Reviewerly/exHarmony huggingface dataset:
Online latent semantic hashing for cross-media retrieval

Hashing based cross-media method has been become an increasingly popular technique in facilitating large-scale multimedia retrieval task, owing to its effectiveness and efficiency. Most existing cross-media hashing methods learn hash functions in a batch based mode. However, in practical applications, data points often emerge in a streaming manner, which makes batch based hashing methods loss their efficiency. In this paper, we propose an Online Latent Semantic Hashing (OLSH) method to address this issue. Only newly arriving multimedia data points are utilized to retrain hash functions efficiently and meanwhile preserve the semantic correlations in old data points. Specifically, for learning discriminative hash codes, discrete labels are mapped to a continuous latent semantic space where the relative semantic distances in data points can be measured more accurately. And then, we propose an online optimization scheme towards the challenging task of learning hash functions efficiently on streaming data points, and the computational complexity and memory cost are much less than the size of training dataset at each round. Extensive experiments across many real-world datasets, e.g. Wiki, Mir-Flickr25K and NUS-WIDE, show the effectiveness and efficiency of the proposed method.
Design and Implementation on Hyperledger-Based Emission Trading System

Emission trading policy provides a new approach using economic incentives to control the environmental pollution efficiently. Legal polluters can trade emission permits with each other through a trusted trading system that lacks security and credibility due to its centralization nowadays. Permissioned blockchain utilize a decentralized way to store private data immutably, providing new approaches to solve those defects of the existing centralized systems. In this paper, we propose a Hyperledger-based Emission Trading System (HyperETS) on the permissioned blockchain. Using Hyperledger Fabric as the implementation platform, HyperETS integrates the fine-grained access control, distributed ledger, and consensus protocol, aiming to provide credible trading service for polluters. We achieve the business logic by designing the particular ledger structures and smart contract in blockchain. HyperETS stores all transactions immutably in a chain and makes it easy to share the data between organizations. Finally, several experiments are conducted to evaluate the performances of the proposed demonstration system.
Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network

The depth maps obtained by the consumer-level sensors are always noisy in the low-resolution (LR) domain. Existing methods for the guided depth super-resolution, which are based on the pre-defined local and global models, perform well in general cases (e.g., joint bilateral filter and Markov random field). However, such model-based methods may fail to describe the potential relationship between RGB-D image pairs. To solve this problem, this paper proposes a data-driven approach based on the deep convolutional neural network with global and local residual learning. It progressively upsamples the LR depth map guided by the high-resolution intensity image in multiple scales. A global residual learning is adopted to learn the difference between the ground truth and the coarsely upsampled depth map, and the local residual learning is introduced in each scale-dependent reconstruction sub-network. This scheme can restore the depth structure from coarse to fine via multi-scale frequency synthesis. In addition, batch normalization layers are used to improve the performance of depth map denoising. Our method is evaluated in noise-free and noisy cases. A comprehensive comparison against 17 state-of-the-art methods is carried out. The experimental results show that the proposed method has faster convergence speed as well as improved performances based on the qualitative and quantitative evaluations.
A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation

Image segmentation plays a fundamental role in image processing. Active contour models have been widely used since they handle topological change easily and provide smooth contours. However, noise presents challenges for edge-based level set methods since it leads contours easily passing through objects or falling into local minima. In this paper, we propose a weighted edge-based level set method based on multi-local statistical information to better segment noisy images. Through analysing the deficiencies of constant length and regional coefficients and traditional edge stop function in noisy image segmentation, weighted length and regional coefficients and modified edge stop function are proposed to overcome their shortcomings, respectively. The weighted edge-based level set method is used to segment synthetic and real images that have added different types and levels of noise. The experiments indicate that our method provides higher segmentation accuracies and more accurate segmentation results, which demonstrate its effectiveness and robustness.
Product-Aware Answer Generation in E-Commerce Question-Answering

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.
An Online Market Mechanism for Edge Emergency Demand Response via Cloudlet Control

