Reviewerly/exHarmony huggingface dataset:
Instance-Dependent Partial Label Learning

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels. However, this assumption is not realistic since the candidate labels are always instance-dependent. In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature. The incorrect label with a high degree is more likely to be annotated as the candidate label. Therefore, the latent label distribution is the essential labeling information in partially labeled examples and worth being leveraged for predictive model training. Motivated by this consideration, we propose a novel PLL method that recovers the label distribution as a label enhancement (LE) process and trains the predictive model iteratively in every epoch. Specifically, we assume the true posterior density of the latent label distribution takes on the variational approximate Dirichlet density parameterized by an inference model. Then the evidence lower bound is deduced for optimizing the inference model and the label distributions generated from the variational posterior are utilized for training the predictive model. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method. Source code is available at https://github.com/palm-ml/valen.
IDENTIFICATION OF LEAST CONGESTED AND SHORTEST DISTANCE PATH IN TRAFFIC NETWORK

Traffic congestion, a mobility problem that exists as the traffic volume demand exceeds the capacity of the existing intersections. Conventional approaches such as building flyover and road widening might not be very effective in solving traffic congestion. Therefore, a suggested approach in this study was to change the driverâ€™s behavior in selecting the best path. The shortest path may not be the best path. The added criterion in selecting the best path was the level of service of the path, while the included parameter that determines the level of service was the degree of saturation. Dijkstraâ€™s algorithm was applied to solve the network graph. Excel solver was used to find the solution. Percentage of difference between paths was carried out to find out the difference and significant of the paths. In conclusion, the path with combined criteria of shortest distance and least congested (degree of saturation) would be the best path, instead of solely based on the shortest distance or minimum degree of saturation.&#x0D; Keywords: Degree of Saturation, Distance, Least Congested, Shortest Path, Dijkstraâ€™s Algorithm and Traffic Network
Carbonatite magmatism in subduction zones: Evidence from the Khanneshin volcano in Afghanistan

Carbonatites are typically associated with intraplate magmatism.The Pliocene Khanneshin carbonatite volcano in southern Afghanistan is a rare exception.There, carbonatites erupted above the actively subducting Makran slab that reaches depths of ~180 km beneath the Registan Desert [1].Trace elements, stable isotopes (carbon, oxygen, and thallium: [2]), and radiogenic isotopes (strontium, neodymium, and lead: [3]) indicate that the Khanneshin carbonatites derived from subducted seafloor sediments of the Indus Fan.The inferred deposition age of the sedimentary precursors implies that subducted material recycled to and from mantle depths in less than 30 million years.Efficient carbon recycling via carbonatitic melts helps explain why global subduction zone carbon inputs are not balanced by volcanic arc outputs [4].The Khanneshin volcano may be a rare surface expression of long-lived carbon reservoirs that form in rear-arc lithosphere; carbonatites elsewhere that contain isotopic evidence of recycling [5-7] may derive from such reservoirs that were reactivated by intraplate rifting or hotspot activity.
Pengaruh Potensi Diri, Status Sosial Ekonomi Orang Tua dan Lingkungan Sosial Terhadap Minat Melanjutkan Pendidikanke Perguruan Tinggi Siswa Kelas XI SMA Negeri 8 Padang

The purpose of this research is to determine the level of influence between “Self Potential, Economic Condition’s of Parents and Social Environment” on interest in continuing to the university 11th students of SMA Negeri 8 Padang. The research is a quantitative descriptive study using data collected with questionnaires and documentation. The sample used in this study is 170 students using proportional random sampling technique from 360 population with error rate 5%. The result of research, show that there are significant influence from the “self potential, economic condition’s of parents and social environment” as simultaneous on interest in continuing to the university among 11th students of SMA Negeri 8 Padang 60,9% while 39,1% is influenced bu other variables. “Self Potential there is a significant influence on interest in continuing to the university, Economic Condition’s of Parents (Parental Education and Parents Income) is influential but not significant on interest in continuing to the university and social environment there is a significant influence on interest in continuing to the university 11th students of SMA Negeri 8 Padang”.Keywords : Self Potential, Economic Condition’s Of Parent, Social Environment and Interest in Continuing Education to Higher Education
Smoothing Dialogue States for Open Conversational Machine Reading

Conversational machine reading (CMR) requires machines to communicate with humans through multi-turn interactions between two salient dialogue states of decision making and question generation processes. In open CMR settings, as the more realistic scenario, the retrieved background knowledge would be noisy, which results in severe challenges in the information transmission. Existing studies commonly train independent or pipeline systems for the two subtasks. However, those methods are trivial by using hard-label decisions to activate question generation, which eventually hinders the model performance. In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.
Deep Attention-guided Graph Clustering with Dual Self-supervision

Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of an auto-encoder and a graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning a discriminative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the discriminative information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments validate that our method consistently outperforms state-of-the-art methods on six benchmark datasets. Especially, our method improves the ARI by more than 18.14% over the best baseline.
Transaction Placement in Sharded Blockchains

While many researchers adopt a sharding approach to design scaling blockchains, few works have studied the transaction placement problem incurred by sharding protocols. The widely-used hashing placement algorithm renders an overwhelming portion of transactions as cross-shard. In this paper, we analyze the high cost of cross-shard transactions and reveal that most Bitcoin transactions have simple dependencies and can become single-shard under a placement algorithm taking transaction dependencies into account. In addition, we perform a case study of OptChain, which is the state-of-the-art transaction placement algorithm for sharded blockchains, and find a shortcoming of it. A simple fix is proposed, and our evaluation results demonstrate that the proposed fix effectively helps OptChain overcome the shortcoming and significantly improve the system performance under a special workload. The authors of OptChain made some revisions to the algorithm description after noticing our work. Their updated algorithm does not exhibit the shortcoming under the workloads employed by this paper.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

Performing event and entity coreference resolution across documents vastly increases the number of candidate mentions, making it intractable to do the full n2 pairwise comparisons. Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. As a result cross-document coreference algorithms are rarely applied to downstream tasks. We draw on an insight from discourse coherence theory: potential coreferences are constrained by the reader’s discourse focus. We model the entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. We then use these neighborhoods to sample only hard negatives to train a fine-grained classifier on mention pairs and their local discourse features. Our approach achieves state-of-the-art results for both events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-Document Coreference corpora. Furthermore, training on multiple corpora improves average performance across all datasets by 17.2 F1 points, leading to a robust coreference resolution model that is now feasible to apply to downstream tasks.
Comparative study of various Machine Learning Algorithms using Finance Industry

Finance sector is the wealth backbone of any country, so risk   assessment and fraud detection have great importance. Risk assessment is the process of identifying vulnerabilities to an organization by identifying risk involved in each and every new plans, policies or investments. This paper concentrates on risk level detection of loan application and insurance claim and suggests a predictive model for risk assessment and fraud detection using three efficient machine learning algorithms after applying under sampling technique on data and compares the accuracy difference of them, on imbalanced and resampled data sets with the leading machine learning algorithms Random Forest ad SVM (support vector machine).
Task-Adaptive Neural Network Retrieval with Meta-Contrastive Learning

Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures (network topologies) without searching for optimal parameters. While some NAS methods handle this issue by utilizing a supernet trained on a large-scale dataset such as ImageNet, they may be suboptimal if the target tasks are highly dissimilar from the dataset the supernet is trained on. To tackle this issue, we propose a novel neural network retrieval method, which retrieves the most optimal pre-trained network for a given task and constraints (e.g. number of parameters) from a model zoo. We train this framework by meta-learning a cross-modal latent space with contrastive loss, to maximize the similarity between a dataset and a network that obtains high performance on it, and minimize the similarity between an irrelevant dataset-network pair. We validate the efficacy of our method on ten real-world datasets, against existing NAS baselines. The results show that our method instantly retrieves networks that outperforms models obtained with the baselines with significantly fewer training steps to reach the target performance.
Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task

The Semantic Verbal Fluency Task (SVF) is an efficient and minimally invasive speechbased screening tool for Mild Cognitive Impairment (MCI).In the SVF, testees have to produce as many words for a given semantic category as possible within 60 seconds.Stateof-the-art approaches for automatic evaluation of the SVF employ word embeddings to analyze semantic similarities in these word sequences.While these approaches have proven promising in a variety of test languages, the small amount of data available for any given language limits the performance.In this paper, we for the first time investigate multilingual learning approaches for MCI classification from the SVF in order to combat data scarcity.To allow for cross-language generalisation, these approaches either rely on translation to a shared language, or make use of several distinct word embeddings.In evaluations on a multilingual corpus of older French, Dutch, and German participants (Controls=66, MCI=66), we show that our multilingual approaches clearly improve over single-language baselines.
Generalized 't Hooft-Nobbenhuis Complex Transformation for Emergent Quantum Mechanics

Possible applications of a generalized 't Hooft-Nobbenhuis spacetime complextransformation on subjects such as Emergent Quantum Mechanics, Deformed SpecialRelativity, Quantum Gravity and Unification were considered. Then a new quantuminterpretation was outlined that considers various ideas such as BohmianMechanics, geon theory of matter, quantum particles as sub-microscopic blackholes, hydrodynamical analogy of quantum systems and metric tensor fluctuation.Also a new quantum formalism was proposed with the use of Ricci Flow as astatistical system that can be used to describe the topology change at PlanckScale and the idea that the metric tensor can be considered as a microscopicalstate in a statistical system.
Regularized Softmax Deep Multi−Agent Q−Learning

Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting. In this work, we empirically demonstrate that QMIX, a popular $Q$-learning algorithm for cooperative multi-agent reinforcement learning (MARL), suffers from a more severe overestimation in practice than previously acknowledged, and is not mitigated by existing approaches. We rectify this with a novel regularization-based update scheme that penalizes large joint action-values that deviate from a baseline and demonstrate its effectiveness in stabilizing learning. Furthermore, we propose to employ a softmax operator, which we efficiently approximate in a novel way in the multi-agent setting, to further reduce the potential overestimation bias. Our approach, Regularized Softmax (RES) Deep Multi-Agent $Q$-Learning, is general and can be applied to any $Q$-learning based MARL algorithm. We demonstrate that, when applied to QMIX, RES avoids severe overestimation and significantly improves performance, yielding state-of-the-art results in a variety of cooperative multi-agent tasks, including the challenging StarCraft II micromanagement benchmarks.
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT

Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach that can handle a very large knowledge graph (KG) by partitioning it into smaller sub-graphs and infusing their specific knowledge into various BERT models using lightweight adapters. To leverage the overall factual knowledge for a target task, these sub-graph adapters are further fine-tuned along with the underlying BERT through a mixture layer. We evaluate our MoP with three biomedical BERTs (SciBERT, BioBERT, PubmedBERT) on six downstream tasks (inc. NLI, QA, Classification), and the results show that our MoP consistently enhances the underlying BERTs in task performance, and achieves new SOTA performances on five evaluated datasets.
A TCN-based Spatial-Temporal PV Forecasting Framework with Automated Detector Network Selection

This paper proposes a two-stage PV forecasting framework for MW-level PV farms based on Temporal Convolutional Network (TCN). In the day-ahead stage, inverter-level physics-based model is built to convert Numerical Weather Prediction (NWP) to hourly power forecasts. TCN works as the NWP blender to merge different NWP sources to improve the forecasting accuracy. In the real-time stage, TCN can leverage the spatial-temporal correlations between the target site and its neighbors to achieve intra-hour power forecasts. A scenario-based correlation analysis method is proposed to automatically identify the most contributive neighbors. Simulation results based on 95 PV farms in North Carolina demonstrate the accuracy and efficiency of the proposed method.
ANALISIS MUTU AIR AKIBAT AKTIVITAS PENAMBANG PASIR DENGAN METODE STORET DI SUNGAI SP PADANG OKI

Sand mining in the village of Sp Padang is carried out in an open pit mining model through several processes, for example the washing process which is carried out to separate sand from other components.When the sludge in the form of mud and fine sand enters the river, this part causes the quality of river water around the sand mining location to decline. The purpose of this research was to determine the condition of water quality due to sand mining in the river Sp.Padang.The type of data collected is primary data. The research was conducted at 3 stations on the river Sp.Padang.The physico-chemical parameters of river water are used, namely: turbidity, temperature, pH, TSS, DO, BOD, COD, water discharge and current velocity. The data obtained from the laboratory were then analyzed, then comparisons were made with the Storet method.Sampling for water quality replaces the surrounding area with direct measurements and measurements made in the laboratory.The use of the Storet method refers to PP No. 82 of 2001. The principle of Storet is to integrate river water quality data with river water standards and then adjust it according to its use, by classifying water quality into four classes. The results of the data analysis of the quality of river water in Sp.Padang as a result of the sand mining activities are categorized as good class B (lightly polluted) with a score of -6. Sand mining activity affects the water quality of the Sirah River in Padang Island, OKI Regency.Key words : OKI, sand miners, storet method, water quality
Exploratory Model Analysis Using Data-Driven Neuron Representations

Probing classifiers have been extensively used to inspect whether a model component captures specific linguistic phenomena.This topdown approach is, however, costly when we have no probable hypothesis on the association between the target model component and phenomena.In this study, aiming to provide a flexible, exploratory analysis of a neural model at various levels ranging from individual neurons to the model as a whole, we present a bottomup approach to inspect the target neural model by using neuron representations obtained from a massive corpus of text.We first feed massive amount of text to the target model and collect sentences that strongly activate each neuron.We then abstract the collected sentences to obtain neuron representations that help us interpret the corresponding neurons; we augment the sentences with linguistic annotations (e.g., part-of-speech tags) and various metadata (e.g., topic and sentiment), and apply pattern mining and clustering techniques to the augmented sentences.We demonstrate the utility of our method by inspecting the pre-trained BERT.Our exploratory analysis reveals that i) specific phrases and domains of text are captured by individual neurons in BERT, ii) a group of neurons simultaneously capture the same linguistic phenomena, and iii) deeper-level layers capture more specific linguistic phenomena.
Efficient Nearest Neighbor Language Models

Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints. While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications. In this paper, we take the recently proposed k-nearest neighbors language model as an example, exploring methods to improve its efficiency along various dimensions. Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance. The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs.