Reviewerly/exHarmony huggingface dataset:
New Conditional Cube Attack on Keccak Keyed Modes

The conditional cube attack on round-reduced Keccak keyed modes was proposed by Huang et al. at EUROCRYPT 2017. In their attack, a conditional cube variable was introduced, whose diffusion was significantly reduced by certain key bit conditions. The attack requires a set of cube variables which are not multiplied in the first round while the conditional cube variable is not multiplied with other cube variables (called ordinary cube variables) in the first two rounds. This has an impact on the degree of the output of Keccak and hence gives a distinguisher. Later, the MILP method was applied to find ordinary cube variables. However, for some Keccak based versions with few degrees of freedom, one could not find enough ordinary cube variables, which weakens or even invalidates the conditional cube attack.In this paper, a new conditional cube attack on Keccak is proposed. We remove the limitation that no cube variables multiply with each other in the first round. As a result, some quadratic terms may appear in the first round. We make use of some new bit conditions to prevent the quadratic terms from multiplying with other cube variables in the second round, so that there will be no cubic terms in the first two rounds. Furthermore, we introduce the kernel quadratic term and construct a 6-2-2 pattern to reduce the diffusion of quadratic terms significantly, where the Θ operation even in the second round becomes an identity transformation (CP-kernel property) for the kernel quadratic term. Previous conditional cube attacks on Keccak only explored the CP-kernel property of Θ operation in the first round. Therefore, more degrees of freedom are available for ordinary cube variables and fewer bit conditions are used to remove the cubic terms in the second round, which plays a key role in the conditional cube attack on versions with very few degrees of freedom. We also use the MILP method in the search of cube variables and give key-recovery attacks on round-reduced Keccak keyed modes.As a result, we reduce the time complexity of key-recovery attacks on 7-round Keccak-MAC-512 and 7-round Ketje Sr v2 from 2111, 299 to 272, 277, respectively. Additionally, we have reduced the time complexity of attacks on 9-round KMAC256 and 7-round Ketje Sr v1. Besides, practical attacks on 6-round Ketje Sr v1 and v2 are also given in this paper for the first time.
The Relationship Between Perceived Organizational Support, Employee Engagement, and Organizational Citizenship Behavior: Application of PLS-SEM Approach

Building upon the integration of both social exchange and organizational support theories, the present study was aimed at investigating the role of perceived organizational support as a predictor of employee engagement and organizational citizenship behaviors.This study further examined the mediating role of employee engagement in the relationship between the perceived organizational support (POS) and organizational citizenship behavior (OCB) in the context of the banking sector of Afghanistan.Through a survey instrument, data were collected from 192 frontline employees of Kabul based commercial banks.Partial least squares structural equation modeling (PLS-SEM) was applied to test the study hypotheses using Smart-PLS 3. The study underlined the significant positive effect of POS on both EE and OCB constructs.Moreover, this study also confirmed the mediating role of EE on the relationship between POS and OCB.The paper concluded that when employees perceive that their contributions are valued, and the organization ensures their well-being, they offer positive attitudes toward their organization and their work roles are enhanced (working engagement), leading to OCB.The limitations and recommendations for future research are also included.
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation

Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years.As a countermeasure, adversarial defense has been explored, but relatively few efforts have been made to detect adversarial examples.However, detecting adversarial examples may be crucial for automated tasks (e.g.review sentiment analysis) that wish to amass information about a certain population and additionally be a step towards a robust defense system.To this end, we release a dataset for four popular attack methods on four datasets and four models to encourage further research in this field.Along with it, we propose a competitive baseline based on density estimation that has the highest AUC on 29 out of 30 dataset-attack-model combinations.The source code is released.
Data sharing policies in scholarly publications: interdisciplinary comparisons

Digital sharing of research data is becoming an important research integrity norm. Data sharing is promoted in different avenues, one being the scholarly publication process: journals serve as gatekeepers, recommending or mandating data sharing as a condition for publication. While there is now a sizeable corpus of research assessing the pervasiveness and efficacy of journal data sharing policies in various disciplines, available research is largely piecemeal and mitigates against meaningful comparisons across disciplines. A major contribution of the present research is that it makes direct across-discipline comparisons employing a common methodology. The paper opens with a discussion of the arguments aired in favour and against data sharing (with an emphasis on ethical issues, which stand behind these policies). The websites of 150 journals, drawn from 15 disciplines, were examined for information on data sharing. The results consolidate the notion of the primacy of biomedical sciences in the implementation of data sharing norms and the lagging implementation in the arts and humanities. More surprisingly, they attest to similar levels of norms adoption in the physical and social sciences. The results point to the overlooked status of the formal sciences, which demonstrate low levels of data sharing implementation. The study also examines the policies of the major journal publishers. The paper concludes with a presentation of the current preferences for different data sharing solutions in different fields, in specialized repositories, general repositories, or publishers' hosting area.
Unsupervised Underexposed Image Enhancement via Self-Illuminated and Perceptual Guidance

