Reviewerly/exHarmony huggingface dataset:
Mining High-Utility Sequential Patterns from Big Datasets

High-Utility Sequential Pattern Mining (HUSPM) has become an emerging issue in recent decades since it reveals more information such as the utility and sequence factors for knowledge discovery. For the previous works, many algorithms were presented to speed up the mining performance regarding a single machine with small datasets. In real-world applications, the size of dataset can be collected from many places or devices, such as PC, Internet of Things (IoT), mobile devices, and shopping malls, among others. It is necessary to build an efficient model to handle the big dataset for HUSPM. In this paper, we present a four-stages MapReduce framework based on the Spark platform for mining the high-utility sequential patterns from a very large database. From the experimental results, we then can observe that the designed model outperforms the state-of-the-art approaches for handling the very big dataset.
LSTM guided ensemble correlation filter tracking with appearance model pool

Deep learning based visual trackers have the potential to provide good performance for object tracking. Most of them use hierarchical features learned from multiple layers of a deep network. However, issues related to deterministic aggregation of these features from various layers, difficulties in estimating variations in scale or rotation of the object being tracked, as well as challenges in effectively modeling the object’s appearance over long time periods leaves substantial scope to improve performance. In this paper, we propose a tracker that learns correlation filters over features from multiple layers of a VGG network. A correlation filter for an individual layer is used to predict the target location. We adaptively learn the contribution of an ensemble of correlation filters for the final location estimation using an LSTM. An adaptive approach is advantageous as different layers encode diverse feature representations and a uniform contribution would not fully exploit this contrastive information. To this end, we use an LSTM as it encodes the interactions for past appearances which is useful for tracking. Further, the scale and rotation parameters are estimated using respective correlation filters. Additionally, an appearance model pool is used that prevents the correlation filter from drifting. Experimental results achieved on five public datasets — Object Tracking Benchmark (OTB100), Visual Object Tracking (VOT) Benchmark 2016, VOT Benchmark 2017, Tracking Dataset and UAV123 Dataset, reveal that our approach outperforms state of the art approaches for object tracking.
Tactics-Trend Analysis for Increasing the Possibility of Shooting in a Basketball Match

Basketball is one of the most popular team-sports played on a shared court. It is important to shoot a basket based on a predominant strategy. In terms of individual movement along with the strategy, it is necessary for an offense player to understand how to shoot easily in an organized team-play. In this study, we introduce basic tactics and propose an environment for automatic extraction of scenes applying them from tracking data of players and a ball. We also analyze applied tactics, which are linked to shoot motions and their goals.
Flexible Fashion Product Retrieval Using Multimodality-Based Deep Learning

Typically, fashion product searching in online shopping malls uses meta-information of the product. However, the use of meta-information is not guaranteed to ensure customer satisfaction, because of inherent limitations on the inaccuracy of input meta-information, imbalance of categories, and misclassification of apparel images. These limitations prevent the shopping mall from providing a user-desired product retrieval. This paper proposes a new fashion product search method using multimodality-based deep learning, which can support more flexible and efficient retrieval by combining faceted queries and fashion image-based features. A deep convolutional neural network (CNN) generates a unique feature vector of the image, and the query input by the user is vectorized through a long short-term memory (LSTM)-based recurrent neural network (RNN). Then, the semantic similarity between the query vector and the product image vector is calculated to obtain the best match. Three different forms of the faceted query are supported. We perform quantitative and qualitative analyses to prove the effectiveness and originality of the proposed approach.
Cosine metric learning based speaker verification

