nils-herrmann/clean_scirepeval huggingface dataset:
Building a large Thai text corpus - part of speech tagged corpus: ORCHID

This paper presents a procedure in building a Thai part-of-speech (POS) tagged corpus named ORCHID. It is a collaboration project between Communications Research Laboratory (CRL) of Japan and National Electronics and Computer Technology Center (NECTEC) of Thailand. We proposed a new tagset based on the previous research on Thai parts-of-speech for using in a multi -lingual machine translation project. We marked the corpus in three levels:paragraph, sentence and word. The corpus keeps text information in text information line and numbering line, which are necessary in retrieving process. Since there are no explicit word/sentence boundary, punctuation and inflection in Thai text, we have to separate a paragraph into sentences before tagging the POS. We applied a probabili stic trigram model for simultaneously word segmenting and POS tagging. Rule for syll able construction is additionally used to reduce the number of candidates for computing the probabilit y. The problems in POS assignment are formalized to reduce the ambiguity occurring in case of the similar POSs.
Reinforcement Learning of Theorem Proving

We introduce a theorem proving algorithm that uses practically no domain heuristics for guiding its connection-style proof search. Instead, it runs many MonteCarlo simulations guided by reinforcement learning from previous proof attempts. We produce several versions of the prover, parameterized by different learning and guiding algorithms. The strongest version of the system is trained on a large corpus of mathematical problems and evaluated on previously unseen problems. The trained system solves within the same number of inferences over 40% more problems than a baseline prover, which is an unusually high improvement in this hard AI domain. To our knowledge this is the first time reinforcement learning has been convincingly applied to solving general mathematical problems on a large scale.
Comparison of Production Serverless Function Orchestration Systems

Since the appearance of Amazon Lambda in 2014, all major cloud providers have embraced the “Function as a Service” (FaaS) model, because of its enormous potential for a wide variety of applications. As expected (and also desired), the competition is fierce in the serverless world, and includes aspects such as the run-time support for the orchestration of serverless functions. In this regard, the three major players are currently Amazon Step Functions (December 2016), Azure Durable Functions (June 2017), and IBM Composer (October 2017), still young and experimental projects with a long way ahead. In this article, we will compare and analyze these three serverless orchestration services under a common evaluation framework. We will study their architectures, programming and billing models, and their effective support for parallel execution, among others. Through a series of experiments, we will also evaluate the run-time overhead of the different infrastructures for different types of workflows.
Dialogue Intent Classification with Long Short-Term Memory Networks

Dialogue intent analysis plays an important role for dialogue systems. In this paper,we present a deep hierarchical LSTM model to classify the intent of a dialogue utterance. The model is able to recognize and classify user’s dialogue intent in an efficient way. Moreover, we introduce a memory module to the hierarchical LSTM model, so that our model can utilize more context information to perform classification. We evaluate the two proposed models on a real-world conversational dataset from a Chinese famous e-commerce service. The experimental results show that our proposed model outperforms the baselines.
Hybrid WSN and RFID indoor positioning and tracking system

Wireless sensor networks (WSNs), consisting of a large number of nodes to detect ambient environment, are widely deployed in a predefined area to provide more sophisticated sensing, communication, and processing capabilities, especially concerning the maintenance when hundreds or thousands of nodes are required to be deployed over wide areas at the same time. Radio frequency identification (RFID) technology, by reading the low-cost passive tags installed on objects or people, has been widely adopted in the tracing and tracking industry and can support an accurate positioning within a limited distance. Joint utilization of WSN and RFID technologies is attracting increasing attention within the Internet of Things (IoT) community, due to the potential of providing pervasive context-aware applications with advantages from both fields. WSN-RFID convergence is considered especially promising in context-aware systems with indoor positioning capabilities, where data from deployed WSN and RFID systems can be opportunistically exploited to refine and enhance the collected data with position information. In this papera, we design and evaluate a hybrid system which combines WSN and RFID technologies to provide an indoor positioning service with the capability of feeding position information into a general-purpose IoT environment. Performance of the proposed system is evaluated by means of simulations and a small-scale experimental set-up. The performed analysis demonstrates that the joint use of heterogeneous technologies can increase the robustness and the accuracy of the indoor positioning systems.
Engaging Image Chat: Modeling Personality in Grounded Dialogue

To achieve the long-term goal of machines being able to engage humans in conversation, our models should be engaging. We focus on communication grounded in images, whereby a dialogue is conducted based on a given photo, a setup that is naturally engaging to humans (Hu et al., 2014). We collect a large dataset of grounded human-human conversations, where humans are asked to play the role of a given personality, as the use of personality in conversation has also been shown to be engaging (Shuster et al., 2018). Our dataset, ImageChat, consists of 202k dialogues and 401k utterances over 202k images using 215 possible personality traits. We then design a set of natural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. Automatic metrics and human evaluations show the efficacy of approach, in particular where our best performing model is preferred over human conversationalists 47.7% of the time.
Android Application Protection against Static Reverse Engineering based on Multidexing

