Reviewerly/exHarmony huggingface dataset:
Merging visual features and temporal dynamics in sequential recommendation

With the development of social networking and mobile computing technologies, data analysis in the fashion field has increasingly focused on visual features. The main features currently used in the recommendation methods include non-visual user attributes, item attributes, explicit ratings, and implicit feedbacks. How to understand visual features and integrate them with non-visual features becomes the key to building a good recommender system. In this paper, we consider both non-visual text data and visual image data and their time dynamics to build a large-scale recommender system. An advanced visual Bayesian personalized ranking (aVBPR) model is proposed, which integrates three models. Factorized personalized Markov chains (FPMC) model is used to simulate users' sequence behaviors, intelligent field-aware factorization machine (iFFM) model also put forward by us is used to predict users' preferences based on non-visual features, and visual Bayesian personalized ranking (VBPR) model is used to analyze users' visual preferences. We design a learning algorithm based on AdaGrad method to optimize model aVBPR. The high complexity of the model does not affect the performance of the system by adopting multi-thread technology in the implementation of the learning algorithm. Experimental results of two real-world datasets Women's and Men's Clothing & Accessories from Amazon demonstrate that our model can obtain better recommendation results than the recent popular models for Amazon datasets. Although the model is complicated, multi-thread technique can be used to greatly improve the speed of the implementation.
Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery

Semantic segmentation is a powerful method to facilitate visual scene understanding. Each pixel is assigned a label according to a pre-defined list of object classes and semantic entities. This becomes very useful as a means to summarize large scale overhead imagery. In this paper we present our work on semantic segmentation with applications to overhead imagery. We propose an algorithm that builds and extends upon the DeepLab framework to be able to refine and resolve small objects (relative to the image size) such as vehicles. We have also investigated sensor adaptation as a means to augment available training data to be able to reduce some of the shortcomings of neural networks when deployed in new environments and to new sensors. We report results on several datasets and compare performance with other state-of-the-art architectures.
Limitless HTTP in an HTTPS World

We present new analytic techniques for inferring HTTP semantics from passive observations of HTTPS that can infer the value of important fields including the status-code, Content-Type, and Server, and the presence or absence of several additional HTTP header fields, e.g., Cookie and Referer. Our goals are to improve the understanding of the confidentiality limitations of HTTPS, and to explore benign uses of traffic analysis that could replace HTTPS interception and static private keys in some scenarios. We found that our techniques increase the efficacy of malware detection, but they do not enable more powerful website fingerprinting attacks against Tor. Our broader set of results raises concerns about the confidentiality goals of TLS relative to a user's expectation of privacy, warranting future research. We apply our methods to the semantics of both HTTP/1.1 and HTTP/2 on data collected from automated runs of Firefox 58.0, Chrome 63.0, and Tor Browser 7.0.11 in a lab setting, and from applications running in a malware sandbox. We obtain ground truth plaintext for a diverse set of applications from the malware sandbox by extracting the key material needed for decryption from RAM post-execution. We developed an iterative approach to simultaneously solve several multi-class (field values) and binary (field presence) classification problems, and we show that our inference algorithm achieves an unweighted $F_1$ score greater than 0.900 for most HTTP fields examined.
Binary classification in unstructured space with hypergraph case-based reasoning

Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work with multiple data sources or in non-metric spaces, and accommodates with missing values. As a result, it drastically reduces the need for data preprocessing or feature engineering. Each element to be classified is partitioned according to its interactions with the training set. For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class. Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art. The time complexity is given and empirically validated. Its robustness with regard to hyperparameter sensitivity is studied and compared to standard classification methods. Finally, the limitation of the model space is discussed, and some potential solutions proposed.
MSSTResNet-TLD: A robust tracking method based on tracking-learning-detection framework by using multi-scale spatio-temporal residual network feature model

The performance of tracking task is directly dependent on the appearance features of target object, a robust approach for constructing appearance features is crucial for adaptation the appearance change. To construct an accurate and robust appearance model for visual object tracking, we modify original deep residual learning network architecture and name it Multi-Scale Residual Network (MSResNet). The first video frame image and its related information of the current input video sequence are used to learn a multi-scale appearance model of target object and a loss function is minimized over the appearance features. Meanwhile, spatial information of each video frame and temporal information between successive video frames effectively combine with MSResNet. And thus the features are generated by Multi-Scale Spatio-Temporal Residual Network, which is named MSSTResNet feature model, can adapt to scale variation, illumination variation, background clutters, severe deformation of the target object, and so on. We implement a robust tracking method based on tracking-learning-detection framework by using our proposed MSSTResNet feature model and name it MSSTResNet-TLD tracker. Unlike the previous tracking methods, the MSResNet architecture is not offline pre-trained on a large auxiliary datasets but is directly learned end-to-end with a multi-task loss by using the current input video sequence. Furthermore, the multi-task loss function utilizes the classification loss and regression loss that is more accurate for target localization. Our experimental results demonstrate that the proposed tracking method outperforms the current state-of-the-art tracking methods on Visual Object Tracking Benchmark (VOT-2016), Object Tracking Benchmark (OTB-2015), and Unmanned Aerial Vehicles (UAV20L) test datasets. Furthermore, our MSSTResNet-TLD tracker is faster than previous most trackers based on deep Convolutional Neural Network (ConvNet or CNN) and our tracker is extremely robust to the tiny target object. Our source code is available for download at https://github.com/binger1225/MSSTResNet-TLD-Tracker.
Secure Distributed Computing Made (Nearly) Optimal

