Reviewerly/exHarmony huggingface dataset:
Comparison of multivariate methods for arsenic estimation and mapping in floodplain soil via portable X-ray fluorescence spectroscopy

Rapid, inexpensive, and equally reliable estimates of potentially toxic elements are a necessity; portable X-ray fluorescence (pXRF) spectrometry is a handy tool to help achieve such. The current study sought to compare multiple linear regression with three regularized regression models [Ridge, Lasso, and ElasticNet (ENET)] for the estimation of total arsenic (As) using pXRF datasets in polluted temperate floodplain soils of Příbram, Czech Republic. A total of 158 surface (0–25 cm) floodplain surface soil samples were collected from a specific site in Příbram. Models were evaluated separately and compared based on mean absolute error (MAE), root mean squared error (RMSE) and the coefficient of determination (R2). All four models were able to predict As with good accuracy (MAE and RMSE values of 0.02 and 0.03, respectively, and R2 values ranging from 0.94 to 0.95). As measured via pXRF as well as predicted via the four regression models produced similar spatial variability as shown by the standard laboratory-measured As using ordinary kriging and Conditional Gaussian Simulations (CGS), although the latter produced more details of As spatial distribution in floodplain soils. Future research should include other auxiliary predictors (e.g., soil physicochemical properties, other various sensor data) as well as cover a wider range of soils to improve model robustness.
Video Object Detection Guided by Object Blur Evaluation

In recent years, the excellent image-based object detection algorithms are transferred to the video object detection directly.These frame-by-frame processing methods are suboptimal owing to the degenerate object appearance such as motion blur, defocus and rare poses.The existing works for video object detection mostly focus on the feature aggregation at pixel level and instance level, but the blur impact in the aggregation process has not been exploited well so far.In this paper, we propose an end-to-end blur-aid feature aggregation network (BFAN) for video object detection.The proposed BFAN focuses on the aggregation process influenced by the blur including motion blur and defocus with high accuracy and little increased computation.In BFAN, we evaluate the object blur degree of each frame as the weight for aggregation.Noteworthy, the background is usually flat which has a negative impact on the object blur degree evaluation.Therefore, we introduce a light saliency detection network to alleviate the background interference.The experiments conducted on the ImageNet VID dataset show that BFAN achieves the stateof-the-art detection performance, exactly 79.1% mAP, with 3 points improvement compared to the video object detection baseline.
Bi-Directional Value Learning for Risk-Aware Planning Under Uncertainty

Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learning process provides a long-range and risk-aware base policy. A forward value learning process ensures local optimality and updates the policy via forward simulations. We consider a class of scalable and continuous-space rover navigation problems (RNP) to assess the safety, scalability, and optimality of the proposed algorithm. The results demonstrate the capabilities of the proposed algorithm in evaluating long-range risk/safety of the planner while addressing continuous problems with long planning horizons.
Optimizing the Memory Hierarchy by Compositing Automatic Transformations on Computations and Data

Optimizing compilers exploit the memory hierarchy using loop tiling and fusion, but these two transformations usually interfere with each other due to the oversight of transformations on data in memories. We present a novel composition of loop tiling and fusion in this paper. Unlike existing tiling-after-fusion algorithms that only transform computation spaces, our approach first applies rectangular/parallelogram tiling to live-out computation spaces for fitting the memory hierarchy, followed by the computation of the memory footprints required by each tile. The upwards exposed data extracted from the memory footprints are used to determine the tile shapes of intermediate computation spaces, allowing the construction of arbitrary tile shapes. Finally, our technique implements a post-tiling fusion strategy for maximizing data locality without losing tilability or parallelism of live-out computation spaces, thereby enabling storage reduction and reuse, and optimizing the memory hierarchy. We demonstrate that our approach can achieve superior performance on both CPU and GPU architectures over the state of the art by experimenting on 11 benchmarks extracted from numerous domains including neural networks, image processing, sparse matrix computation and linear algebra. Also, the results of the ResNet-50 model on an AI accelerator show that our approach can obtain 16% performance improvement.
Effects of Transient Loss of Vision on Head and Eye Movements during Visual Search in a Virtual Environment

