Reviewerly/exHarmony huggingface dataset:
Optimal Decision Trees for Nonlinear Metrics

Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes–Mallows index, are often used to evaluate the performance of machine learning models, in particular, when facing imbalanced datasets that contain more samples of one class than the other. Recent optimal decision tree algorithms have shown remarkable progress in producing trees that are optimal with respect to linear criteria, such as accuracy, but unfortunately nonlinear metrics remain a challenge. To address this gap, we propose a novel algorithm based on bi-objective optimisation, which treats misclassifications of each binary class as a separate objective. We show that, for a large class of metrics, the optimal tree lies on the Pareto frontier. Consequently, we obtain the optimal tree by using our method to generate the set of all nondominated trees. To the best of our knowledge, this is the first method to compute provably optimal decision trees for nonlinear metrics. Our approach leads to a trade-off when compared to optimising linear metrics: the resulting trees may be more desirable according to the given nonlinear metric at the expense of higher runtimes. Nevertheless, the experiments illustrate that runtimes are reasonable for majority of the tested datasets.
Paradata in Documentation Standards and Recommendations for Digital Archaeological Visualisations

Abstract Digitalisation of research data and massive efforts to make it findable, accessible, interoperable, and reusable has revealed that in addition to an eventual lack of description of the data itself (metadata), data reuse is often obstructed by the lack of information about the datamaking and interpretation (i.e. paradata). In search of the extent and composition of categories for describing processes, this article reviews a selection of standards and recommendations frequently referred to as useful for documenting archaeological visualisations. It provides insight into 1) how current standards can be employed to document provenance and processing history (i.e. paradata), and 2) what aspects of the processing history can be made transparent using current standards and which aspects are pushed back or hidden. The findings show that processes are often either completely absent or only partially addressed in the standards. However, instead of criticising standards for bias and omissions as if a perfect description of everything would be attainable, the findings point to the need for a comprehensive consideration of the space a standard is operating in (e.g. national heritage administration or international harmonisation of data). When a standard is used in a specific space it makes particular processes, methods, or tools transparent. Given these premises, if the standard helps to document what needs to be documented (e.g. paradata), and if it provides a type of transparency required in a certain space, it is reasonable to deem the standard good enough for that purpose.
Predicting patterns of similarity among abstract semantic relations.

Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
High-temperature digital image correlation techniques for full-field strain and crack length measurement on ceramics at 1200°C: Optimization of speckle pattern and uncertainty assessment

High-temperature mechanical tests coupled with Digital Image Correlation (DIC) on ceramics which exhibit rather low level of strain require to overcome extreme experimental conditions that usually can reduce significantly measurement accuracy. Thermal resistance of speckle pattern, black body radiation and heat haze are three main concerns, which should thus be taken into account while designing a high-temperature image acquisition setup. In this aim, an experimental procedure has been specifically designed in order to minimize the three above-mentioned disturbances. The main objective of this study is to select a suitable high-temperature resistant speckle pattern for mechanical characterization at 1200 °C (or above) on refractory ceramics. Most of tested speckle patterns were performed with white alumina adhesive and dark ceramic grains (silicon carbide or brown fused alumina). Different grain sizes of silicon carbide were tested. At first, different speckle patterns are compared in terms of DIC strain measurement uncertainty by discussing speckle features, some main DIC parameters and two image pre-treatments (low pass filter, image size reduction). Then, these speckle patterns are tested to analyse fracture behaviour of refractories through a Brazilian test. An enhanced digital image correlation technique (2P-DIC), dedicated to monitor the fracture behaviour, is applied to study the evolution of crack length. The best representation of crack progression has been achieved for sample surface covered with a fine SiC powder ranging from 50 to 100 µm. It is then possible to compare the fracture behaviour between 1200 °C and 20 °C and to show that the refractory exhibits more crack branching at 1200 °C in comparison with behaviour at room temperature.
Feature Recognition of Human Motion Behavior Based on Depth Sequence Analysis

