bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Binary Classification Hidden within Partial Label Learning
Authors: Luo Hengheng , Zhang Yabin , Zhao Suyun , Chen Hong , Li Cuiping
Published: July 2022

Partial label learning (PLL) is to learn a discriminative model under incomplete supervision, where each instance is annotated with a candidate label set. The basic principle of PLL is that the unknown correct label y of an instance x resides in its candidate label set s, i.e., P(y ∈ s | x) = 1. On which basis, current researches either directly model P(x | y) under different data generation assumptions or propose various surrogate multiclass losses, which all aim to encourage the model-based Pθ(y ∈ s | x)→1 implicitly. In this work, instead, we explicitly construct a binary classification task toward P(y ∈ s | x) based on the discriminative model, that is to predict whether the model-output label of x is one of its candidate labels. We formulate a novel risk estimator with estimation error bound for the proposed PLL binary classification risk. By applying logit adjustment based on disambiguation strategy, the practical approach directly maximizes Pθ(y ∈ s | x) while implicitly disambiguating the correct one from candidate labels simultaneously. Thorough experiments validate that the proposed approach achieves competitive performance against the state-of-the-art PLL methods.
On the Optimization of Margin Distribution
Authors: Qian Meng-Zhang , Ai Zheng , Zhang Teng , Gao Wei
Published: July 2022

Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin. Recent years have witnessed the increasing empirical studies on the optimization of margin distribution according to different statistics such as medium margin, average margin, margin variance, etc., whereas there is a relative paucity of theoretical understanding.

In this work, we take one step on this direction by providing a new generalization error bound, which is heavily relevant to margin distribution by incorporating ingredients such as average margin and semi-variance, a new margin statistics for the characterization of margin distribution. Inspired by the theoretical findings, we propose the MSVMAv, an efficient approach to achieve better performance by optimizing margin distribution in terms of its empirical average margin and semi-variance. We finally conduct extensive experiments to show the superiority of the proposed MSVMAv approach.
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning
Authors: Rangi Anshuka , Xu Haifeng , Tran-Thanh Long , Franceschetti Massimo
Published: July 2022

To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate any order-optimal learning algorithm towards a targeted policy in episodic RL and examines the potential damage of two natural types of poisoning attacks, i.e., the manipulation of reward or action. We discover that the effect of attacks crucially depends on whether the rewards are bounded or unbounded. In bounded reward settings, we show that only reward manipulation or only action manipulation cannot guarantee a successful attack. However, by combining reward and action manipulation, the adversary can manipulate any order-optimal learning algorithm to follow any targeted policy with \Theta(\sqrt{T}) total attack cost, which is order-optimal, without any knowledge of the underlying MDP. In contrast, in unbounded reward settings, we show that reward manipulation attacks are sufficient for an adversary to successfully manipulate any order-optimal learning algorithm to follow any targeted policy using \tilde{O}(\sqrt{T}) amount of contamination. Our results reveal useful insights about what can or cannot be achieved by poisoning attacks, and are set to spur more work on the design of robust RL algorithms.
Lexicographic Multi-Objective Reinforcement Learning
Authors: Skalse Joar , Hammond Lewis , Griffin Charlie , Abate Alessandro
Published: July 2022

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, and demonstrate their applicability in practical settings. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
Forgiving Debt in Financial Network Games
Authors: Kanellopoulos Panagiotis , Kyropoulou Maria , Zhou Hao
Published: July 2022

We consider financial networks, where nodes correspond to banks and directed labeled edges correspond to debt contracts between banks. Maximizing systemic liquidity, i.e., the total money flow, is a natural objective of any financial authority. In particular, the financial authority may offer bailout money to some bank(s) or forgive the debts of others in order to maximize liquidity, and we examine efficient ways to achieve this. We study the computational hardness of finding the optimal debt-removal and budget-constrained optimal bailout policy, respectively, and we investigate the approximation ratio provided by the greedy bailout policy compared to the optimal one.