Central and peripheral fields of view extract information of different quality and serve different roles during visual tasks. Past research has studied this dichotomy on-screen in conditions remote from natural situations where the scene would be omnidirectional and the entire field of view could be of use. In this study, we had participants looking for objects in simulated everyday rooms in virtual reality. By implementing a gaze-contingent protocol we masked central or peripheral vision (masks of 6 deg. of radius) during trials. We analyzed the impact of vision loss on visuo-motor variables related to fixation (duration) and saccades (amplitude and relative directions). An important novelty is that we segregated eye, head and the general gaze movements in our analyses. Additionally, we studied these measures after separating trials into two search phases (scanning and verification). Our results generally replicate past on-screen literature and teach about the role of eye and head movements. We showed that the scanning phase is dominated by short fixations and long saccades to explore, and the verification phase by long fixations and short saccades to analyze. One finding indicates that eye movements are strongly driven by visual stimulation, while head movements serve a higher behavioral goal of exploring omnidirectional scenes. Moreover, losing central vision has a smaller impact than reported on-screen, hinting at the importance of peripheral scene processing for visual search with an extended field of view. Our findings provide more information concerning how knowledge gathered on-screen may transfer to more natural conditions, and attest to the experimental usefulness of eye tracking in virtual reality.
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

This article first considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentially Private Generative Adversarial Network (DPGAN) are used to mutually benchmark the local credibility of each party and generate initial tokens; during the update stage, Differentially Private SGD (DPSGD) is used to facilitate collaborative privacy-preserving deep learning, and local credibility and tokens of each party are updated according to the quality and quantity of individually released gradients. Experimental results on benchmark datasets under three realistic settings demonstrate that FDPDDL achieves high fairness, yields comparable accuracy to the centralised and distributed frameworks, and delivers better accuracy than the standalone framework.
Entanglement manipulation beyond local operations and classical communication

When a quantum system is distributed to spatially separated parties, it is natural to consider how the system evolves when the parties perform local quantum operations with classical communication (LOCC). However, the structure of LOCC channels is exceedingly complex leaving many important physical problems unsolved. In this paper we consider generalized resource theories of entanglement based on different relaxations to the class of LOCC. The behavior of various entanglement measures is studied under non-entangling channels, as well as the newly introduced classes of dually non-entangling and PPT-preserving channels. In an effort to better understand the nature of LOCC bound entanglement, we study the problem of entanglement distillation in these generalized resource theories. We first show that unlike LOCC, general non-entangling maps can be superactivated, in the sense that two copies of the same non-entangling map can nevertheless be entangling. On the single-copy level, we demonstrate that every NPT entangled state can be converted into an LOCC-distillable state using channels that are both dually non-entangling and having a PPT Choi representation and that every state can be converted into an LOCC-distillable state using operations belonging to any family of polytopes that approximate LOCC. We then turn to the stochastic convertibility of multipartite pure states and show that any two states can be interconverted by any polytope approximation to the set of separable channels. Finally, as an analog to $k$-positive maps, we introduce and analyze the set of $k$-non-entangling channels.
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm

The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the quantum approximate optimization algorithm (QAOA) to find solutions of the BPSP. We demonstrate that QAOA with constant depth is able to beat all known heuristics for the binary paint shop problem on average in the infinite size limit $n\ensuremath{\rightarrow}\ensuremath{\infty}$. We complete our studies by running first experiments of small-sized instances on a trapped-ion quantum computer through Amazon Braket.
The Architectural Design Requirements of a Blockchain-Based Port Community System

