Reviewerly/exHarmony huggingface dataset:
Benchmarking big data systems: A survey

With the enormous growth on the availability and usage of Big Data storage and processing systems, it has become essential to assess the various performance aspects of these systems so that we can carefully understand their strong and weak aspects. In practice, currently, when an individual/enterprise aims to develop a Big Data storage and processing solution for harnessing the knowledge inside their data, they will get challenged by the availability of several frameworks from which they need to select. This is a challenging task which needs to directed by with good knowledge about various perspectives of such systems. Additionally, the choice normally vary from one scenario to another according to the essential needs of the application. In practice, there is no single benchmark study which can cover the different types of big data processing requirements, systems, application scenarios and metrics. Several benchmarks and benchmarking studies have been developed where each study focuses on some representative type of frameworks and only consider some aspects to cover. In this article, we provide a comprehensive survey and analysis of the state-of-the-art of benchmarking the different types of big data systems (e.g., NoSQL databases, Big SQL engines, Big Streaming engines, Big Graph Processing engines, Big Machine/Deep Learning engines). Additionally, we highlight some of the significant open challenges and missing requirements of current benchmarks of big data systems with suggestions of directions for future extensions and improvements.
Fear of Online Consumer Identity Theft: Cross-Country Application and Short Scale Development

The growing internationalization of electronic commerce demands the establishment of the cross-national validity of theoretical concepts. An important concept in e-commerce is consumers’ fear of online identity theft (FOIT), which impedes consumers’ willingness to engage in online transactions and can negatively affect e-commerce revenues. The present study validates the consumer FOIT scale developed in Germany by Hille et al. (2015) in a cross-cultural setting and proposes an abbreviated version of the scale, which is approximately 35% shorter than the original. Established validation procedures with samples of online consumers from Germany, the United States, and Japan demonstrate the reliability, validity, and cross-national applicability of the short FOIT scale. In particular, this study extends Hille et al. (2015) research by examining and revealing the impact of FOIT on consumers’ prevention-focused responses. This research offers implications for both research and e-commerce managers.
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style

Image captioning is a research hotspot where encoder-decoder models combining convolutional neural network (CNN) and long short-term memory (LSTM) achieve promising results. Despite significant progress, these models generate sentences differently from human cognitive styles. Existing models often generate a complete sentence from the first word to the end, without considering the influence of the following words on the whole sentence generation. In this paper, we explore the utilization of a human-like cognitive style, i.e., building overall cognition for the image to be described and the sentence to be constructed, for enhancing computer image understanding. This paper first proposes a Mutual-aid network structure with Bidirectional LSTMs (MaBi-LSTMs) for acquiring overall contextual information. In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner. In the captioning process, the LSTM implicitly utilizes the subsequent semantic information contained in its hidden states. In fact, MaBi-LSTMs can generate two sentences in forward and backward directions. To bridge the gap between cross-domain models and generate a sentence with higher quality, we further develop a cross-modal attention mechanism to retouch the two sentences by fusing their salient parts as well as the salient areas of the image. Experimental results on the Microsoft COCO dataset show that the proposed model improves the performance of encoder-decoder models and achieves state-of-the-art results.
Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication

Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.
Clustering Optimization for Abnormality Detection in Semi-Autonomous Systems

The use of machine learning techniques is fundamental for developing autonomous systems that can assist humans in everyday tasks. This paper focus on selecting an appropriate network size for detecting abnormalities in multisensory data coming from a semi-autonomous vehicle. We use an extension of Growing Neural Gas with the utility measurement (GNG-U) for segmenting multisensory data into an optimal set of clusters that facilitate a semantic interpretation of data and define local linear models used for prediction purposes. A functional that favors precise linear dynamical models in large state space regions is considered for optimization purposes. The proposed method is tested with synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a semi-autonomous vehicle that interacts with pedestrians in a closed environment. Comparisons with a previous work of abnormality detection are provided.
An efficient active learning method for multi-task learning

In multi-task learning, the sharing of information between related tasks affects and promotes the learning of each task. However, the traditional multi-task learning techniques always require sufficient labeled data to improve the learning of each task, and labeling samples is always expensive in practice. In this paper, we propose two variants of active learning methods for multi-task classification. In the uncertainty step, we propose the support vector preservation criterion that evaluates uncertainty at the level of classifier, which is called classifier-level uncertainty (CLU). In the diversity step, we propose two diversity criteria that evaluate diversity by the clustering method and the partition method respectively, which are called clustering-based diversity (CBD) and partition-based diversity (PBD) respectively. Each diversity criterion together with the uncertainty criterion is to form an active learning method for multi-task learning. In addition, the proposed support vector preservation criterion selects local informative samples which determine the hyperplane for each task. Furthermore, in order to maintain the distribution structure of the samples, we put forward the micro-kernel k-means clustering method and partition-based method to select global informative samples from the non-support vectors. By incorporating the local and global informative samples into active learning, we propose the two active learning methods for multi-task problems. We evaluate the effectiveness of the proposed methods by conducting experiments with other active learning methods. The experimental results show that the proposed two methods perform better than other active learning methods.
Generating Captions for Images of Ancient Artworks

