Reviewerly/exHarmony huggingface dataset:
Aligning the interests of newsvendors and forecasters through blockchain-based smart contracts and proper scoring rules

We consider a newsvendor setting where the newsvendor elicits a demand forecast from an expert to determine the optimal inventory level for a product. Since the interests of both parties might not be aligned, we propose a proper scoring rule to elicit the expert's forecast that is tailored to the decision problem the newsvendor faces. Under the proposed scoring rule, both the newsvendor's expected profit and the expert's expected payment increase as the expert exerts a higher effort when gathering information to formulate a forecast. Trust issues arise in this setting because the ex post payment the expert receives is contingent on the accuracy of the reported forecast, which in turn depends on a future realized outcome that might be only available to the newsvendor. We show that coding the proposed proper scoring rule as a blockchain-based smart contract allows the newsvendor to signal trustworthiness while, at the same time, inducing the expert to devote more effort in information gathering. Following the design science research framework, we conclude the paper by suggesting a design and a fully functional prototype of a blockchain-based smart contract that implements the proposed tailored proper scoring rule.
Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval

In this paper, we study the task of visual-text retrieval in the highly practical setting in which labelled visual data with paired text descriptions are available in one domain (the "source"), but only unlabelled visual data (without text descriptions) are available in the domain of interest (the "target"). We propose the Adaptive Cross-Modal Prototypes framework which seeks to enable target domain retrieval by learning cross-modal visual-text representations while minimising both uni-modal and cross-modal distribution shift across the source and target domains. Our approach is built upon two key ideas: first, we encode the inductive bias that the learned cross-modal representations should be compositional with respect to concepts in each modality—this is achieved through clustering pretrained uni-modal features across each domain and designing a careful regularisation scheme to preserve the resulting structure. Second, we employ mutual information maximisation between cross-modal representations in the source and target domains during learning—this provides a mechanism that preserves commonalities between the domains while discarding signal in each that cannot be inferred from the other. We showcase our approach for the task of cross-domain visual-text retrieval, outperforming existing approaches for both images and videos.
Taking That Perfect Aerial Photo: A Synopsis of Interactions for Drone-based Aerial Photography and Video

Personal drones are more and more present in our lives and acting as "flying cameras" is one of their most prominent applications. In this work, we conduct a synopsis of the scientific literature on human-drone interaction to identify system functions and corresponding commands for controlling drone-based aerial photography and video, from which we compile a dictionary of interactions. We also discuss opportunities for more research at the intersection of drone computing, augmented vision, and personal photography.
Code Clone Detection with Hierarchical Attentive Graph Embedding

Code clone serves as a typical programming manner that reuses the existing code to solve similar programming problems, which greatly facilitates software development but recurs program bugs and maintenance costs. Recently, deep learning-based detection approaches gradually present their effectiveness on feature representation and detection performance. Among them, deep learning approaches based on abstract syntax tree (AST) construct models relying on the node embedding technique. In AST, the semantic of nodes is obviously hierarchical, and the importance of nodes is quite different to determine whether the two code fragments are cloned or not. However, some approaches do not fully consider the hierarchical structure information of source code. Some approaches ignore the different importance of nodes when generating the features of source code. Thirdly, when the tree is very large and deep, many approaches are vulnerable to the gradient vanishing problem during training. In order to properly address these challenges, we propose a hierarchical attentive graph neural network embedding model-HAG for the code clone detection. Firstly, the attention mechanism is applied on nodes in AST to distinguish the importance of different nodes during the model training. In addition, the HAG adopts graph convolutional network (GCN) to propagate the code message on AST graph and then exploits a hierarchical differential pooling GCN to sufficiently capture the code semantics at different structure level. To evaluate the effectiveness of HAG, we conducted extensive experiments on public clone dataset and compared it with seven state-of-the-art clone detection models. The experimental results demonstrate that the HAG achieves superior detection performance compared with baseline models. Especially, in the detection of moderately Type-3 or Type-4 clones, the HAG particularly outperforms baselines, indicating the strong detection capability of HAG for semantic clones. Apart from that, the impacts of the hierarchical pooling, attention mechanism and critical model parameters are systematically discussed.
An In-Depth Analysis of Distributed Training of Deep Neural Networks

As the popularity of deep learning in industry rapidly grows, efficient training of deep neural networks (DNNs) becomes important. To train a DNN with a large amount of data, distributed training with data parallelism has been widely adopted. However, the communication overhead limits the scalability of distributed training. To reduce the overhead, a number of distributed training algorithms have been proposed. The model accuracy and training performance of those algorithms can be different depending on various factors such as cluster settings, training models/datasets, and optimization techniques applied. In order for someone to adopt a distributed training algorithm appropriate for her/his situation, it is required for her/him to fully understand the model accuracy and training performance of these algorithms in various settings. Toward this end, this paper reviews and evaluates seven popular distributed training algorithms (BSP, ASP, SSP, EASGD, AR-SGD, GoSGD, and AD-PSGD) in terms of the model accuracy and training performance in various settings. Specifically, we evaluate those algorithms for two CNN models, in different cluster settings, and with three well-known optimization techniques. Through extensive evaluation and analysis, we made several interesting discoveries. For example, we found out that some distributed training algorithms (SSP, EASGD, and GoSGD) have highly negative impact on the model accuracy because they adopt intermittent and asymmetric communication to improve training performance; the communication overhead of some centralized algorithms (ASP and SSP) is much higher than we expected in a cluster setting with limited network bandwidth because of the PS bottleneck problem. These findings, and many more in the paper, can guide the adoption of proper distributed training algorithms in industry; our findings can be useful in academia as well for designing new distributed training algorithms.
Spurt: Scalable Distributed Randomness Beacon with Transparent Setup

Having shared access to high-quality random numbers is essential in many important applications. Yet, existing constructions of distributed random beacons still have limitations such as imperfect security guarantees, strong setup or network assumptions, or high costs. In this paper, we present Spurt, an efficient distributed randomness beacon protocol that does not require any trusted or expensive setup and is secure against a malicious adversary that controls up to one-third of the nodes in a partially synchronous network. We formally prove that each output of Spurt is unpredictable, bias-resistant, and publicly verifiable. Spurt has an amortized total communication cost of $O(\lambda n^{2})$ per beacon output where $\lambda$ is the security parameter. While designing Spurt, we also design a publicly verifiable secret sharing (PVSS) scheme whose security is based on the standard Decisional Bilinear Diffie-Hellman assumption and does not require a Random Oracle. We implement Spurt and evaluate it using a network of up to 128 nodes running in geographically distributed AWS instances. Our evaluation shows that Spurt can produce about 84 beacon outputs per minute in a network of 32 nodes and is comparable to systems with stronger assumptions or weaker security.
Face inpainting based on GAN by facial prediction and fusion as guidance information

Face inpainting, a special case of image inpainting, aims to complete the occluded facial regions with unconstrained pose and orientation. However, existing methods generate unsatisfying results with easily detectable flaws. There are often fuzzy boundaries and details near the holes. Especially, for face inpainting, the face region semantic information (face structure, contour, and content information) has not been fully utilized, which leads to unnatural face images, such as asymmetry eyebrow and different sizes of eyes. This is unrealistic in many practical applications. To solve the problems, a new generative adversarial network by facial prediction and fusion as guidance information, is proposed for large missing regions of face inpainting. In the proposed method, two stages are adopted to complete coarse inpainting and refinement of the face. In Stage-I, we combine generator with a new encoder–decoder network with variational autoencoder-based backbone to predict the face region semantic information (including face structure, contour and content information) and do facial fusion for face inpainting. This could fully explore face region semantic information and generate coordinated coarse face images. Stage-II builds upon Stage-I results to refine face image. Both global and patch discriminators are used to synthesize high-quality photo-realistic inpainting. Experimental results on both CelebA and CelebA-HQ datasets demonstrate the effectiveness and efficiency of our method.
A systematic analysis and guidelines of graph neural networks for practical applications

A graph neural network (GNN) draws attention to deal with many problems in social networks and bioinformatics, as graph data proliferate in a wide variety of applications. Despite the large amount of investigation, it is still difficult to choose the most suitable method for a given problem due to the lack of a thorough analysis on the feasible methods. An anatomical comparison of GNNs would help to devise a prospective method for better solution to real-world problems. In order to give guidelines to make full use of the GNN for graph classification, this paper attempts to analyze the state-of-the-art methods of the GNN and provide practicable guidelines for applications. The representative methods are described with a systematic scheme in four phases for GNN: 1) pre-processing, 2) aggregation, 3) readout, and 4) classification with graph embedding, resulting in a large coverage of more than 1300 methods. The 13 well-known benchmark datasets are categorized into three types with respect to the properties of graph data such as connectivity. In total, more than 3600 runs are executed to systematically analyze and compare the GNN models while changing only one method for each phase. Experimental reproducibility and replicability are also verified by comparing the results with the performance from the literature. Finally, five guidelines for an appropriate model are deduced according to the graph characteristics such as complexity on connectivity and node feature.
A creative approach to understanding the hidden information within the business data using Deep Learning

• The Deep neural network-based invisible text steganalysis performs the fully specified extraction of both features and phrases related to the particular business to resolve such issues in business data sharing communication • A relative information entropy is obtained for a rough feature optimization. The feature matrices are recognized from the generated information entropy • From that matrix, the information in the particular matrix is weighted and based on the weights. The hidden business information is selected and extracted at the receiver end of the business transformation. Crucial business data is an essential asset for each business establishment. Data security is vital when sensitive data are transmitted over the Internet in a business environment. Steganography is the art of obscuring data inside a regular file of similar or different forms. For digital forensics, hiding data has always been necessary. The current information hiding method based on deep learning models can not directly use the original data as carriers, which means the method can not use the prevailing data in big data to hide information. Hence, this paper proposes a Deep neural network-based invisible text steganalysis (DNNITS) for business data hiding. This paper uses a word embedding layer to extract the syntax and semantic word features. A rough set of relative information entropy has been employed based on information features, and the optimized feature matrices are determined. The information in the optimized feature matrices are weighted, and the hiding information weighted feature is acquired. The findings reveal that our model can safely hide secret messages conveniently, quickly, and with no restriction on the business environment's data amount. The experimental results show that the suggested DNNITS model enhances the extraction rate of 95.4%, significance rate of 97.5%, the performance ratio of 89.6%, an efficiency ratio of 98.7%, recall ratio of 90.4%, and the lower error rate of 10.2% compared to other existing models.
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people's life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation. However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results. Thus, to avoid this problem, we propose a Pre-trained Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embedding-based models without accessing triple data in the knowledge graph. Notably, PKGM could also complete knowledge graphs during servicing, thereby overcoming the common incompleteness issue in knowledge graphs. We test PKGM in three knowledge-related tasks including item classification, same item identification, and recommendation. Experimental results show PKGM successfully improves the performance of each task.
Deep reinforced bitrate ladders for adaptive video streaming

In the typical transcoding pipeline for adaptive video streaming, raw videos are pre-chunked and pre-encoded according to a set of resolution-bitrate or resolution-quality pairs on the server-side, where the pair is often named as bitrate ladder. Different from existing heuristics, we argue that a good bitrate ladder should be optimized by considering video content features, network capacity, and storage costs on the cloud. We propose DeepLadder, a per-chunk optimization scheme which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder w.r.t the above concerns. Technically, DeepLadder selects the proper setting for each video resolution autoregressively. We use over 8,000 video chunks, measure over 1,000,000 perceptual video qualities, collect real-world network traces for more than 50 hours, and invent faithful virtual environments to help train DeepLadder efficiently. Across a series of comprehensive experiments on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos, we demonstrate significant improvements in average video quality bandwidth utilization, and storage overhead in comparison to prior work as well as the ability to be deployed in the real-world transcoding framework.
A Deeper Understanding of Visualization‐Text Interplay in Geographic Data‐driven Stories

