Reviewerly/exHarmony huggingface dataset:
BIALL at 50: the Noughties

It is apt to start this review about the 2000s by mentioning ‘a project called UKILELI…it aims to create a database of UK full-text legislation and case transcripts; available via the web. More critically it aims to be free.’ It was later renamed to the more easily pronounced BAILII. BIALL had voted to support the project with a donation and to encourage law libraries to do the same.
Humans are Still the Best Lossy Image Compressors

Lossy image compression has been studied extensively in the context of typical loss functions such as RMSE, MS-SSIM, etc. However, it is not well understood what loss function might be most appropriate for human perception. Furthermore, the availability of massive public image datasets appears to have hardly been exploited in image compression. In this work, we perform compression experiments in which one human describes images to another, using publicly available images and text instructions. These image reconstructions are rated by human scorers on the Amazon Mechanical Turk platform and compared to reconstructions obtained by existing image compressors. In our experiments, the humans outperform the state of the art compressor WebP in the MTurk survey on most images, which shows that there is significant room for improvement in image compression for human perception. The images, results and additional data is available at https://compression.stanford.edu/human-compression.
Reinforcement Learning in a Large Scale Photonic Network

In recent years Neural Networks or Neuromorphic Computing has significantly shifted the limits of what is computationally possible [1]. Recurrent Neural Networks are nonlinear dynamical systems, and as such they are inherently capable to process temporal information or signals. They show excellent performance in the prediction of chaotic trajectories or in the equalization of nonlinearly corrupted communication channels [2].
Conflict resolution via emerging technologies?

Abstract This paper presents a review of the current techniques and approaches adopted in conflict resolution in Multi-Agent Systems (MAS). The review highlights the strength and weaknesses, and thus, their success in fostering cooperation and collaboration in multi-agent systems. We survey alternative approaches to conflict resolution that rely on emerging technologies such as deep learning. From the survey, we discuss the benefits of using these emerging technologies in the conflict resolution process.
Validity and entailment in modal and propositional dependence logics

The computational properties of modal and propositional dependence logicshave been extensively studied over the past few years, starting from a resultby Sevenster showing NEXPTIME-completeness of the satisfiability problem formodal dependence logic. Thus far, however, the validity and entailmentproperties of these logics have remained mostly unaddressed. This paperprovides a comprehensive classification of the complexity of validity andentailment in various modal and propositional dependence logics. The logicsexamined are obtained by extending the standard modal and propositional logicswith notions of dependence, independence, and inclusion in the team semanticscontext. In particular, we address the question of the complexity of validityin modal dependence logic. By showing that it is NEXPTIME-complete we refute anearlier conjecture proposing a higher complexity for the problem.
Blockchains meet distributed hash tables: Decoupling validation from state storage

The first obstacle that regular users encounter when setting up a node for a public blockchain is the time taken for downloading all the data needed for the node to start operating correctly. In fact, this may last from hours to weeks for the major networks. Our contribution is twofold. Firstly, we show a design that enables mining and validation of new blocks keeping only a very small state. Secondly, we show that it is possible to store the state of the blockchain in a distributed hash table obtaining a wide spectrum of trade-offs between storage committed by the nodes and replication factor. Our proposal is independent from the consensus algorithm adopted, and copes well with transactions that involve smart contracts.
Improving Persian Dependency-Based Semantic Role Labeling using Semantic and structural Relations

In the automatic processing of the natural language, understanding the meaning of the sentence is accomplished by the semantic role labeler. The semantic role labeler does this task by examining the semantic connections between words (often verbs and their dependents) and imposing semantic roles on each of them (dependents) according to the occurred event in the sentence. This paper provides a dependency-based semantic role labeler with the help of clustering algorithms for Persian. We have examined the semantic role labeling problem as a classification problem. In our proposed method, for each verbal predicate, the candidate arguments are identified with the help of dependency relationships, and then the feature vector for them is extracted using the information in the dependency trees. In the next step, using the classification algorithms, the appropriate label for each of the arguments is determined. Finally, to improve the results, the sentences are projected to semantic and structural vector spaces and clustering is performed on them. The resulted information from clustering is used to correct semantic role labels. Experiments have been done on the first semantic role corpus in Persian language and the corpus provided by the authors. The achieved Macro-average F 1 -measure is 74.87 for the first corpus and 73.62 for the second one.
Deep Reinforcement Learning in System Optimization.