The neural encoder-decoder framework is widely adopted for image captioning of natural images. However, few works have contributed to generating captions for cultural images using this scheme. In this paper, we propose an artwork type enriched image captioning model where the encoder represents an input artwork image as a 512-dimensional vector and the decoder generates a corresponding caption based on the input image vector. The artwork type is first predicted by a convolutional neural network classifier and then merged into the decoder. We investigate multiple approaches to integrate the artwork type into the captioning model among which is one that applies a step-wise weighted sum of the artwork type vector and the hidden representation vector of the decoder. This model outperforms three baseline image captioning models for a Chinese art image captioning dataset on all evaluation metrics. One of the baselines is a state-of-the-art approach fusing textual image attributes into the captioning model for natural images. The proposed model also obtains promising results for another Egyptian art image captioning dataset.
Atmospheric extinction levels of solar radiation using aerosol optical thickness satellite data. Validation methodology with measurement system

In order to make the concentrating solar power (CSP) more competitive, an accurate prediction of the solar radiation incident on CSP tower plant receivers is necessary. The extinction in the heliostat-receiver pathway plays an important role in the optical loss of the solar field and therefore, in the performance of the plant. In order to correctly operate these kind of plants and to select new potential emplacements, it is necessary to know the on-site levels of extinction. A methodology was developed in another published work, called Extinction AOT method (EAM), with the purpose of finding out the levels of extinction present at a location using AERONET AOT as input data. However, there is no AERONET AOT data available at any potential site of interest for setting up a solar tower plant, so, alternative approaches were necessary. This paper proposes to determine levels of extinction at any location using AOT satellite data instead of AERONET data. These results have been compared with results obtained applying the EAM with AERONET data and using real extinction measurements obtained with the CIEMAT extinction measurement system. The extinction obtained at PSA has been 5% in all cases considering the error rates.
GroundNet

We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and depth estimates. Specifically, our proposed model, GroundNet, first estimates the depth and surface normal in two separate streams, from which two ground plane normals are then computed deterministically. To leverage the geometric correlation between depth and normal, we propose to add a consistency loss on top of the computed ground plane normals. In addition, a ground segmentation stream is used to isolate the ground regions so that we can selectively back-propagate parameter updates through only the ground regions in the image. Our method achieves the top-ranked performance on ground plane normal estimation and horizon line detection on the real-world outdoor datasets of ApolloScape and KITTI, improving the performance of previous art by up to 17.7% relatively.
Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing Via Geo-Indistinguishability

Nowadays, vehicles have been increasingly adopted as participants in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location information to servers to facilitate the utilities of SC services. Traditional location privacy protection mechanisms cannot be directly applied to vehicle-based SC since they assume workers' location on a 2-dimensional plane, which does not take into account the features of vehicle workers' mobility in vehicle road networks. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) in vehicle road networks. Our objective is to design a location obfuscation strategy to minimize the loss of quality-of-service (QoS) due to task distribution with location obfuscation, while guaranteeing geo-indistinguishability to be satisfied. Considering the computational complexity of the VLP problem, by resorting to discretization, we approximate VLP to a linear programming problem that can be solved by existing well-developed approaches (such as the simplex method). To further improve the time efficiency, we reduce the number of constraints in VLP by exploiting key features of geo-indistinguishability in vechicle road networks (such as transitivity). Finally, our experimental results demonstrate that our approach can achieve a reasonable approximation of the minimum QoS loss with location privacy protected, and also outperforms a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.
Hierarchical Edge-Cloud Computing for Mobile Blockchain Mining Game

Computation offloading has been considered as a viable solution to blockchain mining in mobile environments. In this paper, we present a two-layer computation offloading paradigm that includes an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. Two practical scenarios are investigated: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is connected (to the CSP) or standalone, which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation

Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random Field (CRF) model. The two GANs aim at generating distinct and complementary disparity maps and at improving the generation quality via exploiting the adversarial learning strategy. The deep CRF coupling model is proposed to fuse the generative and discriminative outputs from the dual GAN nets. As such, the model implicitly constructs mutual constraints on the two network branches and between the generator and discriminator. This facilitates the optimization of the whole network for better disparity generation. Extensive experiments on the KITTI, Cityscapes, and Make3D datasets clearly demonstrate the effectiveness of the proposed approach and show superior performance compared to state of the art methods. The code and models are available at https://github.com/mihaipuscas/3dv-coupled-crf-disparity.
The communicative modus operandi of online child sexual groomers: Recurring patterns in their language use