This paper presents the value proposition of blockchain for Port Community Systems (PCS) by dissecting the business processes in port logistics and unfolding functionalities of blockchain in lowering the transaction cost. This paper contributes to the research by a detailed technical assessment of the plethora of currently available blockchain platforms and consensus mechanisms, against the identified requirements in this specific use case. The results of this technical assessment highlight the central value proposition of blockchain for landlord ports, which is independency from a central authority as the controlling agent. Bridging between two research domains of Information Technology and Logistics, this paper proposes the preferred architectural design requirements of a blockchain-based PCS, including provisioning private sidechains, modular design with inter-chain interoperability, and encrypted off-chain data storage. Availability—the readiness for correct service, and reliability—the continuity of correct service, are heavily reliant on the right choice being made for blockchain design for such a complex use case. A preliminary comparative analysis among different decentralisation levels in this paper suggests that a permissioned public blockchain offers the best trade-off in performance measures for this use case. This technical review identifies six research agenda from a design perspective.
Measuring and Improving User Experience Through Artificial Intelligence-Aided Design

This paper aims to propose a methodology for measuring User Experience (UX) by using Artificial Intelligence Aided Design (AIAD) technology in mobile application design. Unlike the traditional assistance design tools, AIAD focuses on the rational use of Artificial Intelligence (AI) technology to measure and improve UX. Since conventional data collection methods (such as user interview and user observation) for user behavior data are inefficient and time-consuming. We propose to obtain user behavior data from logs of mobile application. In order to protect the privacy of users, only a few dimensions of information is used in the process of browsing and operating mobile application. The goal of the proposed methodology is to make the deep neural network model simulate the user's experience in the process of operating a mobile application as much as possible. We design and use projected pages of application to train neural networks for specific tasks. These projected pages consist of the click information of all users in the process of completing a certain task. Thus, features of user behavior can be aggregated and mapped in the connection layers and the hidden layers. Finally, the optimized design is executed on the social communication application to verify the efficiency of the proposed methodology.
Transformers-based information extraction with limited data for domain-specific business documents

Information extraction plays an important role for data transformation in business cases. However, building extraction systems in actual cases face two challenges: (i) the availability of labeled data is usually limited and (ii) highly detailed classification is required. This paper introduces a model for addressing the two challenges. Different from prior studies that usually require a large number of training samples, our extraction model is trained with a small number of data for extracting a large number of information types. To do that, the model takes into account the contextual aspect of pre-trained language models trained on a huge amount of data on general domains for word representation. To adapt to our downstream task, the model employs transfer learning by stacking Convolutional Neural Networks to learn hidden representation for classification. To confirm the efficiency of our method, we apply the model to two actual cases of document processing for bidding and sale documents of two Japanese companies. Experimental results on real testing sets show that, with a small number of training data, our model achieves high accuracy accepted by our clients.
Conditional Entropy Coding for Efficient Video Compression

We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not performing any form of explicit transformations between frames and assume each frame is encoded with an independent state-of-the-art deep image compressor. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs while being much faster and easier to implement. We then propose a novel internal learning extension on top of this architecture that brings an additional $$\sim $$ 10% bitrate savings without trading off decoding speed. Importantly, we show that our approach outperforms H.265 and other deep learning baselines in MS-SSIM on higher bitrate UVG video, and against all video codecs on lower framerates, while being thousands of times faster in decoding than deep models utilizing an autoregressive entropy model.
Weavecraft

3D weaving is an emerging technology for manufacturing multilayer woven textiles. In this work, we present Weavecraft: an interactive, simulation-based design tool for 3D weaving. Unlike existing textile software that uses 2D representations for design patterns, we propose a novel weave block representation that helps the user to understand 3D woven structures and to create complex multi-layered patterns. With Weavecraft, users can create blocks either from scratch or by loading traditional weaves, compose the blocks into large structures, and edit the pattern at various scales. Furthermore, users can verify the design with a physically based simulator, which predicts and visualizes the geometric structure of the woven material and reveals potential defects at an interactive rate. We demonstrate a range of results created with our tool, from simple two-layer cloth and well known 3D structures to a more sophisticated design of a 3D woven shoe, and we evaluate the effectiveness of our system via a formative user study.
Deep learning for camera data acquisition, control, and image estimation

