Reviewerly/exHarmony huggingface dataset:
Collage Inference: Using Coded Redundancy for Lowering Latency Variation in Distributed Image Classification Systems

MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular. Hosting pre-trained machine learning models in the cloud enables elastic scalability as the demand grows. But providing low latency and reducing the latency variance is a key requirement. Variance is harder to control in a cloud deployment due to uncertain-ties in resource allocations across many virtual instances. We propose the collage inference technique, which uses a novel convolutional neural network model, collage-cnn, to provide low-cost redundancy. A collage-cnn model takes a collage image formed by combining multiple images and performs multi-image classification in one shot, albeit at slightly lower accuracy. We augment a collection of traditional single image classifier models with a single collage-cnn classifier, which acts as their low-cost redundant backup. Collage-cnn provides backup classification results if any single image classification requests experience a slowdown. Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1.2x to 2x compared to replication-based approaches while providing high accuracy. Variation in inference latency can be reduced by 1.8x to 15x.
A New Privacy-Preserving Payment Protocol for Blockchain Transactions

Ring confidential transaction (RingCT) protocol is widely used in cryptocurrency to protect the privacy of both users&#x2019; identities and transaction amounts. Most recently, a new RingCT protocol (called RingCT 2.0) was proposed by leveraging cryptographic accumulators, which can achieve a constant-size output theoretically but still far from being practical due to the heavy zero-knowledge associated with the accumulator. In this article, we revisit the design of ring confidential transaction protocol and put forward a more efficient privacy-preserving payment protocol, which is built upon an extended version of one-out-of-many proof and a special multi-signature. Compared with previous works, the new protocol is not only more practical, but also does not suffer from a trusted setup. Besides, we show that the protocol satisfies the security requirements provided that the underlying cryptographic primitives are secure in the random oracle model. We implement our new payment protocol in Java, and the experimental results show that it is efficient enough to be used in practice.
Privacy-preserving spatial query protocol based on the Moore curve for location-based service

The cloud computing has attracted a multitude of data owners to outsource computing and acquire location-based services from cloud service providers. The privacy-preserving spatial query on outsourced data has become a research hotspot in the location-based service. The existing schemes may damage the confidentiality of data, or be trapped by the large computation and communication overhead. The majority of them focus on the protection of original data and location information, while ignoring the significance of query records. In order to find a balance between security, efficiency and accuracy, this paper proposes a spatial transformation scheme for location-based services in outsourced environment, and designs a privacy-preserving k-nearest neighbor (k-NN) protocol. We utilize the Moore curve to perform a one-way transformation of the original data, and adopt the encryption to prevent malicious access to the transformed data by untrusted entities. The proposed protocol accomplishes efficient and accurate k-NN query in the transformed space while guaranteeing the confidentiality of outsourced data and the user's location. In addition, we propose a secure optimization method based on the oblivious transfer to protect the privacy of query records. Performance analysis shows that the proposed protocol has considerable efficiency advantage over existing schemes without sacrificing security and accuracy.
Traceable Ciphertext Policy Attribute-based Encryption Scheme with User Revocation for Cloud Storage

Ciphertext policy Attribute-based encryption (CPABE) plays an increasingly important role in the field of fine-grained access control for cloud storage. However, The exiting solution can not balance the issue of user identity tracking and user revocation. In this paper, we propose a CP-ABE scheme that supports association revocation and traceability. This scheme uses identity directory technology to realize single user revocation and associated user revocation, and the ciphertext re-encryption technology guarantees the forward security of revocation without updating the private key. In addition, we can accurately trace the identity of the user according to the decryption private key and effectively solve the problem of key abuse. This scheme is proved to be safe and traceable under the standard model, and can effectively control the computational and storage costs while maintaining functional advantages. It is suitable for the practical scenarios of tracking audit and user revocation.
Anatomy, Concept, and Design Space of Blockchain Networks

