability of the initial feature selection model until the generalization ability of the feature selection model is not improved or all training samples are used up. Then use ℓ2, 1-norm remove redundant feature to improve algorithm efficiency. Experimental results on nine datasets show that our proposed method is superior to the comparison algorithms.
Coordinate CNNs and LSTMs to categorize scene images with multi-views and multi-levels of abstraction

Due to complexities of scene images, scene categorization is a challenging task in the computer vision community. To categorize scene images effectively, in this paper, we propose to coordinate Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) to perform scene categorization with multi-views and multi-levels of abstraction. Specifically, to utilize the complementary properties of features of different levels of abstraction, we employ CNNs to extract features of multi-levels of abstraction based on its hierarchical structure. Furthermore, in order to deal with variations in scene image contents, we represent each image with multiple views, and in order to take correlation between image views into consideration, we treat image view features from the same image as a sequence and employ Long Short-Term Memory networks (LSTMs) to perform classification. Based on the proposed method, information of multi-views and multi-levels of abstraction can be made full use of in a single framework. We evaluate the proposed method on two challenging scene datasets, MIT indoor scene 67 and SUN 397. Obtained results demonstrate the effectiveness of utilizing CNNs and LSTMs to categorize scene images with multi-views and multi-levels of abstraction. Experiments on comparison to state-of-the-art methods show that the proposed method outperforms all the other methods used for comparison.
A New Continuous Discrete Unscented Kalman Filter

The time and measurement update for the discrete time Kalman filter can be formulated in terms of conditional means and covariances. The unscented Kalman filter can be interpreted as calculating these conditional means and covariances by using the unscented transform. This approach can also be directly applied to nonlinear models as an alternative to the discrete time extended Kalman filter. In this paper, a novel method for computing the unscented Kalman filter for a nonlinear model with continuous time dynamics and discrete time measurements is presented. Compared to the existing approaches, this method is far simpler and less computationally demanding, and it performs at least as well.
Multi-label learning of part detectors for occluded pedestrian detection

Despite recent progress of pedestrian detection, it remains a challenging problem to detect pedestrians that are partially occluded due to the uncertainty and diversity of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees which are learned and combined via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors for pedestrian detection. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. For occlusion handling, several methods are explored to integrate the part detectors learned by the proposed approach. Context is also exploited to further improve the performance. The proposed approach is applied to hand-crafted channel features and features learned by a deep convolutional neural network, respectively. Experiments on the Caltech and CUHK datasets show state-of-the-art performance of our approach for detecting occluded pedestrians, especially heavily occluded ones.
Privacy-Preserving Tensor-Based Multiple Clusterings on Cloud for Industrial IoT

Aiming at discovering hidden different data structures in big data from different perspectives, a tensor-based multiple clustering method has been developed recently, which can be widely used in Industrial Internet of Things (IoT) to improve production and service quality. However, due to the high computational cost and huge volume of data, outsourcing computing to relatively inexpensive cloud servers can greatly save local costs, but there is a high risk of revealing user privacy. To address the above problem, a privacy-preserving tensor-based multiple clustering method on the secure hybrid cloud is proposed. The proposed scheme utilizes a homomorphic cryptosystem to encrypt object tensors and, then, employs cloud servers to completely implement multiple clustering calculation over encrypted object tensors. Furthermore, a series of related security subprotocols are proposed to support privacy-preserving tensor-based multiple clusterings. In the proposed scheme, only encryption and removing perturbation are performed on the client, which is very lightweight for users. Experimental results show that the proposed scheme is accurate and efficient when clustering objects to different groups, while no private or additional information is leaked. Moreover, when employing more cloud nodes, the scheme has high scalability; thus, it is very suitable for clustering Industrial IoT big data.