Reviewerly/exHarmony huggingface dataset:
Retrieval of Scientific Documents Based on HFS and BERT

When retrieving scientific documents with mathematical expressions as the main content, both mathematical expressions and their contextual text features require consideration. However, mathematical expressions are different from texts in terms of grammar and semantics. Thus, integrating the above features and realizing scientific document retrieval is difficult. In this study, a retrieval method of scientific documents based on HFS (Hesitation Fuzzy Sets) and BERT (Bidirectional Encoder Representations from Transformer) is proposed. This method is realized through utilizing the advantages of HFS in multi-attribute decision making and BERT in context-dependent similarity calculation. By analyzing mathematical expressions and calculating the membership degree of symbolic multi-attributes, the similarity of mathematical expressions can be obtained, which can improve the accuracy of mathematical expression recall. With the extraction of the text of the expression context, BERT is used to calculate the context similarity. Then, the recalled technical documents are sorted according to the similarity of context, and the final retrieval result can be obtained. Experiments were carried out on 10,372 Chinese and 11,770 English scientific documents in the NTCIR extended data set. The average value of MAP_ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k (k=10)$ </tex-math></inline-formula> for the recall results of scientific documents was 74.13%. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> DCG ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=10$ </tex-math></inline-formula> ) for the ranking of scientific documents was 86.04%.
Self-dual and LCD double circulant and double negacirculant codes over $${\mathbb {F}}_q+u{\mathbb {F}}_q+v{\mathbb {F}}_q$$

Let q be an odd prime power, and denote by $${\mathbb {F}}_q$$ the finite field with q elements. In this paper, we consider the ring $$R={\mathbb {F}}_q+u{\mathbb {F}}_q+v{\mathbb {F}}_q$$ , where $$u^2=u, v^2=v,uv=vu=0$$ and study double circulant and double negacirculant codes over this ring. We first obtain the necessary and sufficient conditions for a double circulant code to be self-dual (resp. LCD). Then we enumerate self-dual and LCD double circulant and double negacirculant codes over R. Last but not the least, we show that the family of Gray images of self-dual and LCD double circulant codes over R are good. Several numerical examples of self-dual and LCD codes over $${\mathbb {F}}_5$$ as the Gray images of these codes over R are given in short lengths.
GENERATOR Breast DataMart—The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into “Not organized, not ‘ontologized’ data”, “Organized, not ‘ontologized’ data”, and “Organized and ‘ontologized’ data”. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.
Financial access, governance and insurance sector development in sub-Saharan Africa

Purpose This study investigates the role of financial access in moderating the effect of governance on insurance consumption in 42 sub-Saharan African countries using data for the period 2004–2014. Design/methodology/approach Two life insurance indicators are used, notably: life insurance and non-life insurance. Six governance measurements are also used, namely: political stability, ‘voice and accountability’, government effectiveness, regulation quality, corruption-control and the rule of law. The empirical evidence is based on the Generalised Method of Moments (GMM) and Least Squares Dummy Variable Corrected (LSDVC) estimators. Findings Estimations from the LSDVC are not significant while the following main findings are established from the GMM. First, financial access promotes life insurance through channels of political stability, ‘voice and accountability’, government effectiveness, the rule of law and corruption-control. Second, financial access also stimulates non-life insurance via governance mechanisms of political stability, ‘voice and accountability’, government effectiveness, regulation quality, the rule of law and corruption-control. Originality/value This research complements the sparse literature on insurance promotion in Africa by engaging the hitherto unexplored role of financial access through governance channels.
Information aggregation and fusion in deep neural networks for object interaction exploration for semantic segmentation

To tackle the semantic segmentation task, which is a fundamental problem in computer vision, various approaches have been proposed. However, how to utilize object interaction information for improving semantic segmentation performances is not paid enough attention to. In this paper, we propose a method for information aggregation and fusion for exploring object interaction information effectively for improving semantic segmentation performances. Specifically, we propose a logit aggregation strategy to explore object interaction information for semantic segmentation. Furthermore, to facilitate object interaction to guide the training of the semantic segmentation model, we propose to fuse features from intermediate layers of the model to aid pixel semantic label predication. And to fuse these features effectively, a buffered layer connection approach is presented. The proposed method is evaluated extensively in experiments. Obtained results demonstrate the effectiveness of the proposed method.
Applications of #SAT Solvers on Feature Models

Product lines are ubiquitous for managing variable systems. The variability of a product line is typically described in terms of a feature model. Analyzing a feature model gives insight into various aspects, such as the validity of a configuration of features. Several analyses have been considered that require computing the number of valid configurations which proves highly inefficient when using regular SAT solvers. A #SAT solver computes the number of satisfying variable assignments of a propositional formula and is specifically optimized for the aforementioned analyses. In this work, we summarize and unify the state-of-the-art on #SAT-based feature-model analyses and propose a variety of new #SAT-based analyses. We provide an exhaustive catalogue for applications of #SAT for feature models serving as a reference for researchers and practitioners. Furthermore, we show that all these 21 applications are based on only three different #SAT queries. Thus, future research can focus on providing solutions and optimizations for those three queries to accelerate #SAT-based applications.
Proof-Carrying Data Without Succinct Arguments

