Reviewerly/exHarmony huggingface dataset:
Hyperbolic Interaction Model for Hierarchical Multi-Label Classification

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.
Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Abstract Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.
Pronounced non-Markovian features in multiply excited, multiple emitter waveguide QED: Retardation induced anomalous population trapping

The Markovian approximation is widely applied in the field of quantum optics due to the weak frequency dependence of the vacuum field amplitude, and in consequence non-Markovian effects are typically regarded to play a minor role in the optical electron-photon interaction. Here, we give an example where non-Markovianity changes the qualitative behavior of a quantum optical system, rendering the Markovian approximation quantitatively and qualitatively insufficient. Namely, we study a multiple-emitter, multiple-excitation waveguide quantum-electrodynamic (waveguide-QED) system and include propagation time delay. In particular, we demonstrate anomalous population trapping as a result of the retardation in the excitation exchange between the waveguide and three initially excited emitters. Allowing for local phases in the emitter-waveguide coupling, this population trapping cannot be recovered using a Markovian treatment, proving the essential role of non-Markovian dynamics in the process. Furthermore, this time-delayed excitation exchange allows for a novel steady state, in which one emitter decays entirely to its ground state while the other two remain partially excited.
Deep Slow Motion Video Reconstruction With Hybrid Imaging System

Slow motion videos are becoming increasingly popular, but capturing high-resolution videos at extremely high frame rates requires professional high-speed cameras. To mitigate this problem, current techniques increase the frame rate of standard videos through frame interpolation by assuming linear object motion which is not valid in challenging cases. In this paper, we address this problem using two video streams as input; an auxiliary video with high frame rate and low spatial resolution, providing temporal information, in addition to the standard main video with low frame rate and high spatial resolution. We propose a two-stage deep learning system consisting of alignment and appearance estimation that reconstructs high resolution slow motion video from the hybrid video input. For alignment, we propose to compute flows between the missing frame and two existing frames of the main video by utilizing the content of the auxiliary video frames. For appearance estimation, we propose to combine the warped and auxiliary frames using a context and occlusion aware network. We train our model on synthetically generated hybrid videos and show high-quality results on a variety of test scenes. To demonstrate practicality, we show the performance of our system on two real dual camera setups with small baseline.
Large fluctuations of the first detected quantum return time

How long does it take a quantum particle to return to its origin? As shown previously under repeated projective measurements aimed to detect the return, the closed cycle yields a geometrical phase which shows that the average first detected return time is quantized. For critical sampling times or when parameters of the Hamiltonian are tuned this winding number is modified. These discontinuous transitions exhibit gigantic fluctuations of the return time. While the general formalism of this problem was studied at length, the magnitude of the fluctuations, which is quantitatively essential, remains poorly characterized. Here, we derive explicit expressions for the variance of the return time, for quantum walks in finite Hilbert space. A classification scheme of the diverging variance is presented, for four different physical effects: the Zeno regime, when the overlap of an energy eigenstate and the detected state is small and when two or three phases of the problem merge. These scenarios present distinct physical effects which can be analyzed with the fluctuations of return times investigated here, leading to a topology-dependent time-energy uncertainty principle.
Business financing and blockchain technology adoption in agroindustry

Purpose This paper aims to explore patterns of business financing and adoption of blockchain technology in the agricultural industry. The adoption of blockchain technology in terms of recording, storing, validating and securing data can solve a variety of agricultural problems such as agricultural business financing. If the banking and insurance industries are connected in real-time to activity data in the agricultural industry, they can create better credit ratings and profile models. So, finally, all parties in the agricultural industry have a greater chance to get business financing from banks. Design/methodology/approach This paper uses a case study research approach with a framework of analysis of the theory of adoption of technology, organization and environment (TOE) and the theory of “mindfulness of adoption”. The case study method has advantages when verification is still questioned or the application of certain theories in practice as phenomena and contexts that occur in the field in accordance with the application of blockchain technology into a relatively new business, both technically and practically in the field. Findings The findings indicate that there are no barriers to the availability of blockchain technology for technology adoption. The characteristics of this technology are very suitable for solving financing and supply chain business problems in the agricultural industry. However, the adoption of blockchain technology in agriculture shows that there is complexity in the organizational context involving internal and external organizations. The number of organizations and small parties involved in the agricultural process challenges the adoption of blockchain technology as new technology. Then, the external environment of technology, especially government regulations in developing countries, is still an obstacle to the adoption of blockchain technology. Research limitations/implications This study faces several limitations, namely, the limited case of implementation of the blockchain technology due to the novelty of technology and government regulation. So that further research related to the adoption of blockchain technology needs to be done using field data such as surveys. Research related to the connectivity of the banking industry and other financial institutions also needs to be explored further, especially in creating a data-based credit risk model of the blockchain system. Originality/value On the practical side, case studies of technology adoption and its relationship with the financing of agricultural business are still little explored so this study contributes to exploring the application of blockchain technology in the agricultural industry. The adoption of blockchain technology has an impact not only on farmers but also on all parties involved in the supply chain including banks, insurance and other financial institutions. In addition, the distributed data exchange business model using blockchain technology is a new business model in the agriculture industry.
Augmented Natural Language for Generative Sequence Labeling

We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike prior discriminative methods, our model naturally incorporates label semantics and shares knowledge across tasks. Our framework general purpose, performing well on few-shot learning, low resource, and high resource tasks. We demonstrate these advantages on popular named entity recognition, slot labeling, and intent classification benchmarks. We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot (75.0% to 90.9%) and 1-shot (70.4% to 81.0%) state-of-the-art results. Furthermore, our model generates large improvements (46.27% to 63.83%) in low resource slot labeling over a BERT baseline by incorporating label semantics. We also maintain competitive results on high resource tasks, performing within two points of the state-of-the-art on all tasks and setting a new state-of-the-art on the SNIPS dataset.
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems

This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the multihead attention (MHA) to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) without the need of retraining. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large scale and require quick decisions.
End User Accounts of Dark Patterns as Felt Manipulation

Manipulation defines many of our experiences as a consumer, including subtle nudges and overt advertising campaigns that seek to gain our attention and money. With the advent of digital services that can continuously optimize online experiences to favor stakeholder requirements, increasingly designers and developers make use of "dark patterns"---forms of manipulation that prey on human psychology---to encourage certain behaviors and discourage others in ways that present unequal value to the end user. In this paper, we provide an account of end user perceptions of manipulation that builds on and extends notions of dark patterns. We report on the results of a survey of users conducted in English and Mandarin Chinese (n=169), including follow-up interviews from nine survey respondents. We used a card sorting method to support thematic analysis of responses from each cultural context, identifying both qualitatively-supported insights to describe end users' felt experiences of manipulative products, and a continuum of manipulation. We further support this analysis through a quantitative analysis of survey results and the presentation of vignettes from the interviews. We conclude with implications for future research, considerations for public policy, and guidance on how to further empower and give users autonomy in their experiences with digital services.
Photonic Spiking Neural Networks and Graphene-on-Silicon Spiking Neurons

Spiking neural networks are known to be superior over artificial neural networks for their computational power efficiency and noise robustness. The benefits of spiking coupled with the high-bandwidth and low-latency of photonics can enable highly-efficient, noise-robust, high-speed neural processors. The landscape of photonic spiking neurons consists of an overwhelming majority of excitable lasers and a few demonstrations on nonlinear optical cavities. The silicon platform is best poised to host a scalable photonic technology given its CMOS-compatibility and low optical loss. Here, we present a survey of existing photonic spiking neurons, and propose a novel spiking neuron based on a hybrid graphene-on-silicon microring cavity. A comparison among a representative sample of photonic spiking devices is also presented. Finally, we discuss methods employed in training spiking neural networks, their challenges as well as the application domain that can be enabled by photonic spiking neural hardware.
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Toxic language detection systems often falsely flag text that contains minority group mentions as toxic, as those groups are often the targets of online hate.Such over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language.To help mitigate these issues, we create TOXIGEN, a new large-scale and machinegenerated dataset of 274k toxic and benign statements about 13 minority groups.We develop a demonstration-based prompting framework and an adversarial classifier-in-the-loop decoding method to generate subtly toxic and benign text with a massive pretrained language model (Brown et al., 2020).Controlling machine generation in this way allows TOXIGEN to cover implicitly toxic text at a larger scale, and about more demographic groups, than previous resources of human-written text.We conduct a human evaluation on a challenging subset of TOXIGEN and find that annotators struggle to distinguish machine-generated text from human-written language.We also find that 94.5% of toxic examples are labeled as hate speech by human annotators.Using three publicly-available datasets, we show that finetuning a toxicity classifier on our data improves its performance on human-written data substantially.We also demonstrate that TOXI-GEN can be used to fight machine-generated toxicity as finetuning improves the classifier significantly on our evaluation subset.
Convergence complexity analysis of Albert and Chib’s algorithm for Bayesian probit regression

The use of MCMC algorithms in high dimensional Bayesian problems has become routine.This has spurred so-called convergence complexity analysis, the goal of which is to ascertain how the convergence rate of a Monte Carlo Markov chain scales with sample size, n, and/or number of covariates, p.This article provides a thorough convergence complexity analysis of Albert and Chib's [J.Amer.Statist.Assoc.88 (1993) 669-679] data augmentation algorithm for the Bayesian probit regression model.The main tools used in this analysis are drift and minorization conditions.The usual pitfalls associated with this type of analysis are avoided by utilizing centered drift functions, which are minimized in high posterior probability regions, and by using a new technique to suppress high-dimensionality in the construction of minorization conditions.The main result is that the geometric convergence rate of the underlying Markov chain is bounded below 1 both as n → ∞ (with p fixed), and as p → ∞ (with n fixed).Furthermore, the first computable bounds on the total variation distance to stationarity are byproducts of the asymptotic analysis.
Mixed integer linear programming for feature selection in support vector machine

This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modeled in this formulation by including a budget constraint that sets in advance a limit on the number of features to be used in the classification process. We propose both an exact and a heuristic procedure to solve this formulation in an efficient way. Finally, the validation of the model is done by checking it with some well-known data sets and comparing it with classical classification methods.
Abstracting Causal Models

We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. (2017) called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the “right” choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions. We show that procedures for combining micro-variables into macro-variables are instances of our notion of strong abstraction, as are all the examples considered by Rubenstein et al.
Speed Control of DC Motor using Fuzzy Logic Controller by PCI 6221 with MATLAB

This thesis demonstrates the importance of Fuzzy Logic Controller. The operation of a DC motor is performed using Fuzzy Logic Controller (FLC) in MATLAB environment. Fuzzy Logic is one of the most successful applications of fuzzy set in which the variables are linguistic rather than numeric. A Fuzzy Logic Controller (FLC) is based on a set of control rules (fuzzy rules) among linguistic variables. The proposed fuzzy controller results in a better response compared to the normal response of DC motor. This thesis consists of two parts; software and hardware implementation. The software part aims to design and develop a Fuzzy Logic Controller in MATLAB Simulink. The hardware Part Consist of DC motor Driver and PCI 6221. The DC drive is used to convert AC voltage into variable DC voltage PCI 6221 is used as the hardware interface between Hardware and Software.
RE-SWOT: From User Feedback to Requirements via Competitor Analysis

[Context & Motivation] App store reviews are a rich source for analysts to elicit requirements from user feedback, for they describe bugs to be fixed, requested features, and possible improvements. Product development teams need new techniques that help them make real-time decisions based on user feedback. [Question/Problem] Researchers have proposed natural language processing (NLP) techniques for extracting and organizing requirements-relevant knowledge from the reviews for one specific app. However, no attention has been paid to studying whether and how requirements can be identified from competing products. [Principal ideas/results] We propose RE-SWOT, a tool-supported method for eliciting requirements from app store reviews through competitor analysis. RE-SWOT combines NLP algorithms with information visualization techniques. We evaluate the usefulness of RE-SWOT with expert product managers from three mobile app companies. [Contribution] Our preliminary results show that competitor analysis is a promising path for research that has direct impact on the requirements engineering practice in modern app development companies.
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments

The paper presents state of the art of energy-aware high-performance computing (HPC), in particular identification and classification of approaches by system and device types, optimization metrics, and energy/power control methods. System types include single device, clusters, grids, and clouds while considered device types include CPUs, GPUs, multiprocessor, and hybrid systems. Optimization goals include various combinations of metrics such as execution time, energy consumption, and temperature with consideration of imposed power limits. Control methods include scheduling, DVFS/DFS/DCT, power capping with programmatic APIs such as Intel RAPL, NVIDIA NVML, as well as application optimizations, and hybrid methods. We discuss tools and APIs for energy/power management as well as tools and environments for prediction and/or simulation of energy/power consumption in modern HPC systems. Finally, programming examples, i.e., applications and benchmarks used in particular works are discussed. Based on our review, we identified a set of open areas and important up-to-date problems concerning methods and tools for modern HPC systems allowing energy-aware processing.
Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition

Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods.