Reviewerly/exHarmony huggingface dataset:
A Tight Uniform Continuity Bound for the Arimoto-Rényi Conditional Entropy and its Extension to Classical-Quantum States

We prove a tight uniform continuity bound for Arimoto’s version of the conditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -Rényi entropy for the range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \in [0, 1$ </tex-math></inline-formula> ). This definition of the conditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -Rényi entropy is the most natural one among the multiple forms which exist in the literature, since it satisfies two desirable properties of a conditional entropy, namely, the fact that conditioning reduces entropy, and that the associated reduction in uncertainty cannot exceed the information gained by conditioning. Furthermore, it has found interesting applications in various information theoretic tasks such as guessing with side information and sequential decoding. This conditional entropy reduces to the conditional Shannon entropy in the limit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \to 1$ </tex-math></inline-formula> , and this in turn allows us to recover the recently obtained tight uniform continuity bound for the latter from our result. Finally, we apply our result to obtain a tight uniform continuity bound for the conditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -Rényi entropy of a classical-quantum state, for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> in the same range as above. This again yields the corresponding known bound for the conditional entropy of the state in the limit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \to 1$ </tex-math></inline-formula> .
Measuring close proximity interactions in summer camps during the COVID-19 pandemic

Policy makers have implemented multiple non-pharmaceutical strategies to mitigate the COVID-19 worldwide crisis. Interventions had the aim of reducing close proximity interactions, which drive the spread of the disease. A deeper knowledge of human physical interactions has revealed necessary, especially in all settings involving children, whose education and gathering activities should be preserved. Despite their relevance, almost no data are available on close proximity contacts among children in schools or other educational settings during the pandemic. Contact data are usually gathered via Bluetooth, which nonetheless offers a low temporal and spatial resolution. Recently, ultra-wideband (UWB) radios emerged as a more accurate alternative that nonetheless exhibits a significantly higher energy consumption, limiting in-field studies. In this paper, we leverage a novel approach, embodied by the Janus system that combines these radios by exploiting their complementary benefits. The very accurate proximity data gathered in-field by Janus, once augmented with several metadata, unlocks unprecedented levels of information, enabling the development of novel multi-level risk analyses. By means of this technology, we have collected real contact data of children and educators in three summer camps during summer 2020 in the province of Trento, Italy. The wide variety of performed daily activities induced multiple individual behaviors, allowing a rich investigation of social environments from the contagion risk perspective. We consider risk based on duration and proximity of contacts and classify interactions according to different risk levels. We can then evaluate the summer camps' organization, observe the effect of partition in small groups, or social bubbles, and identify the organized activities that mitigate the riskier behaviors. [...]
Language Models as Knowledge Embeddings

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods learn representations that preserve the inherent structure of KGs. They cannot well represent abundant long-tail entities in real-world KGs with limited structural information. Description-based methods leverage textual information and language models. Prior approaches in this direction barely outperform structure-based ones, and suffer from problems like expensive negative sampling and restrictive description demand. In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods. We formulate description-based KE learning with a contrastive learning framework to improve efficiency in training and evaluation. Experimental results show that LMKE achieves state-of-the-art performance on KE benchmarks of link prediction and triple classification, especially for long-tail entities.
Primary School Teachers’ Digital Literacy: An Analysis On Teachers’ Skills In Using Technological Devices

This research aims to analyze teachers’ digital literacy in the learning process at public elementary schools in Bengkulu, Indonesia. The focus is on teachers’ use of technological devices, especially their proficiency in operating, designing learning media, and utilizing digital media as a learning tool. This study employed qualitative research approach with descriptive type. The subjects were determined based on purposive with education background as the inclusive criteria. The data was collected using interview techniques where researchers were the primary instrument. The obtained data was analyzed through tabulation, coding, and conclusion withdrawal. Results showed that teachers firstly used technological devices as a learning tool due to the Covid-19 pandemic. They learnt to operate various device to transfer learning materials through visual, audio, and audiovisual methods. Teachers generally used smartphones and laptops, in which they are able to operate from the beginning to the end, including apps operation. Among the apps were Power Point, Canva, and Kinemaster with Zoom, Google Meets, Google Classroom, and WhatsApp as teaching platforms.
CAR: Class-Aware Regularizations for Semantic Segmentation

Recent segmentation methods, such as OCR and CPNet, utilizing “class level” information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules. However, the extracted class-level information was simply concatenated to pixel features, without explicitly being exploited for better pixel representation learning. Moreover, these approaches learn soft class centers based on coarse mask prediction, which is prone to error accumulation. In this paper, aiming to use class level information more effectively, we propose a universal Class-Aware Regularization (CAR) approach to optimize the intra-class variance and inter-class distance during feature learning, motivated by the fact that humans can recognize an object by itself no matter which other objects it appears with. Three novel loss functions are proposed. The first loss function encourages more compact class representations within each class, the second directly maximizes the distance between different class centers, and the third further pushes the distance between inter-class centers and pixels. Furthermore, the class center in our approach is directly generated from ground truth instead of from the error-prone coarse prediction. Our method can be easily applied to most existing segmentation models during training, including OCR and CPNet, and can largely improve their accuracy at no additional inference overhead. Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2.23% mIOU with superior generalization ability. The complete code is available at https://github.com/edwardyehuang/CAR .
Beyond Bitcoin: Recent Trends and Perspectives in Distributed Ledger Technology

In the last four years, the evolution and adoption of blockchain and, more generally, distributed ledger systems have shown the affirmation of many concepts and models with significant differences in system governance and suitable applications. This work aims to analyze distributed ledger technology (DLT) critically. Starting from the topical idea of decentralization, we introduce concepts and building blocks currently adopted in the available systems centering on their functional aspects and impact on possible applications. We present some conceptual framing tools helpful in the application context: a DLT reference architecture, off-chain and on-chain governance models, and classification of consensus protocols. Finally, we introduce the concept of process authenticity, reviewing tools and strategies for integrating DLT with the physical world and proposing a constructive scheme for the authentication of a physical resource through alphanumeric data.
Coupling coordination relationship between geology–geomorphology and ecology in Northeast China

Northeast China is an important ecological barrier and commodity grain base in China. The coupling coordination relationship between geology-geomorphology and ecology has become a critical background condition for ecosystem protection and sustainable development. Taking Northeast China as a case (accounting for about 13% of China's land area), 9 divisions are divided according to the characteristics of regional ecology and geology-geomorphology, and 17 indicators are selected to build an evaluation index system. Methods of analytic hierarchy process, entropy weight and game theory are used to determine the index weights. Based on the coupling coordination degree (CCD) model, the spatial coupling coordination characteristics of geology-geomorphology and ecology are studied. The variation characteristics of the Normalized Difference Vegetation Index (NDVI) are evaluated by Sen+Mann-Kendall (Sen+MK) method. Our results are as follows. (1) The coupling between geology-geomorphology and ecology is strong, but the spatial differentiation of CCD is obvious. Nine divisions are evaluated as two high-level, three medium-level and three low-level coordination types and one mild imbalance type. (2) The plain divisions Ⅰ and Ⅳ where the typical black soil belt is located are high coordination types. Restricted by geology-geomorphological conditions or ecological conditions, mountain divisions Ⅲ and Ⅶ and plain division Ⅴ are moderate coordination types, mountain divisions Ⅱ and Ⅷ and plateau division Ⅸ are low coordination types, and mountain division Ⅵ is mild imbalance type. (3) The variation trend of NDVI shows a significant increase in divisions Ⅲ, Ⅴ, Ⅰ, Ⅱ and Ⅶ. it shows a significant decrease in part of divisions Ⅳ, Ⅵ, Ⅷ and Ⅸ, and ecological management and construction should be strengthened in these divisions. The research shows that the CCD model method is feasible for evaluating the relationship between geology-geomorphology and ecology and can provide eco-geological background information for Northeast China.
Deep recurrent modelling of Granger causality with latent confounding

Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer potential causal mechanisms between different time series. The original definition of Granger causality is restricted to linear processes and leads to spurious conclusions in the presence of a latent confounder. In this work, we harness the expressive power of recurrent neural networks and propose a deep learning-based approach to model non-linear Granger causality by directly accounting for latent confounders. Our approach leverages multiple recurrent neural networks to parameterise predictive distributions and we propose the novel use of a dual-decoder setup to conduct the Granger tests. We demonstrate the model performance on non-linear stochastic time series for which the latent confounder influences the cause and effect with different time lags; results show the effectiveness of our model compared to existing benchmarks.
Monogamy of Quantum Entanglement

Unlike classical correlation, quantum entanglement cannot be freely shared among many parties. This restricted shareability of entanglement among multi-party systems is known as monogamy of entanglement, which is one of the most fundamental properties of entanglement. Here, we summarize recent theoretical progress in the field of monogamy of entanglement. We firstly review the standard CKW-type monogamy inequalities in terms of various entanglement measures. In particular, the squashed entanglement and one-way distillable entanglement are monogamous for arbitrary dimensional systems. We then introduce some generalized version of monogamy inequalities which extend and sharpen the traditional ones. We also consider the dual polygamy inequalities for multi-party systems. Moreover, we present two new definitions to define monogamy of entanglement. Finally, some challenges and future directions for monogamy of entanglement are highlighted.
Can I Sleep Safely in My Smarthome? A Novel Framework on Automating Dynamic Risk Assessment in IoT Environments

Fully automated homes, equipped with the latest Internet of Things (IoT) devices, aiming to drastically improve the quality of lives of those inhabiting such homes, is it not a perfect setting for cyber threats? More than that, this is a fear of many regular citizens and a trending topic for researchers to apply Cyber Threat Intelligence (CTI) for seamless cyber security. This paper focuses on the Risk Assessment (RA) methodology for smarthome environments, targeting to include all types of IoT devices. Unfortunately, existing approaches mostly focus on the manual or periodic formal RA, or individual device-specific cyber security solutions. This paper presents a Dynamic Risk Assessment Framework (DRAF), aiming to automate the identification of ongoing attacks and the evaluation of the likelihood of associated risks. Moreover, DRAF dynamically proposes mitigation strategies when full automation of the decision making is not possible. The theoretical model of DRAF was implemented and tested in smarthome testbeds deployed in several European countries. The resulting data indicate strong promises for the automation of decision making to control the tightly coupled balance between cyber security and privacy compromise in terms of the embedded services’ usability, end-users’ expectations and their level of cyber concerns.
Artificial intelligence for target symptoms of Thai herbal medicine by web scraping

Machine learning (ML) is implementing artificial intelligence (AI) research within medicine that has made dramatic progress in recent years. In addition to standard treatments, the role of complementary and alternative medicine should be mentioned. Traditional Thai medicine has received growing acceptance as a complementary approach to modern medicine by using local herbs. A vast amount of Thai herbal knowledge and information is freely available on the Internet. The reader must evaluate each website and decide to use trustworthy and appropriate information. This study aimed to acquire Thai herbal knowledge recorded in the Thai language system on the Internet by scraping websites using programming techniques. The knowledge was extracted with programming, and the types of Thai herbs were classified corresponding to target symptoms by the machine learning algorithm. The ML method organized the process when sufficient achievement was reached in order to give reliable and high accuracy results from the training data set. The validation of extracted knowledge was achieved by using the part-of-speech tag patterns analysis. This study showed that the programming and machine learning system was appropriate for obtaining and classifying Thai herbal medicines knowledge.
The Impact of Social Media on the Use of Code Mixing by Generation Z

Generally, generation Z mixes English and Indonesia when communicating with each other. This is a sociolinguistic phenomenon called code-mixing, which impacts social media as a part of the development of Information Communication Technology (ICT). Therefore, this study investigates the impact of social media on the use of code-mixing by generation Z. This study was carried out at Serang Raya University, Indonesia, where English is the only foreign language used to teach students. The research also aims to highlight the contribution of social media in code-mixing and determine the reasons this generation uses English and Indonesian in social media. Data were collected through self-completed questionnaires and interviews. The results showed that the use of social media had an impact on code-mixing between Indonesian and English for Gen Z.
Parallel Hybrid Island Metaheuristic Algorithm

