Reviewerly/exHarmony huggingface dataset:
Delving deep into spatial pooling for squeeze-and-excitation networks

Squeeze-and-Excitation (SE) blocks have demonstrated significant accuracy gains for state-of-the-art deep architectures by re-weighting channel-wise feature responses. The SE block is an architecture unit that integrates two operations: a squeeze operation that employs global average pooling to aggregate spatial convolutional features into a channel feature, and an excitation operation that learns instance-specific channel weights from the squeezed feature to re-weight each channel. In this paper, we revisit the squeeze operation in SE blocks, and shed lights on why and how to embed rich (both global and local) information into the excitation module at minimal extra costs. In particular, we introduce a simple but effective two-stage spatial pooling process: rich descriptor extraction and information fusion. The rich descriptor extraction step aims to obtain a set of diverse (i.e., global and especially local) deep descriptors that contain more informative cues than global average-pooling. While, absorbing more information delivered by these descriptors via a fusion step can aid the excitation operation to return more accurate re-weight scores in a data-driven manner. We validate the effectiveness of our method by extensive experiments on ImageNet for image classification and on MS-COCO for object detection and instance segmentation. For these experiments, our method achieves consistent improvements over the SENets on all tasks, in some cases, by a large margin.
0.75 Gbit/s high-speed classical key distribution with mode-shift keying chaos synchronization of Fabry–Perot lasers

High-speed physical key distribution is diligently pursued for secure communication. In this paper, we propose and experimentally demonstrate a scheme of high-speed key distribution using mode-shift keying chaos synchronization between two multi-longitudinal-mode Fabry-Perot lasers commonly driven by a super-luminescent diode. Legitimate users dynamically select one of the longitudinal modes according to private control codes to achieve mode-shift keying chaos synchronization. The two remote chaotic light waveforms are quantized to generate two raw random bit streams, and then those bits corresponding to chaos synchronization are sifted as shared keys by comparing the control codes. In this method, the transition time, i.e., the chaos synchronization recovery time is determined by the rising time of the control codes rather than the laser transition response time, so the key distribution rate is improved greatly. Our experiment achieved 0.75-Gbit/s key distribution rate with a bit error rate of 3.8*10-3 over 160-km fiber transmission with dispersion compensation. The entropy rate of the laser chaos is evaluated as 16 Gbit/s, which determines the ultimate final key rate together with key generation ratio. It is therefore believed that the method pays a way for Gbit/s physical key distribution.
The Importance of the Application of the Metaverse in Education

In the early 90&amp;#39;s, and especially in some American universities, with the emergence of virtual reality, virtual environments and their manipulation began to be implemented, achieving important advances that have led to improvements in research through changes in the perception of the subject, modeling, communication processes and the development of 3D virtual classrooms.&#x0D; These advances have led to the metaverse, as an environment where humans interact socially as avatars, and especially its application in the world of education in the fields of entertainment, tele-education, educational research, learning environments, etc.&#x0D; According to technologists, this year will separate the thinkers from the builders, and then the technical advances of the last few years will produce the first steps this year in making the metaverse a reality. Advances will largely be attributed to ever-better graphics processing units (GPUs), photorealistic 3D engines, faster content generation through volumetric video and artificial intelligence, the increasing prevalence of cloud computing and 5G, as well as a more sophisticated and better understood blockchain infrastructure.&#x0D; But from a human experience perspective, one development stands out above all others: extended reality (XR) technologies. These include virtual reality (VR), augmented reality (AR) and brain-computer interfaces (BCI), which together are positioned as the next computing platforms in their own right.
A Comparison of Pooling Methods for Convolutional Neural Networks

One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.
Efficient Training for Positive Unlabeled Learning

Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.
Quantum codes for quantum simulation of fermions on a square lattice of qubits

Quantum simulation of fermionic systems is a promising application of quantum computers, but in order to program them, we need to map fermionic states and operators to qubit states and quantum gates. While quantum processors may be built as two-dimensional qubit networks with couplings between nearest neighbors, standard Fermion-to-qubit mappings do not account for that kind of connectivity. In this work we concatenate the (one-dimensional) Jordan-Wigner transform with specific quantum codes defined under the addition of a certain number of auxiliary qubits. This yields a novel class of mappings with which any fermionic system can be embedded in a two-dimensional qubit setup, fostering scalable quantum simulation. Our technique is demonstrated on the two-dimensional Fermi-Hubbard model, that we transform into a local Hamiltonian. What is more, we adapt the Verstraete-Cirac transform and Bravyi-Kitaev Superfast simulation to the square lattice connectivity and compare them to our mappings. An advantage of our approach in this comparison is that it allows us to encode and decode a logical state with a simple unitary quantum circuit.
Beyond the Doomsday Economics of “Proof-of-Work” in Cryptocurrencies

This paper discusses the economics of how Bitcoin achieves data immutability, and thus payment finality, via costly computations, i.e., "proof-of-work."Further, it explores what the future might hold for cryptocurrencies modelled on this type of consensus algorithm.The conclusions are, first, that Bitcoin counterfeiting via "double-spending" attacks is inherently profitable, making payment finality based on proof-of-work extremely expensive.Second, the transaction market cannot generate an adequate level of "mining" income via fees as users free-ride on the fees of other transactions in a block and in the subsequent blockchain.Instead, newly minted bitcoins, known as block rewards, have made up the bulk of mining income to date.Looking ahead, these two limitations imply that liquidity is set to fall dramatically as these block rewards are phased out.Simple calculations suggest that once block rewards are zero, it could take months before a Bitcoin payment is final, unless new technologies are deployed to speed up payment finality.Second-layer solutions such as the Lightning Network might help, but the only fundamental remedy would be to depart from proof-of-work, which would probably require some form of social coordination or institutionalisation.
Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering

Generative Adversarial Networks (GAN) have not only achieved a big success in various generation tasks such as images, but also boosted the accuracy of classification tasks by generating additional labeled data, which is called data augmentation. In this paper, we propose a Rating Augmentation framework with GAN, named RAGAN, aiming to alleviate the data sparsity problem in collaborative filtering (CF), eventually improving recommendation accuracy significantly. We identify a unique challenge that arises when applying GAN to CF for rating augmentation: naive RAGAN tends to generate values biased towards high ratings. Then, we propose a refined version of RAGAN, named RAGANBT, which addresses this challenge successfully. Via our extensive experiments, we validate that our RAGANBT is really effective to solve the data sparsity problem, thereby providing existing CF models with great improvement in accuracy under various situations such as basic top-N recommendation, long-tail item recommendation, and recommendation to cold-start users.
LASEC

This paper introduces LASEC, the first technique for instant do-it-yourself fabrication of circuits with custom stretchability on a conventional laser cutter and in a single pass. The approach is based on integrated cutting and ablation of a two-layer material using parametric design patterns. These patterns enable the designer to customize the desired stretchability of the circuit, to combine stretchable with non-stretchable areas, or to integrate areas of different stretchability. For adding circuits on such stretchable cut patterns, we contribute routing strategies and a real-time routing algorithm. An interactive design tool assists designers by automatically generating patterns and circuits from a high-level specification of the desired interface. The approach is compatible with off-the-shelf materials and can realize transparent interfaces. Several application examples demonstrate the versatility of the novel technique for applications in wearable computing, interactive textiles, and stretchable input devices.
Augmented Reality Research in Education: A Bibliometric Study

