nils-herrmann/clean_scirepeval huggingface dataset:
Gated networks: an inventory

Gated networks are networks that contain gating connection s, n which the outputs of at least two neurons are multiplied. Initially, gated net works were used to learn relationships between two input sources, such as pixels fro m two images. More recently, they have been applied to learning activity recog nition or multimodal representations. The aims of this paper are threefold: 1) to explain the basic computations in gated networks to the non-expert, while ado pting a standpoint that insists on their symmetric nature. 2) to serve as a quick reference guide to the recent literature, by providing an inventory of applicatio ns of these networks, as well as recent extensions to the basic architecture. 3) to su ggest future research directions and applications.
Biomedical ontology alignment: an approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.



An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.



Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
BanditSum: Extractive Summarization as a Contextual Bandit

In this work, we propose a novel method for training neural networks to perform singledocument extractive summarization without heuristically-generated extractive labels. We call our approach BANDITSUM as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BANDITSUM is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BANDITSUM performs significantly better than competing approaches when good summary sentences appear late in the source document.
Learning compound noun semantics

This thesis investigates computational approaches for analysing the semantic relations in compound nouns and other noun-noun constructions. Compound nouns in particular have received a great deal of attention in recent years due to the challenges they pose for natural language processing systems. One reason for this is that the semantic relation between the constituents of a compound is not explicitly expressed and must be retrieved from other sources of linguistic and world knowledge. I present a new scheme for the semantic annotation of compounds, describing in detail the motivation for the scheme and the development process. This scheme is applied to create an annotated dataset for use in compound interpretation experiments. The results of a dual-annotator experiment indicate that good agreement can be obtained with this scheme relative to previously reported results and also provide insights into the challenging nature of the annotation task. I describe two corpus-driven paradigms for comparing pairs of nouns: lexical similarity and relational similarity. Lexical similarity is based on comparing each constituent of a noun pair to the corresponding constituent of another pair. Relational similarity is based on comparing the contexts in which both constituents of a noun pair occur together with the corresponding contexts of another pair. Using the flexible framework of kernel methods, I develop techniques for implementing both similarity paradigms. A standard approach to lexical similarity represents words by their co-occurrence distributions. I describe a family of kernel functions that are designed for the classification of probability distributions. The appropriateness of these distributional kernels for semantic tasks is suggested by their close connection to proven measures of distributional lexical similarity. I demonstrate the effectiveness of the lexical similarity model by applying it to two classification tasks: compound noun interpretation and the 2007 SemEval task on classifying semantic relations between nominals. To implement relational similarity I use kernels on strings and sets of strings. I show that distributional set kernels based on a multinomial probability model can be computed many times more efficiently than previously proposed kernels, while still achieving equal or better performance. Relational similarity does not perform as well as lexical similarity in my experiments. However, combining the two models brings an improvement over either model alone and achieves state-of-the-art results on both the compound noun and SemEval Task 4 datasets. 3 4 Acknowledgments The past four years have been a never-boring mixture of learning and relearning, experiments that worked in …
Multiband antenna for Bluetooth/ZigBee/ Wi-Fi/WiMAX/WLAN/X-band applications: Partial ground with periodic structures and EBG

A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.
Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences.

Using a self-administered questionnaire, 149 respondents rated service elements associated with a recently visited store or restaurant on scales that differed only in the number of response categories (ranging from 2 to 11) and on a 101-point scale presented in a different format. On several indices of reliability, validity, and discriminating power, the two-point, three-point, and four-point scales performed relatively poorly, and indices were significantly higher for scales with more response categories, up to about 7. Internal consistency did not differ significantly between scales, but test-retest reliability tended to decrease for scales with more than 10 response categories. Respondent preferences were highest for the 10-point scale, closely followed by the seven-point and nine-point scales. Implications for research and practice are discussed.
Application of a Lightweight Encryption Algorithm to a Quantized Speech Image for Secure IoT

The IoT Internet of Things being a promising technology of the future. It is expected to connect billions of devices. The increased communication number is expected to generate data mountain and the data security can be a threat. The devices in the architecture are fundamentally smaller in size and low powered. In general, classical encryption algorithms are computationally expensive and this due to their complexity and needs numerous rounds for encrypting, basically wasting the constrained energy of the gadgets. Less complex algorithm, though, may compromise the desired integrity. In this paper we apply a lightweight encryption algorithm named as Secure IoT (SIT) to a quantized speech image for Secure IoT. It is a 64-bit block cipher and requires 64-bit key to encrypt the data. This quantized speech image is constructed by first quantizing a speech signal and then splitting the quantized signal into frames. Then each of these frames is transposed for obtaining the different columns of this quantized speech image. Simulations result shows the algorithm provides substantial security in just five encryption rounds. IoT, Security, Encryption, quantized speech image, SNR, PESQ, Histogram, Entropy, Correlation.
Effects of Transformational Leadership Training on Attitudinal and Financial Outcomes : A Field Experiment

A pretest-posttest control-group design (N = 20) was used to assess the effects of transformational leadership training, with 9 and 11 managers assigned randomly to training and control groups, respectively. Training consisted of a 1-day group session and 4 individual booster sessions thereafter on a monthly basis. Multivariate analyses of covariance, with pretest scores as the covariate, showed that the training resulted in significant effects on subordinates' perceptions of leaders' transformational leadership, subordinates' own organizational commitment, and 2 aspects of branch-level financial performance.
Pseudodysphagia Due to Omohyoid Muscle Syndrome

Omohyoid muscle syndrome (OMS) is a rare clinical condition that has the pathognomonic feature of the appearance of a lateral neck mass when swallowing due to dysfunction of the omohyoid muscle (OH). We present two cases of typical OMS with electrophysiologic and dynamic imaging studies. The study results indicate that OMS is caused mainly by the loosening of the fascial attachment to the intermediate tendon of the OH. The characteristic clinical features and pathomechanism underlying OMS are also discussed.
Automatic Chinese Factual Question Generation

Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical and semantic information in a declarative sentence, which is then transformed into questions. Recently, some research has been conducted to investigate Chinese factual question generation with some limited success. Reported performance is poor due to unavoidable errors (e.g., sentence parsing, name entity recognition, and rule-based question transformation errors) and the complexity of long Chinese sentences. This article introduces a novel Chinese question generation system based on three stages, sentence simplification, question generation and ranking, to address the challenge of automatically generating factual questions in Chinese. The proposed approach and system have been evaluated on sentences from the New Practical Chinese Reader corpus. Experimental results show that ranking improves more than 20 percentage of questions rated as acceptable by annotators, from 65 percent of all questions to 87 percent of the top ranked 25 percent questions.
Thin Images Reflected in the Water: Narcissism and Girls' Vulnerability to the Thin-Ideal.

The purpose of this research is to test how adolescent girls' narcissistic traits-characterized by a need to impress others and avoid ego-threat-influence acute adverse effects of thin-ideal exposure. Participants (11-15 years; total N = 366; all female) reported their narcissistic traits. Next, in two experiments, they viewed images of either very thin or average-sized models, reported their wishful identification with the models (Experiment 2), and tasted high-calorie foods in an alleged taste test (both experiments). Narcissism kept girls from wishfully identifying with thin models, which is consistent with the view that narcissistic girls are prone to disengage from thin-ideal exposure. Moreover, narcissism protected vulnerable girls (those who experience low weight-esteem) from inhibiting their food intake, and led other girls (those who consider their appearance relatively unimportant) to increase their food intake. These effects did not generalize to conceptually related traits of self-esteem and perfectionism, and were not found for a low-calorie foods outcome, attesting to the specificity of findings. These experiments demonstrate the importance of narcissism at reducing girls' thin-ideal vulnerability. Girls high in narcissism disengage self-protectively from threats to their self-image, a strategy that renders at least subsets of them less vulnerable to the thin-ideal.
A 450 fs 65-nm CMOS Millimeter-Wave Time-to-Digital Converter Using Statistical Element Selection for All-Digital PLLs

This paper presents a time-to-digital converter (TDC) that operates with a 20–64 GHz input and underpins the phase digitization function in a millimeter-wave all-digital fractional-N frequency synthesizer. A self-calibrated inductor-less frequency divider using dynamic CML latches provides an eight-phase input to a 3-bit “coarse” TDC, which is interfaced to a 5-bit “fine” TDC through a sub-sampling coarse-fine interface circuit. A wide bandwidth low dropout (LDO) on-chip regulator is used to decrease the effect of supply noise on TDC performance. A synthesized digital engine implements calibration using statistical element selection with mean adaptation to alleviate TDC nonlinearity that results from random mismatches and PVT variations. The TDC is fabricated in 65-nm CMOS along with the divider and calibration circuits, and achieves 450-fs resolution. The measured DNL and INL of the TDC are 0.65 and 1.2 LSB, respectively. The TDC consumes 11 mA from 1-V supply voltage. The TDC features a figure-of-merit of 0.167 (0.47) pJ per conversion step without (with) the frequency divider. A single-shot experiment shows that the on-chip LDO reduces the effect of TDC noise by reducing the standard deviation from 0.856 to 0.167 LSB for constant input. The prototype occupies an active area of  $502\times 110~\mu \text{m}^{\mathbf {2}}$  excluding pads.
Biology and therapy of fibromyalgia. New therapies in fibromyalgia