Proof-carrying data (PCD) is a powerful cryptographic primitive that enables mutually distrustful parties to perform distributed computations that run indefinitely. Known approaches to construct PCD are based on succinct non-interactive arguments of knowledge (SNARKs) that have a succinct verifier or a succinct accumulation scheme. In this paper we show how to obtain PCD without relying on SNARKs. We construct a PCD scheme given any non-interactive argument of knowledge (e.g., with linear-size arguments) that has a split accumulation scheme, which is a weak form of accumulation that we introduce. Moreover, we construct a transparent non-interactive argument of knowledge for R1CS whose split accumulation is verifiable via a (small) constant number of group and field operations. Our construction is proved secure in the random oracle model based on the hardness of discrete logarithms, and it leads, via the random oracle heuristic and our result above, to concrete efficiency improvements for PCD. Along the way, we construct a split accumulation scheme for Hadamard products under Pedersen commitments and for a simple polynomial commitment scheme based on Pedersen commitments. Our results are supported by a modular and efficient implementation.
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression.

We propose a new model that estimates uncertainty in a single forward pass and works on both classification and regression problems. Our approach combines a bi-Lipschitz feature extractor with an inducing point approximate Gaussian process, offering robust and principled uncertainty estimation. This can be seen as a refinement of Deep Kernel Learning (DKL), with our changes allowing DKL to match softmax neural networks accuracy. Our method overcomes the limitations of previous work addressing deterministic uncertainty quantification, such as the dependence of uncertainty on ad hoc hyper-parameters. Our method matches SotA accuracy, 96.2% on CIFAR-10, while maintaining the speed of softmax models, and provides uncertainty estimates that outperform previous single forward pass uncertainty models. Finally, we demonstrate our method on a recently introduced benchmark for uncertainty in regression: treatment deferral in causal models for personalized medicine.
A formal analysis of some factor- and precedent-based accounts of precedential constraint

Abstract In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically examined. An alternative to their reason models is proposed which is less expressive but arguably easier to apply in practice.
Dynamic graph embedding for outlier detection on multiple meteorological time series

Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE. Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related. The graph proximity is described as the distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.
Goblint: Thread-Modular Abstract Interpretation Using Side-Effecting Constraints

Abstract Goblint is a static analysis framework for C programs specializing in data race analysis. It relies on thread-modular abstract interpretation where thread interferences are accounted for by means of flow-insensitive global invariants.
Oracle Bone Inscriptions information processing based on multi-modal knowledge graph

To solve the problems of the great learning difficulty, the long learning period, wide range of knowledge points but weak knowledge connection, and low sharing of Oracle Bone Studies (OBS), a solution of constructing a multi-modal knowledge graph is proposed. Because OBS research involves various kinds of modal data, and these modalities need to be combined together to solve some problems. The OBS multi-modal knowledge graph can provide a unified semantic space for multi-source heterogeneous data. Through multi-modal fusion and information complementation, the defects of a single modality in information processing can be resolved. This multi-modal knowledge graph organizes and manages the basic data better to serve Oracle Bone Inscriptions (OBI) information processing research. Taking OBI detection and recognition as examples, we studied the applications of OBS multi-modal knowledge graph. The experimental results demonstrate that the proposed method reaches 81.3% accuracy in detection and 80.43% accuracy in recognition, and it has 3.7% in detection and 14.8% in recognition improved to the conventional methods.
Mapping farmer perceptions, Conservation Agriculture practices and on-farm measurements: The role of systems thinking in the process of adoption

Conservation Agriculture (CA) usage, particularly in Southern Africa, has remained low with lower yield, higher weed pressure and lower soil quality cited as reasons for ‘disadoption’. Using a detailed case study of 50 farmers in two villages in Cabo Delgado (Northern Mozambique), this study seeks to test the hypothesis that farmers' perceptions of CA are associated with distinctly different ‘mental models’ and if these "ways of thinking" overlap with farmers' identified/self-identified groupings (e.g. CA users, ‘disadopters’ and conventional tillage users). Secondly, we examine whether these different mental models (perceptions) are associated with actual differences in on-farm measurements. Finally, we explore the hypothesis that ‘systems thinking’ (i.e., understanding nonlinear causal relationships and internal feedback loops that drive a complex system) and CA usage are positively associated. Fuzzy Cognitive Mapping (FCM) was used to elicit representations of farmers' mental models. To explore the association between farmers' mental models of CA/conventional practices and on-farm measurements we evaluated cowpea aboveground biomass, yield, weed cover, and soil quality parameters from the farmer's main plot. We drew on network analysis to measure structural metrics of cognitive maps that provide important information about a person's mental model (perceptions) of causal interdependencies of farming dynamics. We find evidence of two data-driven distinct clusters of farmers' mental models that are in relative alignment with farmers' identified/self-identified groupings. Cluster 1 mainly consists of conventional users and cluster 2 mainly consists of CA users/disadopters. While no significant differences in socio-demographic variables were observed, clusters of mental models were associated with key differences in on-farm measurements. Importantly, cluster 1, who tended to be conventional users, had lower yields, lower soil cover, significantly lower carbon stock and higher weed coverage than cluster 2. Soil quality indicators were higher in cluster 2 as were farmers' overall revenue per hectare. Moreover, cluster 2 had significantly higher degrees of ‘systems thinking’ (measured through complex network analysis of graphical mental models) than cluster 1 which had higher forms of linear thinking. We argue that higher forms of experiential learning and practice of CA relate to higher degrees of systems thinking and stronger positive perceptions of CA, even among the CA ‘disadopters’. Our findings highlight the importance of systems thinking abilities and inclusion of biophysical, socio-economic and mental modelling variables rather than simple binary measurements which may have led to erroneous conclusions on CA and thus has implications for how CA is understood and promoted in future.
The Helsinki submission to the AmericasNLP shared task

The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs.Our multilingual NMT models reached the first rank on all language pairs in track 1, and first rank on nine out of ten language pairs in track 2. We focused our efforts on three aspects: (1) the collection of additional data from various sources such as Bibles and political constitutions, (2) the cleaning and filtering of training data with the OpusFilter toolkit, and (3) different multilingual training techniques enabled by the latest version of the OpenNMT-py toolkit to make the most efficient use of the scarce data.This paper describes our efforts in detail.
Tolerating Data Missing in Breast Cancer Diagnosis from Clinical Ultrasound Reports via Knowledge Graph Inference

Medical diagnosis through artificial intelligence has been drawing increasing attention currently. For breast lesions, the clinical ultrasound reports are the most commonly used data in the diagnosis of breast cancer. Nevertheless, the input reports always encounter the inevitable issue of data missing. Unfortunately, despite the efforts made in previous approaches that made progress on tackling data imprecision, nearly all of these approaches cannot accept inputs with data missing. A common way to alleviate the data missing issue is to fill the missing values with artificial data. However, the data filling strategy actually brings in additional noises that do not exist in the raw data. Inspired by the advantage of open world assumption, we regard the missing data in clinical ultrasound reports as non-observed terms of facts, and propose a Knowledge Graph embedding based model KGSeD with the capability of tolerating data missing, which can successfully circumvent the pollution caused by data filling. Our KGSeD is designed via an encoder-decoder framework, where the encoder incorporates structural information of the graph via embedding, and the decoder diagnose patients by inferring their links to clinical outcomes. Comparative experiments show that KGSeD achieves noticeable diagnosis performances. When data missing occurred, KGSeD yields the most stable performance over those of existing approaches, showing better tolerance to data missing.
Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework

Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by interacting with a reward-free environment and collects a dataset of transitions by executing the policy. In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment. This framework is suitable for the meta RL setting where there are many reward functions of interest. In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. The success of using Renyi entropy as the objective results from its encouragement to explore the hard-to-reach state-actions. We further deduce a policy gradient formulation for this objective and design a practical exploration algorithm that can deal with complex environments. In the planning phase, we solve for good policies given arbitrary reward functions using a batch RL algorithm. Empirically, we show that our exploration algorithm is effective and sample efficient, and results in superior policies for arbitrary reward functions in the planning phase.
Contrastive Aligned Joint Learning for Multilingual Summarization

Multilingual text summarization requires the ability to understand documents in multiple languages and generate summaries in the corresponding language, which poses more challenges on current summarization systems.However, this problem has been rarely studied due to the lack of large-scale supervised summarization data in multiple languages.In this paper, we first provide a large-scale multilingual summarization corpus MLGSum consisting of 1.1 million articles and summaries in 12 different languages.Based on it, we develop a unified summarization model to understand the document and generate summaries in different languages.We use the contrastive learning strategy to train our multilingual summarization system (CALMS), which consists of two training objectives, contrastive sentence ranking (CSR) and sentence aligned substitution (SAS).The two training objectives are designed to share salient information extractive ability and align sentencelevel representation across different languages.Experimental results indicate that CALMS achieves significant improvement over monolingual models in all languages.We further transfer CALMS to other languages and find that it will also benefit similar languages.Our code and dataset are available at https://github.com/brxx122/CALMS.
Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

We investigate indoor human multi-target tracking in cartesian coordinates based on range, Doppler and Angle-of-Arrival measurements obtained with a four-antenna passive bistatic radar capturing 802.11ax Wi-Fi signals. A reference antenna selection method is described to perform angle processing correctly when dealing with target detection diversity among antennas. The tracking is performed by an Unscented Kalman Filter (UKF) to handle the non-linear relation between the measurement space and the state space. A Joint Probabilistic Data Association Filter is coupled to the UKF to handle the data association between tracks and measurements when dealing with multiple targets. Simulations are performed to determine the tracking parameters under heavy constraints and identify key scenarios. An experimental setup is built using Universal Software Radio Peripherals, featuring an over-the-air phase calibration for angle processing with an anchor antenna. It is used to validate the proposed single and multi-target tracking scheme.