Reviewerly/exHarmony huggingface dataset:
Generalized Feistel Structures Based on Tweakable Block Ciphers

A generalized Feistel structure (GFS) is a classical approach to construct a block cipher from pseudorandom functions (PRFs). Coron et al. at TCC 2010 instantiated a Feistel structure with a tweakable block cipher (TBC), and presented its provable security treatment. GFSs can naturally be instantiated with TBCs, and among several types of GFSs, the provable security result of TBC-based unbalanced GFSs was presented. TBC-based counterparts of the most basic types of GFSs , namely, type-1, type-2, and type-3 GFSs, can naturally be formalized, and the provable security result of these structures is open. In this paper, we present such formalization and show their provable security treatment. We use a TBC of n-bit blocks and n-bit tweaks, and we identify the number of rounds needed to achieve birthday-bound security and beyond-birthday-bound security (with respect to n). The n-bit security can be achieved with a finite number of rounds, in contrast to the case of classical PRF-based GFSs. Our proofs use Patarin’s coefficient-H technique, and it turns out deriving a collision probability of various internal variables is nontrivial. In order to complete the proof, we introduce an approach to first compute a collision probability of one specific plaintext difference (or a ciphertext difference), and then prove that the case gives the maximum collision probability. We fully verify the correctness of our security bounds for a class of parameters by experimentally deriving upper bounds on the collision probability of internal variables. We also analyse the optimality of our results with respect to the number of rounds and the attack complexity.
Proposition d’une adaptation française et premières validations de l’échelle d’utilisabilité Computer System Usability Questionnaire (F-CSUQ)

The measurement of the usability of computer systems is based on a set of methods, including the use of questionnaires. While there are many questionnaires in English, few exist or have been adapted into French. This article proposes a translation of the Computer System Usability Questionnaire (CSUQ), developed within IBM and validated in the 1990s. This 19-item scale is particularly well suited to the evaluation of expert systems, especially internal systems in companies. It measures three dimensions of usability: usefulness, quality of textual information, and quality of the interface. This proposal for a French translation of the CSUQ (named F-CSUQ) was carried out by a committee of three bilingual experts. The first psychometric validations show a very good internal consistency, with a Cronbach's alpha of 0.960. This shows that all the F-CSUQ items converge towards the same concept. However, the principal component analysis (PCA) does not allow the original structure, composed of the 3 dimensions, to be recovered. Only a smaller version of the F-CSUQ, with 13 items and proposed in this article, respects the three-dimensional structure. These results nevertheless converge with those obtained by other translations of the CSUQ.
Blockchain-based SLA Management for Inter-Provider Agreements

With features like immutability and transparency, blockchain and Distributed Ledger Technologies (DLT) can enable the telco industry to exchange services using smart contracts.Consequently, various 6G network stakeholders can participate in a marketplace for inter-provider agreements as either service providers or consumers.As, a blockchain-based 6G network can aid administrative domains in sharing resources (virtual network functions, services, or slices).However, such a dynamic environment requires strict Service Level Agreement (SLA) monitoring and management.Therefore, this paper considers a use case of a smart contract-based inter-provider agreement.We use novel solutions like IOTA Tangle to perform transactions, IPFS to store the hash of use case data files, and chainlink to access off-chain data feeds for SLA monitoring, reducing costs and increasing transparency.We also provide experimental evaluations of and divide the emulation into two phases.Phase 1 consists of choosing the approach and creating a smart contract (SC) (i.e., SC-Marketplace or SC-Auction).Furthermore, phase 2 consists of off-chain data feed to monitor SLA through chainlink.Finally, we measure transaction latency, response time, overall time consumption, and transaction & storage cost.The maximum latency observed in phase 1 is ≈ 25ms and ≈ 15s for phase 2. Similarly, the average response time for both phases is ≈ 14s ∼ 20s.Lastly, the results also explain that using IOTA-EVM, we can have fee-less transactions, and IPFS helps reduce the storage cost by up to ≈ 80%.However, it is concluded that adding chainlink adds additional cost for SLA data feeds.
Who Finds the Short Proof?

