Miracle-dz/UniFAR-FTUs huggingface dataset:
Court Opinion Generation from Case Fact Description with Legal Basis

In this study, we proposed an approach to automatically generating court view from the fact description of a legal case. This is a text-to-text natural language generation problem, and it can help the automatic legal document generation. Due to the specialty of the legal domain, our model exploits the charge and law article information in the generation process, instead of utilizing just the fact description text. The BERT model is used as the encoder and a Transformer architecture is used as decoder. To smoothly integrate these two parts together, we employ two separate optimizers for the two components during the training process. The experiments on two data sets of Chinese legal cases show that our approach outperforms other methods.
Hierarchical Routing Protocols for Wireless Sensor Networks: Functional and Performance Analysis

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, and pressure, and to cooperatively forward the collected information to the destination through the network infrastructure. As sensor nodes are energy constraint devices, therefore, the importance of energy efficient routing protocols has been increased. In order to minimize energy consumption, recently, a number of hierarchical routing protocols are proposed. For instance, LEACH is an elementary hierarchical routing protocol that employs clustering technique to achieve energy efficiency. A lot of research work has been performed to remove shortcomings and to improve the performance of hierarchical routing protocols. Therefore, a comprehensive review is required which can review state-of-the-art technologies, analyze functional and performance aspects, and highlight hierarchical routing protocol issues and challenges in WSNs. This paper proposes a taxonomy for the classification of existing hierarchical routing protocols for WSNs and analyzes the functionality and performance of existing hierarchical routing protocols. Moreover, it compares existing routing protocols to highlight key technological differences and provides performance comparison for the selected LEACH based routing protocols. Finally, the paper spotlights issues and challenges in existing routing protocols of WSNs, which can assist in future research for the selection of appropriate research domain and provide guidance in selection of energy efficient techniques in the design of energy efficient of routing protocols for WSNs.
Improving the Efficiency and Effectiveness for BERT-based Entity Resolution

BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed to fine-tuning pre-trained language models and the deep pair-wise interaction. Albeit being remarkably effective, it comes with a steep increase in computational cost, as the deep-interaction requires to exhaustively compute every tuple pair to search for co-references. For ER task, it is often prohibitively expensive due to the large cardinality to be matched. To tackle this, we introduce a siamese network structure that independently encodes tuples using BERT but delays the pair-wise interaction via an enhanced alignment network. This siamese structure enables a dedicated blocking module to quickly filter out obviously dissimilar tuple pairs, and thus drastically reduces the cardinality of fine-grained matching. Further, the blocking and entity matching are integrated into a multi-task learning framework for facilitating both tasks. Extensive experiments on multiple datasets demonstrate that our model significantly outperforms state-of-the-art models (including BERT) in both efficiency and effectiveness.
Playing Planning Poker in Crowds: Human Computation of Software Effort Estimates

Reliable cost effective effort estimation remains a considerable challenge for software projects. Recent work has demonstrated that the popular Planning Poker practice can produce reliable estimates when undertaken within a software team of knowledgeable domain experts. However, the process depends on the availability of experts and can be time-consuming to perform, making it impractical for large scale or open source projects that may curate many thousands of outstanding tasks. This paper reports on a full study to investigate the feasibility of using crowd workers supplied with limited information about a task to provide comparably accurate estimates using Planning Poker. We describe the design of a Crowd Planning Poker (CPP) process implemented on Amazon Mechanical Turk and the results of a substantial set of trials, involving more than 5000 crowd workers and 39 diverse software tasks. Our results show that a carefully organised and selected crowd of workers can produce effort estimates that are of similar accuracy to those of a single expert.
Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation

Given recent advances in deep music source separation, a feature representation method is proposed that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). A depthwise separable convolutional neural network is trained on a challenging electronic dance music (EDM) data set and its performance is compared to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.
NEO: A System for Identifying New Emerging Occupation from Job Ads

We demonstrate NEO, a tool for automatically enriching the European Occupation and Skill Taxonomy (ESCO) with terms that represents new occupations extracted from million Online Job Advertisements (OJAs). NEO proposes (i) a novel metric that allows one to measure the semantic similarity between words in a taxonomy, and (ii) a set of measures that estimate the adherence of new terms to the most suited taxonomic concept, enabling the user to evaluate the suggestions. To test its effectiveness, NEO has been evaluated over 2M+ 2018 UK job ads, along with a user-study to confirm the usefulness of NEO in the taxonomy enrichment task.
Applications of Reinforcement learning for Medical Decision Making

