Reviewerly/exHarmony huggingface dataset:
Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.
A practitioner’s view: a survey and comparison of lemmatization and morphological tagging in German and Latin

The challenge of POS tagging and lemmatization in morphologically rich languages is examined by comparing German and Latin. We start by defining an NLP evaluation roadmap to model the combination of tools and resources guiding our experiments. We focus on what a practitioner can expect when using state-of-the-art solutions. These solutions are then compared with old(er) methods and implementations for coarse-grained POS tagging, as well as fine-grained (morphological) POS tagging (e.g. case, number, mood). We examine to what degree recent advances in tagger development have improved accuracy – and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-of-domain evaluation. Out-of-domain evaluation is particularly pertinent because the distribution of data to be tagged will typically differ from the distribution of data used to train the tagger. Pipeline tagging is then compared with a tagging approach that acknowledges dependencies between inflectional categories. Finally, we evaluate three lemmatization techniques.
Mobile Learning and Mobile Assisted Language Learning in Focus

Mobile learning (m-learning) has been applied to foreign language education for more than a decade. Now that the emerging technologies and digital environments for learning have led to innovative learning experiences, there has been an exponential growth in the use of mobile applications for language learning. A growing volume of research has been conducted on Mobile Assisted Language Learning (MALL), emphasizing conceptualization of MALL and investigating the learning outcomes of users of MALL applications in foreign language classes . This paper aims to elaborate on the concept of mobile-assisted language learning (MALL) in association with learning theories and challenges, to present a conceptual framework of MALL design principles and dimensions, and to review existing MALL studies.
Improved image selection for stack-based HDR imaging

Stack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exposures. Minimizing the set of images to combine, while ensuring that the resulting HDR image fully captures the scene&#x2019;s irradiance, is important to avoid long image acquisition and postprocessing times. The problem of selecting the set of images has received much attention. However, existing methods either are not fully automatic, can be slow, or can fail to fully capture more challenging scenes. In this paper, we propose a fully automatic method for selecting the set of exposures to acquire that is both fast and more accurate. We show on an extensive set of benchmark scenes that our proposed method leads to improved HDR images as measured against ground truth using the mean squared error, a pixel-based metric, and a visible difference predictor and a quality score, both perception-based metrics.
The search for leakage-free entangling Fibonacci braiding gates

It is an open question if there are leakage-free entangling Fibonacci braiding gates. In this article, we give a construction of a large family of leakage-free braiding gates which are then proved to be non-entangling. We also conducted brute-force numerical searches for braids with a word-length up to seven and found no leakage-free entangling gates. These suggest the negative for the conjecture. On the other hand, we provide a much simpler protocol to generate approximately leakage-free entangling Fibonacci braiding gates than existing algorithms in the literature.
Weighted Multisource Tradaboost

In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Multisource Tradaboost, but weighs the importance of each datapoint taking into account the amount of target and source data available. A comparative study is then presented exposing the performance of four transfer learning methods as well as the proposed Weighted Multisource Tradaboost. The experimental results show that the proposed method is able to outperform the base method as the number of target samples increase. These results are promising in the sense that source-target ratio weighing may be a path to improve current methods of transfer learning. However, against the asymptotic conjecture of [6], all transfer learning methods tested in this work get outperformed by a no-transfer SVM for large number on target samples.
The Impact of Using Multimedia Interactive Based for Learning Media on Teacher and Student Communication in Bandung

Communication between teachers and students today still uses many conventional systems. With the advancement of computer technology, especially in the field of multimedia, it can be used as an innovative learning media. Multimedia created is an interactive film that can be controlled by the user / audience. Interactive films that are made useful to learn basic component parts in a personal computer. Interactive films begin with the basic display of a personal computer. Then the images in the interactive film can be selected by the user/audience to choose the part they want to learn. How to explain the basic components of a computer can be made interactive and interesting, and is preferred by many people. To create a multimedia presentation you can use a markup language, called SMIL (Syncronized Multimedia Integration Language). SMIL is a recommendation from the World Wide Web Consortium (www.w3c.org). SMIL is the same markup language as HTML and is a variant of XML. From the results of the application in several schools and universities in the city of Bandung, yielding positive results. Motivation from students to listen to the lesson material given increases, this makes the absorption rate of the learning material tested by giving test questions make the students' grades increase.
A Formalization of Forcing and the Unprovability of the Continuum Hypothesis.

We describe a formalization of forcing using Boolean-valued models in the Lean 3 theorem prover, including the fundamental theorem of forcing and a deep embedding of first-order logic with a Boolean-valued soundness theorem. As an application of our framework, we specialize our construction to the Boolean algebra of regular opens of the Cantor space $2^{\omega_2 \times \omega}$ and formally verify the failure of the continuum hypothesis in the resulting model.
Estimating Density Models with Complex Truncation Boundaries

Truncated densities are probability density functions defined on truncated domains. They share the same parametric form with their non-truncated counterparts up to a normalizing constant. Since the computation of their normalizing constants is usually infeasible, Maximum Likelihood Estimation cannot be easily applied to estimate truncated density models. Score Matching (SM) is a powerful tool for fitting parameters using only unnormalized models. However, it cannot be directly applied here as boundary conditions used to derive a tractable SM objective are not satisfied by truncated densities. In this paper, we study parameter estimation for truncated probability densities using SM. The estimator minimizes a weighted Fisher divergence. The weight function is simply the shortest distance from the data point to the boundary of the domain. We show this choice of weight function naturally arises from minimizing the Stein discrepancy as well as upperbounding the finite-sample statistical estimation error. The usefulness of our method is demonstrated by numerical experiments and a Chicago crime dataset. We also show that proposed density estimation can correct the outlier-trimming bias caused by aggressive outlier detection methods.
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments. Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization. We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.
Wireless Sensor Network for Fall Prevention on Geriatric Wards: A Report.

With regard to the growing number of older adults, it needs smart solutions for fall prevention. Especially at geriatric institutions, the risk of falling is very high and frequently leads to injuries, resulting in serious consequences. We present the Inexpensive Node for Bed Exit Detection (INBED), a comprehensive signaling system for fall prevention. The INBED system is based on a wireless sensor network infrastructure via IEEE802.15.4 and highly-specialized open hardware in-house developed wearable. The device, which will be attached to the patients, can detect several types of movement. Occurring events are forwarded to the nursing staff immediately by using the self-organizing and scalable network including wide area network integration. The system can help to relieve the staff while the personal freedom of movement and privacy of patients is increased. With this development, the energy-efficient, simple and intuitive mechanisms of proximity communication can be combined with broadband benefits.
Generating precise error specifications for C: a zero shot learning approach

In C programs, error specifications, which specify the value range that each function returns to indicate failures, are widely used to check and propagate errors for the sake of reliability and security. Various kinds of C analyzers employ error specifications for different purposes, e.g., to detect error handling bugs, yet a general approach for generating precise specifications is still missing. This limits the applicability of those tools. In this paper, we solve this problem by developing a machine learning-based approach named MLPEx. It generates error specifications by analyzing only the source code, and is thus general. We propose a novel machine learning paradigm based on transfer learning, enabling MLPEx to require only one-time minimal data labeling from us (as the tool developers) and zero manual labeling efforts from users. To improve the accuracy of generated error specifications, MLPEx extracts and exploits project-specific information. We evaluate MLPEx on 10 projects, including 6 libraries and 4 applications. An investigation of 3,443 functions and 17,750 paths reveals that MLPEx generates error specifications with a precision of 91% and a recall of 94%, significantly higher than those of state-of-the-art approaches. To further demonstrate the usefulness of the generated error specifications, we use them to detect 57 bugs in 5 tested projects.
Short Paper: How to Attack PSD2 Internet Banking

