Reviewerly/exHarmony huggingface dataset:
Glancing Transformer for Non-Autoregressive Neural Machine Translation

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality.However, existing NAT methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup.We propose the Glancing Language Model (GLM) for single-pass parallel generation models.With GLM, we develop Glancing Transformer (GLAT) for machine translation.With only single-pass parallel decoding, GLAT is able to generate high-quality translation with 8×-15× speedup.Note that GLAT does not modify the network architecture, which is a training method to learn word interdependency.Experiments on multiple WMT language directions show that GLAT outperforms all previous single pass non-autoregressive methods, and is nearly comparable to Transformer, reducing the gap to 0.25-0.9BLEU points.
Grid balancing challenges illustrated by two European examples: Interactions of electric grids, photovoltaic power generation, energy storage and power generation forecasting

The past decade has not only witnessed a great shift in the energy mix, with electricity gaining a more and more prominent role, but has also seen electricity coming increasingly from renewable energy sources, whose proportion is forecasted to reach 50% by 2030. As the integration of photovoltaic energy cannot be deemed successful without the electricity supply being both sustainable and secure, such far-reaching developments prompt legislations and policy makers, including those of the European Union, to make changes to accommodate not only ever-changing technologies, including energy storage solutions, but also new players in the market. Currently, battery energy storage systems are not used for enhancing the precision of photovoltaic power generation schedules, so actors in the market find it difficult to make well-grounded decisions on the viability of utilizing batteries for such a purpose. The innovative novelty of the procedure presented in this paper is that it is suitable for the planning, analysis and evaluation of the schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion-based battery energy storage systems of various capacities. By the examples of two European Union countries, this article studied the deviations of day-ahead and intraday photovoltaic power generation forecasts from the actual electricity generation of 1000 MWp photovoltaic systems. The two main objectives of the project were to investigate the amount of the photovoltaic balancing needs, based on actual data compared to the day-ahead and intraday forecasts, and to establish to what extent sodium-sulfur and lithium-ion-based battery storage systems can decrease those requirements. Furthermore, by using a model for simulation, the potentials of a number of different energy storage capacities (nominal net storage capacities from 10 MWh to 10,000 MWh) for reducing grid balancing needs were assessed. The practical use of the model is that its application makes it possible to create a dynamic design, analysis and assessment system mechanism that can allow one to establish the multilevel schedule accuracy enhancing impacts of sodium-sulfur and lithium-ion energy storage systems linked to solar power plant systems. Real photovoltaic data from Belgium and Hungary were used to find out how the accuracy of PV power generation forecasts influence the level of the annual utilization of energy storage systems.
Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer's overhead of implementing each service for different clouds to make it portable.
The effect of supply chain management through social media on competitiveness of the private hospitals in Jordan

The aim of this research is to examine the impact of supply chain management through social media on competitiveness of the private hospitals in Jordan. The population of the study includes managers in various administrative disciplines. Therefore, the complete census method was used in this research to collect the primary data, where the total responses were 438 responses were used for analysis. The questionnaire was used as a basic instrument in the current research, as it was designed electronically through Google Forms and sent to the research sample via email. AMOS software was used to test the research hypotheses. The results showed that all dimensions of supply chain management through social media had a positive impact on competitiveness; the greatest effect was for strategic relations with suppliers. Based on the study results; managers at the examined companies should enhance their usage of social media with suppliers, according to the report, in order to support strategic connections with them.
Energy and Policy Considerations for Deep Learning in NLP

Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.
Neuromorphic Computing Based on Wavelength-Division Multiplexing

Optical neural networks (ONNs), or optical neuromorphic hardware accelerators, have the potential to dramatically enhance the computing power and energy efficiency of mainstream electronic processors, due to their ultralarge bandwidths of up to 10s of terahertz together with their analog architecture that avoids the need for reading and writing data back and forth. Different multiplexing techniques have been employed to demonstrate ONNs, amongst which wavelength division multiplexing (WDM) techniques make sufficient use of the unique advantages of optics in terms of broad bandwidths. Here, we review recent advances in WDM based ONNs, focusing on methods that use integrated microcombs to implement ONNs. We present results for human image processing using an optical convolution accelerator operating at 11 Tera operations per second. The open challenges and limitations of ONNs that need to be addressed for future applications are also discussed.
Brownian reservoir computing realized using geometrically confined skyrmion dynamics

