Reviewerly/exHarmony huggingface dataset:
Inner-Product Functional Encryption with Fine-Grained Access Control

We construct new functional encryption schemes that combine the access control functionality of attribute-based encryption with the possibility of performing linear operations on the encrypted data. While such a primitive could be easily realized from fully fledged functional encryption schemes, what makes our result interesting is the fact that our schemes simultaneously achieve all the following properties. They are public-key, efficient and can be proved secure under standard and well established assumptions (such as LWE or pairings). Furthermore, security is guaranteed in the setting where adversaries are allowed to get functional keys that decrypt the challenge ciphertext. Our first results are two functional encryption schemes for the family of functions that allow users to embed policies (expressed by monotone span programs) in the encrypted data, so that one can generate functional keys to compute weighted sums on the latter. Both schemes are pairing-based and quite generic: they combine the ALS functional encryption scheme for inner products from Crypto 2016 with any attribute-based encryption schemes relying on the dual-system encryption methodology. As an additional bonus, they yield simple and elegant multi-input extensions essentially for free, thereby broadening the set of applications for such schemes. Multi-input is a particularly desirable feature in our setting, since it gives a finer access control over the encrypted data, by allowing users to associate different access policies to different parts of the encrypted data. Our second result builds identity-based functional encryption for inner products from lattices. This is achieved by carefully combining existing IBE schemes from lattices with adapted, LWE-based, variants of ALS. We point out to intrinsic technical bottlenecks to obtain richer forms of access control from lattices. From a conceptual point of view, all our results can be seen as further evidence that more expressive forms of functional encryption can be realized under standard assumptions and with little computational overhead.
Exploring Visual Information Flows in Infographics

Infographics are engaging visual representations that tell an informative story using a fusion of data and graphical elements. The large variety of infographic design poses a challenge for their high-level analysis. We use the concept of Visual Information Flow (VIF), which is the underlying semantic structure that links graphical elements to convey the information and story to the user. To explore VIF, we collected a repository of over 13K infographics. We use a deep neural network to identify visual elements related to information, agnostic to their various artistic appearances. We construct the VIF by automatically chaining these visual elements together based on Gestalt principles. Using this analysis, we characterize the VIF design space by a taxonomy of 12 different design patterns. Exploring in a real-world infographic dataset, we discuss the design space and potentials of VIF in light of this taxonomy.
Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining

Small and Medium-sized Enterprises (SMEs) are playing a vital role in the modern economy. Recent years, financial risk analysis for SMEs attracts lots of attentions from financial institutions. However, the financial risk analysis for SMEs usually suffers data deficiency problem, especially for the mobile financial institutions which seldom collect credit-related data directly from SMEs. Fortunately, although credit-related information of SMEs is hard to be acquired sufficiently, the interactive relationships between SMEs, which may contain valuable information of financial risk, is usually available for the mobile financial institutions. Finding out credit-related relationship of SME from massive interactions helps comprehensively model the SMEs thus improve the performance of financial risk analysis. In this paper, tackling the data deficiency problem of financial risk analysis for SMEs, we propose an innovative financial risk analysis framework with graph-based supply chain mining. Specifically, to capture the credit-related topology structural and temporal variation information of SMEs, we design and employ a novel spatial-temporal aware graph neural network, to mine supply chain relationship on a SME graph, and then analysis the credit risk based on the mined supply chain graph. Experimental results on real-world financial datasets prove the effectiveness of our proposal for financial risk analysis for SMEs.
KLEJ: Comprehensive Benchmark for Polish Language Understanding

In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks.Such a fast pace of research would not be possible without general NLU benchmarks, which allow for a fair comparison of the proposed methods.However, such benchmarks are available only for a handful of languages.To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard.It consists of a diverse set of tasks, adopted from existing datasets for named entity recognition, question-answering, textual entailment, and others.We also introduce a new sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR).To ensure a common evaluation scheme and promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and applications.Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks.Finally, we provide an extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based models.
Scalable and Secure Big Data IoT System Based on Multifactor Authentication and Lightweight Cryptography

Organizations share an evolving interest in adopting a cloud computing approach for Internet of Things (IoT) applications. Integrating IoT devices and cloud computing technology is considered as an effective approach to storing and managing the enormous amount of data generated by various devices. However, big data security of these organizations presents a challenge in the IoT-cloud architecture. To overcome security issues, we propose a cloud-enabled IoT environment supported by multifactor authentication and lightweight cryptography encryption schemes to protect big data system. The proposed hybrid cloud environment is aimed at protecting organizations' data in a highly secure manner. The hybrid cloud environment is a combination of private and public cloud. Our IoT devices are divided into sensitive and nonsensitive devices. Sensitive devices generate sensitive data, such as healthcare data; whereas nonsensitive devices generate nonsensitive data, such as home appliance data. IoT devices send their data to the cloud via a gateway device. Herein, sensitive data are split into two parts: one part of the data is encrypted using RC6, and the other part is encrypted using the Fiestel encryption scheme. Nonsensitive data are encrypted using the Advanced Encryption Standard (AES) encryption scheme. Sensitive and nonsensitive data are respectively stored in private and public cloud to ensure high security. The use of multifactor authentication to access the data stored in the cloud is also proposed. During login, data users send their registered credentials to the Trusted Authority (TA). The TA provides three levels of authentication to access the stored data: first-level authentication - read file, second-level authentication - download file, and third-level authentication - download file from the hybrid cloud. We implement the proposed cloud-IoT architecture in the NS3 network simulator. We evaluated the performance of the proposed architecture using metrics such as computational time, security strength, encryption time, and decryption time.
On the Design of Chaos-Based S-Boxes