Internet banking security is set to take a major step forward: On September 14, 2019, the Regulatory Technical Standards of the Revised Payment Service Directive (PSD2) are going to be effective within the European Union and the European Economic Area. This regulation makes two widely demanded transaction security properties mandatory: two-factor authentication, and the dynamic linking of the authentication code to the transaction’s beneficiary and amount (full transaction authentication). Even though the regulation is undoubtedly a positive development from a security perspective, it does not account for all the technical and human weak points involved in the transaction process. In this paper, we look at a series of attacks targeting online and mobile banking that are possible even in a post-PSD2 era. Despite the regulatory motivation of this work, the presented issues and suggestions to address them are likely to be universal for internet banking in general.
Finite-dimensional pair coherent state engendered via the nonlinear Bose operator realization and its Wigner phase-space distributions

We theoretically analyze the photon number distribution, entanglement entropy, and Wigner phase-space distribution, considering the finite-dimensional pair coherent state (FDPCS) generated in the nonlinear Bose operator realization. Our results show that the photon number distribution is governed by the two-mode photon number sum q of the FDPCS, the entanglement of the FDPCS always increases quickly at first and then decreases slowly for any q , and the nonclassicality of the FDPCS for odd q is more stronger than that for even q .
Explore Entity Embedding Effectiveness in Entity Retrieval

This paper explores entity embedding effectiveness in ad-hoc entity retrieval, which introduces distributed representation of entities into entity retrieval. The knowledge graph contains lots of knowledge and models entity semantic relations with the well-formed structural representation. Entity embedding learns lots of semantic information from the knowledge graph and represents entities with a low-dimensional representation, which provides an opportunity to establish interactions between query related entities and candidate entities for entity retrieval. Our experiments demonstrate the effectiveness of entity embedding based model, which achieves more than 5% improvement than the previous state-of-the-art learning to rank based entity retrieval model. Our further analysis reveals that the entity semantic match feature effective, especially for the scenario which needs more semantic understanding.
Ortaokul Matematik Ders Kitaplarındaki Metinlerin Okunabilirliği ve Anlaşılabilirliği Üzerine Öğretmen-Öğrenci Görüşleri

It is not only the student's responsibility to understand a text. Whether or not the texts presented to students are prepared in accordance with their level directly affect the comprehensibility of the text. Many formulas have been developed to measure the readability of texts. The sentence length is one of the important variables in the readability formulas. The case study method was used in this study and 5 th , 6 th , 7 th , 8 th grade mathematics textbooks which were published by private publishers and approved by Ministry of National Education in 2017-2018 academic year were analyzed. In this context, the relationship between the readability scores of the texts at the level of knowledge, questions and solutions in the middle school mathematics textbooks and the comprehensibility of these texts were investigated. In this regard, the views of 18 mathematics teachers and 181 students at the 5 th , 6 th , 7 th and 8 th grades were taken on the comprehensibility of low, medium and high readability of knowledge texts, questions and solution texts. The students generally found the texts to be readable at a level that would not obstruct the understanding, and the teachers found it to be easily understandable. On the other hand, the students generally found the texts to be understandable at the medium and good level and generally understandable. Results showed that, teachers, except for the structure of the text in the middle school textbooks, want to; be increased the number of the daily life problems; be reduced the use of intensive symbols, and be increased the problems that are appropriate for their age and experience. Moreover, the students, except for the structure of the text in the middle school textbooks, want to be used multiple representations.
PROJECTION OF INCIDENT SURFACE SOLAR RADIATION IN CHINA UNDER A CLIMATE CHANGE SCENARIO

Abstract. We projected incident surface solar radiation (SSR) over China in the middle (2040–2059) and end (2080–2099) of the 21st century in the Representative Concentration Pathway (RCP) 8.5 scenario using a multi-model ensemble derived from the weighted average of seven global climate models (GCMs). The multi-model ensemble captured the contemporary (1979–2005) spatial and temporal characteristics of SSR and reproduced the long-term temporal evolution of the mean annual SSR in China. However, it tended to overestimate values compared to observations due to the absence of aerosol effects in the simulations. The future changes in SSR showed increases over eastern and southern China, and decreases over the Tibetan Plateau (TP) and northwest China relative to the present day. At the end of the 21st century, there were SSR increases of 9–21 W m−2 over northwest, central, and south China, and decreases of 18–30 W m−2 over the TP in June–July–August (JJA). In northeast China, SSR showed seasonal variation with increases in JJA and decreases in December–January–February. The time series of annual SSR had a decreased linear trend for the TP, and a slightly increased trend for China during 2006–2099. The results of our study suggest that solar energy resources will likely decrease in the TP under future climate change scenarios.
Deep Learning for Video Retrieval by Natural Language