In this paper, we study secure distributed algorithms that are nearly optimal, with respect to running time, for the given input graph G. Roughly speaking, an algorithm is secure if the nodes learn only their final output while gaining no information on the input (or output) of other nodes.
Longer distance continuous variable quantum key distribution protocol with photon subtraction at the receiver

One of the limitations of continuous variable quantum key distribution is the relatively short transmission distance of secure keys. Some solutions have been proposed to overcome the limitation including reverse reconciliation, trusted noise concept, and non-Gaussian operation. In this paper, we propose a protocol using photon subtraction at the receiver, which combines the synergetic benefits of the aforementioned approaches. Using simulations, we show that the performance of the proposed protocol outperforms other conventional protocols. The results showed that an improvement in secure key distance can be obtained using a non-Gaussian operation, depending on the position where the operation is performed, similar to the trusted noise concept. Furthermore, the result implies existence of some Gaussian operations which increases security without using a beam splitter.
Determining the Number of Samples Required to Estimate Entropy in Natural Sequences

Calculating the Shannon entropy for symbolic sequences has been widely considered in many fields. For descriptive statistical problems such as estimating the N-gram entropy of English language text, a common approach is to use as much data as possible to obtain progressively more accurate estimates. However, in some instances, only short sequences may be available. This gives rise to the question of how many samples are needed to compute entropy. In this paper, we examine this problem and propose a method for estimating the number of samples required to compute Shannon entropy for a set of ranked symbolic “natural” events. The result is developed using a modified Zipf-Mandelbrot law and the Dvoretzky-Kiefer-Wolfowitz inequality, and we propose an approximation which yields an estimate for the minimum number of samples required to obtain an estimate of entropy with a given confidence level and degree of accuracy.
Dense Light Field Reconstruction from Sparse Sampling Using Residual Network

A light field records numerous light rays from a real-world scene. However, capturing a dense light field by existing devices is a time-consuming process. Besides, reconstructing a large amount of light rays equivalent to multiple light fields using sparse sampling arises a severe challenge for existing methods. In this paper, we present a learning-based method to reconstruct multiple novel light fields between two mutually independent light fields. We indicate that light rays distributed in different light fields have the same consistent constraints under a certain condition. The most significant constraint is a depth related correlation between angular and spatial dimensions. Our method avoids working out the error-sensitive constraint by employing a deep neural network. We predict residual values of pixels on epipolar plane image (EPI) to reconstruct novel light fields. Our method is able to reconstruct 2 to 4 novel light fields between two mutually independent input light fields. We also compare our results with those yielded by a number of alternatives elsewhere in the literature, which shows our reconstructed light fields have better structure similarity and occlusion.
Necessary field size and probability for MDP and complete MDP convolutional codes

It has been shown that maximum distance profile (MDP) convolutional codes have optimal recovery rate for windows of a certain length, when transmitting over an erasure channel. In addition, the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. Since so far general constructions of these codes have only been provided over fields of very large size, there arises the question about the necessary field size such that these codes could exist. In this paper, we derive upper bounds on the necessary field size for the existence of MDP and complete MDP convolutional codes and show that these bounds improve the already existing ones. For some special choices of the code parameters, we are even able to give the exact minimum field size. Moreover, we derive lower bounds for the probability that a random code is MDP respective complete MDP.
Covariance-based dissimilarity measures applied to clustering wide-sense stationary ergodic processes

We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a formal criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
New Methodology for Contextual Features Usage in Duplicate Bug Reports Detection : Dimension Expansion based on Manhattan Distance Similarity of Topics

Duplicate bug report detection is one of the major problems in software triage systems like Bugzilla to deal with end user requests. User request contains some categorical and especially textual fields which need feature extraction for duplicate detection. Contextual and topical features are acquired using calculating cosine similarity between term frequency or inverse document frequency or BM25F technique from a pair of bug reports against some topics. This research proposes the individual Manhattan distance similarity approach instead of cosine distance similarity for every topic in contextual features to expand the feature dimension which can increase the accuracy of the duplicate bug report detection process. The four famous datasets of bug reports have used for evaluation of the proposed method including Android, Eclipse, Mozilla, and Open Office which the experimental results indicate performance improvement for four contextual features including general, cryptography, network, and Java topics.
Few-shot domain adaptation for semantic segmentation