Reservoir computing (RC) has been considered as one of the key computational principles beyond von-Neumann computing. Magnetic skyrmions, topological particle-like spin textures in magnetic films are particularly promising for implementing RC, since they respond strongly nonlinearly to external stimuli and feature inherent multiscale dynamics. However, despite several theoretical proposals that exist for skyrmion reservoir computing, experimental realizations have been elusive until now. Here, we propose and experimentally demonstrate a conceptually new approach to skyrmion RC that leverages the thermally activated diffusive motion of skyrmions. By confining the electrically gated and thermal skyrmion motion, we find that already a single skyrmion in a confined geometry suffices to realize nonlinearly separable functions, which we demonstrate for the XOR gate along with all other Boolean logic gate operations. Besides this universality, the reservoir computing concept ensures low training costs and ultra-low power operation with current densities orders of magnitude smaller than those used in existing spintronic reservoir computing demonstrations. Our proposed concept is robust against device imperfections and can be readily extended by linking multiple confined geometries and/or by including more skyrmions in the reservoir, suggesting high potential for scalable and low-energy reservoir computing.
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer

Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have recently achieved great success in this task. However, their limited receptive field constrains existing network architectures to reason only locally, dampening the effectiveness of the self-supervised paradigm. In the light of the recent successes achieved by Vision Transformers (ViTs), we propose MonoViT, a brand-new framework combining the global reasoning enabled by ViT models with the flexibility of self-supervised monocular depth estimation. By combining plain convolutions with Transformer blocks, our model can reason locally and globally, yielding depth prediction at a higher level of detail and accuracy, allowing MonoViT to achieve state-of-the-art performance on the established KITTI dataset. Moreover, MonoViT proves its superior generalization capacities on other datasets such as Make3D and DrivingStereo. Source code available at https://github.com/zxcqlf/MonoViT
War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education

Developments in the chatbot space have been accelerating at breakneck speed since late November 2022. Every day, there appears to be a plethora of news. A war of competitor chatbots is raging amidst an AI arms race and gold rush. These rapid developments impact higher education, as millions of students and academics have started using bots like ChatGPT, Bing Chat, Bard, Ernie and others for a large variety of purposes. In this article, we select some of the most promising chatbots in the English and Chinese-language spaces and provide their corporate backgrounds and brief histories. Following an up-to-date review of the Chinese and English-language academic literature, we describe our comparative method and systematically compare selected chatbots across a multi-disciplinary test relevant to higher education. The results of our test show that there are currently no A-students and no B-students in this bot cohort, despite all publicised and sensationalist claims to the contrary. The much-vaunted AI is not yet that intelligent, it would appear. GPT-4 and its predecessor did best, whilst Bing Chat and Bard were akin to at-risk students with F-grade averages. We conclude our article with four types of recommendations for key stakeholders in higher education: (1) faculty in terms of assessment and (2) teaching &amp; learning, (3) students and (4) higher education institutions.
Enabling Computation of Correlation Bounds for Finite-Dimensional Quantum Systems via Symmetrization

We present a technique for reducing the computational requirements by several orders of magnitude in the evaluation of semidefinite relaxations for bounding the set of quantum correlations arising from finite-dimensional Hilbert spaces. The technique, which we make publicly available through a user-friendly software package, relies on the exploitation of symmetries present in the optimisation problem to reduce the number of variables and the block sizes in semidefinite relaxations. It is widely applicable in problems encountered in quantum information theory and enables computations that were previously too demanding. We demonstrate its advantages and general applicability in several physical problems. In particular, we use it to robustly certify the non-projectiveness of high-dimensional measurements in a black-box scenario based on self-tests of $d$-dimensional symmetric informationally complete POVMs.
Causal Reasoning from Meta-reinforcement Learning

Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether causal reasoning can emerge via meta-reinforcement learning. We train a recurrent network with model-free reinforcement learning to solve a range of problems that each contain causal structure. We find that the trained agent can perform causal reasoning in novel situations in order to obtain rewards. The agent can select informative interventions, draw causal inferences from observational data, and make counterfactual predictions. Although established formal causal reasoning algorithms also exist, in this paper we show that such reasoning can arise from model-free reinforcement learning, and suggest that causal reasoning in complex settings may benefit from the more end-to-end learning-based approaches presented here. This work also offers new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform -- and interpret -- experiments.
Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. The decentralized classification fusion problem was the reason to use the belief function-based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques, namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are prone to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. Because of these challenges, efficient fault detection methods must be used to detect faults in a WSN in a timely manner. We have induced four types of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault, and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM gave the first best result for the improvement of the belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN provided the second, third, and fourth best results, respectively. The proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics, i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). Simulations show that the proposed methods outperform the existing techniques and give better results for the belief function and fault detection in WSNs.
Designing Transparent and Autonomous Intelligent Vision Systems

To process vast amounts of visual data such as images, videos, etc. in an automatic and computationally efficient way, intelligent vision systems have been developed over the last three decades. However, with the increasing development of complex technologies like companion robots which require advanced machine vision capabilities and, on the other hand, the growing attention to data security and privacy, the design of intelligent vision systems faces new challenges such as autonomy and transparency. Hence, in this paper, we propose to define the main requirements for the new generation of intelligent vision systems (IVS) we demonstrated in a prototype.
Big Data-Based Improved Data Acquisition and Storage System for Designing Industrial Data Platform

Big data-based acquisition and storage system (ASS) plays an important role in the design of industrial data platform.Many big data frameworks have been integrated compression and serialization method.These methods cannot meet the needs of industrial production information management for requiring time-consuming and mass storage.Based on the existing big data frameworks, we propose an enhanced industrial big data platform in order to reduce the data processing time while requiring fewer data storage space.Specifically, this paper focuses on evaluating the impact of multiple compression and serialization methods on big data platform performance and tries to choose optimal compression and serialization methods for the industrial data platform.Compared to the methods integrated in Hadoop and Spark, the experimental results showed the data compression time of the platform has been reduced by 73.9% with a less than 96% the size of data compressed, furthermore, the data serialization time has been reduced by 80.8%.With the increasing amount of data, it takes less time to compare with benchmark methods.
The Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information

This paper presents a framework for the targetless extrinsic calibration of stereo cameras and Light Detection and Ranging (LiDAR) sensors with a non-overlapping Field of View (FOV). In order to solve the extrinsic calibrations problem under such challenging configuration, the proposed solution exploits road markings as static and robust features among the various dynamic objects that are present in urban environment. First, this study utilizes road markings that are commonly captured by the two sensor modalities to select informative images for estimating the extrinsic parameters. In order to accomplish stable optimization, multiple cost functions are defined, including Normalized Information Distance (NID), edge alignment and, plane fitting cost. Therefore a smooth cost curve is formed for global optimization to prevent convergence to the local optimal point. We further evaluate each cost function by examining parameter sensitivity near the optimal point. Another key characteristic of extrinsic calibration, repeatability, is analyzed by conducting the proposed method multiple times with varying randomly perturbed initial points.
No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Abstract Video quality assessment (VQA) is an important element of various applications ranging from automatic video streaming to display technology. Furthermore, visual quality measurements require a balanced investigation of visual content and features. Previous studies have shown that the features extracted from a pretrained convolutional neural network are highly effective for a wide range of applications in image processing and computer vision. In this study, we developed a novel architecture for no-reference VQA based on the features obtained from pretrained convolutional neural networks, transfer learning, temporal pooling, and regression. In particular, we obtained solutions by only applying temporally pooled deep features and without using manually derived features. The proposed architecture was trained based on the recently published Konstanz natural video quality database (KoNViD-1k), which contains 1200 video sequences with authentic distortion unlike other publicly available databases. The experimental results obtained based on KoNViD-1k demonstrated that the proposed method performed better than other state-of-the-art algorithms. Furthermore, these results were confirmed by tests using the LIVE VQA database, which contains artificially distorted videos.
The iPhone Health App from a forensic perspective: can steps and distances registered during walking and running be used as digital evidence?

The iPhone Health App automatically collects data on daily activities for health purposes. Detailed information on the number of steps taken and distances travelled is stored in a database together with timestamps with a time granularity of a couple of minutes. While such information can potentially be very valuable in a forensic investigation, one needs to have a good understanding of its reliability in order to make proper use of it. In this study we investigate the accuracy of steps and distances registered by the Health App under a broad range of experimental conditions for an iPhone 6, iPhone 7 and iPhone 8. For five subjects, we varied carrying location of the telephone, walking distances, walking speed and compared steps and distances registered by the telephone to manually measured steps and the real distance. The results of the experiments were similar for all three telephones. Steps registered by the iPhone Health App agree very closely to those measured manually with an averaged error of about 2%. The reliability of the registered distances, however, depends on a number of factors, including walking speed and walking style of the subject and can deviate up to 30–40% from the true value. These results suggest that, if you take the properties of the iPhone Health App into account, digital traces from the Health App can be used for evidential purposes, for example to make a probability statement about different routes that may have been travelled in a case.
Vision-to-Language Tasks Based on Attributes and Attention Mechanism

Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts and subtle relationships between image regions and natural language elements. To make full use of these information, this paper attempt to exploit the text-guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language. Our method includes two-level attention networks. One is the text-guided attention network which is used to select the text-related regions. The other is SA network which is used to highlight the concept-related regions and the region-related concepts. At last, all these information are incorporated to generate captions or answers. Practically, image captioning and visual question answering experiments have been carried out, and the experimental results have shown the excellent performance of the proposed approach.