The aim of this study is identifying the publications related to Augmented Reality (AR) in education and describing the features of those studies. The Web of Science database was searched for publications on AR in education. Bibliographic coupling of the countries, bibliographic coupling of the institutions, bibliographic coupling of the journals, bibliographic coupling of the publications, bibliographic coupling of the authors, and co-occurrences of the author keywords were examined for the selected publications. Bibliographic visualisation methods were used to analyse and visualise the characteristics of the selected publications. A total of 437 publications were selected following the research criteria between 1999 and 2018. The results revealed that virtual reality, mobile learning, interactive learning environments, and e-learning were among the most studied concepts in AR research. Computers & Education, EJMSTE, Educational Technology and Society, and Interactive Learning Environments have been among the top journals, Spain and Taiwan have been the most important countries, C.C. Tsai and G.J. Hwang have been the most important authors and National Taiwan University of Science and Technology has been the top institution in this research area. The research trend and the progress of the concept of augmented reality in education have been discussed in the paper.
Incorporating Emoji Descriptions Improves Tweet Classification

Tweets are short messages that often include specialized language such as hashtags and emojis.In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words.We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification.Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.
The Olympic Cu-Au Province, Gawler Craton: A Review of the Lithospheric Architecture, Geodynamic Setting, Alteration Systems, Cover Successions and Prospectivity

The Olympic Cu-Au Province is a metallogenic province in South Australia that contains one of the world’s most significant Cu-Au-U resources in the Olympic Dam deposit. The Olympic Cu-Au Province also hosts a range of other iron oxide-copper-gold (IOCG) deposits including Prominent Hill and Carrapateena. This paper reviews the geology of the Olympic Cu-Au Province by investigating the lithospheric architecture, geodynamic setting and alteration systematics. In addition, since the province is almost entirely buried by post-mineral cover, the sedimentary cover sequences are also reviewed. The Olympic Cu-Au Province formed during the early Mesoproterozoic, ca. 1.6 Ga and is co-located with a fundamental lithospheric boundary in the eastern Gawler Craton. This metallogenic event was driven in part by melting of a fertile, metasomatized sub-continental lithospheric mantle during a major regional tectonothermal event. Fluid evolution and multiple fluid mixing resulted in alteration assemblages that range from albite, magnetite and other higher temperature minerals to lower temperature assemblages such as hematite, sericite and chlorite. IOCG mineralisation is associated with both high and low temperature assemblages, however, hematite-rich IOCGs are the most economically significant. Burial by Mesoproterzoic and Neoproterozoic-Cambrian sedimentary successions preserved the Olympic Cu-Au Province from erosion, while also providing a challenge for mineral exploration in the region. Mineral potential modelling identifies regions within the Olympic Cu-Au Province and adjacent Curnamona Province that have high prospects for future IOCG discoveries. Exploration success will rely on improvements in existing potential field and geochemical data, and be bolstered by new 3D magnetotelluric surveys. However, drilling remains the final method for discovery of new mineral resources.
Exploring Numeracy in Word Embeddings

Word embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions.In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding.Numbers are ubiquitous and frequently appear in text.Inspired by cognitive studies on how humans perceive numbers, we develop an analysis framework to test how well word embeddings capture two essential properties of numbers: magnitude (e.g.3<4) and numeration (e.g.3=three).Our experiments reveal that most models capture an approximate notion of magnitude, but are inadequate at capturing numeration.We hope that our observations provide a starting point for the development of methods which better capture numeracy in NLP systems.
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-noise ratios, subcode lengths, channel conditions and system loads, and show the benefit over regular HARQ and existing E-HARQ schemes without machine learning.
Deep Learning With Customized Abstract Syntax Tree for Bug Localization

