Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning with Interactive Feedback in a Human–Robot Environment

Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely utilized in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent-IDeepRL or human-IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.
A Tensor Subspace Representation-Based Method for Hyperspectral Image Denoising

In hyperspectral image (HSI) denoising, subspace-based denoising methods can reduce the computational complexity of the denoising algorithm. However, the existing matrix subspaces, which are generated by the unfolding matrix of the HSI tensor, cannot completely represent a tensor since the unfolding operation will destroy the tensor structure. To overcome this, we design a novel basis tensor that is directly learned from the original tensor and present a tensor subspace representation (TenSR), which is a more authentic representation for delivering the intrinsic structure of the tensor than a matrix subspace representation. Equipped with the TenSR, we then propose a TenSR-based HSI denoising (TenSRDe) model, which simultaneously considers the low-tubal rankness of the HSI tensor and the nonlocal self-similarity of the coefficient tensor. Moreover, we develop an efficient proximal alternating minimization (PAM) algorithm to solve the proposed nonconvex model and theoretically prove that the algorithm globally converges to a critical point. Experiments implemented on simulated and real data sets substantiate the denoising effect and efficiency of the proposed method.
Digital Competence of Future Secondary School Teachers: Differences According to Gender, Age, and Branch of Knowledge

The development of related technological skills in secondary students is perceived as unachievable if the teachers do not have enough technological expertise to guide their students. This study was based on investigating digital competence on the population of graduate students undertaking a Master’s degree in Education to train as teachers for the secondary educational level. The study made it possible to conclude, on the one hand, the homogeneity of university degrees within the scope of the Bologna Plan, with respect to mean levels of digital training. On the other hand, more exhaustively, differences have come to light concerning specific training on the different digital competence areas in the DigComp evaluation system, related to gender, branch of knowledge, age, and by considering the self-perception of individuals on their own capabilities in everyday technological issues. Consequently, the need for incorporating an ICT syllabus into the subjects covered by a Master’s in education has been highlighted, as well as promoting females, older people, and nontechnological degrees. Digital training on critical competence areas for teaching, such as the creation of digital content, suffers from major shortcomings and should also be promoted.
There is no general connection between the quantum speed limit and non-Markovianity

The quantum speed limit sets a bound on the minimum time required for a quantum system to evolve between two states. For open quantum systems this quantity depends on the dynamical map describing the time evolution in presence of the environment, on the evolution time {\tau} , and on the initial state of the system. We consider a general single qubit open dynamics and show that there is no simple relationship between memory effects and the tightness of the quantum speed limit bound. We prove that only for specific classes of dynamical evolutions and initial states, there exists a link between non-Markovianity and the quantum speed limit. Our results shed light on the connection between information back-flow between system and environment and the speed of quantum evolution.
A novel fitness function in genetic programming for medical data classification

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced. Due to this, ML classification techniques give biased classification over the majority class. In this paper, a novel fitness function in Genetic Programming, for medical data classification has been proposed that handles the problem of unbalanced data. Four benchmark medical data-sets named chronic kidney disease (CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer (WDBC) have been taken from the University of California (UCI) machine learning repository. Classification is done using the proposed technique. The proposed technique achieved the best accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%, 99.12%, 85.0%, and 75.36% respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes show an improvement over other GP and SVM methods that confirm the efficiency of our proposed algorithm.
The Use of Blockchains to Enhance Sustainability Reporting and Assurance*

ABSTRACT The changing dynamics of the accounting profession have been strongly influenced by emerging technologies and the demand for nontraditional metrics and information by stakeholders and regulators. In this article, we perform an exploratory content analysis to examine the role that blockchain technology can play in enhancing sustainability reporting and assurance. The benefits to companies and assurance professionals in using the distributed ledger technology of blockchain are increased trust, transparency, and traceability, which matches stakeholders' demands as it relates to sustainability reporting. This article identifies and analyzes potential and current use cases of blockchain in the United States and Canada to assist accountants and auditors in preparing and reviewing sustainability information. We highlight how augmenting traditional reporting systems with blockchain can overcome problems with sustainability reporting. We discuss implications for practice in detail—finding that blockchain is well‐positioned to provide reliable tracking and custodial support as it relates to sustainability information currently being self‐reported by many firms, such as greenhouse gas emissions, conflict mineral disclosure, or product provenance, among others. Expanded adoption of blockchains by companies will lead to higher‐quality information being included in sustainability reports and allow assurance professionals to verify a wider range of information, potentially leading to uniform standards in the evaluation of sustainability reports.
Potential integration of blockchain technology into smart sustainable city (SSC) developments: a systematic review

Purpose Prior literature lacks concrete and systematic review of the current blockchain application in smart sustainable city that covered to the full extent of various components. Thus, this study explores the integration of blockchain technology in making the city smarter, safer and sustainable. Design/methodology/approach This study conducted a systematic literature review of 49 publications published globally. Data were analysed by coding of the publications whereby the codes were generated based on frequency of appearance ( n ). Findings The results showed that smart sustainable city could leverage blockchain technology in several areas such as governance, mobility, asset, utility, healthcare and logistics. Blockchain technology could also aid smart sustainable city in achieving social, environmental and economic sustainability. Originality/value This study proposes a smart sustainable city with blockchain technology framework: guiding city planners and policymakers by deploying blockchain that supports technology within smart sustainable city framework. This facilitates the digital transformation of a city towards smart and sustainable through the use of blockchain.
When good blocks go bad: Managing unwanted blockchain data

Blockchain has been praised for providing the technical infrastructure that enables a group of self-interested entities to share data without relying on intermediaries. Technically, blockchain is a distributed and decentralized append-only database. This latter aspect leads to an important, yet overlooked governance issue, namely what should the network members do when erroneous or malicious data are added to the blockchain ledger? We start by describing three public cases when the above happened. For each case, we elaborate on the adopted solution, which we refer to as the “rollback,” the “do nothing,” and the “overturn” solution. Drawing from these previous cases, discussions with experts, and from our own experience with blockchain research and development, we provide suggestions concerning managerial, technical, and information security policies and practices organizations should follow when contemplating enterprise-level applications of the blockchain technology.
Object Detection with a Unified Label Space from Multiple Datasets

Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical benefits of such an object detector are obvious and significant—application-relevant categories can be picked and merged form arbitrary existing datasets. However, naïve merging of datasets is not possible in this case, due to inconsistent object annotations. Consider an object category like faces that is annotated in one dataset, but is not annotated in another dataset, although the object itself appears in the latter’s images. Some categories, like face here, would thus be considered foreground in one dataset, but background in another. To address this challenge, we design a framework which works with such partial annotations, and we exploit a pseudo labeling approach that we adapt for our specific case. We propose loss functions that carefully integrate partial but correct annotations with complementary but noisy pseudo labels. Evaluation in the proposed novel setting requires full annotation on the test set. We collect the required annotations (Project page: http://www.nec-labs.com/~mas/UniDet This work was part of Xiangyun Zhao’s internship at NEC Labs America.) and define a new challenging experimental setup for this task based on existing public datasets. We show improved performances compared to competitive baselines and appropriate adaptations of existing work.
Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest both academically and in automotive industry. Time efficiency and safety are of key importance and here a two-step procedure is proposed. First, a convex optimization problem is solved, formulated as a support vector machine (SVM), in order to represent the surrounding environment of the ego vehicle and classify the search space as obstacles or obstacle free. This gives a reduced complexity search space and an A* algorithm is used in a state space lattice in 4 dimensions including position, heading angle and velocity for simultaneous path and velocity planning. Further, a heuristic derived from the SVM formulation is used in the A* search and a pruning technique is introduced to significantly improve search efficiency. Solutions from the proposed planner is compared to optimal solutions computed using optimal control techniques. Three traffic scenarios, a roundabout scenario and two complex takeover maneuvers, with multiple moving obstacles, are used to illustrate the general applicability of the proposed method.
Nonlinear quantum neuron: A fundamental building block for quantum neural networks

Quantum computing enables quantum neural networks (QNNs) to have great potential to surpass artificial neural networks. The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are polynomial in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.
Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process-especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neural network (CNN) with three different word representation techniques: Word to Vector (Word2Vec), Global Vector (GloVe), and Embeddings from Language Models (ELMo). Experiments were performed on datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was measured as an evaluation metric. The experimental results suggest that the ELMo-based CNN approach performs best for the bug triage problem. GloVe-based CNN slightly outperforms Word2Vec-based CNN in many cases. Word2Vec-based CNN outperforms GloVe-based CNN when the number of samples per class in the dataset is high enough.
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization

In order to summarize a content video properly, it is important to grasp the sequential structure of video as well as the long-term dependency between frames. The necessity of them is more obvious, especially for unsupervised learning. One possible solution is to utilize a well-known technique in the field of natural language processing for long-term dependency and sequential property: self-attention with relative position embedding (RPE). However, compared to natural language processing, video summarization requires capturing a much longer length of the global context. In this paper, we therefore present a novel input decomposition strategy, which samples the input both globally and locally. This provides an effective temporal window for RPE to operate and improves overall computational efficiency significantly. By combining both Global-and-Local input decomposition and RPE together, we come up with GL-RPE. Our approach allows the network to capture both local and global interdependencies between video frames effectively. Since GL-RPE can be easily integrated into the existing methods, we apply it to two different unsupervised backbones. We provide extensive ablation studies and visual analysis to verify the effectiveness of the proposals. We demonstrate our approach achieves new state-of-the-art performance using the recently proposed rank order-based metrics: Kendall’s $$\tau $$ and Spearman’s $$\rho $$ . Furthermore, despite our method is unsupervised, we show ours perform on par with the fully-supervised method.
Matching Guided Distillation