Videos are everywhere. Video retrieval, i.e., finding videos that meet the information need of a specific user, is important for a wide range of applications including communication, education, entertainment, business, security etc. Among multiple ways of expressing the information need, a natural-language text is the most intuitive to start a retrieval process. For instance, to find video shots showing "a person in front of a blackboard talking or writing in a classroom". Such a query can be submitted easily, by typing or speech recognition, to a video retrieval system. Given a video as a sequence of frames and a query as a sequence of words, a fundamental problem in video retrieval by natural language is how to properly associate visual and linguistic information presented in sequential order. We attempt to address the fundamental problem by decomposing our quest along the following three dimensions: (1) Query representation, (2) Video representation, (3) Common space. The three dimensions also account for major designs in the state-of-the-art systems. We introduce a set of deep learning methods recently developed by our joint team of RUC, ZJGU, UvA and CAS. We evaluate the deep models on the TRECVID Ad-hoc Video Search (AVS) benchmark over the last three years (2016-2018). Much room exists for future research. Compared to video retrieval with semantic representations, deep learning approaches lack an intuitive explanation of the results obtained, in particular when the results are unsatisfactory. As the retrieval performance continues to improve, the accountability of a video retrieval model requires more research attention. While a well-performed deep model can be largely expected given adequate training data, novel algorithms that enable learning a video retrieval model from limited training resource are much in demand. Consider, for instance, visual annotation and retrieval for a target language other than English. Data and code used for this research are available at http://github.com/li-xirong/video-retrieval.
Quantum coherence in quantum dot systems

We investigate the quantum coherence in a vertical quantum dot system (QDS) with the magnetic field considering electron-electron interaction and Coulomb-blocked systems. We show how the important physical parameters can affect the variation of the quantum coherence. Moreover, we demonstrate that there is a temperature optimal value such that the quantum coherence is maximal and that the retardation of the coherence loss may take place by adjusting the physical parameters. Finally, we display the monotonic dependence of the coherence measures and total quantum correlation on the physical parameters according to the projective measurements.
Devices for Virtual and Augmented Reality

Augmented reality offers unique opportunities for connecting the real world with the digital one through a variety of technologies. Many of these can be found in mobile phones and tablets. This chapter provides an overview of the most commonly used devices used in virtual and augmented reality (VR/AR). We will describe the history, the principle of functioning and the possibilities offered by these devices. We will also mention devices that are still under development as prototypes. The most commonly used technologies undoubtedly include LCD and OLED display devices. Nowadays, 3D displays are being developed that use many different technologies. Smart glasses will certainly be used more often in the future. Many input/output devices provide communication with VR/AR devices. These include the touchscreen, microphone, and speaker, as well as increasingly used haptic devices. This chapter will enable readers to better understand the new technologies used in VR/AR devices.
Alexa Does Not Care. Should You?

This article explores the ethical dimension of digital voice assistants from the angle of postphenomenology and the technological mediation approach, whereby technology plays a mediating role in the human-world relations. Digital voice assistants, such as Amazon Echo’s Alexa or Google’s Home, increasingly form an integral part of everyday life for many people. Powered by Artificial Intelligence and based on voice interaction, voice assistants promise constant accompaniment by answering any questions people might have and even managing the physical space of their homes. However, while accompanying daily lives of people, voice assistants also seamlessly redefine the way people talk, interact and perceive each other. In view of their intentionalities, such as interaction by voice, command-based model of communication and development of attachment, digital voice assistant mediate the norms of interaction beyond their immediate use, the way people perceive themselves, those around and form consequent normative expectations. The article argues that understanding how technologies, such as digital voice assistants, mediate our moral landscape forms an essential part of media literacy in the digital age.