Reviewerly/exHarmony huggingface dataset:
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning

A common approach for defining a reward function for multi-objective reinforcement learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then treated as design parameters dependent on the expertise (and preference) of the person performing the learning, with the typical result that a new solution is required for any change in these settings. This paper investigates the relationship between the reward function and the optimal value function for MORL; specifically addressing the question of how to approximate the optimal value function well beyond the set of weights for which the optimization problem was actually solved, thereby avoiding the need to recompute for any particular choice. We prove that the value function transforms smoothly given a transformation of weights of the reward function (and thus a smooth interpolation in the policy space). A Gaussian process is used to obtain a smooth interpolation over the reward function weights of the optimal value function for three well-known examples: Gridworld, Objectworld and Pendulum. The results show that the interpolation can provide robust values for sample states and actions in both discrete and continuous domain problems. Significant advantages arise from utilizing this interpolation technique in the domain of autonomous vehicles: easy, instant adaptation of user preferences while driving and true randomization of obstacle vehicle behavior preferences during training.
Web-Based Learning Design on Folklore Text for Intermediate Indonesian Language for Foreign Speakers (BIPA)

Education in the 21st century is closely related to the use of technology. It is causing changes in the use of learning materials. One of them being web-based learning material which happens in learning in the Indonesian Language for Foreign Speakers (BIPA). This research created web-based learning on folklore texts for Intermediate BIPA students. Meanwhile, the specific purpose of this research is to describe the user's needs for web-based learning materials and describe the design of the web site. Overall, this research procedure based on R&D method through the 4-D model (Define, Design, Develop, Disseminate). Specifically, this article elaborates about define and design steps of web-based learning on folklore text for intermediate BIPA students, by ragamceritarakyat.com. Three BIPA teachers were the participant of this research. The available website of folklore learning materials also used as a source of the research data. The result shows on two-point. There are need analysis and web design description. This is important because the use of web-based learning is considered to be able to increase students' experience in using technology, facilitate independent learning, and increase knowledge about the material in it, especially in folklore text material.
Implementation of IDMP Standards as a Means of Creating a Unified Information Space in the Field of Drug Circulation

The article presents approaches to creating a unified electronic drug registry based on drug identification standards (IDMP) in the market of the countries of the Eurasian Economic Union. Authors describe the approaches to coding the main accounting units of the created catalog, the entities that describe the drug included in the directory levels. The measures necessary for standardization and normalization of historical data of registration certificates are given, concerning international nonproprietary named, dosage forms, dosages, company names and others. Unsolved problems with the use of a unified catalog are described also, as well as the ways to solve them in the future are given. This work was supported by a grant from the Ministry of Education and Science of the Russian Federation, a unique project identifier RFMEFI60819X0278.
Attention and Graph Matching Network for Retrieval-Based Dialogue System with Domain Knowledge

Building an effective and friendly human-machine dialogue system is one of the major challenges in Artificial Intelligence. This work proposes a new model named Graph and Attention Matching Network (AGMN) for response selection in retrieval-based dialogue system. AGMN model consists of two parts: cross attention mechanism and knowledge representation extractor. Specifically, the cross attention mechanism is exploited to obtain the dual representation from context and response words because these representations can provide the useful matching information for determining whether the next utterance is suitable response or not. Besides, the domain knowledge relationships which are extracted from Linux manuals are incorporated into the word representation by graph attention mechanism. Experimental results on Ubuntu Dialogue Corpus showed that both cross attention mechanism and domain knowledge can contribute to the performance of response selection and the AGMN model proposed in this paper outperforms the state-of-art approaches.
Demonstration of Robust Self-Referenced Continuous Variable Quantum Key Distribution over 25km Fiber Link

We experimentally demonstrate a practical self-referenced continuous variable quantum key distribution system composed of embedded data conversion/acquisition units and standard telecommunication components over a 25km optical fiber, with a predicted key rate of 649kbps.
Mobile Augmented Reality to Enhance Customer Experience while Purchasing Furniture

With the rapid advancement in technology, the younger generations are more inclined to using their gadgets in almost all the activity they do. While buying furniture requires more consideration and is time-consuming, they tend to search for every information that they need on internet first, before taking further action of going to the physical store. Through the field research and interviews, it can be proven that customers in average need two or more trips before finally deciding to purchase. The proposed application is designed to help user find their way to the shop with a sufficient product knowledge and also reducing their consideration time to a faster purchase conversion. Augmented Reality will be used on the mobile application to let user see the color compatibility to the room and allow approximate size measuring. This is done by using a specific target size as a comparison to real life object size. Small size usability testing was conducted to several user, and the final result received great feedback as it was easily understood, self-explanatory and aided user to make decision faster. Eventually, the aim is that when the furniture arrives, customer will feel satisfied as a conclusion of their furniture purchase journey.
Discriminative Viewer Identification using Generative Models of Eye Gaze

We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model.
A New Frequency Hopping Scheme to Secure the Physical Layer in The Internet of Things (IoT)

In this paper, the conventional frequency hopped spread spectrum (FHSS), uncoordinated frequency hopping (UFH), and the message-driven frequency hopping (MDFH) have been simulated and analyzed for bit error rates against single and multiband disguised jamming attacks. The results confirm the superiority of the MDFH over the FHSS. The MDFH also relaxes the two communicating legitimate node's need for frequency synchronization which increases system complexity. However, those schemes are not feasible for the Internet of Things applications due to the lack of computation capability, size, and energy consumption. Because of that we designed and simulated a new frequency hopping scheme which takes into consideration the IoT node's limitations and at the same time maintains a satisfying bit error rate. The new scheme idea is to build a look-up table of orthogonal frequencies in the transmitter and the receiver. Before any transmission, the transmitter uses the UFH scheme to send a lightweight encrypted key to the receiver; the receiver then uses that key to determine the frequency sequence. After successfully receiving the key the receiver should send back acknowledgment; otherwise, the transmitter must send the key again. There are three benefits of the new scheme. First, it is hard on the jammer and/or the eavesdropper to destroy and/or percept the sequence key due to the encryption and the frequency hopping mechanisms. Second, it is robust against the disguised jamming, which considered the worst. Third, it is very simple and affordable. The three schemes have been simulated and the results show that the proposed scheme has provided a bit error rate close to the MDFH. However, the MDFH fails easily in detecting the strongest signal to predict the legitimate transmitted carrier if the jammer attacks all the filter banks on the receiver side at the same time with a disguised jamming signal. We address this problem in our proposed scheme by waiving the use of the filters.
CCA-Secure Leakage-Resilient Identity-Based Encryption without q-Type Assumptions

In this paper, we propose a new leakage-resilient identity-based encryption (IBE) scheme that is secure against chosen-ciphertext attacks (CCA) in the bounded memory leakage model. The security of our scheme is based on the external k-Linear assumption. It is the first CCA-secure leakage-resilient IBE scheme which does not depend on q-type assumptions. The leakage rate 1/10 is achieved under the XDLIN assumption (k=2).
Epochal Signatures for Deniable Group Chats

In this work we take a formal look at deniability in group chat applications and introduce the concept of "epochal signatures" that allows to turn many secure group chat protocols into deniable ones. Intuitively, the transform works for protocols that use signatures for authentication and that become deniable if the signatures are removed. In contrast to previous proposals that use signatures for entity authentication, like mpOTR (CCS’09), our construction does not require pairwise key establishment of participants and allows to add and remove participants without requiring to re-initialize the chat. These properties allow the deployment in protocols that are also designed to scale to very large groups. Finally, we construct a practical epochal signature scheme from generic primitives and prove it secure.
An Automatic Meter Recognition Method for In-Orbit Application

With the development of machine vision technology, more and more vision-based recognition methods have appeared. However, the artificial meter readings are still widespread in the astronautic application. Astronauts’ works are complex, and the meter readings reduce their efficiencies. To solve this problem, in this paper, we propose a method that can automatically recognize the meter readings. The automatic recognition method includes the following three major components: (1) the digit extraction, (2) the digit segmentation, and (3) the digit recognition. The experiment results show that the digits of the meter can be successfully recognized. The automatic readings can reduce astronauts’ works and make them more convenient while working. This new automatic recognition system has great promise for the practical application.
The Use of Volume in the Measurement of Grain in Early Imperial China

Officials managing grain in early imperial China used both capacity and volume units to quantify the spatial extension of grain. This chapter aims to explain why for the same thing, practitioners used two types of units that appear to us as equivalent. To begin with, we establish that volumes were magnitudes expressed with measurement units wholly different from capacities. In fact, there were no specific units devoted to the expression of volume, but simply a specific way of using measurement units for length. Of all types of magnitudes, for which ancient texts contain expressions of quantities, volume stands out for this reason. It also stands out because, as a rule, there is no standard with which volume can be measured. Expressions of volume derive from computation, in which geometric shape plays a crucial part. By contrast, amounts of capacity were expressed using the decimal system of dan (later on, named hu), dou and sheng, and they could be measured with vessels. Officially promulgated vessels included Shang Yang’s cuboidal sheng and Wang Mang’s cylindrical hu. We, then, examine the uses of volume in relation to grain. First, amounts of volume were used to define a second meaning of dan (later on, hu), that is, dan and hu as measurement units of value. Inscriptions on official vessels also used volume amounts to refer to the space they contained. This shows that these vessels did not only define units of capacity, but also units of value for a “standard” state of grain. Hence, these vessels also attest to a practice that connected extension and value through the use of volume. The third use of volume for grain likewise brought geometric shape into play and its primary goal was to assess an amount in value units. In conclusion, we suggest that using volume for grain occurred primarily in relation to the assessment of the value of an amount of grain, and geometric shape was systematically introduced to carry out the operation. This suggests some hypotheses about the management of granaries.
DIOPT: Extremely Fast Classification Using Lookups and Optimal Feature Discretization

For low dimensional classification problems we propose the novel DIOPT approach which considers the construction of a discretized feature space. Predictions for all cells in this space are obtained by means of a reference classifier and the class labels are stored in a lookup table generated by enumerating the complete space. This then leads to extremely high classification throughput as inference consists only of discretizing the relevant features and reading the class label from the lookup table index corresponding to the concatenation of the discretized feature bin indices. Since the size of the lookup table is limited due to memory constraints, the selection of optimal features and their respective discretization levels is paramount. We propose a particular supervised discretization approach striving to achieve maximal class separation of the discretized features, and further employ a purpose-built memetic algorithm to search towards the optimal selection of features and discretization levels. The inference run time and classification accuracy of DIOPT is compared to benchmark random forest and decision tree classifiers in several publicly available data sets. Orders of magnitude improvements are recorded in classification runtime with insignificant or modest degradation in classification accuracy for many of the evaluated binary classification tasks.
Automated relative geological time modeling towards geological prospect identification: Case study, Neuquén Basin, Argentina

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Automated relative geological time modeling towards geological prospect identification: Case study, Neuquén Basin, ArgentinaAuthors: Facundol PaganFernanda RaggioLoeiza GicquelNicolas DaynacFacundol PaganYPFSearch for more papers by this author, Fernanda RaggioYPFSearch for more papers by this author, Loeiza GicquelEliisSearch for more papers by this author, and Nicolas DaynacEliisSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428184.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe following paper aims at applying a semi-automated method for comprehensive seismic interpretation on a Neuquen Basin case study, onshore Argentina. The performed workflow is based on the creation of a signal driven Relative Geological Time (RGT) model (Pauget et al, 2009). RGT model outputs such as Thinning attribute, Wheeler diagram and isochrones enable to emphasize two prograding systems in the Cuyo Group and Quintuco-Vaca Muerta Formation. A 3D stratigraphic framework is built from boundary delineation with bedset termination management, and unit modeling with sedimentary dynamics assignment. This interpretation of depositional settings then leads to a dynamic analysis of isochrones through a stratal slicing performed at a sub-seismic sample scale. The analysis of seismic amplitudes, spectral decomposition results and RGT attributes finally reveals the presence of Cuyo sub-marine canyons interpreted as turbidite systems.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 3:55 PMLocation: Poster Station 10Presentation Type: PosterKeywords: geomodel, sequence stratigraphy, deep-sea fanPermalink: https://doi.org/10.1190/segam2020-3428184.1FiguresReferencesRelatedDetailsCited byBenefits of semi-automatic seismic interpretation workflow for fast-paced decision-making environments: A U.S. onshore case studyAlfredo Fernandez, Jake Marson, Vianney Savajol, and Christi Gell15 August 2022 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Facundol Pagan, Fernanda Raggio, Loeiza Gicquel, and Nicolas Daynac, (2020), "Automated relative geological time modeling towards geological prospect identification: Case study, Neuquén Basin, Argentina," SEG Technical Program Expanded Abstracts : 1150-1154. https://doi.org/10.1190/segam2020-3428184.1 Plain-Language Summary Keywordsgeomodelsequence stratigraphydeep-sea fanPDF DownloadLoading ...
DOS-GAN: A Distributed Over-Sampling Method Based on Generative Adversarial Networks for Distributed Class-Imbalance Learning

Class-imbalance Learning is one of the hot research issues in machine learning. In the practical application of distributed class-imbalance learning, data continues to arrive, which often leads to class-imbalance situations. The imbalance problem in the distributed scenario is particular: the imbalanced state of different nodes may be complementary. The imbalanced states of different nodes may be complementary. Using this complementary relationship to do oversampling to change the imbalanced state is a valuable method. However, the data island limits data sharing in this case between the nodes. To this end, we propose DOS-GAN, which can take turns to use the data of one same class data on multiple nodes to train the global GAN model, and then use this GAN generator to oversampling the class without the original data being exchanged. Extensive experiments confirm that DOS-GAN outperforms the combination of traditional methods and achieves classification accuracy closes to the method of data aggregating.
Building Non-player Character Behaviors By Imitation Using Interactive Case-Based Reasoning

The creation of believable characters is one of the most challenging problems in the interactive entertainment industry. Although there are different tools available for designers and programmers to define the behavior of non-player characters, it remains a complex and error prone process that requires a high level of technical knowledge. Learning from Demonstration is a promising field that studies how to build intelligent agents that are able to replicate behaviors, learning from demonstration of human experts. This approach is interesting for developers who do not have a computer science background, alleviating the need of representing tasks and knowledge in a formal way. In this work we present an online and case-based reasoning agent that learns how to imitate real players of Pac-Man using an interactive approach in which both the human player and the computational agent take turns controlling the main character. In our previous work, the agent was in complete control of the learning process so it decided when to give up or regain control of the character. Now the system have been improved so the player can also regain control of the character and go back in time to correct improper behaviors manifested by the agent whenever they are detected. We also present an evaluation of the system performed by three professional video game designers, followed by the main insights we have gained.
Domus

In this paper, we present Domus, an educational game for multi-touch tables that makes use of a Tangible User Interface (TUI). In particular, it uses as game pieces physical objects called tangibles, which have conductive feet arranged in different patterns so that the device can recognize their position/rotation.
Towards Selective Data Enhanced Implicit Discourse Relation Recognition via Reinforcement Learning

As a fundamental task in NLP, recognizing implicit discourse relations remains a challenging problem for years. One of the most important reasons is the limited amounts of annotated data. On one hand, most existing methods use multi-task methods to enlarge data. External datasets are often fully introduced to the training process which may lead to a negative transfer of noisy data. On the other hand, some previous researches on selecting data mostly focus on designing rules with heuristic methods. It should be difficult to cover all aspects of good samples. Another drawback is that data selection can hardly generally fit well in any other recognition model since it was specialized with a specific discourse relation classifier.In this paper, we propose a novel selective data enhanced model (SDE) based on reinforcement learning. Our model is a general framework, composed of two parts: 1) Discourse relation classifier is designed to identify relations, including multi-level representation module and relation recognition module. 2) Pseudo labeled data selector is designed to pick out data that can enhance the discourse relation classifier. We conduct joint learning alternately to optimize both of the classifier and the selector. Our model is able to expand data selectively. And classifier part can be replaced with any other complicated networks. To further exploit interact signals between arguments, we also present a multi-level representation based on BERT. Experiments show that our model achieves better performance than state-of-the-art methods.
Hdr Image Saliency Estimation By Convex Optimization

In this paper, we propose a convex optimization-based method for the visual saliency prediction of high dynamic range (HDR) images, which allows straightforward reuse of any existing saliency estimation methods for typical images with low dynamic range (LDR). First, the proposed method decomposes a given HDR image into multiple LDR images with different levels of intensity using a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical LDR images. Finally, the saliency map of each decomposed image is integrated into a single map by solving convex optimization problems. The proposed method is applied to actual HDR images and its effectiveness is demonstrated.