Reviewerly/exHarmony huggingface dataset:
Towards blockchain led decentralized autonomous organization (DAO) business model innovations

Purpose Today, business model innovations leverage digital technologies to gain a competitive advantage and transform business processes. Blockchain is still gaining attention in specific fields and bringing value to business models. There is a dearth of research on how blockchain decentralized autonomous organizations impact organization business model innovations. This study attempts to contribute the body of knowledge based on a review of decentralized autonomous organizations and the business model innovation literature using the integrative and generative approach. Design/methodology/approach The paper offers an analysis of decentralized autonomous organizations based on digital business models built on the well-established work by Osterwalder and Pigneur (2010). The practical multilayered decentralized autonomous organizations architectural implementation model design is achieved using practical archetypes depicted in the proposed decentralized autonomous organizations business model. The paper evaluates a marketplace comprising 13 decentralized autonomous organizations led platforms with core functionalities. Findings The paper delivers decentralized autonomous organizations led digital business model canvas elements to explain decentralized autonomous organization business model innovations. It presents the underlying multilayered decentralized autonomous organizations architectural implementation model required to conceptualize a practical business model with an enterprise-ready target operating model. Research limitations/implications The paper contributes directly to the practical decentralized autonomous organizations business model canvas, exemplifying the nine elements of decentralized autonomous organizations’ characteristics for any organizational transformation. The tools and accelerators (business model, layered architecture, target operating model and product mapping) developed in the paper address the managerial challenges of redesigning the decentralized business models. Originality/value The proposed decentralized autonomous organizations smart contract powered business model provide a digital platform to adhere to rules, follow policies, preserve principles and develop consensus without human interventions. The paper shapes the first of its kind decentralized autonomous organizations marketplace evaluation while mapping it to decentralized autonomous organizations layered architecture product requirement considering business model dimension to adopt actionable target operating model.
KGNN

This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs. We address the limitations by proposing the Kernel-based Graph Neural Network (KGNN). A KGNN consists of a GNN-based network as well as a kernel-based network parameterized by a memory network. The GNN-based network performs classification through learning graph representations to implicitly capture the similarity between query graphs and labeled graphs, while the kernel-based network uses graph kernels to explicitly compare each query graph with all the labeled graphs stored in a memory for prediction. The two networks are motivated from complementary perspectives, and thus combing them allows KGNN to use labeled graphs more effectively. We jointly train the two networks by maximizing their agreement on unlabeled graphs via posterior regularization, so that the unlabeled graphs serve as a bridge to let both networks mutually enhance each other. Experiments on a range of well-known benchmark datasets demonstrate that KGNN achieves impressive performance over competitive baselines.
Switchable Novel Object Captioner

Image captioning aims at automatically describing images by sentences.It often requires lots of paired image-sentence data for training.However, trained captioning models can hardly be applied to new domains in which some novel words exist.In this paper, we introduce the zero-shot novel object captioning task, where the machine generates descriptions about novel objects without extra training sentences.To tackle the challenging task, we mimic the way that babies talk about something unknown, i.e., using the word of a similar known object.Following this motivation, we build a key-value object memory by detection models, containing visual information and corresponding words for objects in the image.For those novel objects, we use words of most similar seen objects as proxy visual words to solve the out-of-vocabulary issue.We then propose a Switchable LSTM that incorporates knowledge from the object memory into sentence generation.The model has two switchable working modes, i.e., 1) generating the sentences like standard LSTMs and 2) retrieving proper nouns from the key-value memory.Thus our model is learned to fully disentangle language generation from training objects, and requires zero training sentence in describing novel objects.Experiments on three large-scale datasets demonstrate the ability of our method to describe novel concepts.
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation

Traditional object detectors are ill-equipped for incremental learning. However, fine-tuning directly on a well-trained detection model with only new data will lead to catastrophic forgetting. Knowledge distillation is a flexible way to mitigate catastrophic forgetting. In Incremental Object Detection (IOD), previous work mainly focuses on distilling for the combination of features and responses. However, they under-explore the information that contains in responses. In this paper, we propose a response-based incremental distillation method, dubbed Elastic Response Distillation (ERD), which focuses on elastically learning responses from the classification head and the regression head. Firstly, our method transfers category knowledge while equipping student detector with the ability to retain localization information during incremental learning. In addition, we further evaluate the quality of all locations and provide valuable responses by the Elastic Response Selection (ERS) strategy. Finally, we elucidate that the knowledge from different responses should be assigned with different importance during incremental distillation. Extensive experiments conducted on MS COCO demonstrate our method achieves state-of-the-art result, which substantially narrows the performance gap towards full training. Code is available at https://github.com/Hi-FT/ERD.
A Dynamic Model of Aspectual Composition

