bluuebunny/crossref_metadata_2025 huggingface dataset:
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Authors: Li Jiangnan , Meng Fandong , Lin Zheng , Liu Rui , Fu Peng , Cao Yanan , Wang Weiping , Zhou Jie
Published: July 2022

Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs to overcome implicit contextual modelling of the original entailment style. Following the previous work, we further introduce the emotion information into graphs. Emotion information can markedly promote the detection of causal utterances whose emotion is the same as the targeted utterance. However, it is still hard to detect causal utterances with different emotions, especially neutral ones. The reason is that models are limited in reasoning causal clues and passing them between utterances. To alleviate this problem, we introduce social commonsense knowledge (CSK) and propose a Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two utterances. As not all CSK is emotionally suitable for utterances, we therefore propose a sentiment-realized knowledge selecting strategy to filter CSK. To process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph networks. Experimental results show that our method outperforms baselines and infers more causes with different emotions from the targeted utterance.
Low-Resource NER by Data Augmentation With Prompting
Authors: Liu Jian , Chen Yufeng , Xu Jinan
Published: July 2022

Named entity recognition (NER) is a fundamental information extraction task that seeks to identify entity mentions of certain types in text.

Despite numerous advances, the existing NER methods rely on extensive supervision for model training, which struggle in a low-resource scenario with limited training data.

In this paper, we propose a new data augmentation method for low-resource NER, by eliciting knowledge from BERT with prompting strategies.

Particularly, we devise a label-conditioned word replacement strategy that can produce more label-consistent examples by capturing the underlying word-label dependencies, and a prompting with question answering method to generate new training data from unlabeled texts.

The experimental results have widely confirmed the effectiveness of our approach.

Particularly, in a low-resource scenario with only 150 training sentences, our approach outperforms previous methods without data augmentation by over 40% in F1 and prior best data augmentation methods by over 2.0% in F1. Furthermore, our approach also fits with a zero-shot scenario, yielding promising results without using any human-labeled data for the task.
Group Wisdom at a Price: Jury Theorems with Costly Information
Authors: Michelini Matteo , Haret Adrian , Grossi Davide
Published: July 2022

We study epistemic voting on binary issues where voters are characterized by their competence, i.e., the probability of voting for the correct alternative, and can choose between two actions: voting or abstaining. In our setting voting involves the expenditure of some effort, which is required to achieve the appropriate level of competence, whereas abstention carries no effort. We model this scenario as a game and characterize its equilibria under several variations. Our results show that when agents are aware of everyone's incentives, then the addition of effort may lead to Nash equilibria where wisdom of the crowds is lost. We further show that if agents' awareness of each other is constrained by a social network, the topology of the network may actually mitigate this effect.
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
Authors: Wu Ning , Liang Yaobo , Ren Houxing , Shou Linjun , Duan Nan , Gong Ming , Jiang Daxin
Published: July 2022

Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a simple but effective monolingual pretraining task called contrastive context prediction (CCP) to learn sentence representation by modeling sentence level contextual relation. By pushing the embedding of sentences in a local context closer and pushing random negative samples away, different languages could form isomorphic structure, then sentence pairs in two different languages will be automatically aligned. Our experiments show that model collapse and information leakage are very easy to happen during contrastive training of language model, but language-specific memory bank and asymmetric batch normalization operation play an essential role in preventing collapsing and information leakage, respectively. Besides, a post-processing for sentence embedding is also very effective to achieve better retrieval performance. On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data. Our model also shows larger gain on Tatoeba when transferring between non-English pairs. On two multi-lingual query-passage retrieval tasks, XOR Retrieve and Mr.TYDI, our model even achieves two SOTA results in both zero-shot and supervised setting among all pretraining models using bilingual data.
CauAIN: Causal Aware Interaction Network for Emotion Recognition in Conversations
Authors: Zhao Weixiang , Zhao Yanyan , Lu Xin
Published: July 2022

Emotion Recognition in Conversations has attained increasing interest in the natural language processing community. Many neural-network based approaches endeavor to solve the challenge of emotional dynamics in conversations and gain appealing results. However, these works are limited in capturing deep emotional clues in conversational context because they ignore the emotion cause that could be viewed as stimulus to the target emotion. In this work, we propose Causal Aware Interaction Network (CauAIN) to thoroughly understand the conversational context with the help of emotion cause detection. Specifically, we retrieve causal clues provided by commonsense knowledge to guide the process of causal utterance traceback. Both retrieve and traceback steps are performed from the perspective of intra- and inter-speaker interaction simultaneously. Experimental results on three benchmark datasets show that our model achieves better performance over most baseline models.
None Class Ranking Loss for Document-Level Relation Extraction
Authors: Zhou Yang , Lee Wee Sun
Published: July 2022

Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good document-level RE performance, it is crucial to distinguish such none class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of "no relation". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large margins of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.
Adaptive Information Belief Space Planning
Authors: Barenboim Moran , Indelman Vadim
Published: July 2022

Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms either cannot reason about uncertainty explicitly, or do so with high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an approximation, namely an abstract observation model, that uses an aggregation scheme to alleviate computational costs. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection in a fraction of the computational time.
Tight Bounds for Hybrid Planning
Authors: Bercher Pascal , Lin Songtuan , Alford Ron
Published: July 2022

Several hierarchical planning systems feature a rich level of language features making them capable of expressing real-world problems. One such feature that's used by several current planning systems is causal links, which are used to track search progress. The formalism combining Hierarchical Task Network (HTN) planning with these links known from Partial Order Causal Link (POCL) planning is often referred to as hybrid planning. In this paper we study the computational complexity of such hybrid planning problems. More specifically, we provide missing membership results to existing hardness proofs and thereby provide tight complexity bounds for all known subclasses of hierarchical planning problems. We also re-visit and correct a result from the literature for plan verification showing that it remains NP-complete even in the absence of a task hierarchy.
Shared Autonomy Systems with Stochastic Operator Models
Authors: Costen Clarissa , Rigter Marc , Lacerda Bruno , Hawes Nick
Published: July 2022

We consider shared autonomy systems where multiple operators (AI and human), can interact with the environment, e.g. by controlling a robot. The decision problem for the shared autonomy system is to select which operator takes control at each timestep, such that a reward specifying the intended system behaviour is maximised. The performance of the human operator is influenced by unobserved factors, such as fatigue or skill level. Therefore, the system must reason over stochastic models of operator performance. We present a framework for stochastic operators in shared autonomy systems (SO-SAS), where we represent operators using rich, partially observable models. We formalise SO-SAS as a mixed-observability Markov decision process, where environment states are fully observable and internal operator states are hidden. We test SO-SAS on a simulated domain and a computer game, empirically showing it results in better performance compared to traditional formulations of shared autonomy systems.
Offline Time-Independent Multi-Agent Path Planning
Authors: Okumura Keisuke , Bonnet François , Tamura Yasumasa , Défago Xavier
Published: July 2022

This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a graph and a set of start-goal pairs, the problem consists in assigning a path to each agent such that every agent eventually reaches their goal without blocking each other, regardless of how the agents are being scheduled at runtime. The motivation stems from the nature of distributed environments that agents take actions fully asynchronous and have no knowledge about those exact timings of other actors. We present solution conditions, computational complexity, solvers, and robotic applications.
Reinforcement Learning for Cross-Domain Hyper-Heuristics
Authors: Mischek Florian , Musliu Nysret
Published: July 2022

In this paper, we propose a new hyper-heuristic approach that uses reinforcement learning to automatically learn the selection of low-level heuristics across a wide range of problem domains.

We provide a detailed analysis and evaluation of the algorithm components, including different ways to represent the hyper-heuristic state space and reset strategies to avoid unpromising areas of the solution space.

Our methods have been evaluated using HyFlex, a well-known benchmarking framework for cross-domain hyper-heuristics, and compared with state-of-the-art approaches.

The experimental evaluation shows that our reinforcement-learning based approach produces results that are competitive with the state-of-the-art, including the top participants of the Cross Domain Hyper-heuristic Search Competition 2011.
Learning Cluster Causal Diagrams: An Information-Theoretic Approach
Authors: Niu Xueyan , Li Xiaoyun , Li Ping
Published: July 2022

Many real-world phenomena arise from causal relationships among a set of variables. As a powerful tool, Bayesian Network (BN) has been successful in describing high-dimensional distributions. However, the faithfulness condition, enforced in most BN learning algorithms, is violated in the settings where multiple variables synergistically affect the outcome (i.e., with polyadic dependencies). Building upon recent development in cluster causal diagrams (C-DAGs), we initiate the formal study of learning C-DAGs from observational data to relax the faithfulness condition. We propose a new scoring function, the Clustering Information Criterion (CIC), based on information-theoretic measures that represent various complex interactions among variables. The CIC score also contains a penalization of the model complexity under the minimum description length principle. We further provide a searching strategy to learn structures of high scores. Experiments on both synthetic and real data support the effectiveness of the proposed method.
Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space
Authors: Sonar Chinmay , Suri Subhash , Xue Jie
Published: July 2022

We consider multiwinner elections in Euclidean space using the minimax Chamberlin-Courant rule.

In this setting, voters and candidates are embedded in a d-dimensional Euclidean space,

and the goal is to choose a committee of k candidates so that the rank of any voter's

most preferred candidate in the committee is minimized. (The problem is also equivalent to the

ordinal version of the classical k-center problem.)

We show that the problem is NP-hard in any dimension d >= 2, and also provably hard to approximate.

Our main results are three polynomial-time approximation schemes, each of which finds a committee

with provably good minimax score. In all cases, we show that our approximation bounds are tight or close to tight.

We mainly focus on the 1-Borda rule but some of our results also hold for the more general r-Borda.
Near-Tight Algorithms for the Chamberlin-Courant and Thiele Voting Rules
Authors: Sornat Krzysztof , Williams Virginia Vassilevska , Xu Yinzhan
Published: July 2022

We present an almost optimal algorithm for the classic Chamberlin-Courant multiwinner voting rule (CC) on single-peaked preference profiles. Given n voters and m candidates, it runs in almost linear time in the input size improving the previous best O(nm^2) time algorithm. We also study multiwinner voting rules on nearly single-peaked preference profiles in terms of the candidate-deletion operation. We show a polynomial-time algorithm for CC where a given candidate-deletion set D has logarithmic size. Actually, our algorithm runs in 2^|D| * poly(n,m) time and the base of the power cannot be improved under the Strong Exponential Time Hypothesis. We also adapt these results to all non-constant Thiele rules which generalize CC with approval ballots.
High-Resolution and Arbitrary-Sized Chinese Landscape Painting Creation Based on Generative Adversarial Networks
Authors: Luo Peixiang , Zhang Jinchao , Zhou Jie
Published: July 2022

This paper outlines an automated creation system for Chinese landscape paintings based on generative adversarial networks. The system consists of three cascaded modules: generation, resizing, and super-resolution. The generation module first generates a square-shaped painting, then the resizing module predicts an appropriate aspect ratio for it and performs resizing, and finally the super-resolution module is used to increase the resolution and improve the quality. After training each module with the images we collected from the web, our system can create high-resolution landscape paintings in arbitrary sizes.
Maxmin Participatory Budgeting
Authors: Sreedurga Gogulapati , Ratan Bhardwaj Mayank , Narahari Yadati
Published: July 2022

Participatory Budgeting (PB) is a popular voting method by which a limited budget is divided among a set of projects, based on the preferences of voters over the projects. PB is broadly categorised as divisible PB (if the projects are fractionally implementable) and indivisible PB (if the projects are atomic). Egalitarianism, an important objective in PB, has not received much attention in the context of indivisible PB. This paper addresses this gap through a detailed study of a natural egalitarian rule, Maxmin Participatory Budgeting (MPB), in the context of indivisible PB. Our study is in two parts: (1) computational (2) axiomatic. In the first part, we prove that MPB is computationally hard and give pseudo-polynomial time and polynomial-time algorithms when parameterized by certain well-motivated parameters. We propose an algorithm that achieves for MPB, additive approximation guarantees for restricted spaces of instances and empirically show that our algorithm in fact gives exact optimal solutions on real-world PB datasets. We also establish an upper bound on the approximation ratio achievable for MPB by the family of exhaustive strategy-proof PB algorithms. In the second part, we undertake an axiomatic study of the MPB rule by generalizing known axioms in the literature. Our study leads to the proposal of a new axiom, maximal coverage, which captures fairness aspects. We prove that MPB satisfies maximal coverage.
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
Authors: Vyas Anoushka , Bandyopadhyay Sambaran
Published: July 2022

Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.
Argo: Towards Small Vessel Detection for Humanitarian Purposes
Authors: Moser Elisabeth , Meyer Selina , Schmidhuber Maximilian , Ketterer Daniel , Eberhardt Matthias
Published: July 2022

Refugees trying to get to Europe via the Mediterranean often face human rights violations. The present situation is not in line with the UN's SDG's 10 and 16. We present Argo: a semi-automatically created vessel classification dataset focused on small boats, with the aim to enable NGOs and the public to detect refugee boats in satellite imagery. We achieve a classification recall of 91% on small ships. With a tool developed on top of the results presented here, NGOs could collect information and hold institutions participating in illegal activities accountable.
Deep Cooperation of CDCL and Local Search for SAT (Extended Abstract)
Authors: Cai Shaowei , Zhang Xindi
Published: July 2022

Modern SAT solvers are based on a paradigm named conflict driven clause learning (CDCL), while local search is an important alternative. Although there have been attempts combining these two methods, this work proposes deeper cooperation techniques. First, we relax the CDCL framework by extending promising branches to complete assignments and calling a local search solver to search for a model nearby. More importantly, the local search assignments and the conflict frequency of variables in local search are exploited in the phase selection and branching heuristics of CDCL. We use our techniques to improve three typical CDCL solvers (glucose, MapleLCMDistChronoBT and Kissat). Experiments on benchmarks from the Main tracks of SAT Competitions 2017-2020 and a real world benchmark of spectrum allocation show that the techniques bring significant improvements, particularly on satisfiable instances. A resulting solver won the Main Track SAT category in SAT Competition 2020 and also performs very well on the spectrum allocation benchmark. As far as we know, this is the first work that meets the standard of the challenge ``Demonstrate the successful combination of stochastic search and systematic search techniques, by the creation of a new algorithm that outperforms the best previous examples of both approaches.'' (AAAI 1997) on standard application benchmarks.
Rx-refill Graph Neural Network to Reduce Drug Overprescribing Risks (Extended Abstract)
Authors: Zhang Jianfei , Kuo Ai-Te , Zhao Jianan , Wen Qianlong , Winstanley Erin , Zhang Chuxu , Ye Yanfang
Published: July 2022

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.
Who Says What to Whom: A Survey of Multi-Party Conversations
Authors: Gu Jia-Chen , Tao Chongyang , Ling Zhen-Hua
Published: July 2022

Multi-party conversations (MPCs) are a more practical and challenging scenario involving more than two interlocutors. This research topic has drawn significant attention from both academia and industry, and it is nowadays counted as one of the most promising research areas in the field of dialogue systems. In general, MPC algorithms aim at addressing the issues of Who says What to Whom, specifically, who speaks, say what, and address whom. The complicated interactions between interlocutors, between utterances, and between interlocutors and utterances develop many variant tasks of MPCs worth investigation. In this paper, we present a comprehensive survey of recent advances in text-based MPCs. In particular, we first summarize recent advances on the research of MPC context modeling including dialogue discourse parsing, dialogue flow modeling and self-supervised training for MPCs. Then we review the state-of-the-art models categorized by Who says What to Whom in MPCs. Finally, we highlight the challenges which are not yet well addressed in MPCs and present future research directions.
Recent Advances on Neural Network Pruning at Initialization
Authors: Wang Huan , Qin Can , Bai Yue , Zhang Yulun , Fu Yun
Published: July 2022

Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune a randomly initialized network. This paper offers the first survey concentrated on this emerging pruning fashion. We first introduce a generic formulation of neural network pruning, followed by the major classic pruning topics. Then, as the main body of this paper, a thorough and structured literature review of PaI methods is presented, consisting of two major tracks (sparse training and sparse selection). Finally, we summarize the surge of PaI compared to PaT and discuss the open problems. Apart from the dedicated literature review, this paper also offers a code base for easy sanity-checking and benchmarking of different PaI methods.
On Quantifying Literals in Boolean Logic and its Applications to Explainable AI (Extended Abstract)
Authors: Darwiche Adnan , Marquis Pierre
Published: July 2022

Quantified Boolean logic results from adding operators to Boolean logic for existentially and universally quantifying variables. This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. We complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification and discuss the interplay between variable/literal and existential/universal quantification. We further identify classes of Boolean formulas and circuits that allow efficient quantification. Literal quantification is more fine-grained than variable quantification, which leads to a refinement of quantified Boolean logic with literal quantification as its primitive.
Making Sense of Raw Input (Extended Abstract)
Authors: Evans Richard , Bošnjak Matko , Buesing Lars , Ellis Kevin , Pfau David , Kohli Pushmeet , Sergot Marek
Published: July 2022

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure.

However, the original formulation of the apperception task had one fundamental limitation: it assumed the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information?

The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience.

First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules).
Envy-Free and Pareto-Optimal Allocations for Agents with Asymmetric Random Valuations
Authors: Bai Yushi , Gölz Paul
Published: July 2022

We study the problem of allocating m indivisible items to n agents with additive utilities. It is desirable for the allocation to be both fair and efficient, which we formalize through the notions of envy-freeness and Pareto-optimality. While envy-free and Pareto-optimal allocations may not exist for arbitrary utility profiles, previous work has shown that such allocations exist with high probability assuming that all agents’ values for all items are independently drawn from a common distribution. In this paper, we consider a generalization of this model where each agent’s utilities are drawn independently from a distribution specific to the agent. We show that envy-free and Pareto-optimal allocations are likely to exist in this asymmetric model when m=Ω(n log n), which is tight up to a log log gap that also remains open in the symmetric subsetting. Furthermore, these guarantees can be achieved by a polynomial-time algorithm.
Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases (Extended Abstract)
Authors: Grant John , Martinez Maria Vanina , Molinaro Cristian , Parisi Francesco
Published: July 2022

We define and investigate new inconsistency measures that are particularly suitable for dealing with inconsistent spatio-temporal information, as they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions, and thus define ``dimension-aware'' counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (Extended Abstract)*
Authors: Tolan Songül , Pesole Annarosa , Martínez-Plumed Fernando , Fernández-Macías Enrique , Hernández-Orallo José , Gómez Emilia
Published: July 2022

We present a framework for analysing the impact of AI on occupations. This framework maps 59 generic tasks from different occupational datasets to 14 cognitive abilities and these to a comprehensive list of 328 AI benchmarks used to evaluate research intensity in AI. The use of cognitive abilities as an intermediate layer allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly programmed. We provide insights into the abilities through which AI is most likely to affect jobs, and we show how some of the abilities where AI research is currently very intense are linked to tasks with comparatively limited labour input in the labour markets of advanced economies.
Multivariate Times Series Classification Using Multichannel CNN
Authors: Oh YongKyung
Published: July 2022

Multivariate time series classification is an important and demanding task in sequence data mining. We focus on the multichannel representation of the time series and its corresponding convolutional neural network (CNN) classifier. The proposed method transforms multivariate time series into multichannel analogous image and it is fed into a pretrained multichannel CNN with transfer learning. To verify the efficacy of the proposed method, we compared it with recent deep learning-based time series classification models on five datasets with small amounts of training data. The results indicate that the proposed method provides improved performance on average compared with the other methods when incorporated with transfer learning.
Anchors Selection for Cross-lingual Embedding Alignment through Time
Authors: Pallucchini Filippo
Published: July 2022

In recent years, vector representations of words have proven to be extremely useful across a wide range of NLP applications. Because of the broad interest in the topic, it became essential to answer the following question: is it possible to align different embeddings, in order to compare terms belonging to different vector spaces and their relations? While embedding alignment received considerable attention in the literature, how to find the best anchors for this process is still an open problem; in this paper, we propose an unsupervised, automatic method to select words belonging to different corpora that are close from a semantic point of view, and can be used as anchors for aligning their respective embedding spaces.
Knowledge-Based News Event Analysis and Forecasting Toolkit
Authors: Hassanzadeh Oktie , Awasthy Parul , Barker Ken , Bhardwaj Onkar , Bhattacharjya Debarun , Feblowitz Mark , Martie Lee , Ni Jian , Srinivas Kavitha , Yip Lucy
Published: July 2022

We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting.
Learn to Reverse DNNs from AI Programs Automatically
Authors: Chen Simin , Khanpour Hamed , Liu Cong , Yang Wei
Published: July 2022

With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs automatically, we propose NNReverse, the first learning-based method which can reverse DNNs from AI programs without domain knowledge. NNReverse trains a representation model to represent the semantics of binary code for DNN layers. By searching the most similar function in our database, NNReverse infers the layer type of a given function’s binary code. To represent assembly instructions semantics precisely, NNReverse proposes a more fine-grained embedding model to represent the textual and structural-semantic of assembly functions.
Anaerobic Ammonium Oxidation Performance in Shrimp Pond Wastewater Treatment
Authors: Zulkarnaini Zulkarnaini , Gumelar Guno , Zainuddin Elmi
Published: July 2022

Intensive shrimp culture waste contains high nitrogen, reducing water quality and environmental carrying capacity. Anaerobic ammonium oxidation (anammox) is a potential technology for nitrogen removal. This research aimed to analyze nitrogen removal performance in a filter bioreactor (FtBR). Ammonium and nitrite concentrations of 70-100 mg-N/L were added to sterilized seawater as artificial wastewater and flowed to the reactor with HRT 24-hour. After 120 days of the experiment, the maximum nitrogen removal performance with parameters ACE, NRE, NRR were 82.48%, 72.58%, 0.12 kg-N/m3·d, respectively. The nitrogen stoichiometric ratio NH4+-N:NO2--N:NO3--N was 1:1,40:0,12, which was close to the stoichiometry of anammox process. The anammox process can be a new method for intensive shrimp culture wastewater treatment.
Rekomendasi Strategi Sosialisasi Program Studi Melalui Jalur Undangan Menggunakan Algoritma ID3 dan K-Means
Authors: Hairudin Muhammad Azhar , Zainuddin Hazriani , Wabula Yuyun
Published: July 2022

Based on data obtained from SPAN-PTKIN registrants in 2018 and 2019, the number of interested people through the invitation path who chose the study program at UIN Alauddin as the first choice was 30523 records. Analysis using the ID3 algorithm found that those who interested in the study of religions were more dominant from vocational students. While analysis using the K-Means shows the regions / regencies from which those interested in study programs of religions are spread in 35 regencies / cities. It can be concluded that the socialization of study programs of religions through the invitation path is recommended to be more focused on SMAs that are located in 33 districts / cities as identified in cluster 3. The study programs of religions are prioritized, because these study programs experienced the lowest number of registrants. It is expected that by implementing this recommended strategy, the number of interested prospective new students will draw a significant increase in the future.
ADBR: Accelerated Depth-Based Routing for Underwater Sensor Networks
Authors: Andalib Abdolreza , Abbasi Nia Fariba , Jamshidi Mojtaba , Shaltooki Abdusalam Abdulla , Esnaashari Mehdi
Published: July 2022

It is challenging to propose an efficient routing algorithm for Underwater Wireless Sensor Networks (UWSNs) in terms of packet delivery ratio, end-to-end delay of packet delivery from the source to the destination, and energy consumption. The reasons of that are UWSNs have unique characteristics (e.g. using acoustic channels instead of radio channels for communications), and they have dynamic topology due to the movement of the sensor by the water flow. Depth-Based Routing (DBR) considers one of the well-known algorithms in this context. DBR is a very simple algorithm; however, it is inefficient in terms of packet delivery rate, end-to-end delay, and energy consumption. This study we developed DBR by adding an accelerated routine to it to improve its efficiency, the proposed algorithm; called Accelerated Depth-Based Routing (ADBR). In ADBR, a simple probabilistic mechanism is used to accelerate packet forwarding and provide more multi-path to the destination. In ADBR, each node immediately delivers received packet to the destination with a probability of and follows the DBR routine with a probability of 1 – Pf. The performance of ADBR is evaluated via a set of experiments by using J-SIM simulator. Experimental results indicate the superiority of the ADBR over the DBR algorithm.
Robot Pembersih Lantai Menggunakan Sensor LM393 dan Ultrasonik Berbasis Arduino Uno
Authors: Mardiati Rina , Aziz Nur , Mulyana Edi , Yusuf Teddy
Published: July 2022

Pada era zaman modern saat ini teknologi robot mengalami kemajuan yang sangat pesat khususnya pada robot pembersih lantai. Robot pembersih lantai ini berguna untuk meringankan pekerjaan manusia di bidang rumah tangga. Pada penelitian ini dikembangkan sebuah prototipe robot pembersih lantai dengan menggunakan sensor LM393 dan sensor ultrasonik. Robot ini memiliki fitur alat pembersih sebagai vakum dan alat pel. Sensor ultrasonik digunakan sebagai alat pendeteksi jarak, sedangkan sensor LM393 digunakan sebagai penghitung putaran motor sehingga kecepatan motor stabil sesuai dengan yang diprogramkan pada mikrokontroler arduino uno. Input yang digunakan pada sistem ini adalah jarak dari sensor samping kanan, kiri dan depan yang diperoleh dari sensor ultrasonik. Sementara itu, output dari sistem ini adalah PWM motor DC kiri, PWM motor DC kanan, dan LCD yang menampilkan posisi gerakan robot. Pada penelitian ini dilakukan beberapa pengujian meliputi pengujian modul sensor ultrasonik dan sensor LM393, pengujian kinerja fitur vacum, pengujian kinerja alat pel, serta pengujian sistem secara keseluruhan.  Berdasarkan hasil pengujian yang dilakukan terhadap sensitifitas jarak pantul sensor ultrasonik memiliki nilai selisih error 0,898% yang menandakan sensor ini berfungsi dengan baik. Hasil dari pengujian vacum diperoleh bahwa vacum dapat menghisap debu rata-rata 2,4 gram dari lima percobaan yang menunjukkan kinerja vacum cukup baik. Hasil pengujian alat pel dan pengujian keseluruhan menunjukan bahwa selisih error kemampuan robot membersihkan ruangan berukuran 2 m x 2 m dengan bahan kain pel greenwoll ditempuh dalam waktu 4,036 menit sedangkan menggunakan bahan katun adalah 4,31 menit.
Kepatuhan terhadap Protokol Kesehatan Covid-19 di Ruang Publik
Authors: Hanandini Dwiyanti , Pramono Wahyu
Published: July 2022

Population non-compliance in carrying out strict health protocols in public spaces has the potential to be infected with covid 19, therefore the government limits population crowds in public spaces. This article aims to explain the level of community compliance with health protocols in public spaces. The survey was conducted on 100 residents using questionnaires and observations in public places. Data were analyzed using the statistical packed for social sciences (SPSS) program. The research was conducted in the city of Padang. The research findings show that the level of compliance of the population in complying with the covid-19 health protocol is generally quite high, although there are several health protocols that are not adhered to in certain public areas. In particular, residents are very obedient in implementing the covid-19 health protocol when at malls and recreational areas. While places of worship are places where many residents violate the covid-19 health protocol.
Persepsi Sivitas Akademika Universitas Andalas Terhadap Mahasiswi Bercadar
Authors: Sari Athiyah Kurnia , Miko Alfan , Indraddin Indraddin
Published: July 2022

This research is motivated by the perception of Andalas University academics towards veiled female students who disagree with the use of the veil. This study aims to determine the perception of the academics of Andalas University towards veiled female students from the aspects of knowledge, attitudes and actions. The study was conducted using a quantitative approach with descriptive analysis design. The population in the study were students and lecturers at Andalas University from five faculties. The sample taken amounted to 380 people consisting of lecturers and students, using the proportionate random sampling technique and in collecting data using a questionnaire. The theory used is Functional Structure pioneered by Emile Durkheim, which is classified as a social fact. In analyzing this functionalist theory, society is a system consisting of interconnected, related and balanced parts, then all social structures are integrated into a unity and have a function that different but still creates order and the whole elements will adapt to each other's changes in society both internal and external. This social fact is a way of thinking, a way of feeling, a way of acting, a way of being that is outside the individual (external), general, and coercive that can explain the perception of the academics of Andalas University towards veiled female students who are social facts. The average score (mean) is 3.72 where this score shows the perception of Andalas University academic community towards veiled students on a positive or good interval scale, which is seen from the level of knowledge, attitudes and actions.
Eksklusi Sosial di Bidang Pendidikan Pada Anak-Anak Pengungsi di Indonesia
Authors: Hidayat Taufik
Published: July 2022

Education is significant. Education could give beneficial outcomes for children's future and prevent stress and treat trauma experienced by refugee children. This article examines the process of inequitable education on refugee children in Indonesia by using social exclusion theoretical framework. Different from previous studies that used the law perspective, this study fills the gap in social studies. Literature reviews through Google Scholar and Scopus are used to map inequality education for refugee children in Indonesia. This article reveals that refugee children still hard to enter formal education in Indonesia. However, the Ministry of Education and Culture has already published the regulation that allows refugee children to take formal education. This article also recommends inclusive education for refugee children in Indonesia.
Pendampingan Hidroponik 55 untuk Tumbuh Kembang dengan Usaha Produksi Sayur Sehat dan Menjadi Tujuan Eco-Eduwisata di Cupak Tangah, Padang
Authors: Mailani Fitri , Eros Donny , Jauharry Jauharry , Muliantino Mulyanti Roberto
Published: July 2022

The advantages of this hydroponic vegetable product are that it is free of chemical pesticides, the taste of vegetables is crunchy and sweet, clean, environmentally friendly, and affordable. However, the public's knowledge and understanding regarding hydroponic vegetables is still very little and not evenly distributed in all circles, so that marketing is only limited to certain groups. This service aims to assist and assist Hydroponics 55 partners in producing healthy vegetables and developing into an Eco-tourism area. This service activity is planned to last for 3 years (2021-2024). With the following activity plans: 1) designing a green-house in agricultural areas, 2) increasing online and offline promotion and education, 3) submitting a proposal for collaboration with restaurants, cafes, hospitals and supermarkets in the city of Padang, 4) developing Hydroponic 55 businesses, in processing healthy food derived from hydroponic vegetables, 5) developing agricultural areas into Eco-tourism areas. Activities that have been carried out this year include making educational posters and promoting hydroponic vegetables and distributing them through social media, conducting FGDs with stakeholders, inviting speakers regarding greenhouse preparation and providing knowledge dissemination related to marketing tricks and eco-tourism concepts and facilitating partners in develop business licenses in processing healthy food products. The results of this activity are in the form of educational and promotional posters, proposals for making green-houses, proposals for developing healthy food processing and proposals for developing eco-tourism areas.
Pendampingan Persiapan Pembukaan Sekolah Tatap Muka Terbatas untuk Pencegahan Penularan Covid-19 di SDN 09 Simpang Tigo, Kecamatan Sungai Pua, Kabupaten Agam
Authors: Sari Putri Nilam , Alfian Azyyati Ridha , Firdani Fea , Uswat Anisa Rahmatul , Raisa Puti Oneyna
Published: July 2022

Almost all communities around SDN 09 Simpang Tigo choose to do classroom learning for elementary school children. In fact, until April 7, 2021, Agam Regency was still in the orange zone (moderate risk) because there were still many new cases of COVID-19 in this area. From initial observations, many school residents have not implemented good and correct health protocols and increase the risk of spreading COVID-19. The purpose of this activity was to assess the readiness of schools to carry out limited classroom learning, increase school commitment in implementing health protocols, increase student knowledge about COVID-19, maintain a healthy environment, and train themselves to wash hands with soap and running water. The activity was carried out using the lecture method using media posters. Schools were provided with a handwashing system with a tread system. The activity results showed that schools had begun to implement health protocols but had not been maximized. After counseling, there was an increase in students' knowledge from 45.9% to 85.9%. Students had also been able to practice handwashing with soap properly. Students who were given counseling are expected to become motor drivers to reduce the transmission of COVID-19 in schools and living areas.
Program Penguatan Bisnis BUMNag Sungai Kamuyang Kenagarian Sungai Kamuyang Kabupaten Lima Puluh Kota
Authors: Yuzaria Dwi , Khairati Rusjda , Rastosari Adisti , Rias A. Irzal , Edwiza Daz
Published: July 2022

BUMDes is a legal entity established by the village to manage a business, utilize assets, develop investment and productivity, provide services, and provide other types of business for the most excellent welfare of the village community. The BUMDes itself is named as BUMNag in West Sumatra. The purpose of the program was to improve the business capability of the management team in managing the BUMNag business professionally. This program was designed as a training and mentoring of adult learning method. This program has been implemented from October 11, 2021, to November 30, 2021. The results achieved from this program include 1) mapping of BUMNag business indicators, 2) business training for BUMNag management teams, 3) business mentoring and consultation for BUMNag management teams, assisting the management team in identifying potential business units, and compiling a draft business plan. The number of participants involved in this activity was up to 28 people, consisting of the head of Sungai Kamuyang village and his apparatus, the BUMNag management team, and community leaders in Sungai Kamuyang, Lima Puluh Kota regency.