nils-herrmann/clean_scirepeval huggingface dataset:
Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction

Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language.  natural language error correction, recurrent neural networks, encoderdecoder models, attention mechanism
Execution-Guided Neural Program Decoding

We present a neural semantic parser that translates natural language questions into executable SQL queries with two key ideas. First, we develop an encoder-decoder model, where the decoder uses a simple type system of SQL to constraint the output prediction, and propose a value-based loss when copying from input tokens. Second, we explore using the execution semantics of SQL to repair decoded programs that result in runtime error or return empty result. We propose two modelagnostics repair approaches, an ensemble model and a local program repair, and demonstrate their effectiveness over the original model. We evaluate our model on the WikiSQL dataset and show that our model achieves close to state-of-the-art results with lesser model complexity.
Author Name Disambiguation by Using Deep Neural Network

Author name ambiguity is one of the problems that decrease the quality and reliability of information retrieved from digital libraries. Existing methods have tried to solve this problem by predefining a feature set based on expert’s knowledge for a specific dataset. In this paper, we propose a new approach which uses deep neural network to learn features automatically for solving author name ambiguity. Additionally, we propose the general system architecture for author name disambiguation on any dataset. We evaluate the proposed method on a dataset containing Vietnamese author names. The results show that this method significantly outperforms other methods that use predefined feature set. The proposed method achieves 99.31% in terms of accuracy. Prediction error rate decreases from 1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with other methods that use predefined feature set (Table 3).
Reducing Internet Latency: A Survey of Techniques and Their Merits

Latency is increasingly becoming a performance bottleneck for Internet Protocol (IP) networks, but historically, networks have been designed with aims of maximizing throughput and utilization. This paper offers a broad survey of techniques aimed at tackling latency in the literature up to August 2014, as well as their merits. A goal of this work is to be able to quantify and compare the merits of the different Internet latency reducing techniques, contrasting their gains in delay reduction versus the pain required to implement and deploy them. We found that classifying techniques according to the sources of delay they alleviate provided the best insight into the following issues: 1) The structural arrangement of a network, such as placement of servers and suboptimal routes, can contribute significantly to latency; 2) each interaction between communicating endpoints adds a Round Trip Time (RTT) to latency, particularly significant for short flows; 3) in addition to base propagation delay, several sources of delay accumulate along transmission paths, today intermittently dominated by queuing delays; 4) it takes time to sense and use available capacity, with overuse inflicting latency on other flows sharing the capacity; and 5) within end systems, delay sources include operating system buffering, head-of-line blocking, and hardware interaction. No single source of delay dominates in all cases, and many of these sources are spasmodic and highly variable. Solutions addressing these sources often both reduce the overall latency and make it more predictable.
Optimally sparse representation in general (nonorthogonal) dictionaries via l minimization.

Given a dictionary D = {d(k)} of vectors d(k), we seek to represent a signal S as a linear combination S = summation operator(k) gamma(k)d(k), with scalar coefficients gamma(k). In particular, we aim for the sparsest representation possible. In general, this requires a combinatorial optimization process. Previous work considered the special case where D is an overcomplete system consisting of exactly two orthobases and has shown that, under a condition of mutual incoherence of the two bases, and assuming that S has a sufficiently sparse representation, this representation is unique and can be found by solving a convex optimization problem: specifically, minimizing the l(1) norm of the coefficients gamma. In this article, we obtain parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems. We sketch three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.
Neural correlates of gratitude

Gratitude is an important aspect of human sociality, and is valued by religions and moral philosophies. It has been established that gratitude leads to benefits for both mental health and interpersonal relationships. It is thus important to elucidate the neurobiological correlates of gratitude, which are only now beginning to be investigated. To this end, we conducted an experiment during which we induced gratitude in participants while they underwent functional magnetic resonance imaging. We hypothesized that gratitude ratings would correlate with activity in brain regions associated with moral cognition, value judgment and theory of mind. The stimuli used to elicit gratitude were drawn from stories of survivors of the Holocaust, as many survivors report being sheltered by strangers or receiving lifesaving food and clothing, and having strong feelings of gratitude for such gifts. The participants were asked to place themselves in the context of the Holocaust and imagine what their own experience would feel like if they received such gifts. For each gift, they rated how grateful they felt. The results revealed that ratings of gratitude correlated with brain activity in the anterior cingulate cortex and medial prefrontal cortex, in support of our hypotheses. The results provide a window into the brain circuitry for moral cognition and positive emotion that accompanies the experience of benefitting from the goodwill of others.
Hierarchical Quantized Representations for Script Generation

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modelingbased method.
DOUBLE VITERBI: WEIGHT ENCODING FOR HIGH COMPRESSION RATIO AND FAST ON-CHIP RECON-

Weight pruning has been introduced as an efficient model compression technique. Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information. Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently. As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested. Decoding non-zero weights, however, is still sequential in Viterbi-based pruning. In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix. The proposed sparse matrix is constructed by combining pruning and weight quantization. For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19× using the proposed sparse matrix format compared to the baseline model. Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders. Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.
Chatbot for university related FAQs

Chatbots are programs that mimic human conversation using Artificial Intelligence (AI). It is designed to be the ultimate virtual assistant, entertainment purpose, helping one to complete tasks ranging from answering questions, getting driving directions, turning up the thermostat in smart home, to playing one's favorite tunes etc. Chatbot has become more popular in business groups right now as they can reduce customer service cost and handles multiple users at a time. But yet to accomplish many tasks there is need to make chatbots as efficient as possible. To address this problem, in this paper we provide the design of a chatbot, which provides an efficient and accurate answer for any query based on the dataset of FAQs using Artificial Intelligence Markup Language (AIML) and Latent Semantic Analysis (LSA). Template based and general questions like welcome/ greetings and general questions will be responded using AIML and other service based questions uses LSA to provide responses at any time that will serve user satisfaction. This chatbot can be used by any University to answer FAQs to curious students in an interactive fashion.
A Review of Current Studies on Internet Finance in China

The Internet and finance are accelerating into integration in the 21 Century. Internet Finance was firstly proposed by Ma Weihua, the former president of China Merchants Bank in July 2012. On the basis of 74 latest research literatures selected from CSSCI Journals, Chinese Core Journals, authoritative magazines and related newspapers, this paper summarizes the current domestic research progress and trend about Internet Finance according to three dimensions, such as the sources of journals, research subjects and research contents. This research shows that the current domestic researches are not only shallow and superficial, but also lack the theoretical analyses and model applications; and the wealth-based and bank-based Internet Finance will be the research focus in the future.
A scalable approach for statistical learning in semantic graphs

Increasingly, data is published in the form of semantic graphs. The most notable example is the Linked Open Data (LOD) initiative where an increasing number of data sources are published in the Semantic Web’s Resource Description Framework and where the various data sources are linked to reference one another. In this paper we apply machine learning to semantic graph data and argue that scalability and robustness can be achieved via an urn-based statistical sampling scheme. We apply the urn model to the SUNS framework which is based on multivariate prediction. We argue that multivariate prediction approaches are most suitable for dealing with the resulting high-dimensional sparse data matrix. Within the statistical framework, the approach scales up to large domains and is able to deal with highly sparse relationship data. We summarize experimental results using a friend-of-a-friend data set and a data set derived from DBpedia. In more detail, we describe novel experiments on disease gene prioritization using LOD data sources. The experiments confirm the ease-of-use, the scalability and the good performance of the approach.
The Cut and Dominating Set Problem in A Steganographer Network

A steganographer network corresponds to a graphic structure that the involved vertices (or called nodes) denote social entities such as the data encoders and data decoders, and the associated edges represent any real communicable channels or other social links that could be utilized for steganography. Unlike traditional steganographic algorithms, a steganographer network models steganographic communication by an abstract way such that the concerned underlying characteristics of steganography are quantized as analyzable parameters in the network. In this paper, we will analyze two problems in a steganographer network. The first problem is a passive attack to a steganographer network where a network monitor has collected a list of suspicious vertices corresponding to the data encoders or decoders. The network monitor expects to break (disconnect) the steganographic communication down between the suspicious vertices while keeping the cost as low as possible. The second one relates to determining a set of vertices corresponding to the data encoders (senders) such that all vertices can share a message by neighbors. We point that, the two problems are equivalent to the minimum cut problem and the minimum-weight dominating set problem.
Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees

Maximally Stable Extremal Regions (MSERs) have achieved great success in scene text detection. However, this low-level pixel operation inherently limits its capability for handling complex text information efficiently (e. g. connections between text or background components), leading to the difficulty in distinguishing texts from background components. In this paper, we propose a novel framework to tackle this problem by leveraging the high capability of convolutional neural network (CNN). In contrast to recent methods using a set of low-level heuristic features, the CNN network is capable of learning high-level features to robustly identify text components from text-like outliers (e.g. bikes, windows, or leaves). Our approach takes advantages of both MSERs and slidingwindow based methods. The MSERs operator dramatically reduces the number of windows scanned and enhances detection of the low-quality texts. While the sliding-window with CNN is applied to correctly separate the connections of multiple characters in components. The proposed system achieved strong robustness against a number of extreme text variations and serious real-world problems. It was evaluated on the ICDAR 2011 benchmark dataset, and achieved over 78% in F-measure, which is significantly higher than previous methods.
Design of synchronous reluctance motors with improved power factor

Synchronous reluctance motor (SynRM) have rather poor power factor. This paper investigates possible methods to improve the power factor (pf) without impacting its torque density. The study found two possible aspects to improve the power factor with either refining rotor dimensions and followed by current control techniques. Although it is a non-linear mathematical field, it is analysed by analytical equations and FEM simulation is utilized to validate the design progression. Finally, an analytical method is proposed to enhance pf without compromising machine torque density. There are many models examined in this study to verify the design process. The best design with high performance is used for final current control optimization simulation.
Building and Destroying Continental Mantle Cin -

Continents, especially their Archean cores, are underlain by thick thermal boundary layers that have been largely isolated from the convecting mantle over billion-year timescales, far exceeding the life span of oceanic thermal boundary layers. This longevity is promoted by the fact that continents are underlain by highly melt-depleted peridotites, which result in a chemically distinct boundary layer that is intrinsically buoyant and strong (owing to dehydration). This chemical boundary layer counteracts the destabilizing effect of the cold thermal state of continents. The compositions of cratonic peridotites require formation at shallower depths than they currently reside, suggesting that the building blocks of continents formed in oceanic or arc environments and became “continental” after significant thickening or underthrusting. Continents are difficult to destroy, but refertilization and rehydration of continental mantle by the passage of melts can nullify the unique stabilizing composition of continents.
Effectiveness of mHealth interventions for maternal, newborn and child health in low– and middle–income countries: Systematic review and meta–analysis

To assess the effectiveness of mHealth interventions for maternal, newborn and child health (MNCH) in low- and middle-income countries (LMIC).



16 online international databases were searched to identify studies evaluating the impact of mHealth interventions on MNCH outcomes in LMIC, between January 1990 and May 2014. Comparable studies were included in a random-effects meta-analysis.



Of 8593 unique references screened after de-duplication, 15 research articles and two conference abstracts met inclusion criteria, including 12 intervention and three observational studies. Only two studies were graded at low risk of bias. Only one study demonstrated an improvement in morbidity or mortality, specifically decreased risk of perinatal death in children of mothers who received SMS support during pregnancy, compared with routine prenatal care. Meta-analysis of three studies on infant feeding showed that prenatal interventions using SMS/cell phone (vs routine care) improved rates of breastfeeding (BF) within one hour after birth (odds ratio (OR) 2.01, 95% confidence interval (CI) 1.27-2.75, I(2) = 80.9%) and exclusive BF for three/four months (OR 1.88, 95% CI 1.26-2.50, I(2) = 52.8%) and for six months (OR 2.57, 95% CI 1.46-3.68, I(2) = 0.0%). Included studies encompassed interventions designed for health information delivery (n = 6); reminders (n = 3); communication (n = 2); data collection (n = 2); test result turnaround (n = 2); peer group support (n = 2) and psychological intervention (n = 1).



Most studies of mHealth for MNCH in LMIC are of poor methodological quality and few have evaluated impacts on patient outcomes. Improvements in intermediate outcomes have nevertheless been reported in many studies and there is modest evidence that interventions delivered via SMS messaging can improve infant feeding. Ambiguous descriptions of interventions and their mechanisms of impact present difficulties for interpretation and replication. Rigorous studies with potential to offer clearer evidence are underway.
The Simple MicroEconomics of Public-Private Partnerships

We build a unified theoretical framework to analyze the main incentive issues in Public Private Partnerships (PPPs) and the shape of optimal contracts in those contexts. We present a basic model of procurement in a multitask environment in which a risk-averse agent chooses unobservable efforts in cost reduction and quality improvement. We begin by studying the effect on incentives and risk transfer of bundling building and operation into a single contract, allowing for different assumptions on the contractual framework and the quality of the information held by the government. We then extend the basic model in several directions. We consider the factors that affect the optimal allocation of demand risk and their implications for the use of user charges and the choice of contract length. We study the relationship between the operator and its financiers and the impact of private finance. We discuss the trade-off between incentive and flexibility in long-term PPP agreements and the dynamics of PPP contracts, including cost overruns. We also consider how the institutional environment, and specifically the risk of regulatory opportunism, affects contract design and incentives. We conclude with some policy implications on the desirability of PPPs.
Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research

This paper presents a systematic literature review of the state-of-the-art of research on learning dashboards in the fields of Learning Analytics and Educational Data Mining. Research on learning dashboards aims to identify what data is meaningful to different stakeholders and how data can be presented to support sense-making processes. Learning dashboards are becoming popular due to the increased use of educational technologies, such as Learning Management Systems (LMS) and Massive Open Online Courses (MOOCs). The initial search of five main academic databases and GScholar resulted in 346 papers out of which 55 papers were included in the final analysis. Our review distinguishes different kinds of research studies as well as various aspects of learning dashboards and their maturity regarding evaluation. As the research field is still relatively young, most studies are exploratory and proof-of-concept. The review concludes by offering a definition for learning dashboards and by outlining open issues and future lines of work in the area of learning dashboards. There is a need for longitudinal research in authentic settings and studies that systematically compare different dashboard designs.
A System for Aspect-based Opinion Mining of Hotel Reviews

Online travel portals are becoming important parts for sharing travel information. User generated content and information in user reviews is valuable to both travel companies and to other people and can have a substantial impact on their decision making process. The automatic analysis of used generated reviews can provide a deeper understanding of users attitudes and opinions. In this paper, we present a work on the automatic analysis of user reviews on the booking.com portal and the automatic extraction and visualization of information. An aspect based approach is followed where latent dirichlet allocation is utilized in order to model topic opinion and natural language processing techniques are used to specify the dependencies on a sentence level and determine interactions between words and aspects. Then Naïve Bayes machine learning method is used to recognize the polarity of the user’s opinion utilizing the sentence’s dependency triples. To evaluate the performance of our method, we collected a wide set of reviews for a series of hotels from booking.com. The results from the evaluation study are very encouraging and indicate that the system is fast, scalable and most of all accurate in analyzing user reviews and in specifying users’ opinions and stance towards the characteristics of the hotels and can provide comprehensive hotel information.