Unsupervised domain adaptation (UDA) has attracted increasing attention in recent years because it can liberate the labor force of annotating data. However, in many cases, the target data is scarce because it is difficult to obtain, at this time, the supervised domain adaptation (SDA) becomes attractive. In this work, we propose a novel few-shot supervised domain adaptation framework for semantic segmentation. The main idea is to exploit adversarial learning to align the features extracted from networks. To address the challenge of scarce data, we propose a pairing method of creating pairs using source data and target data. We design our framework as a two-stage structure to enhance the adaptation of the low-level features. In order to ensure the stable and effective training, we employ the spectral normalization in the discriminators and propose an alternately training strategy for the whole framework. Our proposed framework can work well even when there is only one sample per category. We evaluate our proposed method on a challenging synthetic dataset to real-world dataset adaptation where the results demonstrate the effectiveness of our method.
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? a Comparison between Dialog and Translation

This paper addresses the question: In neural dialog systems, why do sequence-to-sequence (Seq2Seq) neural networks generate short and meaningless replies for open-domain response generation? We conjecture that in a dialog system, due to the randomness of spoken language, there may be multiple equally plausible replies for one utterance, causing the deficiency of a Seq2Seq model. To evaluate our conjecture, we propose a systematic way to mimic the dialog scenario in machine translation systems with both real datasets and toy datasets generated elaborately. Experimental results show that we manage to reproduce the phenomenon of generating short and meaningless sentences in the translation setting.
On Topic Difficulty in IR Evaluation

In a test collection setting, topic difficulty can be defined as the average effectiveness of a set of systems for a topic. In this paper we study the effects on the topic difficulty of: (i) the set of retrieval systems; (ii) the underlying document corpus; and (iii) the system components. By generalizing methods recently proposed to study system component factor analysis, we perform a comprehensive analysis on topic difficulty and the relative effects of systems, corpora, and component interactions. Our findings show that corpora have the most significant effect on topic difficulty.
Automatic Detection of Urban Features from Wheelchair Users’ Movements

Providing city navigation instructions to people with motion disabilities requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. Our preliminary study on wheelchair users shows that an automatic crowdsourcing mechanism is needed, avoiding users' involvement.In this contribution we present a solution to crowdsource urban features from inertial sensors installed on a wheelchair. Activity recognition techniques based on decision trees are used to process the sensors data stream. Experimental results, conducted with data acquired from 10 real wheelchair users navigating in an outdoor environment show that our solution is effective in detecting urban features with precision around 0.9, while it is less reliable when classifying some fine-grained urban feature characteristics, like a step height. The experimental results also present our investigation aimed at identifying the best parameters for the given problem, which include number, position and type of inertial sensors, classifier type, segmentation parameters, etc.
Data reduction and stacking for imbalanced data classification

Class imbalance arises when the number of examples belonging to one class is much greater than the number of examples belonging to another. The discussed approach focuses on combining several techniques including data reduction and stacking with the aim of improving the performance of the machine c lassification in the case of imbalanced data. The paper proposes a cluster-based data reduction approach assuming that the instances are selected from a cluster, the data reduction is carried-out on instances belonging to the majority classes, and the aim of the instance selection is to reduce the imbalance ratio between the majority and minority classes. The process of instance selection is carried out with using an agent-based population learning algorithm. To increase performance and generalization ability of the prototype-based machine learning classification it was decided to use the stacking technique. The proposed approach is validated experimentally using several benchmark datasets from the KEEL repository. Advantages and main features of the approach are discussed considering the results of the computational experiment.
Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions

Bayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field.
Differentially private sequential pattern mining considering time interval for electronic medical record systems

Electronic medical record (EMR) systems have now been widely adopted to support medical workers. There also has been much interest in the machine-based generation of clinical pathways that can utilize sequential pattern mining (SPM) to extract them from historical EMR systems. However, the existing methods do not protect individual privacy, even though they involve sensitive medical data. To ensure the privacy of individual data, this paper describes two algorithms that deploy differential privacy by adding noise during calculations in the SPM considering time interval for guaranteeing privacy. The proposals can limit the amount of added noise by adding noise to the frequency calculations of only a part of candidate closed sequences. Experiments on real medical datasets show that our proposal can ensure the robust and high utility of mining process even with minimum privacy budget and amount of added noise.
ARIS: Authentication for Real-Time IoT Systems

Efficient authentication is vital for IoT applications with stringent minimum-delay requirements (e.g., energy delivery systems). This requirement becomes even more crucial when the IoT devices are battery-powered, like small aerial drones, and the efficiency of authentication directly translates to more operation time. Although some fast authentication techniques have been proposed, some of them might not fully meet the needs of the emerging delay-aware IoT. In this paper, we propose a new signature scheme called ARIS that pushes the limits of the existing digital signatures, wherein a commodity hardware can verify 83,333 signatures per second. ARIS also enables the fastest signature generation along with the lowest energy consumption and end-to-end delay among its counterparts. These significant computational advantages come with a larger storage requirement, which is a favorable trade-off for some critical delay-aware applications. These desirable features are achieved by harnessing message encoding with cover-free families and a special elliptic curve based oneway function. We prove the security of ARIS under the hardness of the elliptic curve discrete logarithm problem in the random oracle model. We provide an open-sourced implementation of ARIS on commodity hardware and 8-bit AVR microcontroller for public testing and verification.