Reviewerly/exHarmony huggingface dataset:
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function

We consider the problem of local planning in fixed-horizon Markov Decision Processes (MDPs) with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generative model provides a restricted, “local” access to the MDP: The planner can ask for random transitions from previously returned states and arbitrary actions, and the features are also only accessible for the states that are encountered in this process. As opposed to previous work (e.g. Lattimore et al. (2020)) where linear realizability of all policies was assumed, we consider the significantly relaxed assumption of a single linearly realizable (deterministic) policy. A recent lower bound by Weisz et al. (2020) established that the related problem when the action-value function of the optimal policy is linearly realizable requires an exponential number of queries, either in H (the horizon of the MDP) or d (the dimension of the feature mapping). Their construction crucially relies on having an exponentially large action set. In contrast, in this work, we establish that poly(H, d) planning is possible with state value function realizability whenever the action set has a constant size. In particular, we present the TensorPlan algorithm which uses poly((dH/δ)^A) simulator queries to find a δ-optimal policy relative to any deterministic policy for which the value function is linearly realizable with some bounded parameter (with a known bound). This is the first algorithm to give a polynomial query complexity guarantee using only linear-realizability of a single competing value function. Whether the computation cost is similarly bounded remains an interesting open question. We also extend the upper bound to the near-realizable case and to the infinite-horizon discounted MDP setup. The upper bounds are complemented by a lower bound which states that in the infinite-horizon episodic setting, planners that achieve constant suboptimality need exponentially many queries, either in the dimension or the number of actions.
Navigation Of Health Services Location On The Android-Based Using Location-Based Services

Location-Based Services (LBS) is a concept where its context is modeled on location. Location-Based Services can be accessed on the mobile device through cellular network ability and by using geographical position of mobile devices. By using latitude and longitude from Global Positioning System (GPS), the user can get information a location in accordance with GPS. This research will develop navigation applications for the community that requires guide in the routes towards and locations of health services in city of Semarang. It is based on the literature review that the community does not know all of the locations of the health services because such information is insufficient. The application developed is preceded by a phase of conducting analysis for user requirements including analysis of the ongoing system, analysis of the findings of interview as well as identification of information necessity and system requirements. The designing phase is focused on the Graphical User Interface design of mobile device and Web Admin design. Our design method uses case diagram, Activity diagram, Sequence diagram, and Class diagram. The research has resulted in an application which can give the facility knowledge of the layout and geographic positions nearest to the health service located around the users with supporting information through mobile based Android. This application is based on mobile application and web services with programming language Java, PHP, XML and use MySQL.
Autonomous Driving using CNN

Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.
Deep learning-based image inpainting with structure map

In the past few years, deep learning-based image inpainting has made significant progress. However, many existing methods do not take into account the rationality of the structure and the fineness of the texture, which leads to the scattered structure or excessive smoothness of the repaired image. To solve this problem, we propose a two-stage image inpainting model composed of structure generation network and texture generation network. The structure generation network focuses on the structure and color domain and uses the damaged structure map extracted from the mask image to reasonably fill the mask area to generate a complete structure map. The texture generation network uses the repaired structure map to guide the refinement process. We train the two-stage network on the public datasets Places2, CelebA, and Paris StreetView, and the experimental results show the superiority of our method over the previous methods.
Assessing the applicability of annotation projection methods for coreference relations

The main goal of this thesis is to explore the feasibility of using cross-lingual annotation projection as a method of alleviating the task of manual coreference annotation. To reach our goal, we build a first trilingual parallel coreference corpus that encompasses multiple genres. For the annotation of the corpus, we develop common coreference annotation guidelines that are applicable to three languages (English, German, Russian) and include a novel domain-independent typology of bridging relations as well as state-of-the-art near-identity categories.Thereafter, we design and perform several annotation projection experiments. In the first experiment, we implement a direct projection method with only one source language. Our results indicate that, already in a knowledge-lean scenario, our projection approach is superior to the most closely related work of Postolache et al. (2006). Since the quality of the resulting annotations is to a high degree dependent on the word alignment, we demonstrate how using limited syntactic information helps to further improve mention extraction on the target side. As a next step, in our second experiment, we show how exploiting two source languages helps to improve the quality of target annotations for both language pairs by concatenating annotations projected from two source languages. Finally, we assess the projection quality in a fully automatic scenario (using automatically produced source annotations), and propose a pilot experiment on manual projection of bridging pairs.For each of the experiments, we carry out an in-depth error analysis, and we conclude that noisy word alignments, translation divergences and morphological and syntactic differences between languages are responsible for projection errors. We systematically compare and evaluate our projection methods, and we investigate the errors both qualitatively and quantitatively in order to identify problematic cases. Finally, we discuss the applicability of our method to coreference annotations and propose several avenues of future research.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness.