Online child sexual groomers manipulate their targets into partaking in sexual activity online and, in some cases, offline. To do so they use language (and other semiotic means, such as images) strategically. This study uses a Corpus-Assisted Discourse Studies methodology to identify recurring patterns in online groomers' language use, mapping them to the specific grooming goal that their use in context fulfils. The analysis of the groomers' language (c. 3.3 million words) within 622 conversations from the Perverted Justice website newly identifies 70 such recurring linguistic patterns (three-word collocations), as well as their relative strength of association to one or more grooming goals. The results can be used to inform computational models for detecting online child sexual grooming language. They can also support the development of training resources that raise awareness of typical language structures that characterise online sexual groomers' communicative modus operandi.
Collective optimization for variational quantum eigensolvers

A variational quantum eigensolver (VQE) optimizes parametrized eigenstates of a Hamiltonian on a quantum processor by updating parameters with a classical computer. Such a hybrid quantum-classical optimization serves as a practical way to leverage classical algorithms in exploiting the power of near-term quantum devices. Here, aiming to solve a group of Hamiltonians more efficiently, we develop an extension of the conventional VQE. A snake algorithm is incorporated to couple optimizing processes for VQEs of different Hamiltonians by gradient descent. Such a so-called collective VQE (CVQE) is applied to simulate molecules with varied bond lengths for demonstration. Numeral simulations show that the CVQE exhibits clear collective behavior in the optimization process of updating parameters. Remarkably, the CVQE tends to avoid a single VQE task to be trapped in the local minimum. The collective optimization utilizes intrinsic relations between related tasks and may inspire advanced hybrid quantum-classical algorithms for solving practical problems with current quantum technologies.
Blockchain Enabled AI Marketplace: The Price You Pay for Trust

There has been a considerable amount of interest in exploring blockchain technologies for enabling marketplaces of different kinds. In this work, we provide a blockchain implementation that enables an "AI marketplace": a platform where consumers and data providers can transact data and/or models and derive value. Preserving privacy and trust during these transactions is a paramount concern. As an enabling use case, we consider a transfer learning setting. In this setting, a consumer entity wants to acquire a large training set, from different private data providers, that matches a small validation dataset provided by the consumer. Data providers expect fair value for their contribution and the consumer also wants to maximize its benefit. We implement a distributed protocol on a blockchain that provides guarantees on privacy and consumer's benefit. We also demonstrate that our blockchain implementation plays a crucial role in addressing the issue of fair value attribution and privacy in a trustable way. We consider three different designs for a blockchain implementation that trades off trust requirements on different entities and the overhead in terms of time taken for completion of the task. The first design provides no trust guarantees. The second one guarantees trust with respect to other participants if the platform is trustworthy. The third one guarantees complete trust with no requirements. Our experiments show that the performance in the second and third cases, with partial/complete trust guarantees, degrade by roughly 2× and 5× respectively, compared to the baseline with no trust guarantees.
EF-Dedup: Enabling Collaborative Data Deduplication at the Network Edge

The advent of IoT and edge computing will lead to massive amounts of data that need to be collected and transmitted to online storage systems. To address this problem, we push data deduplication to the network edge. Specifically, we propose a new technique for collaborative edge-facilitated deduplication (EF-dedup), wherein we partition the resource-constrained edge nodes into disjoint clusters, maintain a deduplication index structure for each cluster using a distributed key-value store and perform decentralized deduplication within those clusters. This is a challenging partitioning problem that addresses a novel tradeoff: edge nodes with highly correlated data may not always be within the same edge cloud, with non-trivial network cost among them. We address this challenge by first formulating an optimization problem to partition the edge nodes, considering both the data similarities across the nodes and the inter-node network cost. We prove that the problem is NP-Hard, provide bounded heuristics to solve it and build a prototype EF-dedup system. Our experiments on EF-dedup, performed on edge nodes in AT&T research lab and a central cloud at AWS, demonstrate that EF-dedup achieves 38.3-118.5% better deduplication throughput than sole cloud-based techniques and achieves 43.4-60.2% lesser aggregate cost in terms of the network-storage tradeoff as compared to approaches that solely favor one over the other.
Energy-Aware and Context-Aware Video Streaming on Smartphones

Although streaming video at a higher bitrate (resolution) can lead to better Quality of Experience (QoE), a larger amount of data will have to be downloaded and processed on smartphones and thus consuming more energy. On a moving bus where the wireless signal is weak, more energy will have to be spent on maintaining high bitrate video streaming than at a static environment such as at home or a cafe where the wireless signal is strong. On the other hand, the user perceived QoE does not increase too much by watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), because the perception of video quality is affected by the environment such as the vibration or shaking on a moving bus. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded sensors (e.g., accelerometer) in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impacts of video bitrate and vibration level on QoE, and model the impacts of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the energy-aware and context-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we further propose an online bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithm can significantly outperform existing approaches when considering both energy and QoE.