nils-herrmann/clean_scirepeval huggingface dataset:
Harnessing Constraint Programming for Poetry Composition

Constraints are a major factor shaping the conceptual space of many areas of creativity. We propose to use constraint programming techniques and off-the-shelf constraint solvers in the creative task of poetry writing. We show how many aspects essential in different poetical forms, and partially even in the level of language syntax and semantics can be represented as interacting constraints. The proposed architecture has two main components. One takes input or inspiration from the user or the environment, and based on it generates a specification of the space and aesthetic of a poem as a set of declarative constraints. The other component explores the specified space using a constraint solver. We provide an elementary set of constraints for composition of poetry, we illustrate their use, and we provide examples of poems generated with different sets of constraints.
Discriminative Word Alignment with Conditional Random Fields

In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estimated on a small supervised training set. The CRF is conditioned on both the source and target texts, and thus allows for the use of arbitrary and overlapping features over these data. Moreover, the CRF has efficient training and decoding processes which both find globally optimal solutions. We apply this alignment model to both French-English and Romanian-English language pairs. We show how a large number of highly predictive features can be easily incorporated into the CRF, and demonstrate that even with only a few hundred word-aligned training sentences, our model improves over the current state-ofthe-art with alignment error rates of 5.29 and 25.8 for the two tasks respectively.
Hand grasp and motion for intent expression in mid-air virtual pottery

We describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the potprofile to the shape of the user’s hand represented as a point-cloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Through our approach, we address two specific problems: (a) determining start and end of deformation without explicit clutching and declutching, and (b) identifying user’s intent by characterizing grasp and motion of the hand on the pot. We evaluated our approach’s performance in terms of intent classification, users’ behavior, and users’ perception of controllability. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping and deforming the pot. Further, the use of PCL as mid-air input allows for using common physical objects as tools for pot deformation. Users particularly enjoyed this aspect of our method for shaping pots.
A Domain Model for eParticipation

The introduction of information and communication technologies (ICTs) in the field of public participation led to the emergence of electronic participation (eparticipation). The area of eparticipation is currently a rapidly evolving one. Although, a few eparticipation-related frameworks have been developed they actually aimed to address specific purposes. As a result, there is no work aiming to model the whole eparticipation domain. In this paper, a first attempt to model the domain of eparticipation is presented. For this purpose, we identify and describe the most significant aspects that characterize the eparticipation domain according to the relevant literature. In addition, we define a domain model in order to formally describe these aspects and the relationships between them. This model is illustrated using a set of Unified Modeling Language (UML) package and class diagrams.
An Empirical Evaluation of Thompson Sampling

Thompson sampling is one of oldest heuristic to address the e xploration / exploitation trade-off, but it is surprisingly unpopular in t he literature. We present here some empirical results using Thompson sampling on simu lated and real data, and show that it is highly competitive. And since this heuris tic is very easy to implement, we argue that it should be part of the standard bas eline to compare against.
X10: an object-oriented approach to non-uniform cluster computing

It is now well established that the device scaling predicted by Moore's Law is no longer a viable option for increasing the clock frequency of future uniprocessor systems at the rate that had been sustained during the last two decades. As a result, future systems are rapidly moving from uniprocessor to multiprocessor configurations, so as to use parallelism instead of frequency scaling as the foundation for increased compute capacity. The dominant emerging multiprocessor structure for the future is a Non-Uniform Cluster Computing (NUCC) system with nodes that are built out of multi-core SMP chips with non-uniform memory hierarchies, and interconnected in horizontally scalable cluster configurations such as blade servers. Unlike previous generations of hardware evolution, this shift will have a major impact on existing software. Current OO language facilities for concurrent and distributed programming are inadequate for addressing the needs of NUCC systems because they do not support the notions of non-uniform data access within a node, or of tight coupling of distributed nodes.We have designed a modern object-oriented programming language, X10, for high performance, high productivity programming of NUCC systems. A member of the partitioned global address space family of languages, X10 highlights the explicit reification of locality in the form of places}; lightweight activities embodied in async, future, foreach, and ateach constructs; a construct for termination detection (finish); the use of lock-free synchronization (atomic blocks); and the manipulation of cluster-wide global data structures. We present an overview of the X10 programming model and language, experience with our reference implementation, and results from some initial productivity comparisons between the X10 and Java™ languages.
Donna Interactive Chat-bot acting as a Personal Assistant

Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files.
Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rank-based in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach.
Extreme Learning Machine for Multilayer Perceptron

Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via ℓ1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.
Interpretable Decision Sets: A Joint Framework for Description and Prediction

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model's prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems.
 Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules.
 Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency.
Rawlsian Fairness for Machine Learning

Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls’ notion of “fair equality of opportunity”. In the context of a simple model of online decision making, we give an algorithm that satisfies this fairness constraint, while still being able to learn at a rate that is comparable to (but necessarily worse than) that of the best algorithms absent a fairness constraint. We prove a regret bound for fair algorithms in the linear contextual bandit framework that is a significant improvement over our technical companion paper , which gives black-box reductions in a more general setting. We analyze our algorithms both theoretically and experimentally. Finally, we introduce the notion of a “discrimination index”, and show that standard algorithms for our problem exhibit structured discriminatory behavior, whereas the “fair” algorithms we develop do not. ∗majos, mkearns, jamiemor, aaroth@cis.upenn.edu. Department of Computer and Information Sciences, University of Pennsylvania. †sethneel@wharton.upenn.edu. Department of Statistics, The Wharton School, University of Pennsylvania. 1 ar X iv :1 61 0. 09 55 9v 1 [ cs .L G ] 2 9 O ct 2 01 6
Predicting Success in Dialogue

Task-solving in dialogue depends on the linguistic alignment of the interlocutors, which Pickering & Garrod (2004) have suggested to be based on mechanistic repetition effects. In this paper, we seek confirmation of this hypothesis by looking at repetition in corpora, and whether repetition is correlated with task success. We show that the relevant repetition tendency is based on slow adaptation rather than short-term priming and demonstrate that lexical and syntactic repetition is a reliable predictor of task success given the first five minutes of a taskoriented dialogue.
Impact of delaying school start time on adolescent sleep, mood, and behavior.

To examine the impact of a 30-minute delay in school start time on adolescents' sleep, mood, and behavior.



Participants completed the online retrospective Sleep Habits Survey before and after a change in school start time.



An independent high school in Rhode Island.



Students (n = 201) in grades 9 through 12.  Institution of a delay in school start time from 8 to 8:30 am.



Sleep patterns and behavior, daytime sleepiness, mood, data from the Health Center, and absences/tardies.



After the start time delay, mean school night sleep duration increased by 45 minutes, and average bedtime advanced by 18 minutes (95% confidence interval, 7-29 minutes [t(423) = 3.36; P < .001]); the percentage of students getting less than 7 hours of sleep decreased by 79.4%, and those reporting at least 8 hours of sleep increased from 16.4% to 54.7%. Students reported significantly more satisfaction with sleep and experienced improved motivation. Daytime sleepiness, fatigue, and depressed mood were all reduced. Most health-related variables, including Health Center visits for fatigue-related complaints, and class attendance also improved.



A modest delay in school start time was associated with significant improvements in measures of adolescent alertness, mood, and health. The results of this study support the potential benefits of adjusting school schedules to adolescents' sleep needs, circadian rhythm, and developmental stage.
Natural language reporting for ETL processes

The conceptual design of the Extract -- Transform -- Load (ETL) processes is a crucial, burdensome, and challenging procedure that takes places at the early phases of a Data Warehouse project. Several models have been proposed for the conceptual design and representation of ETL processes, but all share two inconveniences: they require intensive human effort from the designers to create them, as well as technical knowledge from the business people to understand them. In a previous work, we have relaxed the former difficulty by working on the automation of the conceptual design leveraging Semantic Web technology. In this paper, we built upon our previous results and we tackle the second issue by investigating the application of natural language generation techniques to the ETL environment. In particular, we provide a method for the representation of a conceptual ETL design as a narrative, which is the most natural means of communication and does not require knowledge of any specific model. We discuss how linguistic techniques can be used for the establishment of a common application vocabulary. Finally, we present a flexible and customizable template-based mechanism for generating natural language representations for the ETL process requirements and operations.
Cooperative Mobile Robotics: Antecedents and Directions

There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource con ict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this eld, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.
An X-band to Ka-band SPDT switch using 200 nm SiGe HBTs

This paper presents the design and measured performance of an X-band to Ka-band SiGe HBT SPDT switch. The proposed SPDT switch was fabricated using a 200 nm, 150 GHz peak fT silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) BiCMOS technology. The SPDT switch design uses diode-connected SiGe HBTs in a series-shunt configuration to improve the switch bandwidth and isolation. Between 8 and 40 GHz, this SPDT switch achieves an insertion loss of less than 4.3 dB, an isolation of more than 20.3 dB, and a return loss of more than 9 dB.
Sequential Self-Folding Structures by 3D Printed Digital Shape Memory Polymers

Folding is ubiquitous in nature with examples ranging from the formation of cellular components to winged insects. It finds technological applications including packaging of solar cells and space structures, deployable biomedical devices, and self-assembling robots and airbags. Here we demonstrate sequential self-folding structures realized by thermal activation of spatially-variable patterns that are 3D printed with digital shape memory polymers, which are digital materials with different shape memory behaviors. The time-dependent behavior of each polymer allows the temporal sequencing of activation when the structure is subjected to a uniform temperature. This is demonstrated via a series of 3D printed structures that respond rapidly to a thermal stimulus, and self-fold to specified shapes in controlled shape changing sequences. Measurements of the spatial and temporal nature of self-folding structures are in good agreement with the companion finite element simulations. A simplified reduced-order model is also developed to rapidly and accurately describe the self-folding physics. An important aspect of self-folding is the management of self-collisions, where different portions of the folding structure contact and then block further folding. A metric is developed to predict collisions and is used together with the reduced-order model to design self-folding structures that lock themselves into stable desired configurations.
Prediction of Yelp Review Star Rating using Sentiment Analysis

Yelp aims to help people find great local businesses, e.g. restaurants. Automated software is currently used to recommend the most helpful and reliable reviews for the Yelp community, based on various measures of quality, reliability, and activity. However, this is not tailored to each customer. Our goal in this project is to apply machine learning to predict a customer’s star rating of a restaurant based on his/her reviews, as well as other customers’ reviews/ratings, to recommend other restaurants to the customer, as shown in Figure 1.
First Demonstration of 28 GHz and 39 GHz Transmission Lines and Antennas on Glass Substrates for 5G Modules

High-performance and ultra-miniaturized mm-wave building block structures were demonstrated on panel-scale processed 3D glass packages for high-speed 5G communication standards at 28 and 39 GHz bands. To demonstrate the benefits of glass for 5G communications, various topologies of microstrip-fed patch antennas for different resonant frequencies and compact conductor-backed co-planar waveguides were modeled and designed for high bandwidth and efficiency in the mm-wave bands. The simulation results for insertion loss, antenna gain, and bandwidth are consistent with the measured values on the glass substrates. The fabricated conductor-backed coplanar waveguides show insertion losses of 0.2 –0.3 dB/mm with a channel length of 1.86 mm, and the fabricated antennas have more than around 6% bandwidth in the frequency range of 35 to 39 GHz.
A DUAL BAND CIRCULARLY POLARIZED RING ANTENNA BASED ON COMPOSITE RIGHT AND LEFT HANDED METAMATERIALS

In this paper a dual band circularly polarized antenna is designed, fabricated, and measured. Based on the concept of composite right and left handed (CRLH) metamaterials, the same phase constants and current distributions on the ring antenna are achieved at two frequency bands. Thus, the antenna has similar radiation patterns at both bands. The circular polarization is implemented by feeding two vertical ports from power dividers that provide equal magnitudes and quadrature phase excitations.
Generative Adversarial Network for Abstractive Text Summarization

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.
LabelMe video: Building a video database with human annotations

Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well as from missing comprehensive annotated video databases for benchmarking. We designed an online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos. The annotations are also complemented with knowledge from static image databases to infer occlusion and depth information. Using this system, we have built a scalable video database composed of diverse video samples and paired with human-guided annotations. We complement this paper demonstrating potential uses of this database by studying motion statistics as well as cause-effect motion relationships between objects.