Ensemble-based adversarial training is a principled approach to achieve robustness against adversarial attacks. An important technique of this approach is to control the transferability of adversarial examples among ensemble members. We propose in this work a simple yet effective strategy to collaborate among committee models of an ensemble model. This is achieved via the secure and insecure sets defined for each model member on a given sample, hence help us to quantify and regularize the transferability. Consequently, our proposed framework provides the flexibility to reduce the adversarial transferability as well as to promote the diversity of ensemble members, which are two crucial factors for better robustness in our ensemble approach. We conduct extensive and comprehensive experiments to demonstrate that our proposed method outperforms the state-of-the-art ensemble baselines, at the same time can detect a wide range of adversarial examples with a nearly perfect accuracy.
Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

This paper presents a simple unsupervised visual representation learning method with a pretext task of discriminating all images in a dataset using a parametric, instance-level classifier. The overall framework is a replica of a supervised classification model, where semantic classes (e.g., dog, bird, and ship) are replaced by instance IDs. However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy. This work presents several novel techniques to handle these difficulties. First, we introduce a hybrid parallel training framework to make large-scale training feasible. Second, we present a raw-feature initialization mechanism for classification weights, which we assume offers a contrastive prior for instance discrimination and can clearly speed up converge in our experiments. Finally, we propose to smooth the labels of a few hardest classes to avoid optimizing over very similar negative pairs. While being conceptually simple, our framework achieves competitive or superior performance compared to state-of-the-art unsupervised approaches, i.e., SimCLR, MoCoV2, and PIC under ImageNet linear evaluation protocol and on several downstream visual tasks, verifying that full instance classification is a strong pretraining technique for many semantic visual tasks.
Measuring the wedge of flat optical windows

A novel interferometric technique to measure small wedge angle of transparent windows is proposed. The technique is based on the measurement of the shift of the interference pattern generated by two sheared copies of a laser beam after crossing the optical wedge. A simple lens is used to combine the two beams in their focal plane and measurements are taken for different shears. It is not necessary to perform a spatial filtering of the laser beam since correlations are used to determine the parameters of the experiment. A sample with a wedge slightly greater than one minute is used to illustrate the method.
A Visual Approach to the UCN Industrial Playground

The demand for Industry 4.0 skills has increased over the last couple of years. This paper will focus on the multidisciplinary skillsets of IT, Production and Automation Engineering that are being trained on the UCN Industrial Playground (UCN IP). This platform has become an interesting common ground for understanding the knowledge gap in SMEs, when the idea of Industry 4.0 is explored and discussed. But with unexpected consequences led by COVID-19, an emergent need for new procedures start the discussion without having the foundational tools to set off the initial discussion.This paper will present an approach towards a virtual learning factory and present a digital twin platform setup with hardware-in-loop and thought into the ISA95 automation triangle, cyber-physical systems, and simulation timing issues. The paper will also explore the balance between time invested and skills learned – when going from an understanding from a physical setup to a visual approach.
Attending via both Fine-tuning and Compressing

Though being a primary trend for enhancing interpretability of neural networks, attention mechanism's reliability and validity are still under debate.In this paper, we try to purify attention scores to obtain a more faithful explanation of downstream models.Specifically, we propose a framework consisting of a learner and a compressor, which performs finetuning and compressing iteratively to enhance the performance and interpretability of the attention mechanism.The learner focuses on learning better text representations to achieve good decisions by fine-tuning, while the compressor aims to perform compressions over the representations to retain the most useful clues for explanations with a Variational information bottleneck ATtention (VAT) mechanism.Extensive experiments on eight benchmark datasets show the great advantages of our proposed approach in terms of both performance and interpretability.
PROJECT-BASED LEARNING COLLABORATED WITH DIGITAL MEDIA FOR INDONESIAN EFL LEARNERS’ SELF-CONFIDENCE AND COMMUNICATION SKILL

Indonesian EFL learners are still unconvinced to communicate by using English because being able to communicate requires the students to integrate self-confidence, and producing the meaningful idea at the same time. Fortunately, digital stuffs completely brings a new concept in ELT context since the use of digital in ELT clasroom can improve EFL learners’ communication skill. This research aims to reveal Indonesian EFL learners’ communication skill by integrating Project-based learning and digital media to siginificantly affect on students’ self-confidence on communication skill. To achieve the objective of the research, quantitative research method with factorial design 2X2 is applied. Then, the intruments used are interview with Likert scale and students’ communication achievement that have been distributed both normally and homogenously, thus, all the data are processed by using multivariate anova with sig 0,000 < 0,05. Based on the finding of the research, it is found that the using of Project-based Learning integrated with Telegram as media digital to share video absolutely affects on students’ communication skill because the students are able to think deeply in creating video project and feel comfortable to share with others on Telegram twice a week, it indicates that familiarity with their interlocutors and the community existed on Telegram group  leads them to prefectly communicate with other in the real classroom context, and allows them to improve the ability to produce a meaningful idea to deliver.
Simon Deakin and Christopher Markou (eds), Is Law Computable: Critical Perspectives on Law and Artificial Intelligence, Oxford: Hart Publishing, 2020, xxi + 320 pages, hb £72.00

The Modern Law ReviewEarly View REVIEW Simon Deakin and Christopher Markou (eds), Is Law Computable: Critical Perspectives on Law and Artificial Intelligence, Oxford: Hart Publishing, 2020, xxi + 320 pages, hb £72.00 Bernard Keenan, Corresponding Author b.keenan@bbk.ac.uk Lecturer in Law, Birkbeck College. email: b.keenan@bbk.ac.ukSearch for more papers by this author Bernard Keenan, Corresponding Author b.keenan@bbk.ac.uk Lecturer in Law, Birkbeck College. email: b.keenan@bbk.ac.ukSearch for more papers by this author First published: 29 June 2021 https://doi.org/10.1111/1468-2230.12659 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
A Distant Supervised Approach for Relation Extraction in Farsi Texts

The volume of Farsi information on the Internet has been increasing in recent years. However, most of this information is in the form of unstructured or semi-structured free text. For quick and accurate access to the vast knowledge contained in these texts, the information extraction methods are essential to generate knowledge bases. In recent years, relation extraction as a sub-task of information extraction has received much attention. While many of these systems were developed in English and other well-known languages, the systems for information extraction in Farsi have received less attention from researchers. In this systematic research for semi-automatic relation extraction, Persian Wikipedia articles were presented as reliable and semi-structured sources. In this system, the relation extraction is performed with the assistance of patterns that are automatically obtained with an approach based on distant supervised. In order to apply the distant supervised, the vast knowledge base of Wikidata has been used as a source in perfect synchronization with Wikipedia. The results show that the average precision value for all relations is 76.81%, which indicates an enhancement of precision compared to other methods in Farsi.
A proposed system for machine translation by crowdsourcing

To enjoy video content, the hearing impaired and viewers of different languages need video captioning. Existing automatic captioning systems have already been employed on commercial platforms such as YouTube. These systems generate captions from the audio data of the videos. The voice data handled by existing systems is a synthesis of multiple sounds, making it difficult to caption. In addition, the insufficient accuracy of machine-translated captions cannot correctly express the meaning of the original conversation. In this paper, we propose a new captioning system using crowdsourcing to improve the accuracy of foreign language captions in conversations with multiple people. The proposed system includes a step to create machine translation adaptive sentences to increase accuracy. We compared and evaluated the accuracy of captions created by the proposed system and YouTube’s existing system. In this paper, we demonstrate that our proposed system has higher accuracy than the existing system.
E-Komik Hikayat Patani: Putera Pewaris Kerajaan (Satu Analisis Tekstual, Kepentingan dan Cabaran)

In the era of globalization, physical comics have undergone a transformation from being available in a printed format to the e-comics production that are accessible online. However, e-comic-related studies are still limited. Looking at this gap, this study is conducted on the e-comic, Hikayat Patani: Putera Pewaris Kerajaan, published by Dewan Bahasa dan Pustaka (DBP). This study involved three main analyses, namely on the text, importance and challenges. The textual analysis uses five comic principles introduced by Hall in his writing Five Essential Principles of Comics and Cartooning. A library research, meanwhile, was conducted to identify the importance of e-comic and challenges. The analysis in textual aspect results found that the e-comic fulfils all the five comic principles by Hall. This shows that the e-comics have a high quality. In fact, the created illustrations are filled with abundant aspects of Malay identity and not be influenced by any other form of comic technique while presenting the content using an interesting storyline. The e-comic may be indicated as vital to attract the youth generation to learn about the traditional text of Hikayat Patani and the history of the Patani state while becoming a tool in fostering the interest among the younger generation to read and acquire knowledge. However, poor internet network, low chapter production and high cost of producing e-comics are challenges which must be overcome. Thus, this study find it is important to produce more e-comics based on historical sources or traditional texts so that the nation's civilization may continue to be preserved. Keywords: E-comic, Hikayat Patani: Putera Pewaris Kerajaan, textual, importance, challenges.
Interactive two-stream graph neural network for skeleton-based action recognition

Action recognition has wide applications in fields such as human–computer interaction, virtual reality, and robotics. Since human actions can be represented as a sequence of skeleton graphs, approaches based on graph neural networks (GNNs) have attracted considerable attention in the research action recognition. Recent studies have demonstrated the effectiveness of two-stream GNNs in which discriminative features for action recognition are extracted from both the joint stream and the bone stream. Each stream is generated by GNNs that support message passing along fixed connections between vertices. However, existing two-stream approaches have two limitations: no interaction is allowed between the two streams and temporary contacts between joints or bones cannot be modeled. To address these issues, we propose the interactive two-stream graph neural network, which employs a joint–bone communication block to accelerate the interaction between the joint stream and the bone stream. Furthermore, an adaptive strategy is introduced to enable dynamic connections between vertices. Extensive experiments on three large-scale datasets have demonstrated the effectiveness of our proposed method.
The implementation of k-means algorithm to determine the quality of teak wood in image based on the texture

Abstract Teak is a type of tree that produces high quality wood. Large tree, straight trunk, can grow to a height of 30-40 m. In the developing teak parquet industry, the process of selecting raw materials is very important. This is due to the many types, types and classes of wood texture. The choice of wood texture is still widely used by human workers, most of which require a long time and the results obtained are still inaccurate. This study aims to solve the problem of quality teak grouping based on image texture by creating a computerized system or application using the K-means algorithm. With this method, it is hoped that the classification of the quality of teak can be described more easily based on the texture of the image. Based on the results of system design and testing, the application can group the quality of teak wood based on the texture and color of the image
Exploring Unsupervised Pretraining Objectives for Machine Translation

Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data.Most approaches adapt masked-language modeling (MLM) to sequence-to-sequence architectures, by masking parts of the input and reconstructing them in the decoder.In this work, we systematically compare masking with alternative objectives that produce inputs resembling real (full) sentences, by reordering and replacing words based on their context.We pretrain models with different methods on English↔German, English↔Nepali and English↔Sinhala monolingual data, and evaluate them on NMT.In (semi-) supervised NMT, varying the pretraining objective leads to surprisingly small differences in the finetuned performance, whereas unsupervised NMT is much more sensitive to it.To understand these results, we thoroughly study the pretrained models and verify that they encode and use information in different ways.We conclude that finetuning on parallel data is mostly sensitive to few properties that are shared by most models, such as a strong decoder, in contrast to unsupervised NMT that also requires models with strong cross-lingual abilities.