Reviewerly/exHarmony huggingface dataset:
Swin-HSTPS: Research on Target Detection Algorithms for Multi-Source High-Resolution Remote Sensing Images

Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations.
Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis

Early detection of breast cancer is a powerful tool towards decreasing its socioeconomic burden. Although, artificial intelligence (AI) methods have shown remarkable results towards this goal, their "black box" nature hinders their wide adoption in clinical practice. To address the need for AI guided breast cancer diagnosis, interpretability methods can be utilized. In this study, we used AI methods, i.e., Random Forests (RF), Neural Networks (NN) and Ensembles of Neural Networks (ENN), towards this goal and explained and optimized their performance through interpretability techniques, such as the Global Surrogate (GS) method, the Individual Conditional Expectation (ICE) plots and the Shapley values (SV). The Wisconsin Diagnostic Breast Cancer (WDBC) dataset of the open UCI repository was used for the training and evaluation of the AI algorithms. The best performance for breast cancer diagnosis was achieved by the proposed ENN (96.6% accuracy and 0.96 area under the ROC curve), and its predictions were explained by ICE plots, proving that its decisions were compliant with current medical knowledge and can be further utilized to gain new insights in the pathophysiological mechanisms of breast cancer. Feature selection based on features' importance according to the GS model improved the performance of the RF (leading the accuracy from 96.49% to 97.18% and the area under the ROC curve from 0.96 to 0.97) and feature selection based on features' importance according to SV improved the performance of the NN (leading the accuracy from 94.6% to 95.53% and the area under the ROC curve from 0.94 to 0.95). Compared to other approaches on the same dataset, our proposed models demonstrated state of the art performance while being interpretable.
Recommended method study based on incorporating complex network ripple net

The RippleNet network models user preferences and is well applied in the recommended system. But Ripplenet didn't take into account the weight of entities in the knowledge graph, resulting in the inaccurate recommendation results. A RippleNet model incorporating the influence of the complex network nodes is proposed. After constructing the complex networks based on the knowledge maps, the maximum subnet model is extracted, the influence of the nodes in the map network is calculated, and the weight of the nodes is added to the RippleNet model as an entity. The experimental results showed that the present method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, made up for the problem that no entity influence was considered in the RippleNet network, and made the recommended results more in line with users' expectations.
Organization Benefit as an Outcome of Organizational Security Adoption: The Role of Cyber Security Readiness and Technology Readiness

The increasing number of cyber-attacks has become a serious threat to organizations, organizations that are not prepared to face cyber-attacks on their organizational resources will experience huge losses and reduce organizational performance. It is a big challenge for organizations to combat cyber-attacks by improving cyber security, but there is still little research examining the factors that affect an organization’s cyber security readiness from a holistic point of view. This study integrates a framework based on technology, organization, environment, and technology readiness to examine various factors that affect cyber security readiness in organizations, as well as their impact on organizational performance, where the impact is in the form of tangible and intangible benefits. This study proposes 4 hypotheses to test the framework that has been built. A total of 260 data have been validated from an online questionnaire survey given to organizations and companies. This study applied quantitative approach, while the main method used was SEM-PLS and the software involved was SmartPLS V2. The results of the study indicate that the overall hypotheses proposed have a significant impact, cyber security readiness and technology have a positive impact on organizational security performance, which in turn has an impact on the intangible benefits and tangible benefits. The results of this study can be used by organizations as a guide in improving cyber security to achieve superior performance in organizations and improve understanding of references related to cyber security in organizations. Meanwhile, this research has impact to the society because the good cooperation and good organization will be achieved. it also increases the social cooperation responsibility.
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments.Meanwhile, efforts to improve generation models tend to depend on simple n-gram overlap metrics (e.g., BLEU, ROUGE).We argue that new advances on models and metrics should each more directly benefit and inform the other.We therefore propose a generalization of leaderboards, bidimensional leaderboards (BILLBOARDs), that simultaneously tracks progress in language generation models and metrics for their evaluation.Unlike conventional unidimensional leaderboards that sort submitted systems by predetermined metrics, a BILLBOARD accepts both generators and evaluation metrics as competing entries.A BILLBOARD automatically creates an ensemble metric that selects and linearly combines a few metrics based on a global analysis across generators.Further, metrics are ranked based on their correlation with human judgments.We release four BILLBOARDs for machine translation, summarization, and image captioning. 1 We demonstrate that a linear ensemble of a few diverse metrics sometimes substantially outperforms existing metrics in isolation.Our mixed-effects model analysis shows that most automatic metrics, especially the reference-based ones, overrate machine over human generation, demonstrating the importance of updating metrics as generation models become stronger (and perhaps more similar to humans) in the future.
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstream tasks. Previous debiasing methods focus mainly on modeling bias and only implicitly consider semantic information while completely overlooking the complex underlying causal structure among bias and semantic components. To address these issues, we propose a novel methodology that leverages a causal inference framework to effectively remove gender bias. The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings. Our comprehensive experiments show that the proposed method achieves state-of-the-art results in gender-debiasing tasks. In addition, our methods yield better performance in word similarity evaluation and various extrinsic downstream NLP tasks.
Causal Augmentation for Causal Sentence Classification

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning.This is worrying since minor linguistic differences in causal sentences can have disparate meanings.Therefore, we propose the generation of counterfactual causal sentences by creating contrast sets (Gardner et al., 2020) to be included during model training.We experimented on two model architectures and predicted on two outof-domain corpora.While our strengthening schemes proved useful in improving model performance, for negation, regular edits were insufficient.Thus, we also introduce heuristics like shortening or multiplying root words of a sentence.By including a mixture of edits when training, we achieved performance improvements beyond the baseline across both models, and within and out of corpus' domain, suggesting that our proposed augmentation can also help models generalize.
An IoT-Enabled Information System for Smart Navigation in Museums

The Internet of Things (IoT) is a new paradigm that connects objects to provide seamless communication and contextual information to anyone, anywhere, at any time (AAA). These Internet-of-Things-enabled automated objects interact with visitors to present a variety of information during museum navigation and exploration. In this article, a smart navigation and information system (SNIS) prototype for museum navigation and exploration is developed, which delivers an interactive and more exciting museum exploration experience based on the visitor's personal presence. The objects inside a museum share the information that assist and navigate the visitors about the different sections and objects of the museum. The system was deployed inside Chakdara Museum and experimented with 381 users to achieve the results. For results, different users marked the proposed system in terms of parameters such as interesting, reality, ease of use, satisfaction, usefulness, and user friendly. Of these 381 users, 201 marked the system as most interesting, 138 marked most realistic, 121 marked it as easy-in-use, 219 marked it useful, and 210 marked it as user friendly. These statistics prove the efficiency of SNIS and its usefulness in smart cultural heritage, including smart museums, exhibitions and cultural sites.
Effectful program distancing

Semantics is traditionally concerned with program equivalence, in which all pairs of programs which are not equivalent are treated the same, and simply dubbed as incomparable. In recent years, various forms of program metrics have been introduced such that the distance between non-equivalent programs is measured as an element of an appropriate quantale. By letting the underlying quantale vary as the type of the compared programs become more complex, the recently introduced framework of differential logical relations allows for a new contextual form of reasoning. In this paper, we show that all this can be generalised to effectful higher-order programs, in which not only the values , but also the effects computations produce can be appropriately distanced in a principled way. We show that the resulting framework is flexible, allowing various forms of effects to be handled, and that it provides compact and informative judgments about program differences.
Satellite data based abundance mapping of mafic and ultramafic rocks in Mettupalayam, Tamil Nadu, India

The mafic and ultramafic rocks of Mettupalayam belong to the southern granulite terrain of India, which is concomitant with vital economic resources. The advantage of Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) data for mapping the litho units are exploited well here for differentiating the rock units with the aid of band combination (1, 3, 6), principal component analysis (5, 1, 6) and band ratioed band combination (2/3, 3/2, 1/5 and (9–8)/1, (8–6)/2, and (9–6)/3). As part of the field study, the collection of samples and ground control points were carried out and in addition to that, the generation of laboratory reflectance spectra for samples was achieved. The Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were performed using ASTER data with the aid of spectra obtained from the laboratory conditions to demarcate the abundance of mafic and ultramafic rocks of the area. The XRF method was used to retrieve the major oxides of the field-collected samples and the spectral absorption characters are validated with it. The results show a vibrant interpretation of the litho units.
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization

Lexical normalization is the task of transforming an utterance into its standardized form.This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation.Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching.Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages.However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup.The MUL-TILEXNORM shared task sets out to fill this gap.We provide the largest publicly available multilingual lexical normalization benchmark including 12 language variants.We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation.As extrinsic evaluation, we use dependency parsing and part-ofspeech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies.The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions.The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin.Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.
MFAQ: a Multilingual FAQ Dataset

In this paper, we present the first multilingual FAQ dataset publicly available.We collected around 6M FAQ pairs from the web, in 21 different languages.Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics.We adopt a similar setup as Dense Passage Retrieval (DPR) (Karpukhin et al., 2020) and test various bi-encoders on this dataset.Our experiments reveal that a multilingual model based on XLM-RoBERTa (Conneau et al., 2019) achieves the best results, except for English.Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones.Our qualitative analysis reveals the brittleness of the model on simple word changes.We publicly release our dataset 1 , model 2 and training script 3 .
Weather Radar Image Superresolution Using a Nonlocal Residual Network

Accurate and high-resolution weather radar images reflecting detailed structure information of radar echo are vital for analysis and forecast of extreme weather. Typically, this is performed by using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated value regardless of the large-scale context feature of weather radar images. Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. The proposed network mainly consists of several nonlocal residual blocks (NLRB), which combine short skip connection (SSC) and nonlocal operation to train the deep network and capture large-scale context information. In addition, long skip connection (LSC) added in the network avoids learning low-frequency information, making the network focus on high-level features. Extensive experiments of ×2 and ×4 super-resolution reconstruction demonstrate that NLRN achieves superior performance in terms of both quantitative evaluation metrics and visual quality, especially for the reconstruction of the edge and detailed information of the weather radar echo.
A Security-Aware Framework for Designing Industrial Engineering Processes

Modern critical infrastructures are complex Cyber-Physical-Systems (CPS) that tightly integrate physical processes with Information and Communication Technology components.Numerous safety mishaps and security attacks in such systems have demonstrated the need to ensure their safety and security from early design stages.Research on CPS has mostly focused on securing existing, implemented industrial systems, while safety and security consideration during the design stages of modern industrial infrastructures has largely gone unnoticed.In this paper, we present a framework that extends previous, preliminary work on the integration of security in industrial engineering design practices, and provide an algorithmic approach that effectively reduces risk during industrial system design lifecycles.We achieve this by analyzing flows of materials and information related to physical processes using three steps: (1) Identifying critical components and flows, (2) prioritizing flows based on their ties to high risk and importance in terms of dependencies, and (3) classifying system components based on their influence on the overall industrial system.To do that, we utilize (i) material flow networks (MFN) for modelling/designing the physical system (ii) dependency risk graphs for analyzing networks dependencies and assessing the system, in terms of risk, (iii) graph minimum spanning trees, and (iv) network centrality metrics.To evaluate our approach, we model and assess the production chain corresponding to an oil refinery plant's Liquefied Petroleum Gas (LPG) purification process.Preliminary findings demonstrate the complex dependencies between cybersecurity vulnerabilities and system safety.
Neuralizing Regular Expressions for Slot Filling

Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses.In this paper, we study the integration of the two approaches for the slot filling task by converting regular expressions into neural networks.Specifically, we first convert regular expressions into a special form of finite-state transducers, then unfold its approximate inference algorithm as a bidirectional recurrent neural model that performs slot filling via sequence labeling.Experimental results show that our model has superior zero-shot and few-shot performance and stays competitive when there are sufficient training data.
Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.
Defending Industry 4.0: An Enhanced Authentication Scheme for IoT Devices

To address the security concerns of Industry 4.0, recently, Garg <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a lightweight authentication protocol, and Akram <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> showed some of its security drawbacks. We continue this line by exposing how Garg <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s protocol suffers from noninvasive and invasive attacks. First, we explain that a passive attacker can trace any two communicating nodes to compromise their location privacy. Next, we show that an active though noninvasive adversary can compromise the integrity of the exchanged messages without being detected and run a de-synchronization attack. Besides, the adversary can extract any shared session key from any pair of nodes in the protocol. We named this attack a pandemic session key disclosure attack, and its consequences are more harmful than the impersonation of a compromised node. Finally, we disclose how the proposed scheme does not guarantee the privacy protection for the keys when we assume an honest but curious server. To overcome those existing security flaws, we finally propose a revised protocol called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TARDIGRADE</monospace> . First, our informal analysis, and then, our formal security analysis using the real-or-random model shows that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TARDIGRADE</monospace> provides the desired security, and likewise, our performance analysis confirms a reasonable cost compared with Garg <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s protocol.