This paper describes results of a dynamic model of aspectual composition that demonstrates how features necessary for planning and controlling actions can also motivate and ground simple analyses of a number of aspectual phenomena. A novel feature of the model is an active computational representation for verb semantics called x-schemas, an extension of the Petri net formalism that can encode goals, resources and other features affecting aspect. Vexing problems of aspectual composition lend themselves to simple analyses in terms of the context-sensitive interaction between verb-specific x-schemas and a controller x-schema that captures important regularities in the evolution of events. The resulting x-schemas can be elaborated and constrained by such factors as tense, temporal modifiers, nominals and pragmatic context, providing a rich representation that supports simulative inference in language understanding.
Bitcoin transaction strategy construction based on deep reinforcement learning

The emerging cryptocurrency market has lately received great attention for asset allocation due to its decentralization uniqueness. However, its volatility and brand new trading mode has made it challenging to devising an acceptable automatically-generating strategy. This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm — proximal policy optimization (PPO). The framework creatively regards the transaction process as actions, returns as awards and prices as states to align with the idea of reinforcement learning. It compares advanced machine learning-based models for static price predictions including support vector machine (SVM), multi-layer perceptron (MLP), long short-term memory (LSTM), temporal convolutional network (TCN), and Transformer by applying them to the real-time bitcoin price and the experimental results demonstrate that LSTM outperforms. Then an automatically-generating transaction strategy is constructed building on PPO with LSTM as the basis to construct the policy. Extensive empirical studies validate that the proposed method perform superiorly to various common trading strategy benchmarks for a single financial product. The approach is able to trade bitcoins in a simulated environment with synchronous data and obtains a 31.67% more return than that of the best benchmark, improving the benchmark by 12.75%. The proposed framework can earn excess returns through both the period of volatility and surge, which opens the door to research on building a single cryptocurrency trading strategy based on deep learning. Visualizations of trading the process show how the model handles high-frequency transactions to provide inspiration and demonstrate that it can be expanded to other financial products.
Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32\% and 92.67\% in terms of geometric mean and accuracy respectively, utilizing less than 10\% of the total feature space. The empirical results show that the modified genetic algorithm outperforms $Chi^2$ and $PCA$ feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.
Partially Relevant Video Retrieval

Current methods for text-to-video retrieval (T2VR) are trained and tested on video-captioning oriented datasets such as MSVD, MSR-VTT and VATEX. A key property of these datasets is that videos are assumed to be temporally pre-trimmed with short duration, whilst the provided captions well describe the gist of the video content. Consequently, for a given paired video and caption, the video is supposed to be fully relevant to the caption. In reality, however, as queries are not known a priori, pre-trimmed video clips may not contain sufficient content to fully meet the query. This suggests a gap between the literature and the real world. To fill the gap, we propose in this paper a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR). An untrimmed video is considered to be partially relevant w.r.t. a given textual query if it contains a moment relevant to the query. PRVR aims to retrieve such partially relevant videos from a large collection of untrimmed videos. PRVR differs from single video moment retrieval and video corpus moment retrieval, as the latter two are to retrieve moments rather than untrimmed videos. We formulate PRVR as a multiple instance learning (MIL) problem, where a video is simultaneously viewed as a bag of video clips and a bag of video frames. Clips and frames represent video content at different time scales. We propose a Multi-Scale Similarity Learning (MS-SL) network that jointly learns clip-scale and frame-scale similarities for PRVR. Extensive experiments on three datasets (TVR, ActivityNet Captions, and Charades-STA) demonstrate the viability of the proposed method. We also show that our method can be used for improving video corpus moment retrieval.
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment

Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper proposes the Gaussian clouded logit adjustment by Gaussian perturbation of different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in a classifier, we therefore propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method. Source code is available at https://github.com/Keke921/GCLLoss.
Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via $\infty$-ESS, and an application to particle Gibbs

Sequential Monte Carlo (SMC) algorithms were originally designed for estimating intractable conditional expectations within state-space models, but are now routinely used to generate approximate samples in the context of general-purpose Bayesian inference.In particular, SMC algorithms are often used as subroutines within larger Monte Carlo schemes, and in this context, the demands placed on SMC are different: control of mean-squared error is insufficient-one needs to control the divergence from the target distribution directly.Towards this goal, we introduce the conditional adaptive resampling particle filter, building on the work of Gordon, Salmond, and Smith (1993), Andrieu, Doucet, and Holenstein (2010), and Whiteley, Lee, and Heine (2016).By controlling a novel notion of effective sample size, the ∞-ESS, we establish the efficiency of the resulting SMC sampling algorithm, providing an adaptive resampling extension of the work of Andrieu, Lee, and Vihola (2018).We apply our results to arrive at new divergence bounds for SMC samplers with adaptive resampling as well as an adaptive resampling version of the Particle Gibbs algorithm with the same geometric-ergodicity guarantees as its nonadaptive counterpart.
Supervised learning of time-independent Hamiltonians for gate design

