nils-herrmann/clean_scirepeval huggingface dataset:
Synchronous Binarization for Machine Translation

Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. The complexity is exponential in the size of individual grammar rules due to arbitrary re-orderings between the two languages, and rules extracted from parallel corpora can be quite large. We devise a linear-time algorithm for factoring syntactic re-orderings by binarizing synchronous rules when possible and show that the resulting rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system.
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.
FrameBase: Representing N-Ary Relations Using Semantic Frames

Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subject-predicate-object triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also make it impossible to query the data concisely and without prior knowledge of each individual source. We present FrameBase, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment. It also opens possibilities to draw on natural language processing techniques for querying and data mining.
A hierarchical naive Bayes mixture model for name disambiguation in author citations

Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
Wideband planar monopole antennas with dual band-notched characteristics

Wideband planar monopole antennas with dual band-notched characteristics are presented. The proposed antenna consists of a wideband planar monopole antenna and the multiple cup-, cap-, and inverted L-shaped slots, producing band-notched characteristics. In order to generate dual band-notched characteristic, we propose nine types of planar monopole antennas, which have two or three cap (cup or inverted L)-shaped slots in the radiator. This technique is suitable for creating ultra-wideband antenna with narrow frequency notches or for creating multiband antennas
Emotionally Focused Couples Therapy : Status and Challenges

Although the failure to develop a satisfying intimate relationship with one’s partner is the single most frequently presented problem in therapy (Horowitz, 1979), couples therapy, the modality that most directly addresses this problem, is a relatively young discipline. In this discipline, systematic approaches to changing distressed relationships are still being developed and evaluated. At present there are only two clearly delineated treatments for marital distress that have been empirically tested in a number of stud-
Malware Detection Using Deep Transferred Generative Adversarial Networks

Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.
A 330nA energy-harvesting charger with battery management for solar and thermoelectric energy harvesting

Harvesting energy from ambient energy sources such as solar and thermal gradients is one solution to address the dramatic increase in energy consumption of personal electronics. In this paper, an ultra low quiescent current charger and battery management IC that can efficiently extract energy from solar panels and thermoelectric generators to charge batteries and super capacitors is presented. While previous works on energy harvesting [1-5] report efficient DC-DC converters to extract the energy from the harvester, not all of them provide complete battery management functionality for various chemistries. Also, maximum power point tracking (MPPT) which is critical in energy harvesting applications is not provided in some of the published work. In this paper, a charger and battery management IC with 330nA quiescent current is presented. The IC can cold start from 330mV and 5μW of input power. The charger achieves efficiency greater than 80% at single cell solar voltages of 0.5V. A low quiescent current battery management architecture involving sampled circuits, sub regulated rails and clock gating is demonstrated.
Teacher Concerns During Initial Implementation of a One-toOne Laptop Initiative at the Middle School Level

Many schools are initiating projects that place laptop computers into the hands of each student and teacher in the school. These projects entail a great deal of planning and investment by all involved. The teachers in these schools are faced with significant challenges as they prepare for teaching in classrooms where every student has a computer. Using the Concerns-Based Adoption Model of change, this study investigated the concerns of teachers in the early stages of a one-to-one laptop initiative. The results of the study indicate that teachers fall into two relatively well-defined categories in terms of their concerns regarding the innovation. The majority of teachers have genuine concerns about how the introduction of laptop computers into the school environment will impact them personally. A lesser number have concerns about how they will be able to best use the laptops to meet the needs of the students. Implications for professional development include differentiating training based on teacher concerns, ensuring teachers have a voice in the process and are well-informed of decisions pertaining to the adoption, and implementation of the innovation.
Booms , Busts , and Fraud

We examine firm managers’ incentives to commit fraud in a model where firms seek funding from investors and investors can monitor firms at a cost in order to get more precise information about firm prospects. We show that fraud incentives are highest when business conditions are good, but not too good: in exceptionally good times, even weaker firms can get funded without committing fraud, and in bad times investors are more vigilant and it is harder to commit fraud successfully. As investors’ monitoring costs decrease, the region in which fraud occurs shifts towards better business conditions. It follows that if business conditions are sufficiently strong, a decrease in monitoring costs actually increases the prevalence of fraud. If investors can only observe current business conditions with noise, then the incidence of fraud will be highest when investors begin with positive expectations that are disappointed ex post. Finally, increased disclosure requirements can exacerbate fraud. Our results shed light on the incidence of fraud across the business cycle and across different sectors.
Early validation of computational thinking pattern analysis

End-user game design affords teachers a unique opportunity to integrate computational thinking concepts into their classrooms. However, it is not always apparent in game and simulation projects what computational thinking-related skills students have acquired. Computational Thinking Pattern Analysis (CTPA) enables teachers to visualize which of nine specific skills students have mastered in game design that can then be used to create simulations. CTPA has the potential to automatically recognize and calculate student computational thinking skills, as well as to map students' computational thinking skill progression, as they proceed through the curriculum. The current research furthers knowledge of CTPA by exploring its validity based on how its performance correlates to human grading of student games. Initial data from this validation study indicates that CTPA correlates well with human grading and that it can even be used to predict students' future achievement levels given their current skill progression, making CTPA a potentially invaluable computational thinking evaluation tool for teachers.
Event Identification and Tracking in Social Media Streaming Data

In recent years, the growing popularity and active use of social media services on the web have resulted in massive amounts of user-generated data. With these data available, there is also an increasing interest in analyzing it and to extract information from it. Since social media analysis is concerned with investigating current events around the world, there is a strong emphasis on identifying these evens as quickly as possible, ideally in real-time. In order to scale with the rapidly increasing volume of social media data, we propose to explore very simple event identification mechanisms, rather than applying the more complex approaches that have been proposed in the literature. In this paper, we present a first investigation along this motivation. We discuss a simple sliding window model, which uses shifts in the inverse document frequency (IDF) to capture trending terms as well as to track the evolution and the context around events. Further, we present an initial experimental evaluation of the results that we obtained by analyzing real-world data streams from Twitter.
Combining Multiple Learning Strategies for Effective Cross Validation

Parameter tuning through cross-validation becomes very diicult when the validation set contains no or only a few examples of the classes in the evaluation set. We address this open challenge by using a combination of classiiers with diierent performance characteristics to eeectively reduce the performance variance on average of the overall system across all classes, including those not seen before. This approach allows us to tune the combination system on available but less-representative validation data and obtain smaller performance degradation of this system on the evaluation data than using a single-method classiier alone. We tested this approach by applying k-Nearest Neighbor, Rocchio and Language Modeling classiiers and their combination to the event tracking problem in the Topic Detection and Tracking (TDT) domain, where new classes (events) are created constantly over time, and representative validation sets for new classes are often diicult to obtain on time. When parameters tuned on an early benchmark TDT corpus were evaluated on a later TDT benchmark corpus with no overlapping events, we observed a 38-65% reduction in tracking cost (a weighted combination of errors) by the combined system over the individual methods evaluated under the same conditions, strongly suggesting the robustness of this approach as a solution for improving cross-class performance consistency of statistical classi-ers when standard cross-validation fails due to the lack of representative validation sets.
Cooperation of the HDAC inhibitor vorinostat and radiation in metastatic neuroblastoma: efficacy and underlying mechanisms.

Histone deacetylase (HDAC) inhibitors can radiosensitize cancer cells. Radiation is critical in high-risk neuroblastoma treatment, and combinations of HDAC inhibitor vorinostat and radiation are proposed for neuroblastoma trials. Therefore, we investigated radiosensitizing effects of vorinostat in neuroblastoma. Treatment of neuroblastoma cell lines decreased cell viability and resulted in additive effects with radiation. In a murine metastatic neuroblastoma in vivo model vorinostat and radiation combinations decreased tumor volumes compared to single modality. DNA repair enzyme Ku-86 was reduced in several neuroblastoma cells treated with vorinostat. Thus, vorinostat potentiates anti-neoplastic effects of radiation in neuroblastoma possibly due to down-regulation of DNA repair enzyme Ku-86.
An attribute-based community search method with graph refining

In many complex networks, there exist diverse network topologies as well as node attributes. However, the state-of-the-art community search methods which aim to find out communities containing the query nodes only consider the network topology, but ignore the effect of node attributes. This may lead to the inaccuracy of the predicted communities. In this paper, we propose an attribute-based community search method with graph refining technique, called AGAR. First, we present the concepts of topology-based similarity and attribute-based similarity to construct a TA-graph. The TA-graph can reflect both the relations between nodes from the respect of the network topology and that of the node attributes. Then, we construct AttrTCP-index based on the structure of TA-graph. Finally, by querying the AttrTCP-index, we can find out the communities for the query nodes. Experimental results on real-world networks demonstrate AGAR is an effective and efficient community search method by considering both the network topology and node attributes.
Efficiently inferring community structure in bipartite networks

Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.
Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called ”Sashimi” that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called ”Sukiyaki” that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license athttp://mil-tokyo.github.io/.
Dynamic XML documents with distribution and replication

The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1, 3, 22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.
A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control

Converters for photovoltaic (PV) systems usually consist of two stages: a dc/dc booster and a pulsewidth modulated (PWM) inverter. This cascade of converters presents efficiency issues, interactions between its stages, and problems with the maximum power point tracking. Therefore, only part of the produced electrical energy is utilized. In this paper, the authors propose a single-phase H-bridge multilevel converter for PV systems governed by a new integrated fuzzy logic controller (FLC)/modulator. The novelties of the proposed system are the use of a fully FLC (not requiring any optimal PWM switching-angle generator and proportional-integral controller) and the use of an H-bridge power-sharing algorithm. Most of the required signal processing is performed by a mixed-mode field-programmable gate array, resulting in a fully integrated System-on-Chip controller. The general architecture of the system and its main performance in a large spectrum of practical situations are presented and discussed. The proposed system offers improved performance over two-level inverters, particularly at low-medium power.
PDA: Privacy-Preserving Data Aggregation in Wireless Sensor Networks

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.
Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time

Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analysing their influence over time. For this purpose we develop a saliency model which is based on the internal representation of a recent early spatial vision model to measure the low-level bottom-up factor. To measure the influence of high-level bottom-up features, we use a recent DNN-based saliency model. To account for top-down influences, we evaluate the models on two large datasets with different tasks: first, a memorisation task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: The first saccade, an initial guided exploration characterised by a gradual broadening of the fixation density, and an steady state which is reached after roughly 10 fixations. Saccade target selection during the initial exploration and in the steady state are related to similar areas of interest, which are better predicted when including high-level features. In the search dataset, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties and as early as 200ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later this high-level bottom-up control can be overruled by top-down influences.