Reviewerly/exHarmony huggingface dataset:
Material Culture and Typology

The active and discursive nature of material culture is the subject of this paper. It will, however, be approached from the point of view of typology and in particular the debate about the 'Swedish Typology’ (Gräslund 1974). Typology is probably the archaeological method or theory through which the discipline has most explicitly stated its view on the nature of the archaeological object. Inspired by the idea of naturalised epistemology as the basis for understanding how knowledge is constructed within the sciences (as discussed by Thomas 1996: 194), it is here argued that what we do, as archaeologists, is of importance rather than the theorising about our actions. Through a discussion of typology as expressed in archaeological practice, this paper will propose that the relationship between the object and typology is much simpler and more complex than our habitual use of the concept tends to suggest. It is proposed that the creation of typologies reveals the quite decisive influence which the object has upon the archaeological constructions. Typologies, moreover, are intimately connected to prehistoric production strategies. It is the relationship between these two dimensions of typologies, that we must understand in order to fully realise their potentials and understand their roles in archaeological practice.
Verifying Post-Quantum Signatures in 8 kB of RAM

In this paper, we study implementations of post-quantum signature schemes on resource-constrained devices. We focus on verification of signatures and cover NIST PQC round-3 candidates Dilithium, Falcon, Rainbow, GeMSS, and SPHINCS $$^+$$ . We assume an ARM Cortex-M3 with 8 kB of memory and 8 kB of flash for code; a practical and widely deployed setup in, for example, the automotive sector. This amount of memory is insufficient for most schemes. Rainbow and GeMSS public keys are too big; SPHINCS $$^+$$ signatures do not fit in this memory. To make signature verification work for these schemes, we stream in public keys and signatures. Due to the memory requirements for efficient Dilithium implementations, we stream in the public key to cache more intermediate results. We discuss the suitability of the signature schemes for streaming, adapt existing implementations, and compare performance.
Query2Prod2Vec: Grounded Word Embeddings for eCommerce

We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop.We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature.Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge

The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity.Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context.Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it.Our model outperforms several baselines as judged by both automatic metrics and humans.
Barrier-Free Wheelchair with a Mechanical Transmission

Wheelchairs are an important means of transportation for the elderly and disabled. However, the movement of wheelchairs on long curbs and stairs is restricted. In this study, a wheelchair for climbing stairs was developed based on a mechanical transmission system that rotates the entire driving part through a link structure and an actuator to change the speed. The first mode drives the caterpillar, and the second mode drives the wheels. When driving on flat ground, it uses landing gears and wheels, and when climbing stairs, it uses the caterpillar; accordingly, a stable driving is possible. The stability of the transmission is confirmed through stress analysis. The method used in our study makes it is possible to manufacture lightweight wheelchairs because a single motor drives both the wheel and caterpillar through the transmission system.
A Framework for Evaluating Earthquake Early Warning for an Infrastructure Network: An Idealized Case Study of a Northern California Rail System

Earthquake early warning (EEW) systems provide a few to tens of seconds of warning before shaking hits a site. Despite the recent rapid developments of EEW systems around the world, the optimal alert response strategy and the practical benefit of using EEW are still open-ended questions, especially in areas where EEW systems are new or have not yet been deployed. Here, we use a case study of a rail system in California’s San Francisco Bay Area to explore potential uses of EEW for rail systems. Rail systems are of particular interest not only because they are important lifeline infrastructure and a common application for EEW around the world, but also because their geographically broad yet networked infrastructure makes them almost uniquely well suited for utilizing EEW. While the most obvious potential benefit of EEW to the railway is to prevent derailments by stopping trains before the arrival of shaking, the lead time for warnings is usually not long enough to significantly reduce a train’s speed. In reality, EEW’s greatest impact is preventing derailment by alerting trains to slow down or stop before they encounter damaged track. We perform cost-benefit analyses of different decision-making strategies for several EEW system designs to find an optimal alerting strategy. On-site EEW provides better outcomes than source-parameter-based EEW when warning at a threshold of 120 gal (the level of shaking at which damage might occur) regardless of false alarm tolerance. A source-parameter-based EEW system with a lower alerting threshold (e.g., 40 gal) can reduce the exposure to potentially damaged track compared to an on-site system alerting at 120 gal, but a lower alerting threshold comes at the cost of additional precautionary system stops. The optimal EEW approach for rail systems depends strongly on the ratio of the cost of stopping the system unnecessarily to the potential loss from traversing damaged tracks.
Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation

Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high-dimensional state or function approximation. Quantum–classical hybrid algorithms have been proposed in recent years for this purpose under the noisy intermediate-scale quantum computer (NISQ) environment. Under this scheme, the role played by the classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent-based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presenting the same or even higher test accuracy. Meanwhile, the proposed theoretical scheme was successfully implemented on the 20-qubit quantum computer of IBM Q, ibmq_johannesburg. The experimental results reveal that the gate error, especially the CNOT gate error, strongly affects the derived gradient accuracy. The regression accuracy performed on the IBM Q becomes lower with the increase in the number of measurement shot times due to the accumulated gate noise error.
Variational graph autoencoders for multiview canonical correlation analysis

We present a novel approach for multiview canonical correlation analysis based on a variational graph neural network model. We propose a nonlinear model which takes into account the available graph-based geometric constraints while being scalable to large-scale datasets with multiple views. This model combines the probabilistic interpretation of CCA with an autoencoder architecture based on graph convolutional neural network layers. Experiments with the proposed method are conducted on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state-of-the-art multiview representation learning techniques, in addition to being scalable and robust to instances with missing views.
Universal Approach to Solution of Optimization Problems by Symbolic Regression

Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms using arithmetic operations of multiplication and addition are not used there. The search of optimal solution is performed on the space of codes. It is proposed that the principle of small variations of basic solution be applied as a universal approach to create search algorithms. Small variations cause a neighborhood of a potential solution, and the solution is searched for within this neighborhood. The concept of inheritance property is introduced. It is shown that for non-Euclidean search space, the application of evolution and small variations of possible solutions is effective. Examples of using the principle of small variation of basic solution for different symbolic regression methods are presented.
Digital Literacy Status of Turkish Teachers

The aim of this study is to determine the digital literacy status of Turkish teachers and to examine them in terms of various variables.In line with this purpose, the digital literacy status of Turkish teachers was determined and examined whether these situations differ according to variables such as gender, age, years of service in the profession, technology usage level, daily time of internet usage, using distance education platforms and social media.Scanning model was used in the research.The "Digital Literacy Scale" which was developed by Ng (2012) and adapted by Hamutoğlu, Güngören, Uyanık and Erdoğan (2017) was used as the data collection tool.Study group of the research consist of 188 Turkish language teachers who work actively in public schools in Turkey.The data obtained from the Digital Literacy Scale in the research were analyzed with the SPSS 21 package program.As a result of the research, it was found that Turkish teachers' digital literacy levels are high; it was determined that digital literacy levels indicate significant differences according to variables of age, years of service in the profession, technology usage level, daily internet usage time, using distance education platforms and social media.The fact that digital literacy levels decline as participants' age and years of service increase and also those who have high digital literacy levels use distance education platforms and social media more effectively are important results of the research.
Robustness Verification of Quantum Classifiers

Abstract Several important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers. However, quantum noise is a major obstacle to the practical implementation of quantum machine learning. In this work, we define a formal framework for the robustness verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. In particular, this algorithm can find adversarial examples during checking. Our approach is implemented on Google’s TensorFlow Quantum and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises, derived from the surrounding environment. The effectiveness of our robust bound and algorithm is confirmed by the experimental results, including quantum bits classification as the “Hello World” example, quantum phase recognition and cluster excitation detection from real world intractable physical problems, and the classification of MNIST from the classical world.
Inference Time Style Control for Summarization

How to generate summaries of different styles without requiring corpora in the target styles, or training separate models?We present two novel methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model.( 1) Decoder state adjustment instantly modifies decoder final states with externally trained style scorers, to iteratively refine the output against a target style.(2) Word unit prediction constrains the word usage to impose strong lexical control during generation.In experiments of summarizing with simplicity control, automatic evaluation and human judges both find our models producing outputs in simpler languages while still informative.We also generate news headlines with various ideological leanings, which can be distinguished by humans with a reasonable probability.
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network

Relational triple extraction is a crucial task for knowledge graph construction.Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions.This will lead to serious incompleteness of the constructed knowledge graphs.Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations.Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world.In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples.To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory.To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network.We conduct experiments on several benchmark datasets, and the results prove the validity of our method.
Improving Generation and Evaluation of Visual Stories via Semantic Consistency

Story visualization is an underexplored task that falls at the intersection of many important research directions in both computer vision and natural language processing.In this task, given a series of natural language captions which compose a story, an agent must generate a sequence of images that correspond to the captions.Prior work has introduced recurrent generative models which outperform text-to-image synthesis models on this task.However, there is room for improvement of generated images in terms of visual quality, coherence and relevance.We present a number of improvements to prior modeling approaches, including (1) the addition of a dual learning framework that utilizes video captioning to reinforce the semantic alignment between the story and generated images, (2) a copy-transform mechanism for sequentiallyconsistent story visualization, and (3) MARTbased transformers to model complex interactions between frames.We present ablation studies to demonstrate the effect of each of these techniques on the generative power of the model for both individual images as well as the entire narrative.Furthermore, due to the complexity and generative nature of the task, standard evaluation metrics do not accurately reflect performance.Therefore, we also provide an exploration of evaluation metrics for the model, focused on aspects of the generated frames such as the presence/quality of generated characters, the relevance to captions, and the diversity of the generated images.We also present correlation experiments of our proposed automated metrics with human evaluations. 1
Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System