Abstract This paper reports on an exploration of Boolos’ Curious Inference, using higher-order automated theorem provers (ATPs). Surprisingly, only suitable shorthand notations had to be provided by hand for ATPs to find a short proof. The higher-order lemmas required for constructing a short proof are automatically discovered by the ATPs. Given the observations and suggestions in this paper, full proof automation of Boolos’ and related examples now seems to be within reach of higher-order ATPs.
Multivariate Time Series Anomaly Detection with Few Positive Samples

Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances. In practical settings, particularly industrial time series anomaly detection, we often encounter situations where a large amount of normal operation data is available along with a small number of anomaly events collected over time. This practical situation calls for methodologies to leverage these small number of anomaly events to create a better anomaly detector. In this paper, we introduce two methodologies to address the needs of this practical situation and compared them with recently developed state of the art techniques. Our proposed methods anchor on representative learning of normal operation with autoregressive (AR) model along with loss components to encourage representations that separate normal versus few positive examples. We applied the proposed methods to two industrial anomaly detection datasets and demonstrated effective performance in comparison with approaches from literature. Our study also points out additional challenges with adopting such methods in practical applications.
Quantum master equations for a fast particle in a gas

The propagation of a fast particle in a low-density gas at thermal equilibrium is studied in the context of quantum mechanics. A quantum master equation in the Redfield form governing the reduced density matrix of the particle is derived explicitly from first principles. Under some approximations, this equation reduces to the linear Boltzmann equation. The issue of the positivity of the time evolution is also discussed by means of a Lindblad form. The Born and Markov assumptions underlying these equations, as well as other approximations regarding the bath correlation function, are discussed in detail. Furthermore, all these master equations are shown to be equivalent with each other if the density matrix of the particle is diagonal in the momentum basis, or if the collision rate is independent of the particle momentum.
Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers

How well do language models deal with quantification? In this study, we focus on 'few'-type quantifiers, as in 'few children like toys', which might pose a particular challenge for language models because the sentence components with out the quantifier are likely to co-occur, and 'few'-type quantifiers are rare. We present 960 English sentence stimuli from two human neurolinguistic experiments to 22 autoregressive transformer models of differing sizes. Not only do all the models perform poorly on 'few'-type quantifiers, but overall the larger the model, the worse its performance. This inverse scaling is consistent with previous work suggesting that larger models increasingly reflect online rather than offline human processing, and we argue that the decreasing performance of larger models may challenge uses of language models as the basis for natural language systems.
E-NER -- An Annotated Named Entity Recognition Corpus of Legal Text

Identifying named entities such as a person, location or organization, in documents can highlight key information to readers. Training Named Entity Recognition (NER) models requires an annotated data set, which can be a time-consuming labour-intensive task. Nevertheless, there are publicly available NER data sets for general English. Recently there has been interest in developing NER for legal text. However, prior work and experimental results reported here indicate that there is a significant degradation in performance when NER methods trained on a general English data set are applied to legal text. We describe a publicly available legal NER data set, called E-NER, based on legal company filings available from the US Securities and Exchange Commission's EDGAR data set. Training a number of different NER algorithms on the general English CoNLL-2003 corpus but testing on our test collection confirmed significant degradations in accuracy, as measured by the F1-score, of between 29.4\% and 60.4\%, compared to training and testing on the E-NER collection.
Full-color holographic system featuring three-dimensional salient object detection based on a U<sup>2</sup>-RAS network

In this study, a 3D salient object detection model is built at the acquisition step in the full-color holographic system, and a deep network architecture <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">U</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> -reverse attention and residual learning (RAS) algorithm is proposed for salient object detection to obtain more efficient and accurate point cloud information. In addition, we also use the point cloud gridding method to improve the hologram generation speed. Compared with the traditional region of interest method, RAS algorithm, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">U</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> -Net algorithm, the computational complexity is significantly reduced. Finally, the feasibility of this method is proved by experiments.
Efficient 3D object recognition in mobile edge environment