Feature distillation is an effective way to improve the performance for a smaller student model, which has fewer parameters and lower computation cost compared to the larger teacher model. Unfortunately, there is a common obstacle—the gap in semantic feature structure between the intermediate features of teacher and student. The classic scheme prefers to transform intermediate features by adding the adaptation module, such as naive convolutional, attention-based or more complicated one. However, this introduces two problems: a) The adaptation module brings more parameters into training. b) The adaptation module with random initialization or special transformation isn’t friendly for distilling a pre-trained student. In this paper, we present Matching Guided Distillation (MGD) as an efficient and parameter-free manner to solve these problems. The key idea of MGD is to pose matching the teacher channels with students’ as an assignment problem. We compare three solutions of the assignment problem to reduce channels from teacher features with partial distillation loss. The overall training takes a coordinate-descent approach between two optimization objects—assignments update and parameters update. Since MGD only contains normalization or pooling operations with negligible computation cost, it is flexible to plug into network with other distillation methods. The project site is http://kaiyuyue.com/mgd .
GIQA: Generated Image Quality Assessment

Generative adversarial networks (GANs) achieve impressive results today, but not all generated images are perfect. A number of quantitative criteria have recently emerged for generative models, but none of them are designed for a single generated image. In this paper, we propose a new research topic, Generated Image Quality Assessment (GIQA), which quantitatively evaluates the quality of each generated image. We introduce three GIQA algorithms from two perspectives: learning-based and data-based. We evaluate a number of images generated by various recent GAN models on different datasets and demonstrate that they are consistent with human assessments. Furthermore, GIQA is available for many applications, like separately evaluating the realism and diversity of generative models, and enabling online hard negative mining (OHEM) in the training of GANs to improve the results.
Deriving Design Feature Vectors for Patent Images Using Convolutional Neural Networks

Abstract The patent database is often used by designers to search for inspirational stimuli for innovative design opportunities because of the large size, extensive variety, and the massive quantity of design information contained in patent documents. Growing work on design-by-analogy has adopted various vectorization approaches for associating design documents. However, they only focused on text analysis and ignored visual information. Research in engineering design and cognitive psychology has shown that visual stimuli may benefit design ideation. In this study, we focus on visual design stimuli and automatically derive the vector space and the design feature vectors representing design images. The automatic vectorization approach uses a novel convolutional neural network architecture named Dual-Visual Geometry Group (VGG) aiming to accomplish two tasks: visual material-type prediction and international patent classification (IPC) section-label predictions. The derived feature vectors that embed both visual characteristics and technology-related knowledge can be potentially utilized to guide the retrieval and use of near-field and far-field design stimuli according to their vector distances. We report the accuracy of the training tasks and also use a case study to demonstrate the advantages of design image retrievals based on our model.
Driver identification using only the CAN-Bus vehicle data through an RCN deep learning approach

In the recent years, many studies claim that humans have a unique driving behavior style that could be used as a fingerprint in recognizing the identity of the driver. With the rising evolution of Machine Learning (ML), the research efforts aiming to take advantage of the human driving style identifiers have been increasing exponentially. For Advanced Driver Assistance Systems (ADAS), this attribute can be an efficient factor to ensure the security and protection of the vehicle. Additionally, it extends the ADAS capabilities by creating different profiles for the drivers, which helps every driver according to his own driving style and improve the ADAS fidelity. Nonetheless, certain problems in the unpredictability of human behavior and the effectiveness of capturing the temporal features of the signal represented an ongoing challenge to accomplish driver identification. In this paper, we propose a novel deep learning approach to driver identification based on a Residual Convolutional Network (RCN). This approach outperforms the existing state of the art methods in less than two hours of training, while simultaneously achieving 99.3% accuracy. The used data are exclusively provided by the Controller Area Network (CAN-Bus) vehicle data that eliminates any privacy invading concerns from the user.
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. However, previous methods model the input space with features extracted from equally divided sub-regions without considering that point cloud is generally non-uniformly distributed over the space. To address this issue, we propose a novel 3D object detection framework with dynamic information modeling. The proposed framework is designed in a coarse-to-fine manner. Coarse predictions are generated in the first stage via a voxel-based region proposal network. We introduce InfoFocus, which improves the coarse detections by adaptively refining features guided by the information of point cloud density. Experiments are conducted on the large-scale nuScenes 3D detection benchmark. Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9.0% mAP on the nuScenes test set.