Substitution boxes (S-boxes) are critical nonlinear elements to achieve cryptanalytic resistance of modern block and stream ciphers.Given their importance, a rich variety of S-box construction strategies exists.In this paper, S-boxes generated by using chaotic functions (CF) are analyzed to measure their actual resistance to linear cryptanalysis.The aforementioned papers emphasize on the average nonlinearity of the S-box coordinates only, ignoring the rest of the S-box components in the process.Thus, the majority of those studies should be re-evaluated.Integrating such S-boxes in a given cryptosystem should be done with a considerable caution.Furthermore, we show that in the context of nonlinearity optimization problem the profit of using chaos structures is negligible.By using two heuristic methods and starting from pseudo-random S-boxes, we repeatedly reached S-boxes, which significantly outperform all previously published CF-based S-boxes, in those cryptographic terms, which the aforementioned papers utilize for comparison.Moreover, we have linked the multi-armed bandit problem to the problem of maximizing an S-box average coordinate nonlinearity value, which further allowed us to reach near-optimal average coordinate nonlinearity values significantly greater than those known in literature.
Chinese Image Caption Generation via Visual Attention and Topic Modeling

Automatic image captioning is to conduct the cross-modal conversion from image visual content to natural language text. Involving computer vision (CV) and natural language processing (NLP), it has become one of the most sophisticated research issues in the artificial-intelligence area. Based on the deep neural network, the neural image caption (NIC) model has achieved remarkable performance in image captioning, yet there still remain some essential challenges, such as the deviation between descriptive sentences generated by the model and the intrinsic content expressed by the image, the low accuracy of the image scene description, and the monotony of generated sentences. In addition, most of the current datasets and methods for image captioning are in English. However, considering the distinction between Chinese and English in syntax and semantics, it is necessary to develop specialized Chinese image caption generation methods to accommodate the difference. To solve the aforementioned problems, we design the NICVATP2L model via visual attention and topic modeling, in which the visual attention mechanism reduces the deviation and the topic model improves the accuracy and diversity of generated sentences. Specifically, in the encoding phase, convolutional neural network (CNN) and topic model are used to extract visual and topic features of the input images, respectively. In the decoding phase, an attention mechanism is applied to processing image visual features for obtaining image visual region features. Finally, the topic features and the visual region features are combined to guide the two-layer long short-term memory (LSTM) network for generating Chinese image captions. To justify our model, we have conducted experiments over the Chinese AIC-ICC image dataset. The experimental results show that our model can automatically generate more informative and descriptive captions in Chinese in a more natural way, and it outperforms the existing image captioning NIC model.
Boba: Authoring and Visualizing Multiverse Analyses

Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency. However, specifying a multiverse is demanding because analysts must manage myriad variants from a cross-product of analytic decisions, and the results require nuanced interpretation. We contribute Boba: an integrated domain-specific language (DSL) and visual analysis system for authoring and reviewing multiverse analyses. With the Boba DSL, analysts write the shared portion of analysis code only once, alongside local variations defining alternative decisions, from which the compiler generates a multiplex of scripts representing all possible analysis paths. The Boba Visualizer provides linked views of model results and the multiverse decision space to enable rapid, systematic assessment of consequential decisions and robustness, including sampling uncertainty and model fit. We demonstrate Boba's utility through two data analysis case studies, and reflect on challenges and design opportunities for multiverse analysis software.
Recency Aware Collaborative Filtering for Next Basket Recommendation

E-commerce and online services are getting more and more ubiquitous day by day. Like many other e-commerce paradigms, online grocery services can highly benefit from recommender systems, especially when it comes to predicting users' shopping behavior. This specific scenario owns peculiar characteristics, such as repetitiveness and loyalty, which makes the task very different from the standard recommendations. In this work, we present an efficient solution to compute the next basket recommendation, under a more general top-n recommendation framework. We propose a set of collaborative filtering based techniques able to capture users' shopping patterns. Furthermore, we analyzed how recency plays a key role in this particular task. We finally compare our method with state-of-the-art algorithms on two online grocery service datasets.
Data-Assisted Persona Construction Using Social Media Data

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.
DeepLRHE: A Deep Convolutional Neural Network Framework to Evaluate the Risk of Lung Cancer Recurrence and Metastasis From Histopathology Images