We also study financial systems from a game-theoretic standpoint. We observe that the removal of some incoming debt might be in the best interest of a bank. Assuming that a bank's well-being (i.e., utility) is aligned with the incoming payments they receive from the network, we define and analyze a game among banks who want to maximize their utility by strategically giving up some incoming payments. In addition, we extend the previous game by considering bailout payments. After formally defining the above games, we prove results about the existence and quality of pure Nash equilibria, as well as the computational complexity of finding such equilibria.
IMO^3: Interactive Multi-Objective Off-Policy Optimization
Authors: Wang Nan , Wang Hongning , Karimzadehgan Maryam , Kveton Branislav , Boutilier Craig
Published: July 2022

Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose Interactive Multi-Objective Off-policy Optimization (IMO^3). The key idea of IMO^3 is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO^3 identifies a near-optimal policy with high probability, depending on the amount of designer's feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems.
Fusion Label Enhancement for Multi-Label Learning
Authors: Zhao Xingyu , An Yuexuan , Xu Ning , Geng Xin
Published: July 2022

Multi-label learning (MLL) refers to the problem of tagging a given instance with a set of relevant labels. In MLL, the implicit relative importance of different labels representing a single instance is generally different, which recently gained considerable attention and should be fully leveraged. Therefore, label enhancement (LE) has been widely applied in various MLL tasks as the ability to effectively mine the implicit relative importance information of different labels. However, due to the fact that the label enhancement process in previous LE-based MLL methods is decoupled from the training process on the predictive models, the objective of LE does not match the training process and finally affects the whole learning system. In this paper, we propose a novel approach named Fusion Label Enhancement for Multi-label learning (FLEM) to effectively integrate the LE process and the training process. Specifically, we design a matching and interaction mechanism which leverages a novel interaction label enhancement loss to avoid that the recovered label distribution does not match the need of the predictive model. In the meantime, we present a unified label distribution loss for establishing the corresponding relationship between the recovered label distribution and the training of the predictive model. With the proposed loss, the label distributions recovered from the LE process can be efficiently utilized for training the predictive model. Experimental results on multiple benchmark datasets validate the effectiveness of the proposed approach.
Membership Inference via Backdooring
Authors: Hu Hongsheng , Salčić Zoran , Dobbie Gillian , Chen Jinjun , Sun Lichao , Zhang Xuyun
Published: July 2022

Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data sample if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data sample was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked samples created by a data owner. Appealingly, MIB requires data owners' marking a small number of samples for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.
Local Differential Privacy Meets Computational Social Choice - Resilience under Voter Deletion
Authors: Tao Liangde , Chen Lin , Xu Lei , Shi Weidong
Published: July 2022

The resilience of a voting system has been a central topic in computational social choice. Many voting rules, like plurality, are shown to be vulnerable as the attacker can target specific voters to manipulate the result. What if a local differential privacy (LDP) mechanism is adopted such that the true preference of a voter is never revealed in pre-election polls? In this case, the attacker can only infer stochastic information about a voter's true preference, and this may cause the manipulation of the electoral result significantly harder. The goal of this paper is to provide a quantitative study on the effect of adopting LDP mechanisms on a voting system. We introduce the metric PoLDP (power of LDP) that quantitatively measures the difference between the attacker's manipulation cost under LDP mechanisms and that without LDP mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to a voting system. We give a full characterization of PoLDP for the voting system with plurality rule and provide general guidance towards the application of LDP mechanisms.
Deexaggeration
Authors: Kong Li , Li Chuanyi , Ng Vincent
Published: July 2022

We introduce a new task in hyperbole processing, deexaggeration, which concerns the recovery of the meaning of what is being exaggerated in a hyperbolic sentence in the form of a structured representation. In this paper, we lay the groundwork for the computational study of understanding hyperbole by (1) defining a structured representation to encode what is being exaggerated in a hyperbole in a non-hyperbolic manner, (2) annotating the hyperbolic sentences in two existing datasets, HYPO and HYPO-cn, using this structured representation, (3) conducting an empirical analysis of our annotated corpora, and (4) presenting preliminary results on the deexaggeration task.
“My nose is running.” “Are you also coughing?”: Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
Authors: Liu Wenge , Cheng Yi , Wang Hao , Tang Jianheng , Liu Yafei , Zhao Ruihui , Li Wenjie , Zheng Yefeng , Liang Xiaodan
Published: July 2022

With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to build the system, recent researches focused on how to build DSMD in a data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interpretability to data-driven DSMD. Specifically, we propose a more interpretable decision process to implement the dialogue manager of DSMD by reasonably mimicking real doctors' inquiry logics, and we devise a model with highly transparent components to conduct the inference. Moreover, we collect a new DSMD dataset, which has a much larger scale, more diverse patterns, and is of higher quality than the existing ones. The experiments show that our method obtains 7.7%, 10.0%, 3.0% absolute improvement in diagnosis accuracy respectively on three datasets, demonstrating the effectiveness of its rational decision process and model design. Our codes and the GMD-12 dataset are available at https://github.com/lwgkzl/BR-Agent.
Relational Triple Extraction: One Step is Enough
Authors: Shang Yu-Ming , Huang Heyan , Sun Xin , Wei Wei , Mao Xian-Ling
Published: July 2022

Extracting relational triples from unstructured text is an essential task in natural language processing and knowledge graph construction. Existing approaches usually contain two fundamental steps: (1) finding the boundary positions of head and tail entities; (2) concatenating specific tokens to form triples. However, nearly all previous methods suffer from the problem of error accumulation, i.e., the boundary recognition error of each entity in step (1) will be accumulated into the final combined triples. To solve the problem, in this paper, we introduce a fresh perspective to revisit the triple extraction task and propose a simple but effective model, named DirectRel. Specifically, the proposed model first generates candidate entities through enumerating token sequences in a sentence, and then transforms the triple extraction task into a linking problem on a ``head -> tail" bipartite graph. By doing so, all triples can be directly extracted in only one step. Extensive experimental results on two widely used datasets demonstrate that the proposed model performs better than the state-of-the-art baselines.
On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools
Authors: Sun Zhoujian , Huang Zhengxing , Ding Nai
Published: July 2022

Dialogue state tracking (DST) is a component of the task oriented dialogue system. It is responsible for extracting and managing slots, where each slot represents a part of the information to accomplish a task, and slot value is updated recurrently in each dialogue turn. However, many DST models cannot update slot values appropriately. These models may repeatedly inherit wrong slot values extracted in previous turns, resulting in the fail of the entire DST task. They cannot update indirectly mentioned slots well, either. This study designed a model with a mentioned slot pool (MSP) to tackle the update problem. The MSP is a slot specific memory that records all mentioned slot values that may be inherited, and our model updates slot values according to the MSP and the dialogue context. Our model rejects inheriting the previous slot value when it predicates the value is wrong. Then, it extracts the slot value from the current dialogue context. As the contextual information accumulates, the new value is more likely to be correct. It also can track the indirectly mentioned slot by picking a value from the MSP. Experimental results showed our model reached state of the art DST performance on MultiWOZ datasets.
Towards Discourse-Aware Document-Level Neural Machine Translation
Authors: Tan Xin , Zhang Longyin , Kong Fang , Zhou Guodong
Published: July 2022

Current document-level neural machine translation (NMT) systems have achieved remarkable progress with document context. Nevertheless, discourse information that has been proven effective in many NLP tasks is ignored in most previous work. In this work, we aim at incorporating the coherence information hidden within the RST-style discourse structure into machine translation. To achieve it, we propose a document-level NMT system enhanced with the discourse-aware document context, which is named Disco2NMT. Specifically, Disco2NMT models document context based on the discourse dependency structures through a hierarchical architecture. We first convert the RST tree of an article into a dependency structure and then build the graph convolutional network (GCN) upon the segmented EDUs under the guidance of RST dependencies to capture the discourse-aware context for NMT incorporation. We conduct experiments on the document-level English-German and English-Chinese translation tasks with three domains (TED, News, and Europarl). Experimental results show that our Disco2NMT model significantly surpasses both context-agnostic and context-aware baseline systems on multiple evaluation indicators.
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation
Authors: Yang Jian , Yin Yuwei , Ma Shuming , Zhang Dongdong , Wu Shuangzhi , Guo Hongcheng , Li Zhoujun , Wei Furu
Published: July 2022

Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages compared to the two-pass pivot translation but often underperforms the pivot-based method. In this paper, we propose a novel method, named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our method unifies source-teacher, target-teacher, and pivot-teacher models to guide the student model for the zero-resource translation. The source teacher and target teacher force the student to learn the direct source-target translation by the distilled knowledge on both source and target sides. The monolingual corpus is further leveraged by the pivot-teacher model to enhance the student model. Experimental results demonstrate that our model of 72 directions significantly outperforms previous methods on the WMT benchmark.
Exploring the Benefits of Teams in Multiagent Learning
Authors: Radke David , Larson Kate , Brecht Tim
Published: July 2022

For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent teams are primarily studied when in conflict; however, organizational psychology (OP) highlights the benefits of teams among human populations for learning how to coordinate and cooperate. In this paper, we propose a new model of multiagent teams for reinforcement learning (RL) agents inspired by OP and early work on teams in artificial intelligence. We validate our model using complex social dilemmas that are popular in recent multiagent RL and find that agents divided into teams develop cooperative pro-social policies despite incentives to not cooperate. Furthermore, agents are better able to coordinate and learn emergent roles within their teams and achieve higher rewards compared to when the interests of all agents are aligned.
Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Authors: Cui Zijun , Yin Naiyu , Wang Yuru , Ji Qiang
Published: July 2022

Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets. As a result, many existing causal discovery methods can fail under limited data. In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian method to estimate mutual information (MI), based on which we propose a robust MI based independence test; 2) Secondly, we consider the Bayesian estimation of hypothesis likelihood and incorporate it into a well-defined statistical test, resulting in a robust statistical testing based independence test. We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples. Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods.
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
Authors: Fan Joshua , Chen Di , Wen Jiaming , Sun Ying , Gomes Carla
Published: July 2022

Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods.
Creating Dynamic Checklists via Bayesian Case-Based Reasoning: Towards Decent Working Conditions for All
Authors: Lund  Flogard Eirik , Mengshoel Ole Jakob , Bach Kerstin
Published: July 2022

Every year there are 1.9 million deaths world-wide attributed to occupational health and safety risk factors. To address poor working conditions and fulfill UN's SDG 8, "protect labour rights and promote safe working environments for all workers", governmental agencies conduct labour inspections, using checklists to survey individual organisations for working environment violations. Recent research highlights the benefits of using machine learning for creating checklists. However, the current methods only create static checklists and do not adapt them to new information that surfaces during use. In contrast, we propose a new method called Context-aware Bayesian Case-Based Reasoning (CBCBR) that creates dynamic checklists. These checklists are continuously adapted as the inspections progress, based on how they are answered. Our evaluations show that CBCBR's dynamic checklists outperform static checklists created via the current state-of-the-art methods, increasing the expected number of working environment violations found in the labour inspections.
Strategy Proof Mechanisms for Facility Location with Capacity Limits
Authors: Walsh Toby
Published: July 2022

An important feature of many real world facility location problems are capacity limits on the number of agents served by each facility. We provide a comprehensive picture of strategy proof mechanisms for facility location problems with capacity constraints that are anonymous and Pareto optimal. First, we prove a strong characterization theorem. For locating two identical facilities with capacity limits and no spare capacity, the INNERPOINT mechanism is the unique strategy proof mechanism that is both anonymous and Pareto optimal. Second, when there is spare capacity, we identify a more general class of strategy proof mechanisms that interpolates smoothly between INNERPOINT and ENDPOINT which are anonymous and Pareto optimal. Third, with two facilities of different capacities, we prove a strong impossibility theorem that no mechanism can be both anonymous and Pareto optimal except when the capacities differ by just a single agent. Fourth, with three or more facilities we prove a second impossibility theorem that no mechanism can be both anonymous and Pareto optimal even when facilities have equal capacity. Our characterization and impossibility results are all minimal as multiple mechanisms exist if we drop one property.
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code
Authors: Niu Changan , Li Chuanyi , Luo Bin , Ng Vincent
Published: July 2022

Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions.
Situation Calculus for Controller Synthesis in Manufacturing Systems with First-Order State Representation (Extended Abstract)
Authors: De Giacomo Giuseppe , Felli Paolo , Logan Brian , Patrizi Fabio , Sardiña Sebastian
Published: July 2022

Manufacturing is transitioning from a mass production model to a service model in which facilities `bid' for previously unseen products. To decide whether to bid for a previously unseen product, a facility must be able to synthesize, on the fly, a process plan controller that delegates abstract manufacturing tasks in a supplied process recipe to the available manufacturing resources. First-order representations of the state are commonly considered in reasoning about action in AI. Here we show that we can leverage the wide literature on the Situation Calculus automatically synthesize such controllers. We identify two important decidable cases---finite domains and bounded action theories---for which we provide practical synthesis techniques.
Abstraction for Non-Ground Answer Set Programs (Extended Abstract)
Authors: Saribatur Zeynep G. , Eiter Thomas , Schüller Peter
Published: July 2022

Abstraction is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and over-approximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.
Building a Visual Semantics Aware Object Hierarchy
Authors: Diao Xiaolei
Published: July 2022

The semantic gap is defined as the difference between the linguistic representations of the same concept, which usually leads to misunderstanding between individuals with different knowledge backgrounds. Since linguistically annotated images are extensively used for training machine learning models, semantic gap problem (SGP) also results in inevitable bias on image annotations and further leads to poor performance on current computer vision tasks. To address this problem, we propose a novel unsupervised method to build visual semantics aware object hierarchy, aiming to get a classification model by learning from pure-visual information and to dissipate the bias of linguistic representations caused by SGP. Our intuition in this paper comes from real-world knowledge representation where concepts are hierarchically organized, and each concept can be described by a set of features rather than a linguistic annotation, namely visual semantic. The evaluation consists of two parts, firstly we apply the constructed hierarchy on the object recognition task and then we compare our visual hierarchy and existing lexical hierarchies to show the validity of our method. The preliminary results reveal the efficiency and potential of our proposed method.
Together about Dementia
Authors: Andersen Nicklas Sindlev , Chiarandini Marco
Published: July 2022

We present ``Together about Dementia'', a mobile health app

that aims to help persons with dementia when they get lost through

the help of caregivers, relatives, and volunteering

citizens. When the app detects that a person with dementia is

disoriented, wandering, and getting lost, it triggers an alarm that

activates a relative and possibly a volunteer in the proximity. A

backend system based on a microservice and serverless architecture

performs the detection of wandering and the subsequent coordination

of users that are put on a mission to the rescue of the person with

dementia. The backend system implements an AI technique for

spatio-temporal anomaly detection based on location data recorded by

the frontend system installed on the portable device of the person

with dementia.
Clinical Evaluation of the Efficacy of Fitostimoline Hydrogel used for the Autolytic Debridement of Venous Ulcers
Authors:   Santa Maria Nuova Hospital, Motolese Alfonso
Published: July 2022

Venous ulcers are the most common type of chronic ulcers, affecting frequently the lower limbs. Chronic venous ulcers significantly impact quality of life because of pain, risk of infections, need of medications and possibility of malignant transformation [1]. Accurate wound assessment is essential to guarantee appropriate goal setting.Debridement and bacterial load control are two initial steps of wound care [2]. There are many procedures for wounds cleaning and to reduce bacterial bioburden. It is important the appropriate treatment choice for any single case. Firstly, venous ulcers often present fibrin, one of the major factor that prevents healing process, and for this reason, a debridement of fibrinous tissue is necessary [3]. There are different type of debridement (enzymatic, autolytic, surgical etc.) and the common choice of not aggressive debridement depends on the impossibility to obtain an adequate anesthesia at home or in first level/private offices. Autolytic debridement is a selective and not aggressive method which causes removal of necrotic tissue due to the physiological moist wound environment, which exerts its own debriding capacity [4]. It is a painless and safe technique with effective action and with low costs because dressing are usually changed every two days.
Inovasi Sterilisasi Ozon Buah Pir Pasca Panen
Authors: Wulandari Sholihah Ayu , Rosyady Ahmad Fahriyannur , Januarta Boby Dwi , Prayoga Bima , Abi Johan Krisbima , Asmiranti Asmiranti
Published: July 2022

Fruit is the most popular food element of Indonesian people, almost everywhere in Indonesia sells a variety of fruits, one of which is apples sold in various stores, ranging from traditional markets and supermarkets.  However, pear sold by traders are not completely sterile from various bacteria on the skin and flesh of pear, another factor buyers hesitate in buying fruit, namely shelf life or durability for how long the fruit. Therefore, research was conducted on fruit sterilizing technology, one of which was obtained was ozone water purifier technology for fruit sterilizing.  From this research, we innovated to develop a user-friendly ozone sterilization tool named ozone box. Several tests of this tool are carried out to determine whether it works correctly or not, the first is an endurance test between fruits sterilized with ozone and fruits that are not sterilized with ozone. This resistance test was conducted over several days and showed results that the physical appearance of fruits sterilized using ozone was much fresher compared to fruits that were not sterilized using ozone or washed clean water.
Perancangan Film Dokumenter Expository Untuk Meningkatkan Pemahaman Masyarakat Terhadap Sejarah Masjid Jamik Sumenep
Authors: Hujairi Ahmad Walid , Rohman Taufikur , Putra Deny Fardiasnyah , Agustien Lusiana
Published: July 2022

Abstrak— Produksi konten multimedia dalam bentuk film dokumenter merupakan salah satu media untuk menyampaikan informasi kepada masyarakat dalam berbagai macam tujuan. Dalam perancangan kali ini film dokumenter disajikan menggunakan pendekatan ekpository untuk memperkenalkan situs peninggalan sejarah Masjid jamik Sumenep yang berdiri sejak tahun 1787 masehi pada masa Panembahan Somala Raja Sumenep ke XXXI. Penelitian ini bertujuan untuk merancang film dokumenter sejarah Masjid Jamik Sumenep menggunakan pendekatan Expository untuk meningkatkan pemahaman masyarakat terhadap sejarah masjid jamik sumenep. Responden dalam penelitian ini adalah masyarakat khususnya masyarakat sumenep berjumlah 40 responden dengan menggunakan convenience sampling. Peneliti menyebarkan kuesioner pretest dan posttest terkait sejarah Masjid Jamik Sumenep secara online untuk mengetahui efektivitas pemahaman masyarakat khususnya masyarakat Sumenep. Berdasarkan hasil analisis data ditemukan hasil peningkatan yang sebanyak 24,3%. Tingkat kepuasan responden terhadap film dokumenter ini sebesar 87% sehingga besar harapan penulis jika film dokumenter ini dapat menjadisalah satu bentuk tambahan media informasi mengenai situs peninggalan sejarah peradaban islam di kabupaten Sumenep.
Pemilihan Desa Terbaik Di Kawasan KPHP Sungai Sembulan Menggunakan Metode SAW
Authors: Yani Fitri , Adiwinoto Bambang , Helmud Ellya , Marini Marini
Published: July 2022

Forest areas that have the potential to be developed as production forests need to be protected because if they are not immediately protected by the government, there will be illegal logging, forest burning and other activities that will damage the forest itself. With the existence of a Production Forest Management Unit or abbreviated as KPHP, it is the government's effort to protect forests that have potential as production forests. However, in its management, it still has not maximized the existing potential so that it has not increased state revenue in the utilization of forest products so that a decision support system is needed to support or maximize government policies. In the decision support system for selecting the best village in the KPHP area, there are several criteria, namely the potential for NTFPs has a weight of 30%, the level of damage has a weight of 35%, forest farmer groups have a weight of 25% and environmental services have a weight of 10%. While the alternatives consist of Lampur with a weight of 0.59, Kerantai with a weight of 0.35, Cracks with a weight of 0.41, Tanjung Pura with a weight of 0.86 and Kemingking 0.66.
Prediksi Pergerakan Ikan Di Pesisir Pulau Madura Menggunakan Metode Gaussian Mixture Model Dan K-Means Clustering
Authors: Prabiantissa Citra Nurina , Yuliastuti Gusti Eka
Published: July 2022

Wilayah Indonesia yang sebagian besar adalah laut (negara maritim) mempunyai potensi yang besar dalam kontribusi hasil perikanan. Potensi ini yang seharusnya dapat dikembangkan dengan baik,  terutama di wilayah pesisir Pulau Madura. Untuk meningkatkan produksi ikan, peneliti melakukan prediksi terhadap keberadaan ikan pada tahun 2015 – 2017 menggunakan data citra satelit Landsat 8. Dari data ini, dilakukan proses pre-processing, ekstraksi fitur, penerapan algoritma k-means dan gaussian mixture model, kemudian melakukan validasi dengan data Dinas Kelautan dan Perikanan Jawa Timur. Hasil dari penelitian ini, keberadaan ikan dengan menggunakan metode gaussian mixture model lebih banyak kemiripannya dengan data perikanan tangkap dari Dinas Kelautan dan Perikanan. Terutama pada tahun 2017 dimana terdapat kemiripan pada bulan bulan Januari, Februari, April, Mei, September, Oktober, November dan Desember.
Real-time Deteksi Masker Berbasis Deep Learning menggunakan Algoritma CNN YOLOv3
Authors: Agustien Lusiana , Rohman Taufikur , Hujairi Ahmad Walid
Published: July 2022

Abstrak—Penggunaan masker merupakan hal sederhana yang dapat dilakukan untuk menekan angka penularan covid-19 namun sering sekali terabaikan. Penerapan prokes (protocol kesehatan) disegala bidang terus diupayakan oleh pemerintah daerah melalui edukasi dan sosialisasi untuk mengubah perilaku individu dan meningkatkan kesadaran masyarakat untuk mengikuti berbagai protokol kesehatan. Penelitian ini diharapkan bisa menjadi salah satu solusi yang cukup baik sehingga dapat membatu proses pengawasan dan nantinya dapat dikembangkan untuk mengatasi pelanggaran penggunaan masker diruang publik menggunakan sistem elektronik. Dalam penelitian ini menggunakan metode yolov3, dengan beberapa skenario pengujian untuk melihat performa yang dihasilkan oleh system. Parameter yang dianalisan untuk menguji performa dari penelitian ini adalah akurasi, recall, presisi, F1 Score, IoU, dan mAP. Sistem ini dapat melakukan prediksi dengan benar, baik di dalam ruangan maupun di luar ruangan dengan beberapa sudut pengujian yaitu 1800, 900,450 terhadap kamera. Nilai confidence yang dihasilkan juga sangat baik dan bergantung pada pencahayaan, ketajaman gambar, dan jarak antara kamera dan objek yang akan di deteksi.
Rancang Bangun Sistem Otomasi Mesin Pompa Air Tambak Menggunakan Protocol MQTT untuk Antisipasi Kelangkaan Air di Lamongan Saat Musim Kemarau
Authors: Mufid Mohammad Robihul , Mawaddah Saniyatul , Aditama Darmawan , Basofi Arif , Ulul Albab Mochammad Jauhar , Islamiyah Nurul
Published: July 2022

Kurang lebih 30% lahan Indonesia digunakan untuk pertanian, dimana total lahan pertanian untuk di daerah Lamongan sendiri adalah sebesar 91%. Pertumbuhan ekonomi dari sektor pertanian di Lamongan sebesar 35.18%, tertinggi dibanding dengan sektor lain. Meskipun demikian, bukan berarti sistem pertanian di Lamongan sama sekali tidak ada masalah. Salah satu masalah yang terjadi setiap tahun adalah terjadinya kekeringan yang mengakibatkan banyak daerah kekurangan air besih dan banyak petani juga jadi gagal panen. Pada penelitian ini akan dibuat sebuah sistem otomasi yang akan diimplementasikan pada mesin pompa air untuk mengisi tambak atau sawah. Sistem otomasi yang diusulkan dengan menggunakan sensor level air yang digunakan sebagai indikator akan ada atau tidaknya air di sungai atau parit. Penelitian ini juga menerapkan protokol MQTT untuk mengirimkan data dari sistem otomasi pompa ke perangkat end user untuk bisa dilakukan monitoring. Untuk mengetahui validasi dari sistem, maka akan dilakukan pengujian dengan 2 metode yaitu metode otomatis dan metode manual. Pengujian metode otomatis dimaksudkan untuk validasi terhadap sistem otomasi, sedangkan metode manual untuk memastikan bahwa sistem pompa bisa diatur dari jarak jauh. Hasil dari pengujian yang dilakukan didapatkan bahwa sistem pompa dapat berjalan dengan baik pada pengujian metode otomatis dan manual.
Sistem Monitoring Suhu dan Pengairan Otomatis Pada Tanaman Stroberi Berbasis Website
Authors: Farizi Akhmad , Susanto Bekti Maryuni , Atmadji Ery Setiyawan Jullev , Hariyanto Agus , Antika Elly
Published: July 2022

Abstract— Strawberry plants need a certain amount of soil moisture to thrive. In the process of watering strawberry plants, farmers have to go to the garden every day to do watering and monitor the condition of the garden manually, the process of watering manually is like using a bucket and a pitcher, this process is a waste of energy and time, if the farmer's house is far from the garden , farmers or cultivators must make observations when the time is right for watering according to the soil conditions of the strawberry plant. This research implements a website-based automatic temperature monitoring and irrigation system where the website can be accessed via the public Internet network, not only on the local network. The results of observations on the growth of strawberry plants showed that plants that were given automatic irrigation based on the Internet of Things (IoT) grew better when compared to plants that were given manual irrigation.

Keywords—strawberry; monitoring; temperature; waterring; microcontroller; Internet of Things.