The performance of speaker verification depends on the overlap region of the decision scores of true and imposter trials. Motivated by the fact that the overlap region can be reduced by maximizing the between-class distance while minimizing the within-class variance of the trials, we present in this paper two cosine metric learning (CML) back-end algorithms. The first one, named m-CML, aims to enlarge the between-class distance with a regularization term to control the within-class variance. The second one, named v-CML, attempts to reduce the within-class variance with a regularization term to prevent the between-class distance from getting smaller. The regularization terms in the CML methods can be initialized by a traditional channel compensation method, e.g., the linear discriminant analysis. These two algorithms are combined with front-end processing for speaker verification. To validate their effectiveness, m-CML is combined with an i-vector front-end since it is good at enlarging the between-class distance of Gaussian score distributions while v-CML is combined with a d-vector or x-vector front-end as it is able to reduce the within-class variance of heavy-tailed score distributions significantly. Experimental results on the NIST and SITW speaker recognition evaluation corpora show that the proposed algorithms outperform their initialization channel compensation methods, and are competitive to the probabilistic linear discriminant analysis back-end in terms of performance. For comparison, we also applied the m-CML and v-CML methods to the i-vector and x-vector front-ends.
Hybrid Encryption Algorithm Based on AES and RSA in File Encryption

File encryption is an effective way to protect the security of personal or enterprise data. The representative encryption algorithms RSA and AES cannot meet the requirements of the efficiency and security of file encryption in separate use. In this paper, a hybrid encryption algorithm combining AES and RSA algorithm is proposed to solve the above problems to solve the efficiency and security problems in file encryption. The experimental results show that the hybrid encryption algorithm of RSA and AES can not only encrypt files, but also have the advantages of algorithm performance and security.
Enabling Efficient Fast Convolution Algorithms on GPUs via MegaKernels

Modern Convolutional Neural Networks (CNNs) require a massive amount of convolution operations. To address the overwhelming computation problem, Winograd and FFT fast algorithms have been used as effective approaches to reduce the number of multiplications. Inputs and filters are transformed into special domains then perform element-wise multiplication, which can be transformed into batched GEMM operation. Different stages of computation contain multiple tasks with different computation and memory behaviors, and they share intermediate data, which provides the opportunity to fuse these tasks into a monolithic kernel. But traditional kernel fusion suffers from the problem of insufficient shared memory, which limits the performance. In this article, we propose a new kernel fusion technique for fast convolution algorithms based on MegaKernel. GPU thread blocks are assigned with different computation tasks and we design a mapping algorithm to assign tasks to thread blocks. We build a scheduler which fetches and executes the tasks following the dependency relationship. Evaluation of modern CNNs shows that our techniques achieve an average of 1.25X and 1.7X speedup compared to cuDNN's two implementations on Winograd convolution algorithm.
Infinite Dropout for training Bayesian models from data streams

The ability to continuously train Bayesian models in streaming environments is highly important in the era of big data. However, it has to face the famous stability-plasticity dilemma and the problem of noisy and sparse data. We propose a novel and easy-to-implement framework, called Infinite Dropout (iDropout), to address these challenges. iDropout has an easy mechanism to balance between old and new information, which allows models to trade off stability against plasticity. Thanks to the ability to reduce overfitting and the ensemble property of Dropout, our framework obtains better generalization, thus effectively handles undesirable effects of noise and sparsity. Further, iDropout is able to adapt quickly to abnormal changes in data streams. We theoretically analyze the equivalence of Dropout in iDropout to a regularizer, well applied to a much larger context than what was known before. Extensive experiments show that iDropout significantly outperforms the state-of-the-art baselines.
A Privacy-Preserving Query System using Fully Homomorphic Encryption with Real-World Implementation for Medicine-Side Effect Search

The preservation of privacy during a search has become a serious problem in recent years. There is an increasing requirement to ensure that user queries are not abused by a third party, including the search provider. Fully homomorphic encryption (FHE) can conduct addition and multiplication directly over ciphertext. Using FHE, privacy, concerning both the user queries and the database of the search provider, can be protected. In this paper, we propose a privacy-preserving query system model. We implemented the proposed model in a real-world medicine side-effect query system. We applied a filtering technique, prior to the query deployment, to reduce the size of the database and used multi-threading to accelerate the search. The system was tested 10,000 times with a random query, using a database comprising 40,000 records of simulation data, and completed 99.84% of the queries within 60 seconds (s), proving the real-world applicability of our system.
How can we reduce information asymmetries and enhance trust in ‘The Market for Lemons’?

The used car market is characterized by information asymmetries and mistrust. Blockchain technology promises to resolve these problems using a system which stores data over the life cycle of a vehicle. However, while blockchain technology is strong in preserving the stored information, sense-making of this information is still essential to bring value to end consumers of the system. In this paper, we take an exploratory approach and create a prototype, which is then evaluated in realistic car sale conversations between buyers and sellers. We demonstrate and discuss how the interplay of different design elements of an application, built on top of a blockchain-based platform, helps to reduce information asymmetries and enhance trust. Our findings suggest that though providing more information about a used product (a car) leads to fewer information asymmetries in general, a reputation mechanism and data analysis are both beneficial in improving the situation further. As for trust, such a system enhances trust between buyers and sellers and, in general, makes the overall purchase process more trustworthy. However, to achieve these positive effects, the quality of the stored information should be guaranteed and properly communicated to the end-user.
Control architecture for embedding reinforcement learning frameworks on industrial control hardware

Using reinforcement learning to find new control strategies for manufacturing processes is a promising approach. However, in order to use reinforcement learning in a manufacturing environment, industrial requirements must be met. In this paper, a new control architecture is proposed that allows reinforcement learning frameworks to be executed on programmable logic controllers, which implies requirements regarding real-time execution. An agent exchange module is presented, that allows to automatically load agents from a non real-time learning environment to a control program running in a real-time environment of a programmable logic controller. The proposed architecture is evaluated on an experimental setup with a non-linear process and commercial off-the-shelf control hardware, to meet industrial requirements. A genetic algorithm is used to learn new control strategies without having prior knowledge of the system.
A Fast Video Image Detection using TensorFlow Mobile Networks for Racing Cars

With the growth of the Internet of Things, we see an increase in the importance of analysis of data from the edge, often with the results needed in real-time. Indy Car series is one of the well-known racing series in North America. All cars are equipped with multiple cameras. The video streams captured by these cameras can be used for detection and predictive tasks to increase race safety and develop better strategies to win the race. Moreover, the data can be used together with the telemetry data to provide better analysis and predictions for the drivers and the teams. In a lot of video analytics tasks, the tasks begin with object detection as its foundation. The existing pretrained object detection models are inadequate to detect IndyCar race cars. Therefore, we have created a new dataset and have compared three different Single Shot Multibox Detector models from TensorFlow Detection Model Zoo. We run experiments on CPU and GPU. Since transferring the data from edge devices to a server, running inference, and sending the result back is time and resource consuming, we also test mobile detection models on an Edge TPU, which is a Google Coral Dev Board. Our initial results show that the Edge TPU gives the best inference time, and it is more suitable for a real-time machine learning task.
Security and trust preserving inter‐ and intra‐cloud VM migrations

Summary This paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure‐as‐a‐service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre‐defined baseline configurations makes our solution compatible with an existing widely accepted standards‐based, security‐focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter‐ and intra‐cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on‐line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev‐Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments.
Deep Recurrent Neural Network (Deep-RNN) for Classification of Nonlinear Data

Data mining is the most challenging approach that uses the method of extracting the most interesting patterns from a large storage of database. Classification, a supervised learning method, is mostly applicable method of data mining. In this paper, we have used different classification techniques to differentiate the results for different data sets. Deep learning or hierarchical learning is the part of machine learning which mainly follows the widely used concepts of a neural network. There are many deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, etc. In this paper, we have used the concept of deep recurrent neural network (Deep-RNN) to train the model for a classification task. RNN follows a method for weight updation which is known as Backpropagation Through Time (BPTT) and we have used the concept of Deep-RNN by following the concepts of both forward pass and backward pass. Simulation results are quite impressive as compared to earlier developed machine learning models.
Generalized Large-Context Language Models Based on Forward-Backward Hierarchical Recurrent Encoder-Decoder Models