The computing frontier is moving from centralized mega datacenters towards distributed cloudlets at the network edge. We argue that cloudlets are well-suited for participation in Emergency Demand Response (EDR) programs due to their enormous energy consumption and flexible workload distribution, while existing EDR mechanisms for clouds and colocation datacenters are not suitable for cloudlets. We propose a novel online market mechanism, EdgeEDR, to incentivize cloudlets to participate in EDR, featuring multiple cloudlet-specific designs. At a high level, we observe that cloudlet operators can dynamically switch on/off entire cloudlets to compensate for the energy reduction required by the power grid. We formulate a long-term social cost minimization problem and decompose it into a series of one-round procurement auctions. In each auction instance, we propose to let the cloudlet tenants bid with cost functions of their service quality degradation tolerance, and let the cloudlet operator choose the service quality, allocate the workload, and shut down the cloudlets. Via rigorous analysis, we exhibit that our bidding policy is individually rational and truthful; our workload distribution algorithm has near-optimal performance in each auction; and our overall online algorithm achieves a provable competitive ratio. We further confirm the performance of our mechanism through extensive trace-driven simulations.
Salient Object Detection With Lossless Feature Reflection and Weighted Structural Loss

Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various realworld applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin.
RiskCog: Unobtrusive Real-Time User Authentication on Mobile Devices in the Wild

Recent hardware advances have led to the development and consumerization of mobile devices, which mainly include smartphones and various wearable devices. To protect the privacy of users, various user authentication mechanisms have been proposed. In particular, biometrics has been widely used for multi-factor authentication. However, biometrics-based authentication mechanisms usually require costly sensors deployed on devices, and rely on explicit user input and Internet connection for performing user authentication. In this article, we propose a system, called RISKCOG, which can authenticate the ownership of mobile devices unobtrusively and in a real-time manner by adopting a learning-based approach. Unlike previous studies on user authentication, for cross-platform deployment, maximum user privacy protection, and unobtrusive authentication, RISKCOG only relies on those widely available and privacy-insensitive motion sensors to capture the data related to the users' daily device usage. It requires no users' explicit input and has no requirement on the users' motion state or the device placement. RISKCOG is also usable in the environment without Internet access by performing offline user identity verification. We conduct comprehensive experiments on smartphones and smartwatches, which show that RISKCOG can authenticate device users rapidly and with high accuracy.
Categorical Variable Segmentation Model for Software Development Effort Estimation

This paper proposes a new software development effort estimation model. The new model's design is based on the function point analysis, categorical variable segmentation (CVS), and stepwise regression. The stepwise regression method is used for the creation of the unique estimation model of each segment. The estimation accuracy of the proposed model is compared to clustering-based models and the international function point user group model. It is shown that the proposed model increases estimation accuracy when compared to baseline methods: non-clustered functional point analysis and clustering-based models. The new CVS model achieves a significantly higher accuracy than the baseline methods.
BasisGen: automatic generation of operator bases

BasisGen is a Python package for the automatic generation of bases of operators in effective field theories. It accepts any semisimple symmetry group and fields in any of its finite dimensional irreducible representations. It takes into account integration by parts redundancy and, optionally, the use of equations of motion. The implementation is based on well-known methods to generate and decompose representations using roots and weights, which allow for fast calculations, even with large numbers of fields and high-dimensional operators. BasisGen can also be used to do some representation-theoretic operations, such as finding the weight system of an irreducible representation from its highest weight or decomposing a tensor product of representations.
Tool testing and reliability issues in the field of digital forensics

The digital forensic discipline is wholly reliant upon software applications and tools designed and marketed for the acquisition, display and interpretation of digital data. The results of any subsequent investigation using such tools must be reliable and repeatable whilst supporting the establishment of fact, allowing criminal justice proceedings the ability to digest any findings during the process of determining guilt or innocence. Errors present at any stage of an examination can undermine an entire investigation, compromising any potentially evidential results. Despite a clear dependence on digital forensic tools, arguably, the field currently lacks sufficient testing standards and procedures to effectively validate their usage during an investigation. Digital forensics is a discipline which provides decision-makers with a reliable understanding of digital traces on any device under investigation, however, it cannot say with 100% certainty that the tools used to undertake this process produce factually accurate results in all cases. This is an increasing concern given the push for digital forensic organisations to now acquire ISO 17025 accreditation. This article examines the current state of digital forensic tool-testing in 2018 along with the difficulties of sufficiently testing applications for use in this discipline. The results of a practitioner survey are offered, providing an insight into industry consensus surrounding tool-testing and reliability.
Emergence of analogy from relation learning