Underexposed images inevitably suffer severe degradation due to light distortion and noise corruption. Motivated by the limited samples of paired datasets, several unsupervised enhancement methods have been developed. However, these techniques heavily rely on pre-defined fixed lightness and noise removal constraints. Correspondingly, they cannot match the image-specific lightness when performing enhancement and can only refine details in a non-perceptual way. In this paper, we propose an Unsupervised Underexposed Image Enhancement Network (U2IENet) with self-illuminated and perceptual guidance. Specifically, to adjust the illumination for matching the image-specific lightness adaptively, we utilize the bright area of the underexposed image as the self-illuminated guidance to constrain the training process and modulate the features. Meanwhile, we introduce the perceptual guidance as a constraint to remove the noise based on illumination distribution, thus refining the details perceptually. Experiments on both underexposed datasets and public low-light datasets demonstrate the superiority of the proposed approach with higher flexibility over state-of- the-art solutions. In addition, our U2IENet also provides a side function that enables users to adjust the lightness via interactive tuning of a single parameter.
OptEmbed

Click-through rate (CTR) prediction model usually consists of three components: embedding table, feature interaction layer, and classifier. Learning embedding table plays a fundamental role in CTR prediction from the view of the model performance and memory usage. The embedding table is a two-dimensional tensor, with its axes indicating the number of feature values and the embedding dimension, respectively. To learn an efficient and effective embedding table, recent works either assign various embedding dimensions for feature fields and reduce the number of embeddings respectively or mask the embedding table parameters. However, all these existing works cannot get an optimal embedding table. On the one hand, various embedding dimensions still require a large amount of memory due to the vast number of features in the dataset. On the other hand, decreasing the number of embeddings usually suffers from performance degradation, which is intolerable in CTR prediction. Finally, pruning embedding parameters will lead to a sparse embedding table, which is hard to be deployed. To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models. Specifically, we propose pruning the redundant embeddings regarding corresponding features' importance by learnable pruning thresholds. Furthermore, we consider assigning various embedding dimensions as one single candidate architecture. To efficiently search the optimal embedding dimensions, we design a uniform embedding dimension sampling scheme to equally train all candidate architectures, meaning architecture-related parameters and learnable thresholds are trained simultaneously in one supernet. We then propose an evolution search method based on the supernet to find the optimal embedding dimensions for each field. Experiments on public datasets show that OptEmbed can learn a compact embedding table which can further improve the model performance.
Changes in photovoltaic potential over China in a warmer future

Abstract Solar photovoltaic (PV) technology offers a promising path for addressing energy demand and mitigating climate change. However, climatic conditions relevant to the productivity of solar power may be changed in a warmer future. Here, we quantify the impact of climate change on PV potential over China based on high-resolution climate projections within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). Our results indicate that the annual mean PV power potential ( PV POT ) over China would decrease by several percent relative to the reference period (1986–2005) under a warmer climate, with the most pronounced decrease over the Tibetan Plateau, which is currently the most solar-rich region in China. However, beyond changes in the mean climate state, we demonstrate a substantial increase in events of extreme low PV power outputs (i.e. those falling below the 10th percentile of the probability distribution of the daily PV POT anomalies). For instance, the frequency of extreme low PV events is projected to reach nearly three times the reference period level over the Tibetan Plateau under the scenario of 3 °C global warming (similar to late-century warming projected based on current mitigation policies). Future changes in PV POT are dominated by changes in surface solar irradiance, while the warming condition further exacerbates the decrease in PV POT . Our results highlight that both the mean and extreme conditions of solar inputs should be considered simultaneously when assessing the impacts of climate change on PV power outputs.
Positioning Method of Pedestrian Dead Reckoning Based on Human Activity Recognition Assistance

Aiming at the problem that the traditional Pedestrian Dead Reckoning (PDR) cannot adapt to the reliable positioning of the target in different motion states, this paper designs and proposes a positioning method based on deep learning for Human Activity Recognition (HAR) assisted PDR. First, the Wavelet-CNN deep learning network is used in the offline stage to preprocess and train the data of the built-in MEMS sensor of the smartphone to obtain the HAR model. Then, in the online real-time positioning stage, the different motion states of the target are identified based on the HAR model, and the pedestrian step detection and step size estimation algorithms are adaptively adjusted. Finally, the HAR-assisted PDR algorithm is implemented on the smart-phone, and a large number of tests and verifications are carried out in the experimental environment. The proposed localization method for HAR-assisted PDR based on deep learning can accurately identify a variety of complex human motion states, and the recognition accuracy is as high as 99.50%. At the same time, the accuracy rate of the step recognition algorithm of state-of-the-art PDR is increased by 10.94 %, and the maximum positioning error is reduced by about 16.2%, which verifies the effectiveness of the proposed algorithm.
Privacy-preserving cancer type prediction with homomorphic encryption