Many real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often sequentially aggregated reward, are often inherently reinforcement learning problems and present the opportunity to leverage the recent substantial advances in deep reinforcement learning. However, in some cases, it is not clear why deep reinforcement learning is a good fit for the problem. Sometimes, it does not perform better than the state-of-the-art solutions. And in other cases, random search or greedy algorithms could outperform deep reinforcement learning. In this paper, we review, discuss, and evaluate the recent trends of using deep reinforcement learning in system optimization. We propose a set of essential metrics to guide future works in evaluating the efficacy of using deep reinforcement learning in system optimization. Our evaluation includes challenges, the types of problems, their formulation in the deep reinforcement learning setting, embedding, the model used, efficiency, and robustness. We conclude with a discussion on open challenges and potential directions for pushing further the integration of reinforcement learning in system optimization.
Needs Analysis of English Department Students in Translating Text for Completing the Final Project at State Polytechnic of Padang

This study discusses aboutNeeds Analysis of English Department Students in Translating Text for Completing The Final Project at State Polytechnic of Padang. The purpose of this research specifically is to find out what are the needs related to the students difficulties when they are translating text, and what are the best procedure, strategy or method used to translate any kinds of text . The data are collected from English department students who are taking translation for their final project. This study can give a contribution for developing translation curriculum at English Department State Polytechnic of Padang.The data collection methods use questionnaires, observations, and interviews. These methods are important to get information about the the difficulties experience during translating text, students skills that need to be improved, and other additional information. By conducting this needs analysis study, the lecturers will know what the proper method or material that needs to be applied in teaching learning process. It also can be used to develop teaching and learning material. Moreover, these identificationscan be used to find and revise the previous course content in translation. Therefore, it is hoped that the students could be helped when they are writing their final project about this topic.This research starts by designing the needs analysis questionnaires for the students, the ex students, and the related parties. Then, it is tested to find out the validity and reliability. After getting the validity and reliability data, the questionnaire is distributed to the students, ex students, and teachers or industry. The result shows that there are three kinds of needs which have to be fulfilled in order to improve students skills in translating text for the final project, they are: objectives needs, learning needs and the learner’s needs.
Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation

Corpora curated by experts have sustained Natural Language Processing mainly in English, but the expensiveness of corpora creation is a barrier for the development in further languages. Thus, we propose a corpus generation strategy that only requires a machine translation system between English and the target language in both directions, where we filter the best translations by computing automatic translation metrics and the task performance score. By studying Reading Comprehension in Spanish and Word Sense Disambiguation in Portuguese, we identified that a more quality-oriented metric has high potential in the corpora selection without degrading the task performance. We conclude that it is possible to systematise the building of quality corpora using machine translation and automatic metrics, besides some prior effort to clean and process the data.
Linguistically independent sentiment analysis using convolutional-recurrent neural networks model

Text classification is a process which analyses text and assigns one or more classes to it based on its content. This paper introduces a linguistically independent text classifier based on convolutional–recurrent neural networks. The classifier works at character level instead of some higher structures such as words, sentences, etc. To evaluate the accuracy of the proposed methodology, the Yelp data set and other multilingual data set obtained from film review databases containing Czech, German and Spanish languages were used. The resulting accuracy on the Yelp data set is 93,64%. We also proved that the proposed model can work for various languages.
Design and Implementation of an AR-Based Inquiry Courseware— Magnetic Field

Magnetic field is an important knowledge point in junior high school physics discipline. Due to its invisible feature, students require to concretely perceive the magnetic field through experiments. However, the traditional experiment has some defects such as inconvenient operation and insignificant effect. Computer simulation technologies can solve these problems by virtualizing them, but it currently has some shortcoming in terms of its experience and credibility. Based on augmented reality technology and the idea of inquiry learning, this paper designs and implements an AR courseware for magnetic field teaching. The experimental experience is enhanced by the fusion of virtual and real while visualizing the magnetic line. Finally, a preliminary application is carried out.
Recommender resources based on acquiring user's requirement and exploring user's preference with Word2Vec model in web service

Traditional recommender algorithms mainly use structured data (resource tag, user feature etc.) to depict the user preference and ignore the semantic relations of resources. In this paper, we present a new idea for acquiring user's requirement and exploring user's preference with Word2Vec model (RP-Word2Vec) to find the interested and personal resource in the web service. We use Word2Vec model to measure the sentiment among keywords and acquire user's requirement as accurately as possible; and we treat resources as the input of Word2Vec model based on history behaviours and adopt a semantic similarity measuring process to recommend interested and personal resource for the user. The experiments results that the presented RP-Word2Vec supports more effective.
Zhuang National Costume Images Generation Method Based On Deep Convolutional Generative Adversarial Network

The style design and color matching of Zhuang national costumes is a time-consuming, labor-intensive but important task. For this problem, this paper proposes a method for generating images of Zhuang national costumes based on deep convolutional generative adversarial network. Firstly, combining the strong feature extraction capabilities of the convolutional neural network and generative adversarial network to learn the potential distribution of complex data, a deep convolutional generative adversarial network is designed and constructed. Secondly, for the problem that the generative adversarial network is difficult to converge, Introducing an activation function that is robust to noise and parameter initialization methods for noise, using a discriminator and generator training strategy with an iteration ratio of 1:3 helps the network converge to a steady state. The experimental results show that the method can converge to a stable state and effectively generate colorful Zhuang national costume images.
Recommendations for Producers Evaluators and Users of Methodological Literature Reviews

We content-analyzed 241 methodological literature reviews published in 54 journals. Based on our review of reviews involving an inductive and iterative process, we derived 11 latent factors, and their 47 observable indicators, that provide information about critical components included in such reviews. We then offer best-practice recommendations that answer the following questions: 1) How can the motivation for and importance of the methodological literature review be justified? 2) What strategies can be used to inform data selection decisions regarding the scope of the review? 3) How can the transparency and replicability of the process used to identify included articles and recommendations be enhanced? 4) What features can be used to report results and improve the usability of a review’s recommendations? and 5) What factors can be considered when evaluating methodological literature reviews? By answering these questions, we provide recommendations that address the needs of four stakeholder groups: (a) researchers who want to author a methodological literature review, (b) journal editors and reviewers tasked with evaluating submissions describing such reviews, (c) substantive researchers who use these reviews for retraining and retooling and in their own work, and (d) instructors who use them to teach and disseminate knowledge about methodological issues.
Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations

Internet of Things simulations play significant roles in the diverse kinds of activities in our daily lives and have been extensively researched. Creating and controlling virtual agents in three-dimensional Internet of Things simulations is a key technology for achieving realism in three-dimensional simulations. Given that traditional virtual agent-based approaches have limitations for realism, it is necessary to improve the realism of three-dimensional Internet of Things simulations. This article proposes a Q-Network-based motivation framework that applies a Q-Network to select motivations from desires and hierarchical task network planning to execute actions based on goals of the selected motivations. The desires are to be identified and calculated based on states. Selected motivations will be chosen to determine the goals that agents must achieve. In the experiments, the proposed framework achieved an average accuracy of up to 85.5% when the Q-Network-based motivation model was trained. To verify the Q-Network-based motivation framework, a traditional Q-learning is also applied in the three-dimensional virtual environment. Comparing the results of the two frameworks, the Q-Network-based motivation framework shows better results than those of traditional Q-learning, as the accuracy of the Q-Network-based motivation is higher by 15.58%. The proposed framework can be applied to the diverse kinds of Internet of Things systems such as a training autonomous vehicle. Moreover, the proposed framework can generate big data on animal behaviors for other training systems.
The effectiveness of learning a foreign language via a distance learning tool: Τesting the Duolingo application

This research is conducted to explore the effectiveness of studying online. Among the distance learning practices there is the Duolingo application to help users learn a foreign language. In particular, this is a case study aiming at exploring whether the Duolingo application is effective enough in learning a foreign language and developing the language skills. The data for this study was collected by researching tools such as questionnaires, interviews, and tests, before and after the learning process. 31 candidates participated in this research and talked about their experience. Collecting and analyzing the data of the study distributed into making some assumptions about the application. Taking into account the participants’ opinions and suggestions, the researcher managed to identify important issues in the application. The application seems to be effective in the attempt of learning a foreign language. However, there are some issues to be explored regarding the exploration of the language skills and other technical issues on the application. These issues need to be discussed and analyzed for the further improvement of it. Additionally, the researcher gives her opinion by personal experience on using the application and then she suggests some changes to make the application more efficient.
A Semi-Automatic Tool for Linked Data Integration