Reinforcement Learning(RL) is used for decision-making by interacting with uncertain/complex environments with the aim of maximizing long-term reward following a certain policy along with evaluative feedback for improvement. RL is advantageous in medical decision making compared to other forms of learning as it focuses on long-term rewards, it is also able to handle long and complex sequential decision-making tasks with sampled, delayed, and exhaustive feedback. It has emerged as a suitable method for developing satisfactory solutions in the healthcare domain. Improvement in the healthcare system can be achieved by integrating traditional health care practices with RL methods by considering health status of a patient. In this paper, we have discussed various applications of RL that would be helpful in providing effective decisions for improving patient health treatment, prognosis, diagnosis, and condition . RL could be effective in the area of healthcare right from medical diagnosis to handling various critical decision-making tasks. The paper provides a broad view of the various applications of RL in the sector of healthcare. The paper illustrates various RL applications that would be effective in improving the existing healthcare sector at same time being efficient in handling complex medical decision-making tasks.
Man in the Middle: Attack and Protection

The purpose of this paper is to take a closer look at Man-In-The-Middle (will be referred to as MITM) attack and defense. MITM also referred to in certain literature as a hijack attack, is one of the most well-known and widespread attacks in cybersecurity, targeting connection between two parties and directly putting into jeopardy the confidentiality and coherence of the data itself. This paper will delve into the current situation of cybersecurity and usage of Man-In-The-Middle attacks, what constitutes a proper MITM attack, why this approach is chosen among many other options, how such an attack is implemented in a real-life scenario and how we can achieve maximal protection for both individuals and systems.
Visual knowledge: an attempt to explore machine creativity

One question that has long puzzled the artificial intelligence (AI) community is: Can AI be creative? Or, can the reasoning process be creative? Starting at noetic science, this paper discusses the issues of visual knowledge representation and its potential applications to machine creativity. In this paper, we enumerate related research on imagery-thinking-based reasoning, then focus on a special type of visual knowledge representation, i.e., visual scene graph, and finally review the problem of visual scene graph construction and its potential applications in detail. All the evidence suggests that visual knowledge and visual thinking not only can improve the performance of current AI tasks but can be used in the practice of machine creativity. AI has ushered in a new era of development. Existing algorithms have achieved fairly good results in clustering, classification, logical reasoning, and proving. However, looking back at the early definition of AI (McCarthy et al., 2006) which aims at machines recognizing, thinking, and learning like humans, there is still a huge gap in current algorithms, especially in terms of human-like creativity. As early as the 1980s, an AI boom was ongoing, while its guiding concepts were still under discussion. One of China’s most prominent scientists Xuesen QIAN proposed that China should establish the field of noetic science to study the laws and forms of human-like thinking activity. Noetic science is the subject of studying the relationship between consciousness and the brain, mind and matter, subjective and objective. QIAN advocated that the development of noetic science should be combined with AI and computers and that it should be addressed by the construction of abstract thinking and imagery (intuitive) thinking, social thinking, and peculiar thinking (inspirational thinking). QIAN’s proposition coincides with early research in brain science in the 1960s (Gazzaniga, 1967); that is, the left brain is responsible for logical thinking, such as language, logical analysis, reasoning, abstraction, computational language memory, and writing, while the right brain is responsible for imagery thinking including intuition, emotion, graphic perception, imagery memory, art, music, vision, physical coordination, and inspiration. His ideas and suggestions broke through the mainstream framework of AI at that time, inspiring the realization of machine creativity. Even today, these ideas and suggestions still bear significant importance and theoretical value guiding future work. In recent years, as AI has developed, mainstream research communities including top journals (such as Science and Nature) and top AI conferences (such as AAAI and IJCAI) have also begun to focus on intelligence that is capable of creativity. The core question is the simulation of creative thinking; that is, can AI be creative? Can the reasoning process be creative? Frontiers of Information Technology & Electronic Engineering www.jzus. zju.edu. cn; engineering.cae. cn; www.springerlink. com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: jzus@zju.edu. cn
A Deep Learning Framework for Improving Lameness Identification in Dairy Cattle

Lameness, characterized by an anomalous gait in cows due to a dysfunction in their locomotive system, is a serious welfare issue for cows and farmers. Prompt lameness detection methods can prevent the development of acute lameness in cattle. In this study, we propose a deep learning framework to help identify lameness based on motion curves of different leg joints on the cow. The framework combines data augmentation and a convolutional neural network using an LeNet architecture. Performance assessed using cross validation showed promising prediction accuracies above 99% and 91% for validation and test sets, respectively. This also demonstrates the usefulness of data generation in cases where the data set is originally small in size and difficult to generate.
Distributed Situation Awareness for Multi-agent Mission in Dynamic Environments: A Case Study of Multi-UAVs Wildfires Searching

This thesis focuses attention on achieving Distributed Situation Awareness (DSA) with minimal resources (energy, processing cost, etc.) using small low-capacity agents (e.g., UAVs) coordinated in a decentralised fashion while conducting searching activity. This is in contrast to the existing works involving convoluted communication and information processing.
Gerricola queenslandensis n. g., n. sp., a new monorchiid trematode from the eastern Australian coast and its life cycle partially elucidated

Abstract Of over 250 species of Monorchiidae Odhner, 1911, just four are known from gerreid fishes. Here, we report adult specimens of a new species infecting Gerres oyena (Forsskål) and Gerres subfasciatus Cuvier from off Heron Island and North Stradbroke Island, Queensland, Australia. The species is morphologically most similar to the concept of Lasiotocus Looss, 1907, which currently comprises eight species, in the possession of an unspined genital atrium, bipartite terminal organ, round oral sucker and unlobed ovary. However, phylogenetic analyses of the 28S ribosomal DNA gene region shows the species to be distantly related to the two sequenced species of Lasiotocus – Lasiotocus mulli (Stossich, 1883) Odhner, 1911 and Lasiotocus trachinoti Overstreet & Brown, 1970 – and that it clearly requires a distinct genus; thus, we propose Gerricola queenslandensis n. g. , n. sp. Morphologically, G. queenslandensis n. g. , n. sp. differs significantly from L. mulli and L. trachinoti only in the possession of distinctly longer caeca, which terminate in the post-testicular region, and in the absence of a distinct gap in the terminal organ spines. The remaining species of Lasiotocus possess caeca that also terminate in the post-testicular region, which might warrant their transfer to Gerricola n. g. However, doubt about their monophyly due to a combination of significant morphological variation, a lack of information on some features and infection of a wide range of hosts, lead us to retain these taxa as species of Lasiotocus until molecular sequence data are available to better inform their phylogenetic and taxonomic positions. Sporocysts and cercariae of G. queenslandensis n. g. , n. sp. were found in a lucinid bivalve, Codakia paytenorum (Iredale), from Heron Island. Sexual adult and intramolluscan stages were genetically matched with the ITS2 ribosomal DNA and cox1 mitochondrial DNA regions. This is the second record of the Lucinidae as a first intermediate host for the Monorchiidae. Additionally, we report sporocysts and cercariae of another monorchiid infection in a tellinid bivalve, Jactellina clathrata (Deshayes), from Heron Island. Molecular sequence data for this species do not match any sequenced species and phylogenetic analyses do not suggest any generic position.
Logic of Machine Learning

The main question is: why and how can we ever predict based on a finite sample? The question is not answered by statistical learning theory. Here, I suggest that prediction requires belief in “predictability” of the underlying dependence, and learning involves search for a hypothesis where these beliefs are violated the least given the observations. The measure of these violations (“errors”) for given data, hypothesis and particular type of predictability beliefs is formalized as concept of incongruity in modal Logic of Observations and Hypotheses (LOH). I show on examples of many popular textbook learners (from hierarchical clustering to k-NN and SVM) that each of them minimizes its own version of incongruity. In addition, the concept of incongruity is shown to be flexible enough for formalization of some important data analysis problems, not considered as part of ML.
On the antichain tree property

In this note, we investigate a new model theoretical tree property, called the antichain tree property (ATP). We develop combinatorial techniques for ATP. First, we show that ATP is always witnessed by a formula in a single free variable, and for formulas, not having ATP is closed under disjunction. Second, we show the equivalence of ATP and $k$-ATP, and provide a criterion for theories to have not ATP (being NATP). Using these combinatorial observations, we find algebraic examples of ATP and NATP, including pure group, pure fields, and valued fields. More precisely, we prove Mekler's construction for groups, Chatzidakis' style criterion for PAC fields, and the AKE-style principle for valued fields preserving NATP. And we give a construction of an antichain tree in the Skolem arithmetic and atomless Boolean algebras.
Tunable Trajectory Planner Using G3 Curves

Trajectory planning is commonly used as part of a local planner in autonomous driving. This paper considers the problem of planning a continuous-curvature-rate trajectory between fixed start and goal states that minimizes a tunable trade-off between passenger comfort and travel time. The problem is an instance of infinite dimensional optimization over two continuous functions: a path and a velocity profile. We propose a simplification of this problem that facilitates the discretization of both functions. This paper also proposes a method to quickly generate minimal-length paths between start and goal states based on a single tuning parameter: the second derivative of curvature. Further, we discretize the set of velocity profiles along a given path into a selection of longitudinal jerk way-points along the path. Finally, we repeatedly solve the path and velocity profiles in an iterative fashion. Numerical examples are provided to illustrate the benefits of the proposed methods.
Parallel Batch-Dynamic k-Core Decomposition

Maintaining a 𝑘 -core decomposition quickly in a dynamic graph is an important problem in many applications, including social network analytics, graph visualization, centrality measure computations, and community detection algorithms. The main challenge for designing efficient 𝑘 -core decomposition algorithms is that a single change to the graph can cause the decomposition to change significantly. Wepresent the first parallel batch-dynamic algorithm for maintaining an approximate 𝑘 -core decomposition that is efficient in both theory and practice. Given an initial graph with 𝑚 edges, and a batch of 𝐵 updates, our algorithm maintains a ( 2 + 𝛿 ) -approximation of the coreness values for all vertices (for any constant 𝛿 > 0) in 𝑂 ( 𝐵 log 2 𝑚 ) amortized work and 𝑂 ( log 2 𝑚 loglog 𝑚 ) depth (parallel time) with high probability. Our algorithm also maintains a low out-degree orientation of the graph in the same bounds. We implemented and experimentally evaluated our algorithm on a 30-core machine with two-way hyper-threading on 11 graphs of varying densities and sizes. Compared to the state-of-the-art algorithms, our algorithm achieves up to a 114.52x speedup against the best multicore implementation and up to a 497.63x speedup against the best sequential algorithm, obtaining results for graphs that are orders-of-magnitude larger than those used in previous studies. In addition, we present the first approximate static 𝑘 -core algo-rithm with linear work and polylogarithmic depth. We show that on a 30-core machine with two-way hyper-threading, our implementation achieves up to a 3.9x speedup in the static case over the previous state-of-the-art parallel algorithm.
Probing prerecombination physics by the cross-correlation of stochastic gravitational waves and CMB anisotropies

We study the effects of pre-recombination physics on the Stochastic Gravitational Wave Background (SGWB) anisotropies induced by the propagation of gravitons through the large-scale density perturbations and their cross-correlation with Cosmic Microwave Background (CMB) temperature and E-mode polarization ones. As examples of Early Universe extensions to the $\Lambda$CDM model, we consider popular models featuring extra relativistic degrees of freedom, a massless non-minimally coupled scalar field, and an Early Dark Energy component. Assuming the detection of a SGWB, we perform a Fisher analysis to assess in a quantitative way the capability of future gravitational wave interferometers (GWIs) in conjunction with a future large-scale CMB polarization experiment to constrain such variations. Our results show that the cross-correlation of CMB and SGWB anisotropies will help tighten the constraints obtained with CMB alone, with an improvement that significantly depends on the specific model as well as the maximum angular resolution $\ell_{\rm max}^{\rm GW}$ of the GWIs, their designed sensitivity, and the amplitude $A_*$ of the monopole of the SGWB.
A Simple Recipe for Multilingual Grammatical Error Correction

This paper presents a simple recipe to trainstate-of-the-art multilingual Grammatical Error Correction (GEC) models. We achieve this by first proposing a language-agnostic method to generate a large number of synthetic examples. The second ingredient is to use large-scale multilingual language models (up to 11B parameters). Once fine-tuned on language-specific supervised sets we surpass the previous state-of-the-art results on GEC benchmarks in four languages: English, Czech, German and Russian. Having established a new set of baselines for GEC, we make our results easily reproducible and accessible by releasing a CLANG-8 dataset. It is produced by using our best model, which we call gT5, to clean the targets of a widely used yet noisy Lang-8 dataset. cLang-8 greatly simplifies typical GEC training pipelines composed of multiple fine-tuning stages – we demonstrate that performing a single fine-tuning stepon cLang-8 with the off-the-shelf language models yields further accuracy improvements over an already top-performing gT5 model for English.
COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

We present CosmoPower, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than 0.4% in the wavenumber range k ∈ [10−5, 10]Mpc−1, for redshift z ∈ [0, 5]. CosmoPower emulates CMB temperature, polarisation and lensing potential power spectra in the 5σ region of parameter space around the Planck best fit values with an error . 10% of the expected shot noise for the forthcoming Simons Observatory. CosmoPower is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV Euclid-like simulated cosmic shear analysis. For the CMB case, CosmoPower is tested on a Planck 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to O (104) to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the Planck likelihood case. CosmoPower is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available.