Reviewerly/exHarmony huggingface dataset:
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of a single dominant unseen object category in a test image. We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both ‘recognition’ and ‘localization’ of an unseen category. To address this limitation, we introduce a new ‘Zero-Shot Detection’ (ZSD) problem setting, which aims at simultaneously recognizing and locating object instances belonging to novel categories without any training examples. We also propose a new experimental protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to practical issues, e.g., the rarity of unseen objects. To the best of our knowledge, this is the first end-to-end deep network for ZSD that jointly models the interplay between visual and semantic domain information. To overcome the noise in the automatically derived semantic descriptions, we utilize the concept of meta-classes to design an original loss function that achieves synergy between max-margin class separation and semantic space clustering. Furthermore, we present a baseline approach extended from recognition to ZSD setting. Our extensive experiments show significant performance boost over the baseline on the imperative yet difficult ZSD problem.
Cross-Domain Recommendation via Deep Domain Adaptation

The behavior of users in certain services indicates their preferences, which may be used to make recommendations for other services they have never used. However, the cross-domain relation between items and user preferences is not simple, especially when there are few or no common users and items across domains. We propose a content-based cross-domain recommendation method for cold-start users that does not require user- or item-overlap. We formulate recommendations as an extreme classification task, and the problem is treated as an instance of unsupervised domain adaptation. We assess the performance of the approach in experiments on large datasets collected from Yahoo! JAPAN video and news services and find that it outperforms several baseline methods including a cross-domain collaborative filtering method.
Transparency: Motivations and Challenges

Transparency is often deemed critical to enable effective real-world deployment of intelligent systems. Yet the motivations for and benefits of different types of transparency can vary significantly depending on context, and objective measurement criteria are difficult to identify. We provide a brief survey, suggesting challenges and related concerns, particularly when agents have misaligned interests. We highlight and review settings where transparency may cause harm, discussing connections across privacy, multi-agent game theory, economics, fairness and trust.
BeautyGlow: On-Demand Makeup Transfer Framework With Reversible Generative Network

As makeup has been widely-adopted for beautification, finding suitable makeup by virtual makeup applications becomes popular. Therefore, a recent line of studies proposes to transfer the makeup from a given reference makeup image to the source non-makeup one. However, it is still challenging due to the massive number of makeup combinations. To facilitate on-demand makeup transfer, in this work, we propose BeautyGlow that decompose the latent vectors of face images derived from the Glow model into makeup and non-makeup latent vectors. Since there is no paired dataset, we formulate a new loss function to guide the decomposition. Afterward, the non-makeup latent vector of a source image and makeup latent vector of a reference image and are effectively combined and revert back to the image domain to derive the results. Experimental results show that the transfer quality of BeautyGlow is comparable to the state-of-the-art methods, while the unique ability to manipulate latent vectors allows BeautyGlow to realize on-demand makeup transfer.
How does Machine Learning Change Software Development Practices?

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). Based on our findings, we highlight future research directions and provide recommendations for practitioners.
Efficient task-specific data valuation for nearest neighbor algorithms

Given a data set D containing millions of data points and a data consumer who is willing to pay for $ X to train a machine learning (ML) model over D , how should we distribute this $X to each data point to reflect its "value"? In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all N data points, it requires O (2 N ) model evaluations for exact computation and O ( N log N ) for ( ϵ , δ)-approximation. In this paper, we focus on one popular family of ML models relying on K -nearest neighbors ( K NN). The most surprising result is that for unweighted K NN classifiers and regressors, the Shapley value of all N data points can be computed, exactly , in O ( N log N ) time - an exponential improvement on computational complexity! Moreover, for ( ϵ , δ)-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity O ( N h ( ϵ , K ) log N ) when ϵ is not too small and K is not too large. We empirically evaluate our algorithms on up to 10 million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further. We then extend our algorithm to other scenarios such as (1) weighed K NN classifiers, (2) different data points are clustered by different data curators , and (3) there are data analysts providing computation who also requires proper valuation. Some of these extensions, although also being improved exponentially, are less practical for exact computation (e.g., O ( N K ) complexity for weigthed K NN). We thus propose an Monte Carlo approximation algorithm, which is O ( N (log N ) 2 /(log K ) 2 ) times more efficient than the baseline approximation algorithm.
Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Abstract Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time‐consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real‐world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.
Stylistic variation on the Donald Trump Twitter account: A linguistic analysis of tweets posted between 2009 and 2018