We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus transforming the task to an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct analytical solution, providing at the same time a high degree of flexibility over the types of gate-design problems that can be approached. As a significant example, we find generators for the Toffoli gate using only diagonal pairwise interactions, which are easier to implement in some experimental architectures. To showcase the flexibility of the supervised learning approach, we give an example of a nontrivial four-qubit gate that is implementable using only diagonal, pairwise interactions.
Insulator detection and recognition of explosion based on convolutional neural networks

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras can obtain a large number of detailed inspection images. The insulator is an indispensable component in the transmission lines. Detecting insulator in image video quickly and accurately can provide a reliable basis for the ranging and the obstacle avoidance flight of UAV close to the tower and transmission line. At the same time, the insulator is a serious threat to the safety of the power grid due to the multiple faults of the insulator, and the computer technology should be fully utilized to diagnose the fault. Detection of the insulator images with the complex aerial background is implemented by constructing a convolutional neural network (CNN), which has the classic architecture of five modules of convolution and pooling, two modules of fully connected layers. In this paper, we propose a recognition algorithm for explosion fault based on saliency detection, which uses the trained network model to extract the features. Then, we put the saliency maps into a self-organizing feature map (SOM) network and build the mathematical module via super pixel segmentation, contour detection and other image processing methods. The test shows that the algorithm can reduce the error that may be caused by manual analysis. It also demonstrates that the detection of the insulator and the recognition of explosion fault can effectively improve the efficiency and intelligence level.
Learning Plackett-Luce Mixtures from Partial Preferences

We propose an EM-based framework for learning Plackett-Luce model and its mixtures from partial orders. The core of our framework is the efficient sampling of linear extensions of partial orders under Plackett-Luce model. We propose two Markov Chain Monte Carlo (MCMC) samplers: Gibbs sampler and the generalized repeated insertion method tuned by MCMC (GRIM-MCMC), and prove the efficiency of GRIM-MCMC for a large class of preferences.Experiments on synthetic data show that the algorithm with Gibbs sampler outperforms that with GRIM-MCMC. Experiments on real-world data show that the likelihood of test dataset increases when (i) partial orders provide more information; or (ii) the number of components in mixtures of PlackettLuce model increases.
High-Throughput and Area-Optimized Architecture for rBRIEF Feature Extraction

Feature matching is a fundamental step in many real-time computer vision applications such as simultaneous localization and mapping, motion analysis, and stereo correspondence. The performance of these applications depends on the distinctiveness of the visual feature descriptors used, and the speed at which they can be extracted from video frames. When combined with standard key-point detectors, the rotation-aware binary robust independent elementary features (rBRIEF) descriptor has been shown to outperform its counterparts. In this paper, we present a deep-pipelined stream processing architecture that is capable of extracting rBRIEF features from high-throughput video frames. To achieve high processing rate and low complexity hardware, the proposed architecture incorporates an enhanced moving summation strategy to calculate the key-points' patch moments and employs approximate computations to achieve patch rotation. Multiplier-less circuitry is introduced throughout the architecture to avoid the use of costly multipliers. Implementation on the Altera Aria V device demonstrates that the proposed architecture leads to 53.3% reduction in hardware resources (adaptive logic modules), while achieving 50% higher accuracy (in terms of average Hamming distance) when compared to the state-of-the-art architecture. In addition, the proposed architecture is able to process high-resolution ($1920 \times 1080$ ) images at 60 fps, while consuming only 456.15 mW power.
Counting the learnable functions of geometrically structured data

Cover's function counting theorem is a milestone in the theory of artificial neural networks. It provides an answer to the fundamental question of determining how many binary assignments (dichotomies) of p points in n dimensions can be linearly realized. Regrettably, it has proved hard to extend the same approach to more advanced problems than the classification of points. In particular, an emerging necessity is to find methods to deal with geometrically structured data, and specifically with non-point-like patterns. A prominent case is that of invariant recognition, whereby identification of a stimulus is insensitive to irrelevant transformations on the inputs (such as rotations or changes in perspective in an image). An object is thus represented by an extended perceptual manifold, consisting of inputs that are classified similarly. Here, we develop a function counting theory for structured data of this kind, by extending Cover's combinatorial technique, and we derive analytical expressions for the average number of dichotomies of generically correlated sets of patterns. As an application, we obtain a closed formula for the capacity of a binary classifier trained to distinguish general polytopes of any dimension. These results extend our theoretical understanding of the role of data structure in machine learning, and provide useful quantitative tools for the analysis of generalization, feature extraction, and invariant object recognition by neural networks.Received 5 November 2019Accepted 8 April 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.023169Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLearningNeuronal networksPhysical SystemsArtificial neural networksTechniquesCombinatoricsMean-field & cluster methodsNetworksStatistical PhysicsInterdisciplinary Physics