We introduce a robust and light-weight multi-image super-resolution restoration (SRR) method and processing system, called OpTiGAN, using a combination of a multi-image maximum a posteriori approach and a deep learning approach. We show the advantages of using a combined two-stage SRR processing scheme for significantly reducing inference artefacts and improving effective resolution in comparison to other SRR techniques. We demonstrate the optimality of OpTiGAN for SRR of ultra-high-resolution satellite images and video frames from 31 cm/pixel WorldView-3, 75 cm/pixel Deimos-2 and 70 cm/pixel SkySat. Detailed qualitative and quantitative assessments are provided for the SRR results on a CEOS-WGCV-IVOS geo-calibration and validation site at Baotou, China, which features artificial permanent optical targets. Our measurements have shown a 3.69 times enhancement of effective resolution from 31 cm/pixel WorldView-3 imagery to 9 cm/pixel SRR.
Machine Learning Versus Deep Learning Performances on the Sentiment Analysis of Product Reviews

At this current digital era, business platforms have been drastically shifted toward online stores on internet. With the internet-based platform, customers can order goods easily using their smart phones and get delivery at their place without going to the shopping mall. However, the drawback of this business platform is that customers do not really know about the quality of the products they ordered. Therefore, such platform service often provides the review section to let previous customers leave a review about the received product. The reviews are a good source to analyze customer's satisfaction. Business owners can assess review trend as either positive or negative based on a feedback score that customers had given, but it takes too much time for human to analyze this data. In this research, we develop computational models using machine learning techniques to classify product reviews as positive or negative based on the sentiment analysis. In our experiments, we use the book review data from amazon.com to develop the models. For a machine learning based strategy, the data had been transformed with the bag of word technique before developing models using logistic regression, naïve bayes, support vector machine, and neural network algorithms. For a deep learning strategy, the word embedding is a technique that we used to transform data before applying the long short-term memory and gated recurrent unit techniques. On comparing performance of machine learning against deep learning models, we compare results from the two methods with both the preprocessed dataset and the non-preprocessed dataset. The result is that the bag of words with neural network outperforms other techniques on both non-preprocess and preprocess datasets.
Weighted statistical binary patterns for facial feature representation

Abstract We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k -th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.
Shuffled-token Detection for Refining Pre-trained RoBERTa

State-of-the-art transformer models have achieved robust performance on a variety of NLP tasks.Many of these approaches have employed domain agnostic pre-training tasks to train models that yield highly generalized sentence representations that can be fine-tuned for specific downstream tasks.We propose refining a pre-trained NLP model using the objective of detecting shuffled tokens.We use a sequential approach by starting with the pre-trained RoBERTa model and training it using our approach.Applying random shuffling strategy on the word-level, we found that our approach enables the RoBERTa model achieve better performance on 4 out of 7 GLUE tasks.Our results indicate that learning to detect shuffled tokens is a promising approach to learn more coherent sentence representations. 1
Intrinsic mechanisms for drive-dependent Purcell decay in superconducting quantum circuits

We develop an approach to understanding intrinsic mechanisms that cause the ${T}_{1}$-decay rate of a multilevel superconducting qubit to depend on the photonic population of a coupled, detuned cavity. Our method yields simple analytic expressions for both the coherently driven or thermally excited cases, which are in good agreement with full master equation numerics, and also facilitates direct physical intuition. It also predicts several interesting phenomena. In particular, we find that in a wide range of settings, the cavity-qubit detuning controls whether a nonzero photonic population increases or decreases qubit Purcell decay. Our method combines insights from a Keldysh treatment of the system, and Lindblad perturbation theory.
Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models

Storygeneration is an open-ended and subjective task, which poses a challenge for evaluating story generation models.We present CHOOSE YOUR OWN ADVENTURE, a collaborative writing setup for pairwise model evaluation.Two models generate suggestions to people as they write a short story; we ask writers to choose one of the two suggestions, and we observe which model's suggestions they prefer.The setup also allows further analysis based on the revisions people make to the suggestions.We show that these measures, combined with automatic metrics, provide an informative picture of the models' performance, both in cases where the differences in generation methods are small (nucleus vs. top-k sampling) and large (GPT2 vs. Fusion models).