By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from "big data" (to create semantic features for individual words) and supervised learning from "small data" (to create representations of semantic relations between words). Given as inputs labeled pairs of lexical representations extracted by deep learning, the model creates augmented representations by remapping features according to the rank of differences between values for the two words in each pair. These augmented representations aid in coping with the feature alignment problem (e.g., matching those features that make "love-hate" an antonym with the different features that make "rich-poor" an antonym). The model extracts weight distributions that are used to estimate the probabilities that new word pairs instantiate each relation, capturing the pattern of human typicality judgments for a broad range of abstract semantic relations. A measure of relational similarity can be derived and used to solve simple verbal analogies with human-level accuracy. Because each acquired relation has a modular representation, basic symbolic operations are enabled (notably, the converse of any learned relation can be formed without additional training). Abstract semantic relations can be induced by bootstrapping from nonrelational inputs, thereby enabling relational generalization and analogical reasoning.
Efficient Image Recognition and Retrieval on IoT-Assisted Energy-Constrained Platforms From Big Data Repositories

The advanced computational capabilities of many resource constrained devices, such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous Internet of Things (IoT) applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning-based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola-Jones algorithm with additional face and nonface classifier to eliminate the miss-detection problem. Second, the system uses convolutional layers of a cost effective pretrained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.
Natural Language Processing, Sentiment Analysis, and Clinical Analytics

Recent advances in Big Data have prompted healthcare practitioners to utilize the data available on social media to discern sentiment and emotions’ expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients’ writings on various media. Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis—applied to many other domains—depends heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users’ sentiments on social media. Such sentiments can be culled over a period of time thus minimizing the errors introduced by data input and other stressors. Furthermore, we look at some applications of sentiment analysis and application of NLP to mental health. The reader will also learn about the NLTK toolkit that implements various NLP theories and how they can make the data scavenging process a lot easier.
An integrated recommender system for improved accuracy and aggregate diversity

Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers.
A Novel Class-Imbalance Learning Approach for Both Within-Project and Cross-Project Defect Prediction

Software defect prediction (SDP) is an available way to enhance test efficiency and guarantee software reliability. However, there are more clean instances than defective instances in real software projects, and this results in severe class distribution skews and gets the poor performance of classifiers. So solving the class-imbalance problem in SDP has attracted growing attention from industry and academia in software engineering. In this paper, we propose a novel class-imbalance learning approach for both within-project and cross-project class-imbalance problem. We utilize the thought of stratification embedded in nearest neighbor (STr-NN) to produce evolving training datasets with balanced data. For within-project, we directly employ the STr-NN approach for defect prediction. For cross-project, we first introduce transfer component analysis to mitigate the distribution differences between source and target dataset, and then employ the STr-NN approach on the transferred data. We conduct experiments on PROMISE and NASA datasets using ensemble learning based on weight vote. Experimental results indicate that our approach has higher area under curve (AUC), Recall and comparable probability of a false alarm (pf), and F-measure than some existing methods for the class-imbalance problem.
An efficient image retrieval framework using fused information feature

The tremendous growth in the number of digital images has provoked the need for improvement in search and retrieval of images from a large database. The biggest challenge faced in this process is retrieval of the required images from a large database with maximum precision and minimum retrieval time. Selecting appropriate features plays a significant role in improving the performance of image retrieval systems. In this paper, Fused Information Feature-based Image Retrieval System (FIF-IRS) is proposed, consisting of 8-Directional Gray Level Co-occurrence Matrix (8D-GLCM) and HSV Color Moments (HSVCM) features. The performance of the image retrieval system is evaluated by the metrics Precision, Error Rate, and Retrieval Time. The proposed image retrieval system is tested on benchmark image databases- Corel-1K, Corel-5K, and Corel-10K. The experimental results obtained prove that there is a significant improvement while applying the proposed method when compared to existing state-of-the-art techniques with attained precision of 83.3%, 66.9%, and 56.4% on the three databases respectively.
DataToon

Comics are an entertaining and familiar medium for presenting compelling stories about data. However, existing visualization authoring tools do not leverage this expressive medium. In this paper, we seek to incorporate elements of comics into the construction of data-driven stories about dynamic networks. We contribute DataToon, a flexible data comic storyboarding tool that blends analysis and presentation with pen and touch interactions. A storyteller can use DataToon rapidly generate visualization panels, annotate them, and position them within a canvas to produce a visually compelling narrative. In a user study, participants quickly learned to use DataToon for producing data comics.