Reviewerly/exHarmony huggingface dataset:
Alzette: A 64-Bit ARX-box

S-boxes are the only source of non-linearity in many symmetric primitives. While they are often defined as being functions operating on a small space, some recent designs propose the use of much larger ones (e.g., 32 bits). In this context, an S-box is then defined as a subfunction whose cryptographic properties can be estimated precisely. We present a 64-bit ARX-based S-box called Alzette, which can be evaluated in constant time using only 12 instructions on modern CPUs. Its parallel application can also leverage vector (SIMD) instructions. One iteration of Alzette has differential and linear properties comparable to those of the AES S-box, and two are at least as secure as the AES super S-box. As the state size is much larger than the typical 4 or 8 bits, the study of the relevant cryptographic properties of Alzette is not trivial. We further discuss how such wide S-boxes could be used to construct round functions of 64-, 128- and 256-bit (tweakable) block ciphers with good cryptographic properties that are guaranteed even in the related-tweak setting. We use these structures to design a very lightweight 64-bit block cipher (Crax) which outperforms SPECK-64/128 for short messages on micro-controllers, and a 256-bit tweakable block cipher (Trax) which can be used to obtain strong security guarantees against powerful adversaries (nonce misuse, quantum attacks).
Panoramic Convolutional Long Short-Term Memory Networks for Combat Intension Recognition of Aerial Targets

Aiming at the limitation that traditional methods for combat intention recognition of aerial targets are difficult to effectively capture the essential characteristics of intelligence information, we design a novel deep learning method, Panoramic Convolutional Long Short-Term Memory networks (PCLSTM), to improve the recognition ability. First, based on the characteristics of aerial target intelligence information, a panoramic convolutional layer is designed to extract the loosely coupled characteristics of intelligence information, and a time series pooling layer is designed to reduce the scale of neural network parameters on a large scale. Then, the temporal feature extraction capability of the LSTM layer and the depth feature mining capability of the traditional deep learning layer are combined to construct the PCLSTM neural network. Subsequently, the recognition performance of PCLSTM is analyzed by simulation experiments compared with standard deep net, convolutional neural network and LSTM network as benchmark models. Finally, PCLSTM was used to carry out simulation tests on different truncated data sets of original intelligence information, to analyze the optimal length of truncated data for different combat intention recognition. And then a reasonable aerial target combat intention recognition method is designed. The simulation results show that the method presented in this paper has theoretical significance and reference value for command decision-making.
Toward building recommender systems for the circular economy: Exploring the perils of the European Waste Catalogue

The growth in the number of industries aiming at more sustainable business processes is driving the use of the European Waste Catalogue (EWC). For example, the identification of industrial symbiosis opportunities, in which a user-generated item description has to be annotated with exactly one EWC tag from an a priori defined tag ontology. This study aims to help researchers understand the perils of the EWC when building a recommender system based on natural language processing techniques. We experiment with semantic enhancement (an EWC thesaurus) and the linguistic contexts of words (learned by Word2vec) for detecting term vector similarity in addition to direct term matching algorithms, which often fail to detect an identical term in the short text generated by users. Our in-depth analysis provides an insight into why the different recommenders were unable to generate a correct annotation and motivates a discussion on the current design of the EWC system.
Comparative Analysis and Design of Cybersecurity Maturity Assessment Methodology Using NIST CSF, COBIT, ISO/IEC 27002 and PCI DSS

Data or Information security in today's digital era is crucial in every organization that needs to pay attention. Management of organizational information is one of the components in realizing Good Corporate Governance. The measure of an adequate level of protection is an indicator of the cybersecurity awareness aspects of an organization's business processes in the short, medium, and long term, especially in the field that deals with information and communication technology (ICT). To make this happen, it requires a security standard that is appropriate and follows its needs to help organizations know the maturity level of cybersecurity in protecting its information security. The ABC organization is one of the Government agencies that manage the critical infrastructure and Indonesian digital economies. The organization has currently implemented several international security standards through its planning, implementation, evaluation document, and ICT activities.Â However, based on the national information security readiness assessment, information security management readiness results are still not optimal. In this study, an analysis of the NIST, ISO 27002, COBIT, and PCI DSS security standards has been carried out, which are ABC organizational security standards in managing ICT by assigned tasks and functions. Furthermore, the analysis result is used as materials for drafting a cybersecurity maturity framework through the four standard approaches that have become the basis for ICT management. The proposed concept of twenty-one integrated cybersecurity categories is expected to be a capital in measure ICT management performance in ABC organizations.
Benefits and risks of switching to distance learning in a pandemic

