nils-herrmann/clean_scirepeval huggingface dataset:
Improved Speech-to-Text Translation with the Fisher and Callhome Spanish – English Speech Translation Corpus

Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.
A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.
On the Equivalence Between Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF (with the I-divergence objective function) optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. This provides a theoretical basis for a new hybrid method that runs PLSI and NMF alternatively, each jumping out of local minima of the other method successively, thus achieving a better final solution. Extensive experiments on five real-life datasets show relations between NMF and PLSI, and indicate the hybrid method leads to significant improvements over NMFonly or PLSI-only methods. We also show that at first order approximation, NMF is identical to χ-statistic.
Automatic Attribution of Quoted Speech in Literary Narrative

We describe a method for identifying the speakers of quoted speech in natural-language textual stories. We have assembled a corpus of more than 3,000 quotations, whose speakers (if any) are manually identified, from a collection of 19th and 20th century literature by six authors. Using rule-based and statistical learning, our method identifies candidate characters, determines their genders, and attributes each quote to the most likely speaker. We divide the quotes into syntactic classes in order to leverage common discourse patterns, which enable rapid attribution for many quotes. We apply learning algorithms to the remainder and achieve an overall accuracy of 83%.
Control flow analysis

Any static, global analysis of the expression and data relationships in a program requires a knowledge of the control flow of the program. Since one of the primary reasons for doing such a global analysis in a compiler is to produce optimized programs, control flow analysis has been embedded in many compilers and has been described in several papers. An early paper by Prosser  described the use of Boolean matrices (or, more particularly, connectivity matrices) in flow analysis. The use of “dominance” relationships in flow analysis was first introduced by Prosser and much expanded by Lowry and Medlock [6].  describe compilers which use various forms of control flow analysis for optimization. Some recent developments in the area are reported in  and in .
 The underlying motivation in all the different types of control flow analysis is the need to codify the flow relationships in the program. The codification may be in connectivity matrices, in predecessor-successor tables, in dominance lists, etc. Whatever the form, the purpose is to facilitate determining what the flow relationships are; in other words to facilitate answering such questions as: is this an inner loop?, if an expression is removed from the loop where can it be correctly and profitably placed?, which variable definitions can affect this use?
 In this paper the basic control flow relationships are expressed in a directed graph. Various graph constructs are then found and shown to codify interesting global relationships.
Self-healing soft pneumatic robots

Inspired by the compliance found in many organisms, soft robots are made almost entirely out of flexible, soft material, making them suitable for applications in uncertain, dynamic task environments, including safe human-robot interactions. Their intrinsic compliance absorbs shocks and protects them against mechanical impacts. However, the soft materials used for their construction are highly susceptible to damage, such as cuts and perforations caused by sharp objects present in the uncontrolled and unpredictable environments they operate in. In this research, we propose to construct soft robotics entirely out of self-healing elastomers. On the basis of healing capacities found in nature, these polymers are given the ability to heal microscopic and macroscopic damage. Diels-Alder polymers, being thermoreversible covalent networks, were used to develop three applications of self-healing soft pneumatic actuators (a soft gripper, a soft hand, and artificial muscles). Soft pneumatic actuators commonly experience perforations and leaks due to excessive pressures or wear during operation. All three prototypes were designed using finite element modeling and mechanically characterized. The manufacturing method of the actuators exploits the self-healing behavior of the materials, which can be recycled. Realistic macroscopic damage could be healed entirely using a mild heat treatment. At the location of the scar, no weak spots were created, and the full performance of the actuators was nearly completely recovered after healing.
A Stochastic Model of Airline Operations

We present a stochastic model of the daily operations of an airline. Its primary purpose is to evaluate plans, such as crew schedules, as well as recovery policies in a random environment. We describe the structure of the stochastic model, sources of disruptions, recovery policies, and performance measures. Then, we describe SimAir—our simulation implementation of the stochastic model, and we give computational results. Finally, we give future directions for the study of airline recovery policies and planning under uncertainty.
Applying Attribute-Based Encryption on Publish Subscribe Messaging Patterns for the Internet of Things

With the advent of the Internet of Things (IoT), communication between connected machines has become necessity. We simulate the communication of IoT by short-lived instant messaging for group communication. Group communication security requires such measures as group forward and backward secrecy and perfect forward secrecy. We satisfy these security measures by using a group controller and Attributebased Encryption (ABE) to encrypt data on update procedures. The communication overhead is outsourced to a mediating MQ Telemetry Transport broker. Thus, we decrease the costs for group joins and leaves to T(1). The number of attributes used in the system are reduced to O(log(N)), where N represents the maximum number of members. We provide an intuitive approach to fit the maximum number N = 2k members to our requirements and to increase the maximum size of members, if needed by N = 2k+1.
Using data mining techniques in heart disease diagnosis and treatment

The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. Heart disease is the leading cause of death all over the world in the past ten years. Several researchers are using statistical and data mining tools to help health care professionals in the diagnosis of heart disease. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. Recently, researchers have been investigating the effect of hybridizing more than one technique showing enhanced results in the diagnosis of heart disease. However, using data mining techniques to identify a suitable treatment for heart disease patients has received less attention. This paper identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease.
Pseudo-Constant Switching Frequency in On-Time Controlled Buck Converter with Predicting Correction Techniques

The predicting correction technique (PCT) is proposed to achieve an adaptive on-time control for ripple-based buck converters. The switching frequency fSW variation is suppressed when the PCT technique considers complete parasitic resistances of the components and devices. Even without extra clock-controlled circuits and current sensing circuits, the buck converter operates with a nearly constant fSW over a wide load range. Parasitic resistances almost cause no influence and restriction on f<sub>SW</sub>. Only input voltage is used to predict the adaptive on-time. Measurement results show only 0.32% in Δf<sub>SW</sub>/f<sub>SW</sub> and 5.7 kHz/A in Δf<sub>SW</sub>/ΔI<sub>LOAD</sub> in case of 1.4 A load current change and f<sub>SW</sub> is 2.5 MHz. Consequently, a pseudo-constant f<sub>SW</sub> with well-defined noise spectrum strongly benefits the solution of electromagnetic interference (EMI) for system-on-a-chip (SoC) applications.
Scalable attack graph for risk assessment

The growth in the size of networks and the number of vulnerabilities is increasingly challenging to manage network security. Especially, difficult to manage are multi-step attacks which are attacks using one or more vulnerabilities as stepping stones. Attack graphs are widely used for analyzing multi-step attacks. However, since these graphs had large sizes, it was too expensive to work with. In this paper, we propose a mechanism to manage attack graphs using a divide and conquer approach. To enhance efficiency of risk analyzer working with attack graphs, we converted a large graph to multiple sub-graphs named risk units and provide the light-weighted graphs to the analyzers. As a result, when k order of time complexity algorithms work with an attack graph with n vertices, a division having c of overhead vertices reduces the workloads from nk to r(n + c)k. And the coefficient r becomes smaller geometrically from 2−kdepended on their division rounds. By this workload reduction, risk assessment processes which work with large size attack graphs become more scalable and resource practical.
Blockchain Technology: Transforming Libertarian Cryptocurrency Dreams to Finance and Banking Realities

The financial technology (FinTech) sector sees high potential value in cryptocurrency blockchain protocols, or distributed-ledger technology (DLT). However, the requirements and guarantees of blockchains for cryptocurrencies do not match those of FinTech—from transaction throughput to security primitives and privacy. The author explores how blockchain research beyond Bitcoin is closing these gaps and some of the challenges that remain.
Visual terrain classification for selecting energy efficient gaits of a hexapod robot

Legged robots need to be able to classify and recognize different terrains to adapt their gait accordingly. Recent works in terrain classification use different types of sensors (like stereovision, 3D laser range, and tactile sensors) and their combination. However, such sensor systems require more computing power, produce extra load to legged robots, and/or might be difficult to install on a small size legged robot. In this work, we present an online terrain classification system. It uses only a monocular camera with a feature-based terrain classification algorithm which is robust to changes in illumination and view points. For this algorithm, we extract local features of terrains using either Scale Invariant Feature Transform (SIFT) or Speed Up Robust Feature (SURF). We encode the features using the Bag of Words (BoW) technique, and then classify the words using Support Vector Machines (SVMs) with a radial basis function kernel. We compare this feature-based approach with a color-based approach on the Caltech-256 benchmark as well as eight different terrain image sets (grass, gravel, pavement, sand, asphalt, floor, mud, and fine gravel). For terrain images, we observe up to 90% accuracy with the feature-based approach. Finally, this online terrain classification system is successfully applied to our small hexapod robot AMOS II. The output of the system providing terrain information is used as an input to its neural locomotion control to trigger an energy-efficient gait while traversing different terrains.
Box-Office Opening Prediction of Movies based on Hype Analysis through Data Mining

Box-office performance of a movie is mainly determined by the amount the movie collects in the opening weekend and Pre-Release hype is an important factor as far as estimating the openings of the movie are concerned. This can be estimated through user opinions expressed online on sites such as Twitter which is an online micro-blogging site with a user base running into millions. Each user is entitled to his own opinion which he expresses through his tweets. This paper suggests a novel way to mine and analyze the opinions expressed in these tweets with respect to a movie prior to its release, estimate the hype surrounding it and also predict the box-office openings of the movie.
Bioelectrics-new applications for pulsed power technology

Electric phenomena play an important role in biophysics. Bioelectric processes control the ion transport processes across membranes, and are the basis for information transfer along neurons. These electrical effects are generally triggered by chemical processes. However, it is also possible to control such cell functions and transport processes by applying pulsed electric fields. This area of bioengineering, bioelectrics, offers new applications for pulsed power technology. One such application is prevention of biofouling, an effect that is based on reversible electroporation of cell membranes. Pulsed electric fields of several kV/cm amplitude and submicrosecond duration have been found effective in preventing the growth of aquatic nuisance species on surfaces. Reversible electroporation is also used for medical applications, e.g. for delivery of chemotherapeutic drugs into tumor cells, for gene therapy, and for transdermal drug delivery. Higher electric fields cause irreversible membrane damage. Pulses in the microsecond range with electric field intensities in the tens of kV/cm are being used for bacterial decontamination of water and liquid food. A new type of field-cell interaction, "Intracellular Electromanipulation", by means of nanosecond pulses at electric fields exceeding 50 kV/cm has been recently added to known bioelectric effects. It is based on capacitive coupling to cell substructures, has therefore the potential to affect transport processes across subcellular membranes, and may be used for gene transfer into cell nuclei. There are also indications that it triggers intracellular processes, such as programmed cell death, an effect, which can be used for cancer treatment. In order to generate the required electric fields for these processes, high voltage, high current sources are required. The pulse duration needs to be short to prevent thermal effects. Pulse power technology is the enabling technology for bioelectrics. The field of bioelectrics, therefore opens up a new research area for pulse power engineers, with fascinating applications in biology and medicine.
Goal-Driven Conceptual Blending: A Computational Approach for Creativity

Conceptual blending has been proposed as a creative cognitive process, but most theories focus on the analysis of existing blends rather than mechanisms for the efficient construction of novel blends. While conceptual blending is a powerful model for creativity, there are many challenges related to the computational application of blending. Inspired by recent theoretical research, we argue that contexts and context-induced goals provide insights into algorithm design for creative systems using conceptual blending. We present two case studies of creative systems that use goals and contexts to efficiently produce novel, creative artifacts in the domains of story generation and virtual characters engaged in pretend play respectively.
Community based educational data repositories and analysis tools

This workshop will explore community based repositories for educational data and analytic tools that are used to connect researchers and reduce the barriers to data sharing. Leading innovators in the field, as well as attendees, will identify and report on bottlenecks that remain toward our goal of a unified repository. We will discuss these as well as possible solutions. We will present LearnSphere, an NSF funded system that supports collaborating on and sharing a wide variety of educational data, learning analytics methods, and visualizations while maintaining confidentiality. We will then have hands-on sessions in which attendees have the opportunity to apply existing learning analytics workflows to their choice of educational datasets in the repository (using a simple drag-and-drop interface), add their own learning analytics workflows (requires very basic coding experience), or both. Leaders and attendees will then jointly discuss the unique benefits as well as the limitations of these solutions. Our goal is to create building blocks to allow researchers to integrate their data and analysis methods with others, in order to advance the future of learning science.
Developing a Comprehensive Engagement Framework of Gamification for Reflective Learning

Engagement is a key reason for introducing gamification to learning and thus serves as an important measurement of its effectiveness. Based on a literature review and meta-synthesis, this paper proposes a comprehensive framework of engagement in gamification for learning. The framework sketches out the connections among gamification strategies, dimensions of engagement, and the ultimate learning outcome. It also elicits other task - and user - related factors that may potentially impact the effect of gamification on learner engagement. To verify and further strengthen the framework, we conducted a user study to demonstrate that: 1) different gamification strategies can trigger different facets of engagement; 2) the three dimensions of engagement have varying effects on skill acquisition and transfer; and 3) task nature and learner characteristics that were overlooked in previous studies can influence the engagement process. Our framework provides an in-depth understanding of the mechanism of gamification for learning, and can serve as a theoretical foundation for future research and design.
The MiPACQ clinical question answering system.

The Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) is a QA pipeline that integrates a variety of information retrieval and natural language processing systems into an extensible question answering system. We present the system's architecture and an evaluation of MiPACQ on a human-annotated evaluation dataset based on the Medpedia health and medical encyclopedia. Compared with our baseline information retrieval system, the MiPACQ rule-based system demonstrates 84% improvement in Precision at One and the MiPACQ machine-learning-based system demonstrates 134% improvement. Other performance metrics including mean reciprocal rank and area under the precision/recall curves also showed significant improvement, validating the effectiveness of the MiPACQ design and implementation.