Blockchain technologies have been increasingly adopted by enterprises to increase operational efficiency and enable new business models. These enterprise blockchain applications generally run on dedicated blockchain networks due to regulations and security requirements. The design process of these networks involves many decisions and trade-offs that impact security, governance, and performance of applications that run on them. The challenge is further exacerbated by the lack of a common architecture and concept map to communicate about blockchain networks, as blockchain technologies tend to use different concepts and architecture. This paper presents a concept map, an anatomy and the principal dimensions of the design space of blockchain networks. We applied the proposed design space in a case study about designing and deploying a blockchain network for an ad-hoc IoT infrastructure. We found that the design space brought structure to the design process and the analysis of design alternatives. The presented concept map, anatomy and design space are intended to help improve the blockchain network design practice and lay a foundation for future research on the design process and deployment automation of blockchain networks.
Symmetry-Breaking for Airflow Control Optimization of an Oscillating-Water-Column System

Global optimization problems are mostly solved using search methods. Therefore, decreasing the search space can increase the efficiency of their solving. A widely exploited technique to reduce the search space is symmetry-breaking, which helps impose constraints on breaking existing symmetries. The present article deals with the airflow control optimization problem in an oscillating-water-column using the Particle Swarm Optimization (PSO). In an effort to ameliorate the efficiency of the PSO search, a symmetry-breaking technique has been implemented. The results of optimization showed that shrinking the search space helped to reduce the search time and ameliorate the efficiency of the PSO algorithm.
Route Planning for Automatic Indoor Driving of Smart Cars

While various types of traffic navigation maps play an important role in our life, the complex layout of large number of buildings in the city makes it difficult for people to find destinations and walking routes indoors. Since GPS does not work very accurately inside the buildings, for indoor navigation problem we still have to depend on the traditional front desk human resource consultation. In recent years, the rapid development of artificial intelligence technology has made it possible for smart car navigation instead of the manual consultation. Not only that, smart cars can even handle short-distance goods transportation. Combining indoor positioning technology with different video signals or any other non-GPS satellite positioning technology for directing the smart car to the designated position is a hot research direction these days. In this paper, we mainly study the realization and application of a smart car in the indoor automated driving system. Our proposed smart car, built using the Raspberry Pi 3, uses a series of famous path planning algorithms such as Dijkstra's algorithm, Best priority search and A*(A star) algorithm to plan the driving path of the car's automatic driving. Finally, the real-time obstacle avoidance function is also realized by using ultrasonic ranging, infrared ranging and pan-tilt camera. Several experiments were conducted to compare the efficiency of using different path finding algorithms and the results of using infrared ranging and ultrasonic ranging during obstacle avoidance.
A Unified Framework for Multi-view Spectral Clustering

In the era of big data, multi-view clustering has drawn considerable attention in machine learning and data mining communities due to the existence of a large number of unlabeled multi-view data in reality. Traditional spectral graph theoretic methods have recently been extended to multi-view clustering and shown outstanding performance. However, most of them still consist of two separate stages: learning a fixed common real matrix (i.e., continuous labels) of all the views from original data, and then applying K-means to the resulting common label matrix to obtain the final clustering results. To address these, we design a unified multi-view spectral clustering scheme to learn the discrete cluster indicator matrix in one stage. Specifically, the proposed framework directly obtain clustering results without performing K-means clustering. Experimental results on several famous benchmark datasets verify the effectiveness and superiority of the proposed method compared to the state-of-the-arts.
Factors Affecting Accuracy of Convolutional Neural Network Using VGG-16

In this paper, performance evaluation of image data-set using 2-layer Convolutional Neural Network Architecture and transfer learning method, is studied on Fashion MNIST dataset. Fashion MNIST is a dataset of images consisting 70000 28 $$\times $$ 28 gray-scale images, associated with label of 10 classes. The area of research of this paper in transfer learning is limited to pre-trained neural network VGG-16. The accuracy and respective losses are evaluated using the two mentioned methods. The work under this paper is inspired by widely famous Image-net Large Scale Visual Recognition Challenge, although due to constraint on time, resources, a smaller data set i.e. Fashion MNIST is taken for the study. The work is dependent on Keras Functional API and Tensor Flow. With the accuracy 88.24% and loss 29.02 as per CNN model, the model can be utilised by online clothing stores to classify their articles under right category.
Semantic Access Control for Privacy Management of Personal Sensing in Smart Cities