Abstract 3D object recognition has great research and application value in the fields of automatic drive, virtual reality, and commercial manufacturing. Although various deep models have been exploited and achieved remarkable results for 3D object recognition, their computational cost is too high for most mobile applications. This paper combines edge computing and 3D object recognition into a powerful and efficient framework. It consists of a cloud-based rendering stage and a terminal-based recognition stage. In the first stage, inspired by the cloud-based rendering technique, we upload the 3D object data from the mobile device to the edge cloud server for multi-view rendering. The rendering stage utilizes the powerful computing resource in the edge cloud server to generate multiple view images of the given 3D object from different views by parallel high-quality rendering. During the terminal-based recognition stage, we integrate a lightweight CNN architecture and a neural network quantization technique into a 3D object recognition model based on the multiple images rendered in the edge cloud server, which can be executed fast in the mobile device. To reduce the cost of network training, we propose a novel semi-supervised 3D deep learning method with fewer labeled samples. Experiments demonstrate that our method achieves competitive performance compared to the state-of-the-art methods with low latency running in the mobile edge environment.
Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study

Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malayalam dataset has been created by extracting 2,000 tweets from Twitter. The bidirectional encoder representations from transformers (BERT) is a pretrained model that has been used for various natural language processing tasks. This work employs a transformer-based BERT model for Malayalam sentiment analysis. The efficacy of BERT in analyzing the sentiments latent in Malayalam texts has been studied by comparing the performance of BERT with various machine learning models as well as deep learning models. By analyzing the results, it is found that a substantial increase in accuracy of 5% for BERT when compared with that of Bi-GRU, which is the next bestperforming model.
Social Media Use in Higher Education During the COVID-19 Pandemic: A Systematic Literature Review

This study reviews the literature to gain an in-depth understanding of the pedagogical role of social media in higher education institutions (HEI’s) during the COVID-19 pandemic. A systematic search in the Web of Science, Scopus, and EBSCO databases yielded 34 relevant empirical studies published between January 2020 and April 2021. The findings reveal that: a) the innovative possibilities furnished through social media facilitated the transition to a complete online learning setting, b) the majority of studies are oriented towards the perspectives of students, c) the lack of well-defined policy hinders the effective utilization of social media in the pedagogical process, and d) questionnaires were the mostly used data collection method overlooking the significance of digital tracing as a rich source of data. This article provides a research agenda to advance the knowledge of the pedagogical possibilities of social media, especially that these platforms were not used to their full potential for teaching and learning during the pandemic. This study also has practical implications for HEI’s and policymakers to recognize the significance of social media in maintaining educational sustainability
The development and validation of a scale measuring mobile phone use in an academic environment

The purpose of this study was to fill the gap in current research on educational mobile phone use within the framework for the rational analysis of the mobile education (FRAME) model. The paper developed and validated the Mobile Phone Use in Academic Environment Scale (MPUAES) to measure both positive and negative aspects of educational use of mobile phones. The data was collected from all faculties and all grade levels of Middle East Technical University in Ankara. The inclusion criterion for the participation in the study was owning a smartphone. The exploratory and confirmatory factor analyses were run with two different groups in a total of 1887 undergraduate students. Three factors structure with 18 items were obtained, which were labeled as facilitator, distractor, and connectedness. These three factors explained 63.42% of the total variance. For confirmation of the factor structure, confirmatory factor analysis was performed with the second sample. The Cronbach alpha coefficient of each factor ranged between .90 and .74. To conclude, the findings of the study proposed that the scores obtained from the developed scale were valid and reliable in measuring undergraduate students’ mobile phone use in an academic environment.
Distributed Quantum Computing: a Survey

Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundred of noisy qubits available. Yet -- to fully unveil the potential of quantum computing out of the labs and into business reality -- the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding the thousands (if not millions) of noise-free qubits. To this aim, there exists a broad consensus among both academic and industry communities about considering the distributed computing paradigm as the key solution for achieving such a scaling, by envision multiple moderate-to-small-scale quantum processors communicating and cooperating to execute computational tasks exceeding the computational resources available within a single processing device. The aim of this survey is to provide the reader with an overview about the main challenges and open problems arising with distributed quantum computing, and with an easy access and guide towards the relevant literature and the prominent results from a computer/communications engineering perspective.
CONTRIBUIÇÕES MINERALÓGICAS E GEOQUÍMICAS SOBRE A ORIGEM DO MINÉRIO DE MANGANÊS DO MORRO DO URUCUM (CORUMBÁ, BRASIL)

This article was prepared in 2011 and its publication at this time represents a reminder to Prof. doctor Herbert Pöllmann, who contributed to the field and analytical activities. The manganese ore from Urucum, in Mato Grosso do Sul, constitutes an important reserve of this mineral good and in recent years has been the scene of many constant researches in several publications, mainly in the last five years, with emphasis on both Mn and Fe. At the time we analyzed the few samples of this unpretentious article, publications were still scarce. Our analyzes are restricted to a few samples and the sampling is not representative of the different levels at which Mn ore occurs. The mineralogical and chemical results of these samples indicate mineralogy dominated by cryptomelana and low concentrations of most trace elements, especially REE. Anomalous are mainly Ba, Sr and Co. The origin o this ore is undoubtedly sedimentary, with little or no terrigenous contribution.
Context Aware Image Sentiment Classification using Deep Learning Techniques

Objectives: To propose context aware sentiment classification using deep learning techniques.Methods: We used EfficientNetB-7 deep learning framework for caption generation for the input image and to classify the sentiment of generated caption using machine learning techniques.First, we employ several real-time and synthetic image datasets, then apply pre-processing and normalization for data balancing.Then efficient module implementation for feature extraction and selection using convolutional and pooling layers were done.Despite this proceeding, it generates the caption for respective images.The various feature extraction and selection Natural Language Processing (NLP) techniques such as TF-IDF, lemmas, dependency and correlational features have been used and classify the sentiment label using attention model and greedy approach.Finally, generating the blue score for the entire testing dataset and show the effectiveness of the proposed system.Findings: Our model gives higher accuracy with different deep learning techniques which is demonstrated in result section.The proposed model archives 73.80% average accuracy for EMOTIC dataset.The module has evaluated with different features and deep learning classification algorithms proposed earlier.Novelty: This research is the collaboration of Deep learning and machine learning classification techniques.We first extract the visual features from the input image using deep learning and classify with machine learning with the collaboration of NLP processes.We also carried out various feature extraction techniques such as Ngram, dependency features, co-relational features and determined the sentiment of generated captions.
Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm

To solve the problem that recommendation algorithms based on knowledge graph ignore the information of the entity itself and the user information during information aggregating, we propose a double interaction graph neural network recommendation algorithm based on knowledge graph. First, items in the dataset are selected as user-related items and then they are integrated into user features, which are enriched. Then, according to different user relationship weights and the influence weights of neighbor entities on the central entity, the graph neural network is used to integrate the features of nodes in the knowledge graph to obtain neighborhood information. Secondly, user features are interacted and aggregated with the entity’s own information and neighborhood information, respectively. Finally, the label propagation algorithm is used to train the edge weights to assist entity features learning. Experiments on two real datasets commonly used in recommended algorithms were conducted and showed that the model is better than the existing baseline models. The values of AUC and F1 on MoviesLens-1M are 0.905 and 0.835 and on the Book-Crossing are 0.698 and 0.640. Compared with the baseline model, the Precision@K index improved by 1.3–3% and the Recall@K index improved by 2.2~11.2% on the MoviesLens-1M dataset, while the Precision@K index improved by 0.6~1.6% and the Recall@K index improved by 4.5~10.8% on the Book-Crossing dataset. The model also achieves strong performance in data-sparse scenarios.
Dual-Word Embedding Model Considering Syntactic Information for Cross-Domain Sentiment Classification

The purpose of cross-domain sentiment classification (CDSC) is to fully utilize the rich labeled data in the source domain to help the target domain perform sentiment classification even when labeled data are insufficient. Most of the existing methods focus on obtaining domain transferable semantic information but ignore syntactic information. The performance of BERT may decrease because of domain transfer, and traditional word embeddings, such as word2vec, cannot obtain contextualized word vectors. Therefore, achieving the best results in CDSC is difficult when only BERT or word2vec is used. In this paper, we propose a Dual-word Embedding Model Considering Syntactic Information for Cross-domain Sentiment Classification. Specifically, we obtain dual-word embeddings using BERT and word2vec. After performing BERT embedding, we pay closer attention to semantic information, mainly using self-attention and TextCNN. After word2vec word embedding is obtained, the graph attention network is used to extract the syntactic information of the document, and the attention mechanism is used to focus on the important aspects. Experiments on two real-world datasets show that our model outperforms other strong baselines.