Predicting the recurrence risk of lung cancer is critical for appropriate adjuvant therapy of lung cancer patients after surgical resection. However, traditional circulating tumor cell (CTC) detection or next generation sequencing-based methods are usually expensive and time-inefficient, which urges the need for more efficient computational models. In this study, we have established a convolutional neural network (CNN) framework called DeepLRHE to predict the recurrence risk of lung cancer through analyzing histopathology images. DeepLRHE consists of a few steps including automatic tumor region identification, image normalization, biomarker identification, and sample classification. In practice, we used 110 lung cancer samples downloaded from the TCGA database to train and validate our CNN model. The area under curve (AUC) for the 2-fold cross-validation is 0.85, suggesting a relative good prediction performance. Our study demonstrates that the features extracted from histopathology images could be well used to predict lung cancer recurrence after surgical resection, and help classify patients who should receive additional adjuvant therapy.
An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction.The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR.The adaptive adjustment of the parameters has always been a research hotspot.However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars.The contradiction of big data prediction is especially prominent.In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA).Eight high-and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated.The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets.It ranks first with a score of 0.024 in the overall evaluation.The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR.KMCM, CSMA and SVR are skillfully integrated in this work and perform well.Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity.This work validated the tremendous potential of the proposed approach in the prediction field.
Evaluation of Online Teaching Quality of Basic Education Based on Artificial Intelligence

In the age of the Internet, basic education faces several new challenges: the lack of deep integration of artificial intelligence (AI), and the relatively poor quality of online teaching. To cope with these challenges, this paper designs an evaluation method for online teaching quality of basic education in the context of the AI. Firstly, the application of the AI in basic education was analyzed, and the promoting effect of online teaching on basic education was confirmed. On this basis, the entropy weight method and grey clustering analysis were introduced to evaluate the online teaching quality of basic ed-ucation. Based on the proposed model, several strategies were proposed to improve the quality of online teaching in basic education. The research re-sults provide a good reference for the application of online teaching and AI in basic education.
HittER: Hierarchical Transformers for Knowledge Graph Embeddings

This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph.We propose HittER, a Hierarchical Transformer model to jointly learn Entityrelation composition and Relational contextualization based on a source entity's neighborhood.Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block.We further design a masked entity prediction task to balance information from the relational context and the source entity itself.Experimental results show that HittER achieves new stateof-the-art results on multiple link prediction datasets.We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.
On the quantum correlations in two-qubit XYZ spin chains with Dzyaloshinsky–Moriya and Kaplan–Shekhtman–Entin-Wohlman–Aharony interactions

The anisotropic Heisenberg two-spin-1/2 model in an inhomogeneous magnetic field with both antisymmetric Dzyaloshinsky–Moriya and symmetric Kaplan–Shekhtman–Entin-Wohlman–Aharony cross interactions is considered at thermal equilibrium. Using a group-theoretical approach, we find fifteen spin Hamiltonians and as many corresponding Gibbs density matrices (quantum states) whose eigenvalues are expressed only through square radicals. We also found local unitary transformations that connect nine of this fifteen state collection, and one of them is the X quantum state. Since such quantum correlations as quantum entanglement, quantum discord, one-way quantum work deficit, and others are known for the X state, this allows to get the quantum correlations for any member from the nine state family. Further, we show that the remaining six quantum states are separable and that they are also connected by local unitary transformations, but, however, now the case with known correlations beyond entanglement is generally not available.
Towards a Quantum Software Modeling Language

We set down the principles behind a modeling language for quantum software. We present a minimal set of extensions to the well-known Unified Modeling Language (UML) that allows it to effectively model quantum software. These extensions are separate and independent of UML as a whole. As such they can be used to extend any other software modeling language, or as a basis for a completely new language. We argue that these extensions are both necessary and sufficient to model, abstractly, any piece of quantum software. Finally, we provide a small set of examples that showcase the effectiveness of the extension set.
Augmented Reality as a Sustainable Technology to Improve Academic Achievement in Students with and without Special Educational Needs

Virtual reality has impacted education, where progressively more educational institutionsconsider its inclusion. The research problem derives from the need to study the educational possibilitiesprovided by integrating augmented reality into the curriculum, and its effect on academic achievement ina diverse class, specifically in the chemistry subject. This study examines 60 school-age participants withandwithout special educational needs, and addresses three overarching questions: (a)Would integratingaugmented reality (AR) technology result in better academic achievement? (b)Would knowledge beretained longer by using AR? (c) Is there any relationship between academic achievement, acceptanceand motivation regarding the use of this technology? Embracing the socio-constructivist theory oflearning and collaborative and immersive learning as a framework, this study was carried out usinga quantitative approach and a pre-experimental design. The AR VR Molecules Editor applicationwas used in chemistry lessons. Main results showed significant immediate academic achievementand content retention. Despite classroom diversity, immersive technologies enhance students’ learningregardless of whether they have special educational needs (SEN) or not. They also acknowledge that ARis a suitable sustainable technology that may foster social and cognitive justice and inclusive education,and train students that are equally prepared for the dynamic future.