 

Abstrak— Tanaman stroberi membutuhkan kelembapan tanah tertentu agar dapat berkembang dengan baik. Pada proses penyiraman tanaman stroberi, petani harus pergi ke kebun setiap hari  untuk melakukan penyiraman dan memonitoring kondisi kebun secara manual, proses penyiraman secara manual seperti menggunakan ember dan teko kocor, proses ini  sangatlah membuang tenaga dan juga waktu, apabila rumah petani jauh dari kebun tersebut, petani atau pembudidaya harus melakukan pengamatan kapan waktu yang tempat untuk melakukan penyiraman sesuai dengan kondisi tanah dari tanaman stroberi. Penelitian ini mengimplementasikan sistem monitoring suhu dan pengairan otomatis berbasis website dimana website dapat diakses melalui jaringan publik Internet bukan hanya di jaringan lokal. Hasil pengamatan pertumbuhan tanaman stroberi menunjukkan bahwa tanaman yang diberikan pengairan otomatis berbasis Internet of Things (IoT) tumbuh lebih baik jika dibandingkan dengan tanaman yang diberikan pengairan secara manual.

Keywords—stroberi; monitoring; suhu; pengairan; mikrokontroller; Internet of Things.
Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi
Authors: Orisa Mira , Faisol Ahmad
Published: July 2022

Algoritma Partitioning Around Medoid dikenal dengan K-medoids. Algoritma K-medoids lebih cocok digunakan pada Dataset yang memiliki outlier. Karena K-medoids merupakan perbaikan dari algoritma K-means pada clustering yang kurang baik dalam menangani dataset yang memiliki outliers. Algoritma K-medoids menentukan pusat cluster berdasarkan perwakilan objek cluster yang disebut dengan medoid. Medoid adalah objek cluster yang terletak paling sentral, dengan jumlah jarak minimum ke titik lain. Untuk menutupi kelemahan metode K-medoids dalam menentukan jumlah k awal secara random digunakan metode elbow. Evaluasi kelayakan algoritma K-medoids dalam pembentukan klasterisasi dilakukan pengukuran terhadap silhouette coefficient dan Davies-Bouldin index.
Interdisciplinary study of the impact of the COVID-19 pandemic on family and marriage institutions
Authors: Loboda Polina Evgen'evna , Prokudina Regina Olegovna
Published: July 2022

The object of the study is the family. The subject of the study is the institute of divorce. The authors consider in detail such aspects of the topic as the role of the impact of the COVID-19 pandemic on the institution of divorce. The analysis of the legislation of the Russian Federation on the directions of state development in the field of economics, demography, as well as regulation of family relations is carried out. The article reveals the content of the psychological aspect. In particular, it is said about the formation of psychological tension that arose due to the need to stay in a confined space for a long time. As a result, stress begins in subjects, which affects not only the psychological state of a person, but also the physical one, it can reduce immunity and resistance to coronavirus. A special contribution of the authors to the study of the topic is to establish the indirect role of the COVID-19 pandemic on the dissolution of marriages. The main conclusion of the study is that coronavirus cannot be considered as an independent cause of marital divorce, but it aggravates the negative trends that occur in every family. Despite this, an increase in the number of divorces is inevitable, so it is necessary to take measures to optimize this process. A significant contribution was made by innovative changes in the field of notary in 2021. One of the innovations was remote notarial actions. Thanks to this, the notary actively implements electronic technologies in its activities, allowing not only to speed up the passage of all stages of the procedure, but also to increase the security and reliability of legally important information. Such changes have had a positive impact on the existing system, since they have made it the most simplified and convenient.
The problem of the role-based intrapersonal conflict of a working woman.
Authors: Pachkolina Anastasiya Vladimirovna
Published: July 2022

The subject of this study is the role conflict of a working woman in modern Russian society. The objective of the study is to assess the severity of the role conflict and its dependence on the locus of the role conflict, as well as the relationship in the distribution of internal family roles in the group of working women mothers. The author considers the role conflict of a working woman as a historically changeable problem caused by both external and internal processes occurring with a woman's personality. The main conditions contributing to the emergence of role conflict that take place in the lives of women who combine both professional and family responsibilities are highlighted.    The scientific novelty lies in the attempt to comprehend the role conflict of a working woman in modern Russian society. Attention is focused on the problem of a woman combining many contradictory personal qualities that lead to the emergence and/or strengthening of role conflict. In the course of the study, statistically significant data were obtained indicating that the locus of role conflict used to resolve conflict situations affects the degree of involvement of the spouse in family roles, which is a factor in reducing role conflict in women. The analysis of the results showed that the structure of the role conflict of a working woman-mother has a complex nature and contains many components, the problem needs further, more detailed study.