Cancer genomics tailors diagnosis and treatment based on an individual's genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients' data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet .
Efficient convolutional neural networks on Raspberry Pi for image classification

With the good performance of deep learning in the field of computer vision (CV), the convolutional neural network (CNN) architectures have become main backbones of image recognition tasks. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices. This paper proposes a lightweight convolutional neural network TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept of block connections in ThreshNet, the newly proposed network model compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. Our proposed TripleNet and other State-of-the-Art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%, respectively. The detail codes of this work are available at https://github.com/RuiyangJu/TripleNet .
Dialog chatbot as an interactive online tool in enhancing ESP vocabulary learning

Purpose Based on an experimental study on English for Specific Purposes (ESP) students, at the Business Department at the University of Bisha, the purpose of the study is to examine the effect of chatbot use on learning ESP in online classrooms during COVID-19 and find out how Dialogflow chabot can be a useful and interactive online platform to help ESP learners in learning vocabulary well. Design/methodology/approach The research paper is based on an experimental study of two groups, an experiential group and a controlled group. Two tests were carried out. Pre-tests and post-test of vocabulary knowledge were conducted for both groups to explore the usefulness of using the Dialogflow chatbot in learning ESP vocabulary. A designed chatbot content was prepared and included all the vocabulary details related to words' synonyms and a brief explanation of words’ meanings. An informal interview is another tool used in the study. The purpose of using the interview with the participants was to elicit more data from the participants about using the chatbot and about how and in what aspects chatbot using the conversational program was useful and productive. Findings The findings of the study explored that the use of chatbots plays a major role in enhancing and learning ESP vocabulary. That was clear as the results showed that the students who used the chatbot Dialogflow in the experimental group outperformed their counterparts in the control group. Research limitations/implications The study displays an important pedagogical implication as the use of chatbots could be applied in several settings to improve language learning in general or learning ESP courses in particular. Chatbot creates an interesting environment to foster build good interactions where negotiation of meaning takes place clearly seems to be of great benefit to help learners advance in their L2 lexical development. Originality/value Examining and exploring whether the use of chatbots plays a major role in enhancing and learning ESP vocabulary in English as Foreign Language setting.
Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases

Abstract Objectives To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. Methods In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. Results A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. Conclusions The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. Key Points • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.
An Efficient Threshold Access-Structure for RLWE-Based Multiparty Homomorphic Encryption

Abstract We propose and implement a multiparty homomorphic encryption (MHE) scheme with a $$t$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> </mml:math> -out-of- $$N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>N</mml:mi> </mml:math> -threshold access-structure that is efficient and does not require a trusted dealer in the common random string model. We construct this scheme from the ring-learning-with-error assumptions and as an extension of the MHE scheme of Mouchet et al. (PETS 21). By means of a specially adapted share re-sharing procedure, this extension can be used to relax the $$N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>N</mml:mi> </mml:math> -out-of- $$N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>N</mml:mi> </mml:math> -threshold access-structure of the original scheme into a $$t$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> </mml:math> -out-of- $$N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>N</mml:mi> </mml:math> -threshold one. This procedure introduces only a single round of communication during the setup phase, after which any set of at least t parties can compute a t -out-of- t additive sharing of the secret-key with no interaction; this new sharing can be used directly in the scheme of Mouchet et al. We show that, by performing Shamir re-sharing over the MHE ciphertext-space ring with a carefully chosen exceptional set, this reconstruction procedure can be made secure and has negligible overhead. Moreover, it only requires the parties to store a constant-size state after its setup phase. Hence, in addition to fault tolerance, lowering the corruption threshold also yields considerable efficiency benefits, by enabling the distribution of batched secret-key operations among the online parties. We implemented and open-sourced our scheme in the Lattigo library.
Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration

Objective: This paper explored the relationship between chatbots and flipped learning in an educational setting. It also identifies the benefits and drawbacks of using chatbots in a flipped learning context and the ethical and privacy concerns related to their use. Method: The study utilized a theoretical analysis approach, which included a comprehensive review of relevant literature from Scopus and World of Science databases. The data collected from the literature review was analyzed using a qualitative approach. Results: The study found that chatbots can potentially enhance student engagement and learning outcomes in a flipped learning context by providing personalized support, facilitating group discussions and collaborations, providing feedback and assessment on student work, supporting self-directed learning, and enhancing student engagement and motivation. However, using chatbots in a flipped learning context also raises ethical and privacy concerns, including data privacy, data security, and student anonymity. Novelty: Contributes to the existing research on using chatbots in education by providing insights into the potential benefits and drawbacks of using chatbots in a flipped learning context. The study highlights the importance of considering the ethical and privacy concerns and the future potential of chatbots in a flipped learning context and proposes future research directions.