This study introduces a novel Parallel Hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base. The corresponding hybrid algorithm is called Parallel Hybrid Island Metaheuristic Algorithms (PHIMA). The hybrid parallel structure exploits the characteristics of the individual metaheuristic algorithms to boost robustness and diversity. Island Genetic Algorithm has been combined with Particle Swarm Optimization and Fireworks Algorithm to build three different PHIMA algorithms: PSO-GA (PHIMA-PGA), FWA-GA (PHIMA-FGA) and FWA-PSO-GA (PHIMA-FPGA). Further, another implementational variation known as “co-evolution” is applied to the sub-GA islands of PHIMA-FPGA to improve the performance on multi-modal high-dimensional problems. This variation is referred to as PHIMA-FPGA-Co. Each PHIMA Algorithm exhibits different advantages and characteristics, and the parallel hybridization using the island model is found to improve robustness and population diversity. The performances of the four new algorithms are compared with each other and that of the traditional Island GAs and all four proposed PHIMA algorithms show better result quality.
Dimensionality Reduction: Challenges and Solutions

The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between data sets, covariance, etc. Dimensionality reduction entails mapping a set of high dimensional data features onto low dimensional data. Motivated by the lack of learning models’ performance due to the high dimensionality data, this study encounters five distinct dimensionality reduction methods. Besides, a comparison between reduced dimensionality data and the original one using statistical and machine learning models is conducted thoroughly.
Privacy-Preserving Public Auditing for Shared Cloud Data With Secure Group Management

With cloud storage services, users can store their data in the cloud and efficiently access the data at any time and any location. However, when data are stored in the cloud, there is a risk of data loss because users lose direct control over their data. To solve this problem, many cloud storage auditing techniques have been studied. In 2019, Tian <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a public auditing scheme for shared data that supports data privacy, identity traceability, and group dynamics. In this paper, we point out that their scheme is insecure against tag forgery or proof forgery attacks, which means that, even if the cloud server has deleted some outsourced data, it can still generate valid proof that the server had accurately stored the data. We then propose a new scheme that provides the same functionalities and is secure against the above attacks. Moreover, we compare the results with other schemes in terms of computation and communication costs.
Study on spatio-temporal characteristics of earthquakes in southwest China based on <i>z</i>-value

Abstract In this study, earthquakes with magnitudes above 3.0 on the Richter scale in southwest China from 1900 to 2013 are taken as research objects. The spatio-temporal migration characteristics of seismic activity in the area of concern are studied by using geostatistics method to explore the rules of seismic activity. From the perspective of geostatistics and based on z -value, eight major seismic zones in southwest China were studied zone-wise by using the geostatistics tool ArcGIS and the seismic analysis tool ZMAP to explore the spatial and temporal migration characteristics of earthquakes. It was found that each seismic zone had its own quiet period with relatively inactive seismic activity. Simultaneously, through the study of the time-varying characteristics of the seismic belt, it was found that the time-varying regularity of the seismic frequency of the fault belt with the same or related geological structure background is roughly consistent. In conclusion, the seismic activity in southwest China will still be active in the future.
A note on uncertainty relations of metric-adjusted skew information

The uncertainty principle is one of the fundamental features of quantum mechanics and plays a vital role in quantum information processing. We study uncertainty relations based on metric-adjusted skew information for finite quantum observables. Motivated by the paper [Physical Review A 104, 052414 (2021)], we establish tighter uncertainty relations in terms of different norm inequalities. Naturally, we generalize the method to uncertainty relations of metric-adjusted skew information for quantum channels and unitary operators. As both the Wigner-Yanase-Dyson skew information and the quantum Fisher information are the special cases of the metric-adjusted skew information corresponding to different Morozova-Chentsov functions, our results generalize some existing uncertainty relations. Detailed examples are given to illustrate the advantages of our methods.