Personal and home sensors generate valuable information that could be used in Smart Cities. Unfortunately, typically, this data is locked out and used only by application/system developer. While vendors are to blame, one should consider also the "binary nature" of data access. Specifically, either owner has full control over her data (e.g. in a "closed system"), or she completely looses control, when the data is "opened". In this context, we propose, a semantic technologies-based, authorization and privacy control framework that enables user to maintain flexible, yet manageable data access control policies. The proposed approach is described in detail, including implementation and testing.
Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts

This article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.
Bidirectional Broad Learning System

As we all know, the learning efficiency and learning speed of traditional neural networks are far below requirements, which has become the main bottleneck of many applications. Recently, Chen et al. proposed a simple and effective learning method called Broad Learning System (BLS). This model experiment shows that this model can greatly reduce the training time compared with the traditional method. However, there are still many open questions in the field of BLS, such as whether the number of hidden layer nodes can be minimized without affecting the learning effect. This article proposes a new learning algorithm called the bidirectional learning system(B-BLS). In this algorithm, the feature layer nodes in the BLS are directly embedded by random projection, while the enhancement layer nodes are generated by random projection and the other half are obtained by reducing network residuals. Experiment results show the effectiveness of the method.
Social Collaborative Mutual Learning for Item Recommendation

Recommender Systems (RSs) provide users with item choices based on their preferences reflected in past interactions and become important tools to alleviate the information overload problem for users. However, in real-world scenarios, the user–item interaction matrix is generally sparse, leading to the poor performance of recommendation methods. To cope with this problem, social information is introduced into these methods in several ways, such as regularization, ensemble, and sampling. However, these strategies to use social information have their limitations. The regularization and ensemble strategies may suffer from the over-smoothing problem, while the sampling-based strategy may be affected by the overfitting problem. To overcome the limitations of the previous efforts, a novel social recommendation model, namely, Social Collaborative Mutual Learning (SCML), is proposed in this article. SCML combines the item-based CF model with the social CF model by two well-designed mutual regularization strategies. The embedding-level mutual regularization forces the user representations in two models to be close, and the output-level mutual regularization matches the distributions of the predictions in two models. Extensive experiments on three public datasets show that SCML significantly outperforms the baseline methods and the proposed mutual regularization strategies can embrace the advantages of the item-based CF model and the social CF model to improve the recommendation performance.
Decoding Task From Oculomotor Behavior In Virtual Reality

In the present study, we aim to explore whether and how well we can predict tasks based on eye movements in a virtual environment. We designed four different tasks in which participants had to align two cubes of different sizes. To define where participants looked, we used a ray-based method to calculate the point-of-regard (POR) on each cube at each time point. Using leave-one-subject-out cross-validation, our model performed well with an f1-score of 0.51 ± 0.17 (chance level 0.25) in predicting the four alignment types. Results suggest that the type of task can be decoded based on the aggregation of PORs. We further discuss the implications of object size on task inference and thus set an exciting road-map for how to design intention recognition experiments in virtual reality.
Generalization Bounds of Multitask Learning From Perspective of Vector-Valued Function Learning

In this article, we study the generalization performance of multitask learning (MTL) by considering MTL as a learning process of vector-valued functions (VFs). We will answer two theoretical questions, given a small size training sample: 1) under what conditions does MTL perform better than single-task learning (STL)? And 2) under what conditions does MTL guarantee the consistency of all tasks during learning? In contrast to the conventional task-summation based MTL, the introduction of VF form enables us to detect the behavior of each task and the task-group relatedness in MTL. Specifically, the task-group relatedness examines how the success (or failure) of some tasks affects the performance of the other tasks. By deriving the specific deviation and symmetrization inequalities for VFs, we obtain a generalization bound for MTL to the upper bound of the joint probability that there is at least one task with a large generalization gap. To answer the first question, we discuss how the synergic relatedness between task groups affects the generalization performance of MTL and shows that MTL outperforms STL if almost any pair of complementary task groups is predominantly synergic. Moreover, to answer the second question, we present a sufficient condition to guarantee the consistency of each task in MTL, which requires that the function class of each task should not have high complexity. In addition, our findings provide a strategy to examine whether the task settings will enjoy the advantages of MTL.
Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning

A novel two-stage automobile insurance fraud detection system is proposed that initially extracts a test set from the original imbalanced insurance dataset. A genetic algorithm based optimized fuzzy c-means clustering is then applied on the remaining data set for undersampling the majority samples by eliminating the outliers among them. Thereafter, the detection of the fraudulent claims occurs in two stages. In the first stage, each insurance record is passed to the clustering module that identifies the claim as genuine, malicious, or suspicious. The genuine and malicious samples are removed and only the suspicious instances are further scrutinized in the second stage by four trained supervised classifiers − Decision Tree, Support Vector Machine, Group Method for Data Handling and Multi-Layer Perceptron individually for final decision making. Extensive experiments and comparative analysis with another recent approach using a real-world automobile insurance dataset justifies the effectiveness of the proposed system.
Deterministic Gibbs Sampling for Data Association in Multi-Object Tracking

In multi-object tracking, multiple objects generate multiple sensor measurements, which are used to estimate the objects’ state simultaneously. Since it is unknown from which object a measurement originates, a data association problem arises. Considering all possible associations is computationally infeasible for large numbers of objects and measurements. Hence, approximation methods are applied to compute the most relevant associations. Here, we focus on deterministic methods, since multi-object tracking is often applied in safety-critical areas. In this work we show that Herded Gibbs sampling, a deterministic version of Gibbs sampling, applied in the labeled multi-Bernoulli filter, yields results of the same quality as randomized Gibbs sampling while having comparable computational complexity. We conclude that it is a suitable deterministic alternative to randomized Gibbs sampling and could be a promising approach for other data association problems.
Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.
Solar Position Identification on Sky Images for Photovoltaic Nowcasting applications

Nowcasting, i.e. very-short term forecasting, of photovoltaic (PV) production has a growing importance. A typical method to perform it, relies on sky cameras and sky images that, duly processed, can provide a forecast of sharp and fast changes in PV production. The first issue that arises while dealing with these analyses regards the identification and forecasting of the solar position in the images, especially when it is covered by clouds. In this paper, three different techniques for solar identification in sky images are proposed, analysed and compared.
General-Purpose Automated Machine Learning for Transportation: A Case Study of Auto-sklearn for Traffic Forecasting

Currently, there are no guidelines to determine what are the most suitable machine learning pipelines (i.e. the workflow from data preprocessing to model selection and validation) to approach Traffic Forecasting (TF) problems. Although automated machine learning (AutoML) has proved to be successful dealing with the model selection problem in other applications areas, only a few papers have explored the performance of general-purpose AutoML methods, purely based on optimisation, when tackling TF. In this paper, we provide a thorough exploration of the benefits of Auto-sklearn for TF, as a general-purpose AutoML method that follows a hybrid search strategy combining optimisation with meta-learning and ensemble learning. Particularly, we focus on how well Auto-sklearn is able to recommend competitive machine learning pipelines to forecast traffic, modelled as a TF multi-class imbalanced classification problem, along different time horizons at two spatial scales (point and road segment) and two environments (freeway and urban). Concretely, we test the following scenarios: I) a hybrid search strategy with the three components (optimisation, meta-learning, ensemble learning), II) a strategy based on meta-learning and ensemble learning, and III) a strategy based on the estimation of the best performing pipeline from those suggested by the meta-learning. Experimental results show that the meta-learning component of Auto-sklearn does not work properly on TF problems, and on the other hand, that the optimisation does not contribute too much to the final performance of predictions.