Abstract Data‐driven stories comprise of visualizations and a textual narrative. The two representations coexist and complement each other. Although existing research has explored the design strategies and structure of such stories, it remains an open research question how the two representations play together on a detailed level and how they are linked with each other. In this paper, we aim at understanding the fine‐grained interplay of text and visualizations in geographic data‐driven stories. We focus on geographic content as it often includes complex spatiotemporal data presented as versatile visualizations and rich textual descriptions. We conduct a qualitative empirical study on 22 stories collected from a variety of news media outlets; 10 of the stories report the COVID‐19 pandemic, the others cover diverse topics. We investigate the role of every sentence and visualization within the narrative to reveal how they reference each other and interact. Moreover, we explore the positioning and sequence of various parts of the narrative to find patterns that further consolidate the stories. Drawing from the findings, we discuss study implications with respect to best practices and possibilities to automate the report generation.
Efficient Directed Densest Subgraph Discovery

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a threethousand- edge graph, it takes three days for one of the best exact algorithms to complete. In this paper, we develop an efficient and scalable DDS solution. We introduce the notion of [x, y]-core, which is a dense subgraph for G, and show that the densest subgraph can be accurately located through the [x, y]-core with theoretical guarantees. Based on the [x, y]-core, we develop both exact and approximation algorithms. We have performed an extensive evaluation of our approaches on eight real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.
Constraints on magic state protocols from the statistical mechanics of Wigner negativity

Abstract Magic states are key ingredients in schemes to realize universal fault-tolerant quantum computation. Theories of magic states attempt to quantify this computational element via monotones and determine how these states may be efficiently transformed into useful forms. Here, we develop a statistical mechanical framework based on majorization to describe Wigner negative magic states for qudits of odd prime dimension processed under Clifford circuits. We show that majorization allows us to both quantify disorder in the Wigner representation and derive upper bounds for magic distillation. These bounds are shown to be tighter than other bounds, such as from mana and thauma, and can be used to incorporate hardware physics, such as temperature dependence and system Hamiltonians. We also show that a subset of single-shot Rényi entropies remain well-defined on quasi-distributions, are fully meaningful in terms of data processing and can acquire negative values that signal magic. We find that the mana of a magic state is the measure of divergence of these Rényi entropies as one approaches the Shannon entropy for Wigner distributions, and discuss how distillation lower bounds could be obtained in this setting. This use of majorization for quasi-distributions could find application in other studies of non-classicality, and raises nontrivial questions in the context of classical statistical mechanics.
Analytics Mistakes that Derail Software Startups

[Context] Software startups are engines of innovation and economy, yet building software startups is challenging and subject to a high failure rate. They need to act and respond fast in highly uncertain business environments. To do so, they need to identify crucial and actionable information that supports them in making correct decisions and reduce uncertainty. So far, the software startup literature focused predominantly on what information to measure from a metrics perspective. Thus, there is a lack of research investigating how to deal with information from an analytics perspective.
Word Embedding-Based Topic Similarity Measures

Topic models aim at discovering a set of hidden themes in a text corpus. A user might be interested in identifying the most similar topics of a given theme of interest. To accomplish this task, several similarity and distance metrics can be adopted. In this paper, we provide a comparison of the state-of-the-art topic similarity measures and propose novel metrics based on word embeddings. The proposed measures can overcome some limitations of the existing approaches, highlighting good capabilities in terms of several topic performance measures on benchmark datasets.
Efficient and Privacy-Preserving Infection Control System for Covid-19-Like Pandemics Using Blockchain

Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this article, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads.