bluuebunny/crossref_metadata_2025 huggingface dataset:
Beyond Strong-Cyclic: Doing Your Best in Stochastic Environments
Authors: Aminof Benjamin , De Giacomo Giuseppe , Rubin Sasha , Zuleger Florian
Published: July 2022

``Strong-cyclic policies" were introduced to formalize trial-and-error strategies and are known to work in Markovian stochastic domains, i.e., they guarantee that the goal is reached with probability 1. We introduce ``best-effort" policies for (not necessarily Markovian) stochastic domains. These generalize strong-cyclic policies by taking advantage of stochasticity even if the goal cannot be reached with probability 1. We compare such policies with optimal policies, i.e., policies that maximize the probability that the goal is achieved, and show that optimal policies are best-effort, but that the converse is false in general. With this framework at hand, we revisit the foundational problem of what it means to plan in nondeterministic domains when the nondeterminism has a stochastic nature. We show that one can view a nondeterministic planning domain as a representation of infinitely many stochastic domains with the same support but different probabilities, and that for temporally extended goals expressed in LTL/LTLf a finite-state best-effort policy in one of these domains is best-effort in each of the domains. In particular, this gives an approach for finding such policies that reduces to solving finite-state MDPs with LTL/LTLf goals. All this shows that ``best-effort" policies are robust to changes in the probabilities, as long as the support is unchanged.
Online Approval Committee Elections
Authors: Do Virginie , Hervouin Matthieu , Lang Jérôme , Skowron Piotr
Published: July 2022

Assume k candidates need to be selected. The candidates appear over time. Each time one appears, it must be immediately selected or rejected---a decision that is made by a group of individuals through voting. Assume the voters use approval ballots, i.e., for each candidate they only specify whether they consider it acceptable or not. This setting can be seen as a voting variant of choosing k secretaries. Our contribution is twofold. (1) We assess to what extent the committees that are computed online can proportionally represent the voters. (2) If a prior probability over candidate approvals is available, we show how to compute committees with maximal expected score.
Lexicographic Entailment, Syntax Splitting and the Drowning Problem
Authors: Heyninck Jesse , Kern-Isberner Gabriele , Meyer Thomas
Published: July 2022

Lexicographic inference is a well-known and popular approach to reasoning with non-monotonic conditionals. It is a logic of very high-quality, as it extends rational closure and avoids the so-called drowning problem. It seems, however, this high quality comes at a cost, as reasoning on the basis of lexicographic inference is of high computational complexity. In this paper, we show that lexicographic inference satisfies syntax splitting, which means that we can restrict our attention to parts of the belief base that share atoms with a given query, thus seriously restricting the computational costs for many concrete queries. Furthermore, we make some observations on the relationship between c-representations and lexicographic inference, and reflect on the relation between syntax splitting and the drowning problem.
On the Ordinal Invariance of Power Indices on Coalitional Games
Authors: Doignon Jean-Paul , Moretti Stefano , Ozturk Meltem
Published: July 2022

In a coalitional game, the coalitions are weakly ordered according to their worths in the game. When moreover a power index is given, the players are ranked according to the real numbers they are assigned by the power index. If any game inducing the same ordering of the coalitions generates the same ranking of the players then, by definition, the game is (ordinally) stable for the power index, which in turn is ordinally invariant for the game. If one is interested in ranking players of a game which is stable, re-computing the power indices when the coalitional worths slightly fluctuate or are uncertain becomes useless. Bivalued games are easy examples of games stable for any power index which is linear. Among general games, we characterize those that are stable for a given linear index. Note that the Shapley and Banzhaf scores, frequently used in AI, are particular semivalues, and all semivalues are linear indices. To check whether a game is stable for a specific semivalue, it suffices to inspect the ordering of the coalitions and to perform some direct computation based on the semivalue parameters.
Fixed-Budget Best-Arm Identification in Structured Bandits
Authors: Azizi MohammadJavad , Kveton Branislav , Ghavamzadeh Mohammad
Published: July 2022

Best-arm identification (BAI) in a fixed-budget setting is a bandit problem where the learning agent maximizes the probability of identifying the optimal (best) arm after a fixed number of observations. Most works on this topic study unstructured problems with a small number of arms, which limits their applicability. We propose a general tractable algorithm that incorporates the structure, by successively eliminating suboptimal arms based on their mean reward estimates from a joint generalization model. We analyze our algorithm in linear and generalized linear models (GLMs), and propose a practical implementation based on a G-optimal design. In linear models, our algorithm has competitive error guarantees to prior works and performs at least as well empirically. In GLMs, this is the first practical algorithm with analysis for fixed-budget BAI.
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Authors: Bae Wonho , Noh Junhyug , Jalali Asadabadi Milad , Sutherland Danica J.
Published: July 2022

Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most existing SWSSS algorithms extract pixel-level pseudo-labels from an image classifier - a very difficult task to do well, hence requiring complicated architectures and extensive hyperparameter tuning on fully-supervised validation sets. We propose a method called prediction filtering, which instead of extracting pseudo-labels, just uses the classifier as a classifier: it ignores any segmentation predictions from classes which the classifier is confident are not present. Adding this simple post-processing method to baselines gives results competitive with or better than prior SWSSS algorithms. Moreover, it is compatible with pseudo-label methods: adding prediction filtering to existing SWSSS algorithms further improves segmentation performance.
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Authors: Chen Deli , Lin Yankai , Li Lei , Ren Xuancheng , Li Peng , Zhou Jie , Sun Xu
Published: July 2022

Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally transferred from other fields like CV or NLP, whose underlying working mechanism remains underexplored. In this work, we first deeply probe the working mechanism of GCL in SSNC, and find that the promotion brought by GCL is severely unevenly distributed: the improvement mainly comes from subgraphs with less annotated information, which is fundamentally different from contrastive learning in other fields. However, existing GCL methods generally ignore this uneven distribution of annotated information and apply GCL evenly to the whole graph. To remedy this issue and further improve GCL in SSNC, we propose the Topology InFormation gain-Aware Graph Contrastive Learning (TIFA-GCL) framework that considers the annotated information distribution across graph in GCL. Extensive experiments on six benchmark graph datasets, including the enormous OGB-Products graph, show that TIFA-GCL can bring a larger improvement than existing GCL methods in both transductive and inductive settings. Further experiments demonstrate the generalizability and interpretability of TIFA-GCL.
Contests to Incentivize a Target Group
Authors: Elkind Edith , Ghosh Abheek , Goldberg Paul W.
Published: July 2022

We study how to incentivize agents in a target subpopulation to produce a higher output by means of rank-order allocation contests, in the context of incomplete information. We describe a symmetric Bayes--Nash equilibrium for contests that have two types of rank-based prizes: (1) prizes that are accessible only to the agents in the target group; (2) prizes that are accessible to everyone. We also specialize this equilibrium characterization to two important sub-cases: (i) contests that do not discriminate while awarding the prizes, i.e., only have prizes that are accessible to everyone; (ii) contests that have prize quotas for the groups, and each group can compete only for prizes in their share. For these models, we also study the properties of the contest that maximizes the expected total output by the agents in the target group.
Function-words Adaptively Enhanced Attention Networks for Few-Shot Inverse Relation Classification
Authors: Dou Chunliu , Wu Shaojuan , Zhang Xiaowang , Feng Zhiyong , Wang Kewen
Published: July 2022

The relation classification is to identify semantic relations between two entities in a given text. While existing models perform well for classifying inverse relations with large datasets, their performance is significantly reduced for few-shot learning. In this paper, we propose a function words adaptively enhanced attention framework (FAEA) for few-shot inverse relation classification, in which a hybrid attention model is designed to attend class-related function words based on meta-learning. As the involvement of function words brings in significant intra-class redundancy, an adaptive message passing mechanism is introduced to capture and transfer inter-class differences.We mathematically analyze the negative impact of function words from dot-product measurement, which explains why the message passing mechanism effectively reduces the impact. Our experimental results show that FAEA outperforms strong baselines, especially the inverse relation accuracy is improved by 14.33% under 1-shot setting in FewRel1.0.
Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation
Authors: Feng Cheng , Zhong Chaoliang , Wang Jie , Zhang Ying , Sun Jun , Yokota Yasuto
Published: July 2022

Existing unsupervised domain adaptation (UDA) studies focus on transferring knowledge in an offline manner. However, many tasks involve online requirements, especially in real-time systems. In this paper, we discuss Online UDA (OUDA) which assumes that the target samples are arriving sequentially as a small batch. OUDA tasks are challenging for prior UDA methods since online training suffers from catastrophic forgetting which leads to poor generalization. Intuitively, a good memory is a crucial factor in the success of OUDA. We formalize this intuition theoretically with a generalization bound where the OUDA target error can be bounded by the source error, the domain discrepancy distance, and a novel metric on forgetting in continuous online learning. Our theory illustrates the tradeoffs inherent in learning and remembering representations for OUDA. To minimize the proposed forgetting metric, we propose a novel source feature distillation (SFD) method which utilizes the source-only model as a teacher to guide the online training. In the experiment, we modify three UDA algorithms, i.e., DANN, CDAN, and MCC, and evaluate their performance on OUDA tasks with real-world datasets. By applying SFD, the performance of all baselines is significantly improved.
Comparison Knowledge Translation for Generalizable Image Classification
Authors: Feng Zunlei , Qiu Tian , Wu Sai , Jin Xiaotuan , He Zengliang , Song Mingli , Wang Huiqiong
Published: July 2022

Deep learning has recently achieved remarkable performance in image classification tasks, which depends heavily on massive annotation. However, the classification mechanism of existing deep learning models seems to contrast to humans' recognition mechanism. With only a glance at an image of the object even unknown type, humans can quickly and precisely find other same category objects from massive images, which benefits from daily recognition of various objects. In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories. Specifically, we investigate a new task termed Comparison Knowledge Translation (CKT). Given a set of fully labeled categories, CKT aims to translate the comparison knowledge learned from the labeled categories to a set of novel categories. To this end, we put forward a Comparison Classification Translation Network (CCT-Net), which comprises a comparison classifier and a matching discriminator. The comparison classifier is devised to classify whether two images belong to the same category or not, while the matching discriminator works together in an adversarial manner to ensure whether classified results match the truth. Exhaustive experiments show that CCT-Net achieves surprising generalization ability on unseen categories and SOTA performance on target categories.
Multi-view Unsupervised Graph Representation Learning
Authors: Gan Jiangzhang , Hu Rongyao , Zhan Mengmeng , Mo Yujie , Wan Yingying , Zhu Xiaofeng
Published: July 2022

Both data augmentation and contrastive loss are the key components of contrastive learning. In this paper, we design a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-view contrastive learning, to address some issues of contrastive learning ignoring the information from feature space. Specifically, the adaptive data augmentation first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. Experimental results verify the effectiveness of our proposed method, compared to state-of-the-art methods.
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems
Authors: Karia Rushang , Srivastava Siddharth
Published: July 2022

Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with deep learning to learn a generalizable Q-function for such problems. The learned Q-function can be efficiently transferred to related problems that have different object names and object quantities, and thus, entirely different state spaces. We show that the learned, generalized Q-function can be utilized for zero-shot transfer to related problems without an explicit, hand-coded curriculum. Empirical evaluations on a range of problems show that our method facilitates efficient zero-shot transfer of learned knowledge to much larger problem instances containing many objects.
Self-Predictive Dynamics for Generalization of Vision-based Reinforcement Learning
Authors: Kim Kyungsoo , Ha Jeongsoo , Kim Yusung
Published: July 2022

Vision-based reinforcement learning requires efficient and robust representations of image-based observations, especially when the images contain distracting (task-irrelevant) elements such as shadows, clouds, and light. It becomes more important if those distractions are not exposed during training. We design a Self-Predictive Dynamics (SPD) method to extract task-relevant features efficiently, even in unseen observations after training. SPD uses weak and strong augmentations in parallel, and learns representations by predicting inverse and forward transitions across the two-way augmented versions. In a set of MuJoCo visual control tasks and an autonomous driving task (CARLA), SPD outperforms previous studies in complex observations, and significantly improves the generalization performance for unseen observations. Our code is available at https://github.com/unigary/SPD.
Declaration-based Prompt Tuning for Visual Question Answering
Authors: Liu Yuhang , Wei Wei , Peng Daowan , Zhu Feida
Published: July 2022

In recent years, the pre-training-then-fine-tuning paradigm has yielded immense success on a wide spectrum of cross-modal tasks, such as visual question answering (VQA), in which a visual-language (VL) model is first optimized via self-supervised task objectives, e.g., masked language modeling (MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream task (e.g., VQA) via a brand-new objective function, e.g., answer prediction. However, the inconsistency of the objective forms not only severely limits the generalization of pre-trained VL models to downstream tasks, but also requires a large amount of labeled data for fine-tuning. To alleviate the problem, we propose an innovative VL fine-tuning paradigm (named Declaration-based Prompt Tuning, abbreviated as DPT), which fine-tunes the model for downstream VQA using the pre-training objectives, boosting the effective adaptation of pre-trained

models to the downstream task. Specifically, DPT reformulates the VQA task via (1) textual adaptation, which converts the given questions into declarative sentence form for prompt-tuning, and (2) task adaptation, which optimizes the objective function of VQA problem in the manner of pre-training phase. Experimental results on GQA dataset show that DPT outperforms the fine-tuned counterpart by a large margin regarding accuracy in both fully-supervised (2.68%) and zero-shot/fewshot (over 31%) settings. All the data and codes will be available to facilitate future research.
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Authors: Xia Haifeng , Wang Pu , Koike-Akino Toshiaki , Wang Ye , Orlik Philip , Ding Zhengming
Published: July 2022

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.
A Polynomial-time Decentralised Algorithm for Coordinated Management of Multiple Intersections
Authors: Iwase Tatsuya , Stein Sebastian , Gerding Enrico H. , Chapman Archie
Published: July 2022

Autonomous intersection management has the potential to reduce road traffic congestion and energy consumption. To realize this potential, efficient algorithms are needed.

However, most existing studies locally optimize one intersection at a time, and this can cause negative externalities on the traffic network as a whole.

Here, we focus on coordinating multiple intersections,

and formulate the problem as a distributed constraint optimisation problem (DCOP). We consider three utility design approaches that trade off efficiency and fairness.

Our polynomial-time algorithm for coordinating multiple intersections reduces the traffic delay by about 41 percentage points compared to independent single intersection management approaches.
Learnability of Competitive Threshold Models
Authors: Wang Yifan , Tong Guangmo
Published: July 2022

Modeling the spread of social contagions is central to various applications in social computing. In this paper, we study the learnability of the competitive threshold model from a theoretical perspective. We demonstrate how competitive threshold models can be seamlessly simulated by artificial neural networks with finite VC dimensions, which enables analytical sample complexity and generalization bounds. Based on the proposed hypothesis space, we design efficient algorithms under the empirical risk minimization scheme. The theoretical insights are finally translated into practical and explainable modeling methods, the effectiveness of which is verified through a sanity check over a few synthetic and real datasets. The experimental results promisingly show that our method enjoys a decent performance without using excessive data points, outperforming off-the-shelf methods.
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation
Authors: Jin Chang , Qiu Shigui , Xiao Nini , Jia Hao
Published: July 2022

In Neural Machine Translation (NMT), data augmentation methods such as back-translation have proven their effectiveness in improving translation performance. In this paper, we propose a novel data augmentation approach for NMT, which is independent of any additional training data. Our approach, AdMix, consists of two parts: 1) introduce faint discrete noise (word replacement, word dropping, word swapping) into the original sentence pairs to form augmented samples; 2) generate new synthetic training data by softly mixing the augmented samples with their original samples in training corpus. Experiments on three translation datasets of different scales show that AdMix achieves significant improvements (1.0 to 2.7 BLEU points) over strong Transformer baseline. When combined with other data augmentation techniques (e.g., back-translation), our approach can obtain further improvements.
Abstract Rule Learning for Paraphrase Generation
Authors: Liu Xianggen , Lei Wenqiang , Lv Jiancheng , Zhou Jizhe
Published: July 2022

In early years, paraphrase generation typically adopts rule-based methods, which are interpretable and able to make global transformations to the original sentence. But they struggle to produce fluent paraphrases. Recently, deep neural networks have shown impressive performances in generating paraphrases. However, the current neural models are black boxes and are prone to make local modifications to the inputs. In this work, we combine these two approaches into RULER, a novel approach that performs abstract rule learning for paraphrasing. The key idea is to explicitly learn generalizable rules that could enhance the paraphrase generation process of neural networks. In RULER, we first propose a rule generalizability metric to guide the model to generate rules underlying the paraphrasing. Then, we leverage neural networks to generate paraphrases by refining the sentences transformed by the learned rules. Extensive experimental results demonstrate the superiority of RULER over previous state-of-the-art methods in terms of paraphrase quality, generalization ability and interpretability.
Document-level Event Factuality Identification via Reinforced Multi-Granularity Hierarchical Attention Networks
Authors: Qian Zhong , Li Peifeng , Zhu Qiaoming , Zhou Guodong
Published: July 2022

Document-level Event Factuality Identification (DEFI) predicts the event factuality according to the current document, and mainly depends on event-related tokens and sentences. However, previous studies relied on annotated information and did not filter irrelevant and noisy texts. Therefore, this paper proposes a novel end-to-end model, i.e., Reinforced Multi-Granularity Hierarchical Attention Network (RMHAN), which can learn information at different levels of granularity from tokens and sentences hierarchically. Moreover, with hierarchical reinforcement learning, RMHAN first selects relevant and meaningful tokens, and then selects useful sentences for document-level encoding. Experimental results on DLEF-v2 corpus show that RMHAN model outperforms several state-of-the-art baselines and achieves the best performance.
Scheduling with Untrusted Predictions
Authors: Bampis Evripidis , Dogeas Konstantinos , Kononov Alexander , Lucarelli Giorgio , Pascual Fanny
Published: July 2022

Using machine-learned predictions to create algorithms with better approximation guarantees is a very fresh and active field. In this work, we study classic scheduling problems under the learning augmented setting. More specifically, we consider the problem of scheduling jobs with arbitrary release dates on a single machine and the problem of scheduling jobs with a common release date on multiple machines. Our objective is to minimize the sum of completion times. For both problems, we propose algorithms which use predictions for taking their decisions. Our algorithms are consistent -- i.e. when the predictions are accurate, the performances of our algorithms are close to those of an optimal offline algorithm--, and robust -- i.e. when the predictions are wrong, the performance of our algorithms are close to those of an online algorithm without predictions. In addition, we confirm the above theoretical bounds by conducting experimental evaluation comparing the proposed algorithms to the offline optimal ones for both the single and multiple machines settings.
General Optimization Framework for Recurrent Reachability Objectives
Authors: Klaska David , Kucera Antonin , Musil Vit , Rehak Vojtech
Published: July 2022

We consider the mobile robot path planning problem for a class of recurrent reachability objectives. These objectives are parameterized by the expected time needed to visit one position from another, the expected square of this time, and also the frequency of moves between two neighboring locations. We design an efficient strategy synthesis algorithm for recurrent reachability objectives and demonstrate its functionality on non-trivial instances.
PG3: Policy-Guided Planning for Generalized Policy Generation
Authors: Yang Ryan , Silver Tom , Curtis Aidan , Lozano-Perez Tomas , Kaelbling Leslie
Published: July 2022

A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy search-based methods with a focus on the score function used to guide the search over policies. We demonstrate limitations of two score functions --- policy evaluation and plan comparison --- and propose a new approach that overcomes these limitations. The main idea behind our approach, Policy-Guided Planning for Generalized Policy Generalization (PG3), is that a candidate policy should be used to guide planning on training problems as a mechanism for evaluating that candidate. Theoretical results in a simplified setting give conditions under which PG3 is optimal or admissible. We then study a specific instantiation of policy search where planning problems are PDDL-based and policies are lifted decision lists. Empirical results in six domains confirm that PG3 learns generalized policies more efficiently and effectively than several baselines.
Transfer Learning Based Adaptive Automated Negotiating Agent Framework
Authors: Sengupta Ayan , Nakadai Shinji , Mohammad Yasser
Published: July 2022

With the availability of domain specific historical negotiation data, the practical applications of machine learning techniques can prove to be increasingly effective in the field of automated negotiation. Yet a large portion of the literature focuses on domain independent negotiation and thus passes the possibility of leveraging any domain specific insights from historical data. Moreover, during sequential negotiation, utility functions may alter due to various reasons including market demand, partner agreements, weather conditions, etc. This poses a unique set of challenges and one can easily infer that one strategy that fits all is rather impossible in such scenarios. In this work, we present a simple yet effective method of learning an end-to-end negotiation strategy from historical negotiation data. Next, we show that transfer learning based solutions are effective in designing adaptive strategies when underlying utility functions of agents change. Additionally, we also propose an online method of detecting and measuring such changes in the utility functions. Combining all three contributions we propose an adaptive automated negotiating agent framework that enables the automatic creation of transfer learning based negotiating agents capable of adapting to changes in utility functions. Finally, we present the results of an agent generated using our framework in different ANAC domains with 100 different utility functions each and show that our agent outperforms the benchmark score by domain independent agents by 6%.
Linear Combinatorial Semi-Bandit with Causally Related Rewards
Authors: Nourani-Koliji Behzad , Ghoorchian Saeed , Maghsudi Setareh
Published: July 2022

In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation
Authors: Schaldenbrand Peter , Liu Zhixuan , Oh Jean
Published: July 2022

Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styled drawings for a given text description where a user can specify a desired drawing style using a sample image. Inspired by a theory in art that style and content are generally inseparable during the creative process, we propose a coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated by optimizing for style and content simultaneously throughout the process as opposed to applying style transfer after creating content in a sequence. Based on human evaluation, the styles of images generated by StyleCLIPDraw are strongly preferred to those by the sequential approach. Although the quality of content generation degrades for certain styles, overall considering both content and style, StyleCLIPDraw is found far more preferred, indicating the importance of style, look, and feel of machine generated images to people as well as indicating that style is coupled in the drawing process itself. Our code, a demonstration, and style evaluation data are publicly available.
Menentukan Modulus Elastisitas Stabilisasi Subgrade Berbasis Defleksi
Authors: Rosyidi Sri Atmaja P. , Wukirasih Niken , Siegfried Siegfried
Published: July 2022

Metode evaluasi perkerasan jalan berbasis teknologi tidak merusak atau non-destructive testing (NDT) merupakan pendekatan pengujian yang diperlukan saat ini dalam menjalankan sistem manajemen perkerasan jalan. Pengujian NDT memiliki keunggulan dari pengujian konvensional berbasis laboratorium maupun pengujian lapangan yaitu lebih cepat, ekonomis dan meminimalisasi gangguan pelaksanaan pengujian di lapangan serta tidak meninggalkan kerusakan pada struktur yang dievaluasi. Makalah ini bertujuan untuk mengukur modulus elastisitas bahan subgrade atau tanah dasar yang distabilisasi dengan menggunakan pengujian NDT Light Weight Deflectometer (LWD) Pusjatan. Penelitian dilaksanakan metode eksperimen dimana pengujian LWD dilaksanakan di model fisik lapisan tanah dasar perkerasan jalan dengan skala 1:1, dengan dimensi 3 × 1 m dan tebal 1 m. Tanah dasar berasal dari tanah lempung ekspansif yang diberikan bahan tambah untuk stabilisasi. Pengujian dilakukan pada 16 titik pengamatan untuk 0 hari, 3 hari dan 7 hari.  Pengujian LWD menganalisis lendutan dengan menilai deformasi vertikal dari beban yang dijatuhkan di atas lapisan perkerasan jalan. Peralatan LWD selain digunakan untuk mengukur kekakuan bahan perkerasan, alat ini mudah dioperasikan dan memiliki dimensi alat yang cukup sederhana sehingga praktis untuk dapat untuk digunakan di lokasi konstruksi dengan berbagai kondisi. Hasil pengujian LWD  merupakan nilai modulus elastisitas dinamis tanah dari analisis lendutan dengan pendekatan Boussineq.  Dari penelitian ini ditemukan bahwa alat LWD secara efektif dapat mengukur kualitas dalam nilai kekakuan lapisan subgrade selama masa peningkatan daya dukung tanah yang distabilisasi. selama tahap konstruksi. Terdapat peningkatan daya dukung tanah lapisan subgrade yang dapat diukur, yaitu nilai modulus elastisitas pada masing-masing hari pengamatan 0, 3 dan 7 hari. Perbandingan empirik pengujian LWD terhadap nilai modulus elastisitas dari korelasi DCP (dynamic cone penetrometer)  menunjukkan nilai korelasi yang baik dengan nilai modulus elastisitas LWD yang lebih besar dibandingkan hasil pengujian DCP.
Perbandingan Perilaku Siklik Elemen Replaceable Link Material Baja Konvesional Dengan Low Yield Point Steel (LYS)
Authors: Kumalasari Aulia Indira , Suswanto Budi , Sugihardjo Hidajat
Published: July 2022

Selama ini di dalam desain struktur Eccentrically Braced Frames (EBF), link selalu didesain secara menyatu dengan balok lantai sehingga menyebabkan terjadinya overstrength pada bagian tersebut. Selain itu, desain tersebut juga menjadikan perbaikan pasca gempa menjadi kurang efisien baik dalam segi waktu serta biaya. Selama satu dekade ini, mulai banyak penelitian yang memodelkan replaceable link dengan berbagai pengembangan untuk mendapatkan elemen yang daktail ketika menghadapi gempa, begitu juga dengan penelitian ini. Penelitian ini bertujuan untuk mengetahui perilaku link dengan berbagai macam material terhadap daktilitas, energi disipasi, deformasi dan rotasi, serta pola kegagalan pasca terjadi gempa. Pada penelitian ini dilakukan pengujian terhadap empat spesimen web bolted replaceable link dengan variasi material baja konvensional dan juga baja low yield pont (LYS). Analisa dilakukan dengan metode numerik yaitu dengan mengaplikasikan beban siklik bolak balik pada elemen link. Beban tersebut kemudian dikontrol trerhadap perpindahan di salah satu ujung link. Kemudian hasil pengujian dianalisa berdasarkan daktilitas, energi disipasi, deformasi, serta pola kegagalan. Hasil pemodelan menunjukkan link dengan tipe sambungan web bolted menghasilkan kurva dengan pinching effect yang disebabkan oleh sambungan baut. Selain itu, link LYS100 menunjukkan nilai rotasi yang besar dikarenakan material tersebut memiliki regangan ultimate yang lebih besar dibanding dengan baja konvensional. Dari pemodelan numerik didapatkan bahwa replaceable link dengan material LYS hasilnya sebanding dengan link dengan baja konvensional serta link tersebut mampu memenuhi persyaratan terkait daktilitas, kemampuan mendisipasi energi, serta besar rotasi yang dihasilkan.
Kajian Kekangan Kawat Anyam Sebagai Material Perbaikan Kerusakan Kolom Beton Bertulang Akibat Beban Aksial Sentris
Authors: Kencanawati Ni Nyoman , Merdana I Nyoman , Darmawan Ngakan Komang Indra
Published: July 2022

Komposit jaringan kawat anyam yang dilas dengan material mortar yang sering disebut juga dengan ferosemen adalah bahan yang dapat dipakai untuk memperbaiki atau memperkuat bagian struktur yang tua atau rusak. Namun, belum ada standar yang dikembangkan untuk komposit ferosemen sebagai bahan perbaikan struktur sampai saat ini. Dengan demikian penelitian pada material komposit ferosemen ini masih terus dituntut untuk dikembangkan. Penelitian ini bermaksud untuk mengetahui kemampuan komposit ferosemen dalam perbaikan kerusakan kolom. Lebih lanjut, penelitian ini bertujuan untuk mengetahui pengaruh kekangan ferosemen terhadap kolom beton bertulang yang telah diberikan kerusakan sebesar 60% akibat beban aksial sentris. Benda uji yang digunakan berupa kolom beton bertulang berbentuk persegi dengan ukuran penampang 150 mm x 150 mm dan panjang 1000 mm. Sebagai perbaikan, benda uji diberikan kekangan dengan kawat anyam yang dilas dengan tiga variasi diameter kawat yaitu 0,5 mm, 1 mm, dan 1,5 mm. Hasil menunjukkan bahwa kemampuan menahan beban pada kolom perbaikan menjadi lebih besar dengan presentase kenaikan kapasitas kolom dalam menahan beban aksial rata-rata sebesar 1,75 kali dibandingkan dengan kolom awal yang tanpa kerusakan. Pemilihan ukuran diameter kawat anyam menjadi salah satu parameter yang harus diperhatikan dalam efektifitas perbaikan kolom dengan menggunakan material ferosemen.
Studi Potensi Kapasitas Tampungan Embung Simarubak Ubak Di Kabupaten Humbang Hasundutan
Authors: Zevri Asril
Published: July 2022

Peningkatan jumlah penduduk dengan perubahan musim yang cukup esktrem di Kabupaten Humbang Hasundutan mengakibatkan jumlah ketersediaan air tidak terkendali dan sangat terbatas untuk memenuhi kebutuhan air baik itu kebutuhan air irigasi dan air baku. Salah satu solusi yang dilakukan yaitu dengan konservasi melalui pembangunan kolam tampungan atau embung. Potensi kolam tampungan embung yang dapat ditingkatkan salah satunya yaitu Embung Simarubak Ubak di Kecamatan Dolok Sanggul dikarenakan catchment area yang cukup luas dan elevasi topografi yang curam. Tujuan dari penelitian untuk menghitung potensi kapasitas tampungan Embung Simarubak Ubak berdasarkan simulasi antara jumlah ketersediaan air (inflow) dengan kebutuhan air (outflow). Pendekatan metodologi dilakukan secara kuantitatif dengan menghitung jumlah ketersediaan air (inflow) berdasarkan curah hujan bulanan, evapotranspirasi, debit andalan sedangkan kebutuhan air (outflow) dihitung untuk menentukan kebutuhan air irigasi dan air baku. Simulasi kapasitas tampungan dianalisa dengan metode analitis numerik antara jumlah inflow dan outflow dalam kurun waktu satu tahun. Hasil analisa simulasi kapasitas tampungan Embung Simarubak Ubak yaitu 93,394.77 m3 dapat memenuhi kebutuhan air baku penduduk sebanyak 1623 jiwa dengan luas daerah irigasi potensial seluas 94.51 Ha.
Faktor-faktor Pengaruh Besaran Estimasi Biaya Tidak langsung Pada Penawaran Pekerjaan Jalan Oleh Kontraktor X
Authors: Adianto Yohanes Lim Dwi , Wimala Mia , Harun Aldo Mayla
Published: July 2022

Estimasi biaya tidak langsung memiliki faktor yang dapat mempengaruhi tingkat akurasi pada harga penawaran kontraktor saat tender. Penelitian terhadap faktor pengaruh estimasi biaya tidak langsung di Indonesia masih terbatas, khususnya pada oleh kontraktor jalan. Hasil dari penelitian ini diharapkan dapat menjawab permintaan kontraktor jalan X dalam mengidentifikasi faktor apa saja yang berpengaruh pada estimasi biaya tidak langsung sehingga dapat diperkirakan dengan lebih akurat untuk proyek-proyek di masa mendatang. Tahap awal akan dimulai dengan kajian literatur untuk mendapatkan faktor yang berpengaruh dalam estimasi biaya tidak langsung dari berbagai proyek konstruksi baik di Indonesia maupun di luar negeri. Selanjutnya, faktor-faktor tersebut akan disusun dalam bentuk kuesioner dan dinilai  tingkat pengaruhnya terhadap estimasi biaya tidak langsung oleh para responden kontraktor jalan yang beroperasi di Jabodetabek. Hasil kajian literatur menunjukkan 51 faktor pengaruh yang dibagi menjadi empat kelompok, yaitu 11 faktor dalam kelompok Proyek, 20 faktor pengaruh dalam kelompok Organisasi, 13 faktor pengaruh dalam kelompok Klien dan Peraturan Pemerintah, dan tujuh faktor pengaruh dalam kelompok Lingkungan. Berdasarkan hasil analisis RII (Relative Importance Index), didapatkan 15 faktor yang  sangat berpengaruh terhadap estimasi biaya tidak langsung pada kontraktor jalan X yaitu ukuran proyek, lingkup pekerjaan, durasi proyek, ketersediaan modal kontraktor, jadwal pembayaran, site layout, ketersediaan pasokan sumber daya, kemudahan untuk dibangun, metode manajemen proyek, posisi keuangan klien, kondisi ekonomi regional, inflasi atau suku bunga, lokasi proyek, kebutuhan kontraktor untuk pekerjaan dan jumlah uang muka. Ukuran proyek dan lokasi proyek merupakan dua faktor yang memiliki perbedaan tingkat pengaruh yang signifikan pada proyek konstruksi jalan dibandingkan jenis proyek konstruksi lainnya.
Analisis Tingkat Kecelakaan Lalu Lintas Menggunakan Metode Accident Rate Dan Equivalent Accident Number (EAN) Di Kota Magelang
Authors: Muto'in Naurahelmi Farihah , Utami Adita
Published: July 2022

Meningkatnya jumlah penduduk, berbanding lurus dengan peningkatan volume kendaraan. Semakin meningkatnya volume kendaraan dapat menyebabkan semakin tingginya kerawanan dan indikasi terjadinya kecelakaan lalu lintas Berdasarkan data SATLANTAS Polres Magelang Kota, jumlah kejadian kecelakaan pada tahun 2015 sebanyak 136 kejadian, tahun 2016 sebanyak 188 kejadian, tahun 2017 sebanyak 186 kejadian, tahun 2018 sebanyak 167 kejadian, tahun 2019 sebanyak 265 kejadian, dan tahun 2020 sebanyak 149 kejadian. Penelitian ini bertujuan untuk mendapatkan lokasi  rawan kecelakaan (blacksite) serta titik rawan kecelakaan (blackspot)pada ruas jalan di kota Magelang . Metode yang digunakan yaitu Accident Rate untuk mengidentifikasi ruas jalan rawan kecelakaan dan Metode Equivalent Accident Number (EAN) sebagai identifikasi titik rawan kecelakaan. Hasil dari penelitian ini adalah titik rawan kecelakaan yang ditinjau pada ruas jalan Ahmad Yani berada di KM 3+800 – KM 4 dengan nilai accident rate sebesar 70,399 , ruas jalan Jenderal Sudirman berada di KM 0+600 – KM 0+800 dengan nilai accident rate sebesar 105,446, ruas jalan Urip Sumoharjo berada di KM 1+800 – KM 2 dengan nilai accident rate sebesar 78,283, ruas jalan Soekarno Hatta berada di KM 0+800 – KM 1 dengan nilai accident rate sebesar 47,424, dan ruas jalan Gatot Subroto berada di KM 1+200 – KM 1+400 dengan nilai accident rate sebesar 59,729.
Pengembangan Produk Kerajinan Kelompok Anyaman Mansiang Taratak Sejahtera dalam Rangka Meningkatkan Kinerja Pemasaran
Authors: Lita Ratni Prima , Henmaidi Henmaidi , Meuthia Meuthia , Rahmi Devi Yulia , Mardiyah Fatma Poni
Published: July 2022

Usaha kerajinan daun mansiang Taratak, Kenagarian Kubang, Kabupaten Lima Puluh Kota Provinsi Sumatera Barat (Sumbar) ada sejak lama dan merupakan usaha turun temurun. Salah kelompok pengrajin anyaman daun Mansiang ini adalah Kelompok Sejahtera. Permasalahan yang dihadapi mitra adalah masalah pengembangan produk yang mampu mendorong kinerja pemasaran Tujuan kegiatan PKM untuk meningkatkan pertumbuhan usaha dan mengembangkan daya saing produk ke pasar luar Kabupaten Lima Puluh Kota, mempercepat difusi teknologi dan manajemen dari Perguruan Tinggi ke mitra. Secara khusus tujuan kegiatan adalah mengembangkan produk kerajinan daun mansiang pada kelompok anyaman mansiang Taratak Sejahtera.

Kegiatan dilaksanakan dengan 3 tahap yaitu tahap persiapan dimulai sosialisasi dan pengurusan ijin dengan institusi terkait, mengontak pengrajin untuk memutuskan jadwal kegiatan dan menyusun modul kegiatan dan mempersiapkan pelaksanaan kegiatan. Tahap pelaksanaan kegiatan pada aspek produksi dengan metode pengontrolan kualitas, metode pelatihan tentang variasi produk, metode praktek merapikan produk, metode pelatihan penciptaan produk, dan konsultasi bisnis. Pada tahap monitoring dan evaluasi dilakukan kegiatan  evaluasi pelaksanaan kegiatan dan monitoring pelaksanaan kegiatan.

Hasil kegiatan adanya pengontrolan kualitas, peningkatan variasi produk, peningkatan kerapian produk dan benang yang sesuai dengan warna anyaman pengembangan inovasi desain dan perbaikan kualitas sehingga bisa membidik segmen menengah dan atas.
Rencana Aksi Nagari Tageh Nagari Lubuk Gadang Selatan Kecamatan Sangir Kabupaten Solok Selatan dalam Menghadapi Pandemi Covid-19
Authors: Syamsurizaldi Syamsurizaldi , Coirala Fillia Agustin , Supriyanto Supriyanto
Published: July 2022

Nagari Tageh merupakan program berkelanjutan membantu nagari di Nagari Lubuk Gadang Selatan Kecamatan Sangir Kabupaten Solok Selatan. Program ini dimaksudkan untuk membantu pemerintahan Nagari dalam meningkatkan tata kelola pemerintahan nagari dan mengelola potensi nagari dan menyusun rencana strategis dalam menghadapi pandemic Covid-19 di Nagari Lubuk Gadang Selatan yang memiliki potensi nagari di sector pertanian khususnya tanaman perkebunan dan peternakan, serta pariwisata dan jasa. Sebagai nagari yang terletak dikawasan strategis, namun sampai saat ini belum berstatus sebagai nagari mandiri. Kegiatan pengabdian kepada masyarakat dilakukan melalui pendampingan dan kemitraan untuk membantu nagari dalam penguatan kapasitas dan kelembagaan untuk penguatan penyelenggaraan pemerintahan dan pembangunan nagari dalam meningkatkan kesejahteraan masyarakat. Pada tahun pertama (2020) telah  dilakukan desimenasi, FGD dan lokarya dengan pemerintahan nagari, lembaga-lembaga kemasyarakatan di tingkat nagari serta stakeholder terkait lainnya di Nagari Lubuk Gadang Selatan dalam menggali permasalahan serta merumuskan Rencana Aksi Nagari Tageh tahun 2021-2023.  Adapun hasil dari program pengabdian masyrakat ini adalah pada tahun pertama (2020) yaitu: (1) Laporan Pengabdian Kepada Masyarakat, (2) Rencana Aksi Nagari Tageh Tahun 2021-2023 yang ditetapkan dengan Keputusan Wali Nagari Lubuk Gadang Selatan, (3) Publikasi kegiatan pengabdian di media online, dan (4) publikasi artikel pada jurnal ilmiah pengabdian kepada masyarakat. Rencana aksi nagari tageh diimplemntasikan melalui pendekatan sinergi dan integrasi dengan Rencana Kerja Pemerintahan ( RKP) dan Anggaran Pendapatan dan Belanja (APB) Nagari Tahun 2021-2023.

Kata kunci: Rencana Aksi, Nagari Tageh, Lubuk Gadang Selatan.