Introduction. The need to maintain social isolation in the spread of COVID-19 required educational organizations to implement accelerated measures to introduce distance learning. The purpose of this study is to assess the level of adaptation of students from Russian universities to new educational practices, the benefits and risks of distance learning in the COVID-19 pandemic. Materials and methods. The study was conducted from May to early July 2020 in the form of a questionnaire survey of senior students of the full-time and part-time modes of study at the Russian State Social University (N=146; girls – 58.9%, boys – 41.1%). Results. The results of the study show the success of students’ adaptation to distance learning; most of the respondents did not experience any difficulties in organizing the educational process remotely. Students’ problems relate to a change in the format of interaction with a teacher, an increase in the teaching load, an insufficient level of material-and-technical equipment of the workplace. The results of the study showed that in the context of distance learning, the emphasis in the teacher’s work shifted onto the demonstration of videos (27.4%). Only 9.6% of the respondents indicated that the teacher used business games at lessons; 5.5% – virtual reality technologies; presentations were mentioned by 1.4% of the respondents. Lack of “live communication” with the teacher, poor quality of feedback with him/her, decreased motivation and interest in learning are the key limitations of distance learning effectiveness. Students’ interest in distance learning is determined by such parameters as the teacher’s charisma (79.5%), his/her ability to keep the listeners’ attention (57.5%), the level of use of digital innovative technologies (72.6%). Conclusions. According to the research results, the authors come to the conclusion that the compensators of the identified problems can be: the widespread use of information and communication technologies in the educational process, increasing the level of interactivity of the lessons, flexibility and an individual approach in organizing training, the teacher’s personal interest, support for student feedback.
Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer

PURPOSE Pancreatic cancer is an aggressive malignancy with patients often experiencing nonspecific symptoms before diagnosis. This study evaluates a machine learning approach to help identify patients with early-stage pancreatic cancer from clinical data within electronic health records (EHRs). MATERIALS AND METHODS From the Optum deidentified EHR data set, we identified early-stage (n = 3,322) and late-stage (n = 25,908) pancreatic cancer cases over 40 years of age diagnosed between 2009 and 2017. Patients with early-stage pancreatic cancer were matched to noncancer controls (1:16 match). We constructed a prediction model using eXtreme Gradient Boosting (XGBoost) to identify early-stage patients on the basis of 18,220 features within the EHR including diagnoses, procedures, information within clinical notes, and medications. Model accuracy was assessed with sensitivity, specificity, positive predictive value, and the area under the curve. RESULTS The final predictive model included 582 predictive features from the EHR, including 248 (42.5%) physician note elements, 146 (25.0%) procedure codes, 91 (15.6%) diagnosis codes, 89 (15.3%) medications, and 9 (1.5%) demographic features. The final model area under the curve was 0.84. Choosing a model cut point with a sensitivity of 60% and specificity of 90% would enable early detection of 58% late-stage patients with a median of 24 months before their actual diagnosis. CONCLUSION Prediction models using EHR data show promise in the early detection of pancreatic cancer. Although widespread use of this approach on an unselected population would produce high rates of false-positive tests, this technique may be rapidly impactful if deployed among high-risk patients or paired with other imaging or biomarker screening tools.
Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.
Federated Learning with Compression: Unified Analysis and Sharp Guarantees

In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices with potentially unreliable or limited communication and heterogeneous data distributions. Two notable trends to deal with the communication overhead of federated algorithms are \emph{gradient compression} and \emph{local computation with periodic communication}. Despite many attempts, characterizing the relationship between these two approaches has proven elusive. We address this by proposing a set of algorithms with periodical compressed (quantized or sparsified) communication and analyze their convergence properties in both homogeneous and heterogeneous local data distributions settings. For the homogeneous setting, our analysis improves existing bounds by providing tighter convergence rates for both \emph{strongly convex} and \emph{non-convex} objective functions. To mitigate data heterogeneity, we introduce a \emph{local gradient tracking} scheme and obtain sharp convergence rates that match the best-known communication complexities without compression for convex, strongly convex, and nonconvex settings. We complement our theoretical results and demonstrate the effectiveness of our proposed methods by several experiments on real-world datasets.
Philosophy of education in a new key: Exploring new ways of teaching and doing ethics in education in the 21st century

Within the rough ground that is the field of education there is a complex web of ethical obligations: to prepare our students for their future work; to be ethical as educators in our conduct and teaching; to the ethical principles embedded in the contexts in which we work; and given the Southern context of this work, the ethical obligations we have to this land and its First Peoples. We put out a call to colleagues whose work has been concerned with the pedagogies of professional ethics, the ethical burdens of institutional injustice, and the application of ethical theory to education’s applied fields. In the responses we received it can be seen that ethical concerns in education are broad ranging, covering terrain varying from the preparation of preservice teachers, ethics in higher education, early childhood and care, educational leadership, relational and communicative ethics. Perhaps it could also be argued that this paper demonstrates Gibbon’s observation that ‘Assumptions about the particularity of this time as new and ripe with opportunity to make a difference through philosophy of education are not new and there’s much to learn from the persistence of wanting to imagine that they are’ (in Peters et al., 2020 Peters, M. A., Arndt, S., Tesar, M., Jackson, L., Hung, R., Mika, C., Ozolins, J. T., Teschers, C., Orchard, J., Buchanan, R., Madjar, A., Novak, R., Besley, T., Sturm, S., Roberts, P., & Gibbons, A (2020). Philosophy of education in a new key. Educational Philosophy and Theory, 1–22. https://doi.org/10.1080/00131857.2020.1759194 [Google Scholar], p. 17). However, while the field of ethics is perennially concerned with human relations and pedagogical interventions to improve these, the responses collected here show that educational ethics is far from static. Educational ethics is a field that continues to develop in response to changing contexts.
Cross-Lingual Abstractive Summarization with Limited Parallel Resources

Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources.Existing methods to do so often adopt sequenceto-sequence networks with multi-task frameworks.Such approaches apply multiple decoders, each of which is utilized for a specific task.However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to lowresource languages.To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting.Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the cross-lingual summarization (CLS) task.In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer.Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios.Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.
Underwater Image Restoration via Contrastive Learning and a Real-World Dataset

Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in recent decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions in capturing clear underwater images, no comprehensive evaluations have been conducted with regard to underwater image restoration. To address this gap, we constructed a large-scale real underwater image dataset, dubbed Heron Island Coral Reef Dataset (‘HICRD’), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employed an accurate water parameter (diffuse attenuation coefficient) to generate the reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Furthermore, we present a novel method for underwater image restoration based on an unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are both publicly available.
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction

Implicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event.However, most prior works focus on capturing direct relations between arguments and the event trigger.The lack of reasoning ability brings many challenges to the extraction of implicit arguments.In this work, we present a Frame-aware Event Argument Extraction (FEAE) learning framework to tackle this issue through reasoning in event frame-level scope.The proposed method leverages related arguments of the expected one as clues to guide the reasoning process.To bridge the gap between oracle knowledge used in the training phase and the imperfect related arguments in the test stage, we further introduce a curriculum knowledge distillation strategy to drive a final model that could operate without extra inputs through mimicking the behavior of a well-informed teacher model.Experimental results demonstrate FEAE obtains new state-ofthe-art performance on the RAMS dataset.
Sphalerite as a record of metallogenic information using multivariate statistical analysis: Constraints from trace element geochemistry

In this study, the dominant geological factor that influences the element distribution in sphalerite was determined and the behaviors of the elements under a single factor were investigated (e.g., temperature). To this end, two multivariate statistical analysis methods, namely, principal component analysis (PCA) and factor analysis (FA), were performed on a published sphalerite dataset containing 1336 analysis points from 52 deposits. The PCA produced five element clusters, which were each controlled by the five dominant factors identified by the FA (including magmatic-derived materials, temperature, pH, element remobilization, and dynamic recrystallization). Furthermore, the influence of temperature on the trace element composition of sphalerite was quantitatively expressed based on the corresponding homogenization temperature of fluid inclusions from 27 deposits, i.e., T FAS (°C) = (32.22 ± 1.88) × FAS + (245.49 ± 3.79) ( R a 2 = 0.918). The above research was then applied to the sphalerite from the Baoshan Cu-Pb-Zn deposit to reveal its metallogenic conditions. The results indicate that from the central-western mining districts to the northern mining districts, the contributions of the magmatic-derived materials, temperature, and f O 2 decrease, whereas the contributions of the basement-derived hydrothermal fluid and pH increase. • The element's content in sphalerite is determined predominantly by five factors. • The extent to which each factor affects the elements is quantified by factor analysis. • Not only Fe, Mn, Ga, Ge, and In, but also Sb is closely correlated with temperature. • The above correlations are strong enough to be used as a geothermometer. • The differences in the composition of sphalerite in the Baoshan deposit are caused by magma, temperature, and pH.
DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring

We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with simulated Gaussian noise, saturated pixels, or JPEG compression artifacts as well as real-world images. Moreover, we present detailed analyses of the benefit of the feature-based Wiener deconvolution and of the multi-scale cascaded feature refinement as well as the robustness of the proposed approach. Our extensive experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep Wiener deconvolution network</i> facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
Are customers happy with robot service? Investigating satisfaction with robot service restaurants during the COVID-19 pandemic

The COVID-19 virus has led to a rapid increase in demand for robot service restaurants. However, given the novelty of both, little is known regarding the impact of this technology on consumer attitudes in the time of the pandemic. Using a sample of customers with dining experiences in Muscat-based robot service restaurants, the current study investigates the factors that affect experience satisfaction and experience extension. The results demonstrate that perceived usefulness, speed of service, and experience novelty directly influence experience satisfaction, while perceived enjoyment and experience satisfaction directly influence experience extension. Moreover, novelty seeking moderates the effects of experience novelty and perceived enjoyment on experience satisfaction, as well as the impact of perceived enjoyment on experience extension. Lastly, perceived risk reduction of infection and trust are found to moderate the impact of experience satisfaction on experience extension. Theoretical and practical implications are offered.
All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning

Abstract In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the lack of energy-efficient nonlinear optical functions. Here, we experimentally demonstrate an all-optical Rectified Linear Unit (ReLU), which is the most widely used nonlinear activation function for deep learning, using a periodically-poled thin-film lithium niobate nanophotonic waveguide and achieve ultra-low energies in the regime of femtojoules per activation with near-instantaneous operation. Our results provide a clear and practical path towards truly all-optical, energy-efficient nanophotonic deep learning.