DEX files are executable files of Android applications. Since DEX files are in the format of Java bytecodes, their Java source codes can be easily obtained using static reverse engineering tools. This results in numerous Android application thefts. There are some tools (e.g. bangcle, ijiami, liapp) that protect Android applications against static reverse engineering utilizing dynamic code loading. These tools usually encrypt classes.dex in an APK file. When the application is launched, the encrypted classes.dex file is decrypted and dynamically loaded. However, these tools fail to protect multidex APKs, which include more than one DEX files (classes2.dex, classes3.dex, ...) to accommodate large-sized execution codes. In this paper, we propose a technique that protects multidex Android applications against static reverse engineering. The technique can encrypt/decrypt multiple DEX files in APK files and dynamically load them. The experimental results show that the proposed technique can effiectively protect multidex APKs.
Advanced battery management system using MATLAB/Simulink

A battery management system (BMS) is a system that manages a rechargeable battery (cell or battery pack), by protecting the battery to operate beyond its safe limits and monitoring its state of charge (SoC) & state of health (SoH). BMS has been the essential integral part of hybrid electrical vehicles (HEVs) & electrical vehicles (EVs). BMS provides safety to the system and user with run time monitoring of battery for any critical hazarder conditions. In the present work, design & simulation of BMS for EVs is presented. The entire model of BMS & all other functional blocks of BMS are implemented in Simulink toolbox of MATLAB R2012a. The BMS presented in this research paper includes Neural Network Controller (NNC), Fuzzy Logic Controller (FLC) & Statistical Model. The battery parameters required to design and simulate the BMS are extracted from the experimental results and incorporated in the model. The Neuro-Fuzzy approach is used to model the electrochemical behavior of the Lead-acid battery (selected for case study) then used to estimate the SoC. The Statistical model is used to address battery's SoH. Battery cycle test results have been used for initial model design, Neural Network training and later; it is transferred to the design & simulation of BMS using Simulink. The simulation results are validated by experimental results and MATLAB/Simulink simulation. This model provides more than 97% accuracy in SoC and reasonably accurate SoH.
Understanding the Characteristics of Internet Short Video Sharing: A YouTube-Based Measurement Study

Established in 2005, YouTube has become the most successful Internet website providing a new generation of short video sharing service. Today, YouTube alone consumes as much bandwidth as did the entire Internet in year 2000 . Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering. In this paper, using traces crawled in a 1.5-year span (from February 2007 to September 2008), we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length, access pattern, to their active life span. The series of datasets also allow us to identify the growth trend of this fast evolving Internet site, which has seldom been explored before. We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching and peer-to-peer distribution schemes to efficiently deliver videos to end users.
User Modeling for Task Oriented Dialogues

We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and system turns into fixed length representations using Recurrent Neural Networks (RNN). It then encodes the dialogue history using another RNN layer. At each turn, user responses are decoded from the hidden representations of the dialogue level RNN. This hierarchical user simulator (HUS) approach allows the model to capture undiscovered parts of the user goal without the need of an explicit dialogue state tracking. We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal. We evaluate the proposed models on movie ticket booking domain by systematically interacting each user simulator with various dialogue system policies trained with different objectives and users.
Eye In-painting with Exemplar Generative Adversarial Networks

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in-painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object. Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power. We show that ExGANs can produce photo-realistic personalized in-painting results that are both perceptually and semantically plausible by applying them to the task of closed-to-open eye in-painting in natural pictures. A new benchmark dataset is also introduced for the task of eye inpainting for future comparisons.
Scientific Information Extraction with Semi-supervised Neural Tagging

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.
Agriculture robotic vehicle based pesticide sprayer with efficiency optimization

This paper deals with the exposition of how robotics can be applied to various fields of agriculture. One of the most important occupations in a developing country like India is agriculture. It is very important to improve the efficiency and productivity of agriculture by replacing laborers with intelligent machines like robots using latest technologies. The paper proposes a new strategy to replace humans in various agricultural operations like detection of presence of pests, spraying of pesticides, spraying of fertilizers, etc there by providing safety to the farmers and precision agriculture. The developed system involves designing a prototype which uses simple cost effective equipments like microprocessors, wireless camera, various motors and terminal equipments which is an aid to the farmers in various crop field activities.
Zero Knowledge Proofs : A Survey

A zero knowledge interactive proof system allows one person to convince another person of some fact without revealing the information about the proof. In particular, it does not enable the verifier to later convince anyone else that the prover has a proof of the theorem or even merely that the theorem is true (much less that he himself has a proof). This paper reviews the field of zero knowledge proof systems giving a brief overview of zero knowledge proof systems and the state of current research in this field.
Future Trends in Software Engineering Research for Mobile Apps