This paper presents a generalized form of large-context language models (LCLMs) that can take linguistic contexts beyond utterance boundaries into consideration. In discourse-level and conversation-level automatic speech recognition (ASR) tasks, which have to handle a series of utterances, it is essential to capture long-range linguistic contexts beyond utterance boundaries. The LCLMs of previous studies mainly focused on utilizing past contexts, and none fully utilized future contexts because LMs typically process words in a time-ordered manner. Our key idea is to introduce the LCLMs into the situation where ASR results of the whole series of utterances are given by a first decoding pass. This situation makes it possible for the LCLMs to leverage future contexts. In this paper, we propose generalized LCLMs (GLCLMs) based on forward-backward hierarchical recurrent encoder-decoder models in which generative probabilities of individual utterances are computed by leveraging not only past contexts but also future contexts beyond utterance boundaries. In order to efficiently introduce GLCLMs to ASR, we also propose a global-context iterative rescoring method that repeatedly rescores the ASR hypotheses of an individual utterance by using surrounding ASR hypotheses. Experiments on discourse-level ASR tasks demonstrate the effectiveness of our GLCLM approach.
An Improved Ant Colony Optimization Algorithm Based on Fractional Order Memory for Traveling Salesman Problems

Ant Colony Optimization (ACO) algorithm has a wide array of applications to solve combinatorial optimization problems, especially Traveling Salesman Problems (TSPs). The major limitations of ACO algorithm are premature convergence, the possibility that trapped in the local optima. In this paper, an improved Ant Colony Optimization algorithm is proposed which uses fractional order difference for pheromone updating and a weighted combined transition probability. The fractional order difference with the characteristic of long-term memory helps the algorithm make full use of the historical information, and the combined transition probability enhances the exploration ability of the algorithm by using the information of a few steps forward. The performance of the proposed algorithm is tested on various data sets from the standard TSP Library compared with the corresponding integer order algorithm and some evolutionary algorithms. According to the empirical results, our algorithm based on fractional order difference overcomes the classic integer order. Furthermore, the results on a number of TSP instances demonstrate that compared with other evolutionary algorithms, the proposed method can obtain the better solutions on most instances with stronger robustness.
Rare Traffic Sign Recognition using Synthetic Training Data

Modern computer vision methods usually require lots of labelled data for training. Besides price of labelling, problems with rare object classes and adaptation to new domain or task arise. One of the promising methods to solve these problems is to generate synthetic training data. In this work we focus on task of traffic sign detection. We consider several methods for generating synthetic data for training traffic sign detectors: random placement of signs of different quality (simple synthetic, CGI based and CGI improved using generative adversarial network). We also propose a method to replace real signs with synthetic signs. Experimental evaluation shows that proposed method improves quality of detection of rare traffic signs and that usage of synthetic data is very helpful for improving training of traffic sign classifier.
TsOutlier: Explaining Outliers with Uniform Profiles over IoT Data

IoT data with timestamps are often found with outliers, such as GPS trajectories or sensor readings. While existing systems mostly focus on detecting temporal outliers without explanations, a decision maker may be more interested in the cause of the outlier appearance such that subsequent actions would be taken, e.g., cleaning unreliable readings or repairing broken devices. Such outlier detection and explanation are expected to be performed in either offline (batch) or online modes (over streaming IoT data with timestamps). In this work, we present TsOutlier, a new prototype system for detecting outliers with explanations over IoT data. The framework defines uniform profiles to explain the outliers detected by various algorithms, including the outliers with variant time intervals. Both batch and streaming processing are supported in a uniform framework. In particular, by varying the block size, it provides a tradeoff between computing the accurate results and approximating with efficient incremental computation. In this paper, we present several case studies of applying TsOutlier in industry, e.g., how this framework works in detecting outliers over the operation data of Shanghai Subway, and how to get reasonable explanations for the detected outliers in tracking excavators.
Multi-label Deep Learning based Side Channel Attack

In this paper, a novel side channel attack method with multi-label deep learning is proposed, and it surpasses the state-of-the-art result in ASCAD benchmark dataset. Our experimental results show that an ingenious modification of the output layer of a neural network can bring several times improvement of attack performance than the original model, even dozens of times when dealing with unaligned traces. In fact, the masked AES implementation of ASCAD only requires about 150, 250 and 350 traces to break respectively, when traces are manually desynchronized with our multi-label classification model. Our model can be considered as an ensemble learning method using the integration of monobit models based on the independence assumption of bits in the intermediate value.