Linked Data (LD) is a set of best practices to publish data in RDF format. Transforming structured datasets into RDF datasets is possible thanks to RDF Mappings. To be able to define such mappings, it is necessary to be familiar with the LD practices and to know perfectly concerned datasets. An obstacle to the democratisation of the LD is that few people satisfy these two conditions.We believe that tools making easy the process of LD integration will foster the LD growth. In this demonstration, we present a chatbot-like tool that can semi-automatically generate RDF mappings for existing structured datasets. The challenge is to automate part of the integration process that requires getting familiar with RDF.
No Right to Remain Silent: Isolating Malicious Mixes

Mix networks are a key technology to achieve network anonymity and private messaging, voting and database lookups. However, simple mix network designs are vulnerable to malicious mixes, which may drop or delay packets to facilitate traffic analysis attacks. Mix networks with provable robustness address this drawback through complex and expensive proofs of correct shuffling but come at a great cost and make limiting or unrealistic systems assumptions. We present Miranda, an efficient mix-net design, which mitigates active attacks by malicious mixes. Miranda uses both the detection of corrupt mixes, as well as detection of faults related to a pair of mixes, without detection of the faulty one among the two. Each active attack -- including dropping packets -- leads to reduced connectivity for corrupt mixes and reduces their ability to attack, and, eventually, to detection of corrupt mixes. We show, through experiments, the effectiveness of Miranda, by demonstrating how malicious mixes are detected and that attacks are neutralized early.
Diffused Label Propagation based Transductive Classification Algorithm for Word Sense Disambiguation

A major natural language processing problem, word sense disambiguation is the task of identifying the correct sense of a polysemous word based on its context. In terms of machine learning, this can be considered as a supervised classification problem. A better alternative can be the use of semi-supervised classifiers since labeled data is usually scarce yet we can access large quantities of unlabeled textual data. We propose an improvement to Label Propagation which is a well-known transductive classification algorithm for word sense disambiguation. Our approach make use of a semantic diffusion kernel. We name this new algorithm as diffused label propagation algorithm (DILP). We evaluate our proposed algorithm with experiments utilizing various sizes of training sets of disambiguated corpora. With these experiments we try to answer the following questions: 1. Does our algorithm with semantic kernel formulation yield higher classification performance than the popular kernels? 2. Under which conditions does a kernel design perform better than others? 3. What kind of regularization methods result with better performance? Our experiments demonstrate that our approach can outperform baseline in terms of accuracy in several conditions.
WLCSSLearn: Learning Algorithm for Template Matching-based Gesture Recognition Systems

Template matching algorithms are well suited for gesture recognition, but unlike other machine learning approaches there are no established methods to optimize their parameters. We present WLCSSLearn: an optimization approach for the WarpingLCSS algorithm based on a genetic algorithms. We demonstrate that WLCSSLearn makes the optimization procedure automatic, fast and suitable for new recognition problems even when there is no a-priori knowledge about suitable range of parameter values. We evaluate WLCSSLearn on three different datasets of gestures. We demonstrated that our method increased the accuracy and F1 score up to 20% compared to previous literature.
PELATIHAN DAN PENDAMPINGAN PENULISAN KARYA ILMIAH BAGI GURU-GURU SMP DI SENTANI

Some of the problems faced by teachers in Sentani are the ability to write or express ideas in writing in general is still very low. Teachers are generally not used to writing. They lacked understanding of the rules of writing scientific papers and generally had no ideas to write. This is very related to the problem of lack of sources of information in the form of books and journals about education. Teachers have difficulty in searching references / libraries in various sources. As well as teachers experiencing obstacles in terms of sending publications. This service program aims to resolve the above problems through training activities in research methodology and mentoring to publish their work and direct the teacher to the journal to be addressed.  The method used for solutions to problems faced by teachers and to support the success of the program is in the form of lectures, question and answer, demonstrations and practice / writing. Participants who have participated in the training will immediately practice the results of the training they get and make scientific papers with the guidance of the trainers so that participants can make scientific papers well.  Keywords : papers, teacher professional development, SMPN Sentani