There has been tremendous growth in the use of mobile devices over the last few years. This growth has fueled the development of millions of software applications for these mobile devices often called as 'apps'. Current estimates indicate that there are hundreds of thousands of mobile app developers. As a result, in recent years, there has been an increasing amount of software engineering research conducted on mobile apps to help such mobile app developers. In this paper, we discuss current and future research trends within the framework of the various stages in the software development life-cycle: requirements (including non-functional), design and development, testing, and maintenance. While there are several non-functional requirements, we focus on the topics of energy and security in our paper, since mobile apps are not necessarily built by large companies that can afford to get experts for solving these two topics. For the same reason we also discuss the monetizing aspects of a mobile app at the end of the paper. For each topic of interest, we first present the recent advances done in these stages and then we present the challenges present in current work, followed by the future opportunities and the risks present in pursuing such research.
The Role of Advertising in Consumer Decision Making

Advertising is a form of communication intended to convince an audience (viewers, readers or listeners) to purchase or take some action upon products, information or services etc. The purchase process is a decision – making process under risk. This paper investigates the relationship between independent variables which are emotional response with attitudinal and behavioral aspect of consumer buying behavior, by tapping the responses of 110 respondents. The basic objective of this research is to assess the influence of advertising through attitudinal buying behavior of consumer (male & female) and analyze the influence of Advertising between male and female.
Noun Phrase Coreference as Clustering

This paper introduces a new, unsupervised algorithm for noun phrase coreference resolution. It differs from existing methods in that it views coreference resolution as a clustering task. In an evaluation on the MUC-6 coreference resolution corpus, the algorithm achieves an F-measure of 53.6%, placing it firmly between the worst (40%) and best (65%) systems in the MUC-6 evaluation. More importantly, the clustering approach outperforms the only MUC-6 system to treat coreference resolution as a learning problem. The clustering algorithm appears to provide a flexible mechanism for coordinating the application of context-independent and context-dependent constraints and preferences for accurate partitioning of noun phrases into coreference equivalence classes.
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association

Chinese pinyin input method engine (IME) lets user conveniently input Chinese into a computer by typing pinyin through the common keyboard. In addition to offering high conversion quality, modern pinyin IME is supposed to aid user input with extended association function. However, existing solutions for such functions are roughly based on oversimplified matching algorithms at word-level, whose resulting products provide limited extension associated with user inputs. This work presents the Moon IME, a pinyin IME that integrates the attentionbased neural machine translation (NMT) model and Information Retrieval (IR) to offer amusive and customizable association ability. The released IME is implemented on Windows via text services framework.
Interpreting Deep Learning: The Machine Learning Rorschach Test?

Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While multilayer, or deep, neural networks (DNNs) originated as models of biological networks in neuroscience [1, 2, 3, 4] and psychology [5, 6], and as engineering methods [7, 8], they have become a centerpiece of the machine learning (ML) toolbox. In ML, DNNs are simultaneously one of the simplest and most complex methods. They consist of many interconnected nodes that are grouped into layers , whose operations are stunningly simple; the nth node of the network at a given layer i, xi(n) is simply a nonlinear function f(·) (e.g. saturating nonlinearity) applied to an affine function of the previous layer
2 Vec : Learning Author Representations by Combining Content and Link Information

In this paper, we consider the problem of learning representations for authors from bibliographic co-authorship networks. Existing methods for deep learning on graphs, such as DeepWalk, suffer from link sparsity problem as they focus on modeling the link information only. We hypothesize that capturing both the content and link information in a unified way will help mitigate the sparsity problem. To this end, we present a novel model ‘Author2Vec’ , which learns lowdimensional author representations such that authors who write similar content and share similar network structure are closer in vector space. Such embeddings are useful in a variety of applications such as link prediction, node classification, recommendation and visualization. The author embeddings we learn are empirically shown to outperform DeepWalk by 2.35% and 0.83% for link prediction and clustering task respectively.
Content Based Image Retrieval using Color and Texture

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.
"You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks

Over the years, the Web has shrunk the world, allowing individuals to share viewpoints with many more people than they are able to in real life. At the same time, however, it has also enabled anti-social and toxic behavior to occur at an unprecedented scale. Video sharing platforms like YouTube receive uploads from millions of users, covering a wide variety of topics and allowing others to comment and interact in response. Unfortunately, these communities are periodically plagued with aggression and hate attacks. In particular, recent work has showed how these attacks often take place as a result of “raids,” i.e., organized efforts coordinated by ad-hoc mobs from third-party communities. Despite the increasing relevance of this phenomenon, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human reviews. In this paper, we propose an automated solution to identify videos that are likely to be targeted by coordinated harassers. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of raid victims. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with high accuracy (AUC up to 94%). Overall, our work paves the way for providing video platforms like YouTube with proactive systems to detect and mitigate coordinated hate attacks.
Concept-Oriented Deep Learning: Generative Concept Representations

Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning. We discuss probabilistic and generative deep learning, which generative concept representations are based on, and the use of variational autoencoders and generative adversarial networks for learning generative concept representations, particularly for concepts whose data are sequences, structured data or graphs.