The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection-based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long- and short-term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low-power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.
Three-dimensional measurement method of color fringe projection based on an improved three-step phase-shifting method

A three-dimensional (3D) measurement method of color fringe projection based on an improved three-step phase-shifting method is proposed. The color fringe pattern is encoded by two cosine fringe patterns with the same frequency but different shifting phase and a uniform gray flat image into three color channels R, G, and B. Although the measurement speed of the traditional three-step phase-shifting method can meet the requirements of measuring 3D objects, it makes the noise and inaccuracy of the captured images increase, and each image will cause measurement error. Therefore, we improve the three-step phase-shifting method and introduce the Hilbert transform into the three-step phase-shift method. The DC component of the fringe pattern is obtained by using the Hilbert transform principle, and the third fringe pattern in the three-step phase-shift method is replaced by the captured light intensity distribution of the DC component. The phase difference of the other two fringe patterns is fixed as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>π<!-- π --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> by the Hilbert transform. The improved three-step phase-shifting method is used to obtain the phase information of the deformed color fringe image, and then the phase-unwrapping algorithm is used to obtain the phase distribution information of the whole field. The results show that the improved method can not only accurately calculate the phase information but also greatly improve the measurement speed and quality.
Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.
Joint Image Dehazing and Super-Resolution: Closed Shared Source Residual Attention Fusion Network

In real world, due to the existence of floating particles such as smoke and dust in the atmosphere, images taken by camera are susceptible to different levels of blurring, low contrast, color distortion and visual degradation, which would be amplified when enlarging the image resolution.Therefore, it is a new trend to join the image dehazing and image super-resolution tasks.To generate sharp high-resolution images from low-resolution images with severe haze, a common way is to connect the dehazing network and the super-resolution network in series.However, two-stage joint approach easily introduces blurring artifacts and is time-consuming.In addition, although there are a few existing one-stage methods, their training modes are relatively complicated and the restoration effects on some texture details are still blurry.In this paper, we focus on exploring one-stage joint model and propose a back-projection network based on shared source attention fusion (BPSAF), which forms a closed frame through back-projection mechanism.BPSAF can remove non-uniform haze and extend the resolution simultaneously.Specifically, a shared source attention fusion (SAF) module is presented to fuse high-frequency information of different level features using shared source skip connections more effectively, which filters out abundant thin haze and low-frequency information from merged images.To enhance the definition of restored images, a feedback error correction module based on error attention mechanism (FEC-EA) is designed to further correct distorted texture details by eliminating the feedback error from initial super-resolution result to the input hazy image.Experimental results demonstrate that our back-projection framework is superior to other existing methods in terms of quantitative indicators and visual quality.
IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News

In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection -Shared Task 1 at CASE 2021. 1 The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish).To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder.We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages.Our systems achieve an average F 1 score of 81.2.Out of thirteen subtask-language tracks, our submissions rank 1 st in nine and 2 nd in four tracks.
A face recognition software framework based on principal component analysis

Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.
Learn the Highest Label and Rest Label Description Degrees

Although Label Distribution Learning (LDL) has found wide applications in varieties of classification problems, it may face the challenge of objective mismatch -- LDL neglects the optimal label for the sake of learning the whole label distribution, which leads to performance deterioration. To improve classification performance and solve the objective mismatch, we propose a new LDL algorithm called LDL-HR. LDL-HR provides a new perspective of label distribution, \textit{i.e.}, a combination of the \textbf{highest label} and the \textbf{rest label description degrees}. It works as follows. First, we learn the highest label by fitting the degenerated label distribution and large margin. Second, we learn the rest label description degrees to exploit generalization. Theoretical analysis shows the generalization of LDL-HR. Besides, the experimental results on 18 real-world datasets validate the statistical superiority of our method.
PhonemeBERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript

Recent years have witnessed significant improvement in ASR systems to recognize spoken utterances. However, it is still a challenging task for noisy and out-of-domain data, where substitution and deletion errors are prevalent in the transcribed text. These errors significantly degrade the performance of downstream tasks. In this work, we propose a BERT-style language model, referred to as PhonemeBERT, that learns a joint language model with phoneme sequence and ASR transcript to learn phonetic-aware representations that are robust to ASR errors. We show that PhonemeBERT can be used on downstream tasks using phoneme sequences as additional features, and also in low-resource setup where we only have ASR-transcripts for the downstream tasks with no phoneme information available. We evaluate our approach extensively by generating noisy data for three benchmark datasets - Stanford Sentiment Treebank, TREC and ATIS for sentiment, question and intent classification tasks respectively. The results of the proposed approach beats the state-of-the-art baselines comprehensively on each dataset.
Adoption of Digital Pathology in Developing Countries: From Benefits to Challenges

Digital pathology and the use of artificial intelligence constitute undisputedly the future of modern pathology.The outcomes and benefits of the whole slide imaging are beyond the scope of traditional microscopy, which the pathologists were using for decades.COVID-19 pandemic has further highlighted the importance of digital pathology as it offers the pathologists to work from their place of comfort and bridges the gap of physical barriers.In addition to the many advantages, there are certain limitations and challenges, which have to be overcomed particularly in the developing world.The major issue is the cost of scanners and technical support and training of staff.However, despite all these problems and challenges that exist, these can be resolved with the passage of time, where the role of world leader organisations will be of great importance in resolving these challenges.
Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.
Ground-mounted photovoltaic solar parks promote land surface cool islands in arid ecosystems

Despite the growth rates of photovoltaic solar parks, their potential to alter land surface temperature remains unclear. Yet, resolving temperature impacts is pivotal to understanding the implications for ecosystem function, and the consequences for society due to perturbations to ecosystem service supply and natural capital stores. Here, for the first time, we demonstrate the existence of a solar park land surface temperature cool island effect that extends beyond the solar park boundary, using Landsat satellite imagery. The cool island effect was quantified for two large ground-mounted solar parks, Longyangxia (850 megawatts) in China and Stateline (300 megawatts) in the United States of America, where the effect was confirmed using field-based measurements. At both sites, the cooling extended up to 730 m away from the solar park boundary with localized reductions in LST of up to 2.3 °C. These cool islands could affect large areas of the land surface as solar parks proliferate across the world, with notable positive or negative impacts on ecosystem function. Given the potential implications for ecosystem processes, including carbon feedbacks to climate change and the carbon intensity of the electricity produced, improved understanding of solar park LST impacts is required. Specifically, this knowledge is needed to inform the development of sustainable land use and energy policies considering the rapid growth of solar park developments.
Lightweight Dual-Stream Residual Network for Single Image Super-Resolution

The deep convolutional neural network has achieved great success in the Single Image Super-resolution task. It is obviously that among the well-known super-resolution methods, the deep learning-based algorithms show the most advanced performance. However, the most advanced algorithms currently use complex networks with a large number of parameters, which makes it difficult to apply deep learning algorithms on mobile devices. To solve this problem, we propose a lightweight dual-residual network(LDRN) for single image super-resolution, which has better reconstruction quality than most current advanced lightweight algorithms. Due to its fewer parameters and computational expense, real-time and mobile applications of our networks can be easily realized. On the basis of the residual module, we propose a new residual unit, which uses two depthwise separable(DW) convolution to obtain better balance between feature extraction capacity and lightweight performance. We further design a dual-stream residual block, which contains a multiplication branch and an addition branch. The dual-stream residual block can improve the reconstruction performance more effectively than expanding the network width. In addition, we also designed a new up-sampling module to simplify the previous up-sampling methods. Extensive experimental results show that our network has better reconstruction performance and lightweight performance than most existing state-of-the-art algorithms. Our code is available at https://github.com/Jiangyichun-cust/pytorch-LDRN.