Twitter was an integral part of Donald Trump's communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections.
Super-Resolution of Single Remote Sensing Image Based on Residual Dense Backprojection Networks

High-resolution (HR) images are always preferred for many remote sensing applications, which can be obtained from their low-resolution (LR) counterparts via a technique referred to as super-resolution (SR). Among SR approaches, single image SR (SISR) methods aim at reconstructing the HR image from only one LR image. In this paper, a residual dense backprojection network (RDBPN)-based SISR method is proposed to promote the resolution of RGB remote sensing images with median- and large-scale factors. The proposed network consists of several residual dense backprojection blocks that contain two kinds of modules, named the upprojection module and the downprojection module, and these modules are densely connected in one block. Different from the chain-connected backprojection structure, the proposed method applies a residual backprojection block structure, which can utilize residual learning in both global and local manners. We further simplify the network by replacing the downprojection unit with the downscaling unit to accelerate the speed of reconstruction, and this implementation is called fast RDBPN (FRDBPN). Several experiments under the UC Merced data set are conducted to validate the effectiveness of the proposed method, and the results indicate that: 1) the proposed residual block structure is superior to the chain-connected structure; 2) FRDBPN achieves a speedup of about 1.3 times with similar and even better-reconstructed performance in comparison with RDBPN; and 3) RDBPN and FRDBPN outperform several state-of-the-art methods in terms of both quantitative evaluation and visual quality.
Blockchain Games: A Survey

With the support of the blockchain systems, the cryptocurrency has changed the world of virtual assets. Digital games, especially those with massive multi-player scenarios, will be significantly impacted by this novel technology. However, there are insufficient academic studies on this topic. In this work, we filled the blank by surveying the state-of-the-art blockchain games. We discuss the blockchain integration for games and then categorize existing blockchain games from the aspects of their genres and technical platforms. Moreover, by analyzing the industrial trend with a statistical approach, we envision the future of blockchain games from technological and commercial perspectives.
Blockchain-Based Distributed Software-Defined Vehicular Networks: A Dueling Deep ${Q}$ -Learning Approach

Vehicular ad hoc networks (VANETs) have become an essential part in smart transportation systems of modern cities. However, because of dynamicity and infrastructure-less of VANETs, the ever increasing number of network security issues become obstacles for the realization of smart cities. Software-defined VANETs have provided a reliable way to manage VANETs dynamically and securely. However, the traditionally centralized control plane makes it vulnerable to malicious nodes and results in performance degradation. Therefore, a distributed control plane is necessary. How to reach a consensus among multiple controllers under complex vehicular environment is an essential problem. In this paper, we propose a novel blockchain-based distributed software-defined VANET framework (block-SDV) to establish a secure architecture to overcome the above issues. The trust features of blockchain nodes, the number of consensus nodes, trust features of each vehicle, and the computational capability of the blockchain are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space and reward function. Since it is difficult to be solved by traditional methods, we propose a novel dueling deep Q-learning (DDQL) with prioritized experience replay approach. Simulation results are presented to show the effectiveness of the proposed block-SDV framework.
Unsupervised Anomaly Localization Using Variational Auto-Encoders

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including >1000 healthy and >250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.
A comparative study of machine translation for multilingual sentence-level sentiment analysis

Sentiment analysis has become a key tool for several social media applications, including, analysis of user’s opinions about products and services, support for politics during campaigns and even identification of market trending. Multiple existing sentiment analysis methods explore different techniques, usually relying on lexical resources or learning approaches. Despite the significant interest in this theme and amount of research efforts in the field, almost all existing methods are designed to work with only English content. Most current strategies in other languages consist of adapting existing lexical resources, without presenting proper validations and basic baseline comparisons. In this work, we take a different step into this field. We focus on evaluating existing efforts proposed to do language specific sentiment analysis with a simple yet effective baseline approach. To do it, we evaluated sixteen methods for sentence-level sentiment analysis proposed for English, and compared them with three language-specific methods. Based on fourteen human labeled language-specific datasets, we provide an extensive quantitative analysis of existing multilingual approaches. Our results suggest that simply translating the input text in a specific language to English and then using one of the existing best methods developed for English can be better than the existing language-specific approach evaluated. We also rank methods according to their prediction performance and identify those that acquired the best results using machine translation across different languages. As a final contribution to the research community, we release our codes, datasets, and the iFeel 3.0 system, a Web framework and tool for multilingual sentence-level sentiment analysis1. We hope our system sets up a new baseline for future sentence-level methods developed in a wide set of languages.
SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection

Real-time 3D object detection is a fundamental technique in numerous applications, such as autonomous driving, unmanned aerial vehicles (UAV) and robot vision. However, current LiDAR-based 3D object detection algorithms allocate inadequate attention to the inhomogeneity of LiDAR point clouds and the shape encoding capability of regional proposal schemes. This paper introduces a novel 3D object detection network called the Shape Attention Regional Proposal Network (SARPNET), which deploys a new low-level feature encoder to remedy the sparsity and inhomogeneity of LiDAR point clouds with an even sample method, and embodies a shape attention mechanism that learns the statistic 3D shape priors of objects and uses them to spatially enhance semantic embeddings. Experimental results show that the proposed one-stage method outperforms state-of-the-art one-stage and even two-stage methods on the KITTI 3D object detection benchmark. It achieved a BEV AP of (87.26%, 62.80%), 3D AP of (75.64%, 60.43%), and orientation AP of (88.86%, 71.01%) for the detection of cars and cyclists, respectively. Besides, the method is the third winner in the nuScenes 3D Detection challenge of CVPR2019 Workshop on Autonomous Driving (WAD).
Triple‐entry accounting with blockchain: How far have we come?

Abstract Although double‐entry accounting has been used for more than 600 years, today’s era of disruptive technological change utilising blockchain and FinTech has led to the emergence of another promising accounting method: triple‐entry accounting. This paper explores triple‐entry accounting, from its conception to the current state of play, using three case studies. We find that: (i) in a blockchain ecosystem, for some accounts, business entities will only need to perform a single entry internally and the opposite entry will be recorded in a public shared ledger; and (ii) triple‐entry accounting is a new and a more efficient way to address fundamental trust and transparency issues that plague current accounting systems. Triple‐entry accounting with blockchain, when properly implemented, can fundamentally improve accounting.
A Survey of Single and Multi-UAV Aerial Manipulation

Aerial manipulation has direct application prospects in environment, construction, forestry, agriculture, search, and rescue. It can be used to pick and place objects and hence can be used for transportation of goods. Aerial manipulation can be used to perform operations in environments inaccessible or unsafe for human workers. This paper is a survey of recent research in aerial manipulation. The aerial manipulation research has diverse aspects, which include the designing of aerial manipulation platforms, manipulators, grippers, the control of aerial platform and manipulators, the interaction of aerial manipulator with the environment, through forces and torque. In particular, the review paper presents the survey of the airborne platforms that can be used for aerial manipulation including the new aerial platforms with aerial manipulation capability. We also classified the aerial grippers and aerial manipulators based on their designs and characteristics. The recent contributions regarding the control of the aerial manipulator platform is also discussed. The environment interaction of aerial manipulators is also surveyed which includes, different strategies used for end-effectors interaction with the environment, application of force, application of torque and visual servoing. A recent and growing interest of researchers about the multi-UAV collaborative aerial manipulation was also noticed and hence different strategies for collaborative aerial manipulation are also surveyed, discussed and critically analyzed. Some key challenges regarding outdoor aerial manipulation and energy constraints in aerial manipulation are also discussed.
Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models.
Augment to Prevent

In this paper, we address the issue of augmenting text data in supervised Natural Language Processing problems, exemplified by deep online hate speech classification. A great challenge in this domain is that although the presence of hate speech can be deleterious to the quality of service provided by social platforms, it still comprises only a tiny fraction of the content that can be found online, which can lead to performance deterioration due to majority class overfitting. To this end, we perform a thorough study on the application of deep learning to the hate speech detection problem: a) we propose three text-based data augmentation techniques aimed at reducing the degree of class imbalance and to maximise the amount of information we can extract from our limited resources and b) we apply them on a selection of top-performing deep architectures and hate speech databases in order to showcase their generalisation properties. The data augmentation techniques are based on a) synonym replacement based on word embedding vector closeness, b) warping of the word tokens along the padded sequence or c) class-conditional, recurrent neural language generation. Our proposed framework yields a significant increase in multi-class hate speech detection, outperforming the baseline in the largest online hate speech database by an absolute 5.7% increase in Macro-F1 score and 30% in hate speech class recall.