We review the impact of deep-learning technologies on camera architecture. The function of a camera is first to capture visual information and second to form an image. Conventionally, both functions are implemented in physical optics. Throughout the digital age, however, joint design of physical sampling and electronic processing, e.g., computational imaging, has been increasingly applied to improve these functions. Over the past five years, deep learning has radically improved the capacity of computational imaging. Here we briefly review the development of artificial neural networks and their recent intersection with computational imaging. We then consider in more detail how deep learning impacts the primary strategies of computational photography: focal plane modulation, lens design, and robotic control. With focal plane modulation, we show that deep learning improves signal inference to enable faster hyperspectral, polarization, and video capture while reducing the power per pixel by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mn>10</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>−<!-- − --></mml:mo></mml:mrow><mml:mn>100</mml:mn><mml:mo>×<!-- × --></mml:mo></mml:math> . With lens design, deep learning improves multiple aperture image fusion to enable task-specific array cameras. With control, deep learning enables dynamic scene-specific control that may ultimately enable cameras that capture the entire optical data cube (the “light field”), rather than just a focal slice. Finally, we discuss how these three strategies impact the physical camera design as we seek to balance physical compactness and simplicity, information capacity, computational complexity, and visual fidelity.
Pomsets with preconditions: a simple model of relaxed memory

Relaxed memory models must simultaneously achieve efficient implementability and thread-compositional reasoning. Is that why they have become so complicated? We argue that the answer is no: It is possible to achieve these goals by combining an idea from the 60s (preconditions) with an idea from the 80s (pomsets), at least for X64 and ARMv8. We show that the resulting model (1) supports compositional reasoning for temporal safety properties, (2) supports all expected sequential compiler optimizations, (3) satisfies the DRF-SC criterion, and (4) compiles to X64 and ARMv8 microprocessors without requiring extra fences on relaxed accesses.
Learning Activation Functions in Deep (Spline) Neural Networks

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions.To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the second-order total-variations of the trainable nonlinearities.The representer theorem for DNNs tells us that the optimal activation functions are adaptive piecewise-linear splines, which allows us to recast the problem as a parametric optimization.The challenging point is that the corresponding basis functions (ReLUs) are poorly conditioned and that the determination of their number and positioning is also part of the problem.We circumvent the difficulty by using an equivalent B-spline basis to encode the activation functions and by expressing the regularization as an 1 -penalty.This results in the specification of parametric activation function modules that can be implemented and optimized efficiently on standard development platforms.We present experimental results that demonstrate the benefit of our approach.
Student perceptions and mobile technology adoption: implications for lower-income students shifting to digital

This paper responds to Joo et al. (Educational Technology Research and Development 64:611-630, 2016) study of the factors predicting South Korean online students' use of a mobile learning management system (m-LMS). The findings show students' continuous intention and their actual usage have implications for how faculty and institutions may need to promote the usefulness of mobile technology for students to accept and adopt the technology. This would be particularly relevant for lower income students with limited digital literacy skills and limited access. For many students, mobile technology is the only means through which they can persist in taking courses while institutions offer classes primarily in online modalities during the COVID-19 pandemic.
Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints

Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the x, y axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by considering kinematics of the obstacles based on measurements from a 2D lidar. Moreover, a novel approach for correcting the heading of the MAV towards the center of the mine tunnel is proposed, while the efficacy of the suggested framework has been evaluated in multiple field trials in an underground mine in Sweden.
A new approach of ECG steganography and prediction using deep learning

In this paper, a new approach of ECG steganography of hiding patient's confidential information is proposed. As steganography results in distortion within the ECG signal which hampers the clinical features, in this work, encryption was performed within TP-segment of ECG. Additionally, segment classification and feature extraction were used for data concealing within normal TP-segments, while keeping abnormal segments unaffected. To reduce the computational complexity and execution time, encryption was performed in time domain signal, using a new approach. Finally, after decryption of hidden data, to predict original sample values of modified TP-segments, a long short-term memory recurrent neural network (LSTM-RNN) was used which efficiently reduced the error between the original and predicted signal. This algorithm was successfully implemented on mitdb, ptbdb and European ST-T database, available in physionet and percent root mean square difference (PRD), PRD normalized (PRDN) were obtained less than 1% along with signal to noise ratio (SNR) and peak SNR (PSNR) more than 80 dB. It was observed that this algorithm provided better result among other frequency domain techniques and recently published works.