Given a bug report, bug localization technique can help developers automatically locate potential buggy files.Information retrieval and deep learning approaches have been applied in bug localization by extracting lexical features in bug reports and syntactic features in source code files, though they fail to utilize the structural and semantic information of source code files.In this paper, we present a bug localization system CAST, which exploits deep learning and customized abstract syntax trees of programs to locate potential buggy source files automatically and effectively.Specifically, CAST extracts both lexical semantics in bug reports (e.g., words) and source files (e.g., method names) and program semantics in source files (e.g., abstract syntax tree, AST).Moreover, CAST enhances the tree-based convolutional neural network (TBCNN) model with customized ASTs, which distinguish between user-defined methods and system-provided ones to reflect their contributions leading to defects.Furthermore, customized ASTs group the syntactic entities with similar semantics and prune the ones with little or redundant semantics in order to facilitate the learning performance.Experimental results on four widely-used software projects show that CAST significantly outperforms the state-of-the-art methods in locating the buggy source files.
A Dual-Cameras-Based Driver Gaze Mapping System With an Application on Non-Driving Activities Monitoring

Characterisation of the driver's non-driving activities (NDAs) is of great importance to the design of the take-over control strategy in Level 3 automation. Gaze estimation is a typical approach to monitor the driver's behaviour since the eye gaze is normally engaged with the human activities. However, current eye gaze tracking techniques are either costly or intrusive which limits their applicability in vehicles. This paper proposes a low-cost and non-intrusive dual-cameras based gaze mapping system that visualises the driver's gaze using a heat map. The challenges introduced by complex head movement during NDAs and camera distortion are addressed by proposing a nonlinear polynomial model to establish the relationship between the face features and eye gaze on the simulated driver's view. The Root Mean Square Error of this system in the in-vehicle experiment for the X and Y direction is 7.80±5.99 pixel and 4.64±3.47 pixel respectively with the image resolution of $1440 \times 1080$ pixels. This system is successfully demonstrated to evaluate three NDAs with visual attention. This technique, acting as a generic tool to monitor driver's visual attention, will have wide applications on NDA characterisation for intelligent design of take over strategy and driving environment awareness for current and future automated vehicles.
Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval

Term frequency is a common method for identifying the importance of a term in a query or document. But it is a weak signal, especially when the frequency distribution is flat, such as in long queries or short documents where the text is of sentence/passage-length. This paper proposes a Deep Contextualized Term Weighting framework that learns to map BERT's contextualized text representations to context-aware term weights for sentences and passages. When applied to passages, DeepCT-Index produces term weights that can be stored in an ordinary inverted index for passage retrieval. When applied to query text, DeepCT-Query generates a weighted bag-of-words query. Both types of term weight can be used directly by typical first-stage retrieval algorithms. This is novel because most deep neural network based ranking models have higher computational costs, and thus are restricted to later-stage rankers. Experiments on four datasets demonstrate that DeepCT's deep contextualized text understanding greatly improves the accuracy of first-stage retrieval algorithms.
Optimization of distance formula in K-Nearest Neighbor method

K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is closest to the object based on comparison between previous and current data. In the learning process, KNN calculates the distance of the nearest neighbor by applying the euclidean distance formula, while in other methods, optimization has been done on the distance formula by comparing it with the other similar in order to get optimal results. This study will discuss the calculation of the euclidean distance formula in KNN compared with the normalized euclidean distance, manhattan and normalized manhattan to achieve optimization results or optimal value in finding the distance of the nearest neighbor.
Exposed soil and mineral map of the Australian continent revealing the land at its barest

Multi-spectral remote sensing has already played an important role in mapping surface mineralogy. However, vegetation - even when relatively sparse - either covers the underlying substrate or modifies its spectral response, making it difficult to resolve diagnostic mineral spectral features. Here we take advantage of the petabyte-scale Landsat datasets covering the same areas for periods exceeding 30 years combined with a novel high-dimensional statistical technique to extract a noise-reduced, cloud-free, and robust estimate of the spectral response of the barest state (i.e. least vegetated) across the whole continent of Australia at 25 m2 resolution. Importantly, our method preserves the spectral relationships between different wavelengths of the spectra. This means that our freely available continental-scale product can be combined with machine learning for enhanced geological mapping, mineral exploration, digital soil mapping, and establishing environmental baselines for understanding and responding to food security, climate change, environmental degradation, water scarcity, and threatened biodiversity.