Reviewerly/exHarmony huggingface dataset:
GraMMy: Graph representation learning based on micro–macro analysis

Graph Neural Networks (GNNs) are robust variants of deep network models, typically designed to learn from graph-structured data. Despite the recent advancement of GNNs, the basic message passing scheme of learning often holds back these models in effectively capturing the influence of the nodes from higher order neighbourhood. Further, the state-of-the-art approaches mostly ignore the contextual significance of the paths through which the message/information propagates to a node. In order to deal with these two issues, we propose GraMMY as a novel framework for hierarchical semantics-driven graph representation learning based on Micro-Macro analysis. The key idea here is to study the graph structure from different levels of abstraction, which not only provides an opportunity for flexible flow of information from both local and higher-order neighbours but also helps in more concretely capturing how information travels within various hierarchical structures of the graph. We incorporate the knowledge gained from micro and macro level semantics into the embedding of a node and use this to perform graph classification. Experimentations on four bio-informatics and two social datasets exhibit the superiority of GraMMy over state-of-the-art GNN-based graph classifiers.
A homotopy gated recurrent unit for predicting high dimensional hyperchaos

Due to recurrent neural network’s (RNN) powerful ability to process sequential data, RNN attracts much attention in various fields. However, the long-term dependency has always been a major challenge for RNN. In this paper, a homotopy gated recurrent unit (H-GRU) model is proposed for predicting hyperchaos, which tries to improve the long-term dependence of RNN. We develop the model in three tasks: Mackey–Glass (τ = 30), hyperchaotic Rössler, and 4D hyperchaos of Chen et al. (2018). Compared with the four pre-existing prediction models, the prediction accuracy of H-GRU is the highest among baseline models, which demonstrates the merit of the proposed model. Furthermore, the proposed model has significant improvement in replicating hyperchaotic attractors. Especially in 4D hyperchaos tasks, not only is the prediction accuracy of H-GRU three orders of magnitude higher than the baseline model, but also H-GRU can replicate hyperchaotic attractors more accurately.
Reinforcement Learning with Option Machines

Reinforcement learning (RL) is a powerful framework for learning complex behaviors, but lacks adoption in many settings due to sample size requirements. We introduce a framework for increasing sample efficiency of RL algorithms. Our approach focuses on optimizing environment rewards with high-level instructions. These are modeled as a high-level controller over temporally extended actions known as options. These options can be looped, interleaved and partially ordered with a rich language for high-level instructions. Crucially, the instructions may be underspecified in the sense that following them does not guarantee high reward in the environment. We present an algorithm for control with these so-called option machines (OMs), discuss option selection for the partially ordered case and describe an algorithm for learning with OMs. We compare our approach in zero-shot, single- and multi-task settings in an environment with fully specified and underspecified instructions. We find that OMs perform significantly better than or comparable to the state-of-art in all environments and learning settings.
Complex Query Answering with Neural Link Predictors (Extended Abstract)*

Neural link predictors are useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries containing logical conjunctions (∧), disjunctions (∨), and existential quantifiers (∃). We propose a framework for efficiently answering complex queries on in- complete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods — black-box models trained on millions of generated queries — without the need for training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across multiple knowledge graphs. We find that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms. All our source code and datasets are available online (https://github.com/uclnlp/cqd).
Temporality Spatialization: A Scalable and Faithful Time-Travelling Visualization for Deep Classifier Training

Time-travelling visualization answers how the predictions of a deep classifier are formed during the training. It visualizes in two or three dimensional space how the classification boundaries and sample embeddings are evolved during training. In this work, we propose TimeVis, a novel time-travelling visualization solution for deep classifiers. Comparing to the state-of-the-art solution DeepVisualInsight (DVI), TimeVis can significantly (1) reduce visualization errors for rendering samples’ travel across different training epochs, and (2) improve the visualization efficiency. To this end, we design a technique called temporality spatialization, which unifies the spatial relation (e.g., neighbouring samples in single epoch) and temporal relation (e.g., one identical sample in neighbouring training epochs) into one high-dimensional topological complex. Such spatio-temporal complex can be used to efficiently train one visualization model to accurately project and inverse-project any high and low dimensional data across epochs. Our extensive experiment shows that, in comparison to DVI, TimeVis not only is more accurate to preserve the visualized time-travelling semantics, but 15X faster in visualization efficiency, achieving a new state-of-the-art in time-travelling visualization.
Enhancing Text Generation via Multi-Level Knowledge Aware Reasoning

How to generate high-quality textual content is a non-trivial task. Existing methods generally generate text by grounding on word-level knowledge. However, word-level knowledge cannot express multi-word text units, hence existing methods may generate low-quality and unreasonable text. In this paper, we leverage event-level knowledge to enhance text generation. However, event knowledge is very sparse. To solve this problem, we split a coarse-grained event into fine-grained word components to obtain the word-level knowledge among event components. The word-level knowledge models the interaction among event components, which makes it possible to reduce the sparsity of events. Based on the event-level and the word-level knowledge, we devise a multi-level knowledge aware reasoning framework. Specifically, we first utilize event knowledge to make event-based content planning, i.e., select reasonable event sketches conditioned by the input text. Then, we combine the selected event sketches with the word-level knowledge for text generation. We validate our method on two widely used datasets, experimental results demonstrate the effectiveness of our framework to text generation.
Study of the Application of User Security Techniques in the Network

Mapping user behavior on the network is one of the fundamental points for the development of security measures against intrusions, however, despite a large number of platforms and programs adopting protection standards, users are still poorly understood about their security, so, this article sought to study user security techniques on the network as a way of identifying to what extent their actions may constitute a risk or protection for their security. of a data collection questionnaire, which was prepared online and later collected responses from 222 users in Manaus-AM. From the data, a certain balance in the behavior of users can be highlighted, indicating that he is more aware of the risks, but performing actions that leave him exposed, it can be seen that through the data obtained, his behavior brings surprises to some people. situations. The main conclusion of the study reveals a certain balance of user actions regarding their security while in the network environment, so the contribution of the research is in the description and presentation of the user's security actions while in the online environment, an area little explored by the academic research. Finally, a proposal for a future research agenda on this topic is also presented.
Strengthening Sustainable Context of Water Management in Educational Building Universitas Indonesia

Abstract This paper aims to strengthen the sustainability related to the water management system in an educational building, an integrated teaching and laboratory building in Universitas Indonesia. It provides recommendations to improve the water management system in the building in achieving the sustainable aspects of the local context. Water needs continuously increase due to population growth and rapid development, which require good water management. Water management is an activity in controlling water resources to use the water effectively and efficiently by minimizing damage to the environment and nature. Water is a natural resource that is very important for the survival of living things, so water for sustainable development should achieve sustainable access. Include sufficient quantities of water, of acceptable quality, at affordable prices to meet the needs of human society while maintaining the integrity of the hydrological cycle and the associated ecosystems. This paper used qualitative approaches carried out by collecting data through interviews, field observations and documents collection. The result of this study indicated that there are potentials to improve water management in the building. Optimizing the use of natural resources that exist in the building environment can strengthen the water management performance towards sustainability.
MultiQuant: Training Once for Multi-bit Quantization of Neural Networks

Quantization has become a popular technique to compress deep neural networks (DNNs) and reduce computational costs, but most prior work focuses on training DNNs at each individual fixed bit-width and accuracy trade-off point. How to produce a model with flexible precision is largely unexplored. This work proposes a multi-bit quantization framework (MultiQuant) to make the learned DNNs robust for different precision configuration during inference by adopting Lowest-Random-Highest bit-width co-training method. Meanwhile, we propose an online adaptive label generation strategy to alleviate the problem of vicious competition under different precision caused by one-hot labels in the supernet training. The trained supernet model can be flexibly set to different bit widths to support dynamic speed and accuracy trade-off. Furthermore, we adopt the Monte Carlo sampling-based genetic algorithm search strategy with quantization-aware accuracy predictor as evaluation criterion to incorporate the mixed precision technology in our framework. Experiment results on ImageNet datasets demonstrate MultiQuant method can attain the quantization results under different bit-widths comparable with quantization-aware training without retraining.
Quaternion Ordinal Embedding

Ordinal embedding (OE) aims to project objects into a low-dimensional space while preserving their ordinal constraints as well as possible. Generally speaking, a reasonable OE algorithm should simultaneously capture a) semantic meaning and b) the ordinal relationship of the objects. However, most of the existing methods merely focus on b). To address this issue, our goal in this paper is to seek a generic OE method to embrace the two features simultaneously. We argue that different dimensions of vector-based embedding are naturally entangled with each other. To realize a), we expect to decompose the D dimensional embedding space into D different semantic subspaces, where each subspace is associated with a matrix representation. Unfortunately, introducing a matrix-based representation requires far more complex parametric space than its vector-based counterparts. Thanks to the algebraic property of quaternions, we are able to find a more efficient way to represent a matrix with quaternions. For b), inspired by the classic chordal Grassmannian distance, a new distance function is defined to measure the distance between different quaternions/matrices, on top of which we construct a generic OE loss function. Experimental results for different tasks on both simulated and real-world datasets verify the effectiveness of our proposed method.
Why Bad Coffee? Explaining BDI Agent Behaviour with Valuings (Extended Abstract)

An important issue in deploying an autonomous system is how to enable human users and stakeholders to develop an appropriate level of trust in the system. It has been argued that a crucial mechanism to enable appropriate trust is the ability of a system to explain its behaviour. Obviously, such explanations need to be comprehensible to humans. Due to the perceived similarity in functioning between humans and autonomous systems, we argue that it makes sense to build on the results of extensive research in social sciences that explores how humans explain their behaviour. Using similar concepts for explanation is argued to help with comprehensibility, since the concepts are familiar. Following work in the social sciences, we propose the use of a folk-psychological model that utilises beliefs, desires, and ``valuings''. We propose a formal framework for constructing explanations of the behaviour of an autonomous system, present an (implemented) algorithm for giving explanations, and present evaluation results.
A Closed-Loop Perception, Decision-Making and Reasoning Mechanism for Human-Like Navigation

Reliable navigation systems have a wide range of applications in robotics and autonomous driving. Current approaches employ an open-loop process that converts sensor inputs directly into actions. However, these open-loop schemes are challenging to handle complex and dynamic real-world scenarios due to their poor generalization. Imitating human navigation, we add a reasoning process to convert actions back to internal latent states, forming a two-stage closed loop of perception, decision-making, and reasoning. Firstly, VAE-Enhanced Demonstration Learning endows the model with the understanding of basic navigation rules. Then, two dual processes in RL-Enhanced Interaction Learning generate reward feedback for each other and collectively enhance obstacle avoidance capability. The reasoning model can substantially promote generalization and robustness, and facilitate the deployment of the algorithm to real-world robots without elaborate transfers. Experiments show our method is more adaptable to novel scenarios compared with state-of-the-art approaches.
Logit Mixing Training for More Reliable and Accurate Prediction

When a person solves the multi-choice problem, she considers not only what is the answer but also what is not the answer. Knowing what choice is not the answer and utilizing the relationships between choices, she can improve the prediction accuracy. Inspired by this human reasoning process, we propose a new training strategy to fully utilize inter-class relationships, namely LogitMix. Our strategy is combined with recent data augmentation techniques, e.g., Mixup, Manifold Mixup, CutMix, and PuzzleMix. Then, we suggest using a mixed logit, i.e., a mixture of two logits, as an auxiliary training objective. Since the logit can preserve both positive and negative inter-class relationships, it can impose a network to learn the probability of wrong answers correctly. Our extensive experimental results on the image- and language-based tasks demonstrate that LogitMix achieves state-of-the-art performance among recent data augmentation techniques regarding calibration error and prediction accuracy.
A benchmark dataset for Turkish data-to-text generation

In the last decades, data-to-text (D2T) systems that directly learn from data have gained a lot of attention in natural language generation. These systems need data with high quality and large volume, but unfortunately some natural languages suffer from the lack of readily available generation datasets. This article describes our efforts to create a new Turkish dataset (Tr-D2T) that consists of meaning representation and reference sentence pairs without fine-grained word alignments. We utilize Turkish web resources and existing datasets in other languages for producing meaning representations and collect reference sentences by crowdsourcing native speakers. We particularly focus on the generation of single-sentence biographies and dining venue descriptions. In order to motivate future Turkish D2T studies, we present detailed benchmarking results of different sequence-to-sequence neural models trained on this dataset. To the best of our knowledge, this work is the first of its kind that provides preliminary findings and lessons learned from the creation of a new Turkish D2T dataset. Moreover, our work is the first extensive study that presents generation performances of transformer and recurrent neural network models from meaning representations in this morphologically-rich language.
School readiness measurement: an implementation of the nijmeegse schoolbekwaamheids test (NST)

Preparing children to enter elementary school is an important thing that must be known and understood by parents. Choosing to send children to school too early without paying attention to their ability and development will cause a fatal impact. Readiness for early childhood schools is certainly different from one child to another. Still, the readiness of children can be seen from the maturity of the different aspects of physical, motoric, cognitive, social, and emotional development that will directly help children in the learning process and adjust to their school environment. This research is a descriptive quantitative study to see the implementation of the Nijmeegse Schoolbekwaamheids Test (NST) as one of the various test tools to measure the readiness of a child to enter elementary school. The results and research findings get the readiness of children to enter elementary school is not only assessed from the age factor. The readiness of children to enter elementary school is assessed by motor skills, observation skills, concentration abilities, understanding abilities, and the ability to assess situations.
Evaluation system of innovation and entrepreneurship education based on the GA-SVM method

The demand for innovative and entrepreneurial talents in today's society is becoming more and more urgent. As the main position for the country to cultivate innovative and entrepreneurial talents, universities need to pay more attention to the cultivation of innovative and entrepreneurial talents. This paper reasonably constructs the evaluation index system of innovation and entrepreneurship education, then uses the analytic hierarchy process to determine the weight of each index, and optimizes the use of GA-SVM to construct the evaluation model of innovation and entrepreneurship education, so as to obtain a reasonable and effective evaluation system of innovation and entrepreneurship education in universities, and provide a reference basis for the cultivation of innovation and entrepreneurship talents in universities.
Hospital health monitoring on cloud using blockchain technology

Block chain is being used to invest in innovation and address trust issues more effectively. It is at the front of a wide-ranging hunt for position to develop and commercial breakthroughs. A wide range of businesses could profit from blockchain-based technology solutions. It demonstrates how blockchain technology could be utilised in the healthcare industry to share medical data and analysis among institutions and research organisations based on patient-defined access policies. In order to protect confidential data, the project involves the use of two types of chains: A private one is the side chain it will keeps the information about the patients, and A public one is the main chain it which stores information about patients health data marked with a ID temporary. Hospital Health Monitoring deals with the hospital branch and patients management. We are using the cloud computing for delivers the convenient, on-demand access and sharing the data, applications and hardware over the internet, it provides the unlimited infrastructure to store and execute the patient data and program. The project applies that blockchain technology which is a decentralized network, where the entire database of health care management is being handled by many users.
Opinion mining based on partial textual entailent

The net is a heap of uncooked facts within the form of multimedia messages such as tweets, blogs and many others. Unordered and unconnected. The information that turns into records is most effective after it is processed, organized and structured in the form of online textual content snippets. According to related research, we determined the current sentiment analysis tools to be sluggish, cumbersome and arithmetically heavy which causes the venture handy to be inefficient. Consequently, to bypass the above-mentioned trouble of reading opinions proficiently, a latest procedure is put-forward in the current tabloid utilizes the conception of Partial Textual Entailment (PTE) to handle this job of Opinion Mining. Among the tweets the method for calculating Semantic resemblance as a way to organize homogeneous tweets simultaneously the utilization of PTE is recommended. To minimize the load on Sentiment analyzer this approach is expected as it also helps to make the processing speed quicker. Additionally, for making countless Natural Language Processing applications we advise an amendment in the current procedure of PTE which can also be additionally accepted.