Fibromyalgia is a chronic, musculoskeletal pain condition that predominately affects women. Although fibromyalgia is common and associated with substantial morbidity and disability, there are no US Food and Drug Administration-approved treatments. However, progress has been made in identifying pharmacological and non-pharmacological treatments for fibromyalgia. Recent pharmacological treatment studies have focused on selective serotonin and norepinephrine reuptake inhibitors, which enhance serotonin and norepinephrine neurotransmission in the descending pain pathways and lack many of the adverse side effects associated with tricyclic medications. Promising results have also been reported for medications that bind to the alpha2delta subunit of voltage-gated calcium channels, resulting in decreased calcium influx at nerve terminals and subsequent reduction in the release of several neurotransmitters thought to play a role in pain processing. There is also evidence to support exercise, cognitive behavioral therapy, education, and social support in the management of fibromyalgia. It is likely that many patients would benefit from combinations of pharmacological and non-pharmacological treatments, but more study is needed.
Modeling Rich Contexts for Sentiment Classification with LSTM

Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships. While few prior study has approached the issue of modeling contexts in tweet, this paper proposes to use a hierarchical LSTM to model rich contexts in tweet, particularly long-range context. Experimental results show that contexts can help us to perform sentiment classification remarkably better.
Coordinated and Reconfigurable Vehicle Dynamics Control

A coordinated reconfigurable vehicle dynamics control (CRVDC) system is achieved by high-level control of generalized forces/moment, distributed to the slip and slip angle of each tire by an innovative control allocation (CA) scheme. Utilizing control of individual tire slip and slip angles helps resolve the inherent tire force nonlinear constraints that otherwise may make the system more complex and computationally expensive. This in turn enables a real-time adaptable, computationally efficient accelerated fixed-point (AFP) method to improve the CA convergence rate when actuation saturates. Evaluation of the overall system is accomplished by simulation testing with a full-vehicle CarSim model under various adverse driving conditions, including scenarios where vehicle actuator failures occur. Comparison with several other vehicle control system approaches shows how the system operational envelope for CRVDC is significantly expanded in terms of vehicle global trajectory and planar motion responses.
Communities' Detection in Social Networks: State of the art and perspectives

Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities and clustering is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature, such as the algorithms of Newman and Girvan. However, most solutions have common problems: the stability and the community structures quality. In this paper, we give a state of the art, from 1970 until 2018 on the approaches as well as the algorithms of communities' detection in social networks. This study aims to gives a comparison between the different proposed architectures;
Balancing control actions for cascaded H-bridge converters with integrated battery energy storage

This paper investigates the ways to handle active power components for control and balancing purposes in three-phase Cascaded H-Bridge converters with embedded battery energy storage systems. The split storage elements are interfaced to the converter modules through a non-isolated DC/DC stage, in order to eliminate the inherent second-order harmonic of the module output current. Both the cases of star and delta converter configurations are analyzed. The topology-specific degrees of freedom are exploited, control algorithms are developed and limitations are established. Potential grid asymmetries are taken into consideration through the utilization of symmetrical components. Several modes of operation are studied, such as independent load feeding, State of Charge balancing as well as converter operation under grid faults.
Annual Report Readability , Earnings , and Stock Returns ∗

This paper examines the relationship between annual report readability and firm performance. This is motivated by the Securities and Exchange Commission’s plain English disclosure regulations that attempt to make corporate disclosures easier to read for ordinary investors. I measure the readability of public company annual reports using the Fog Index and the Kincaid Index from computational linguistics. I find that the annual reports of firms with lower earnings are harder to read. Moreover, the positive earnings of firms with annual reports that are easier to read are more persistent in the next one to four years. This suggests that, consistent with the motivation behind the plain English disclosure regulation of the Securities and Exchange Commission, managers may be opportunistically choosing the readability of annual reports to hide adverse information from investors. However, I do not find any correlation between annual report readability and future stock returns, suggesting that the market does impound the implications of disclosure readability into stock prices.
An improved anomaly detection and diagnosis framework for mobile network operators

The ever increasing complexity of commercial mobile networks drives the need for methods capable of reducing human workload required for network troubleshooting. In order to address this issue, several attempts have been made to develop automatic anomaly detection and diagnosis frameworks for mobile network operators. In this paper, the latest improvements introduced to one of those frameworks are discussed, including more sophisticated profiling and detection capabilities. The new algorithms further reduce the need for human intervention related to the proper configuration of the profiling and anomaly detection apparatus. The main concepts of the new approach are described and illustrated with an explanatory showcase featuring performance data from a live 3 G network.
Combining multispectral aerial imagery and digital surface models to extract urban buildings

This paper presents an automated classification of buildings in Coleraine, Northern Ireland. The classification was generated using very high spatial resolution data (10 cm) from a Digital Mapping Camera (DMC) for March 2009. The visible to near infrared (VNIR) bands of the DMC enabled a supervised classification to be performed to extract buildings from vegetation. A Digital Surface Model (DSM) was also created from the image to differentiate between buildings and other land classes with similar spectral profiles, such as roads. The supervised classification had the lowest classification accuracy (50%) while the DSM had an accuracy of 81%. The combination of the DSM and the supervised classification achieved an overall classification accuracy of 95%. Two spatial metrics (percentage of the landscape and number of patches) were also used to test the level of agreement between the classification and digitised building data. The results suggest that fine resolution multispectral aerial imagery can automatically detect buildings to a very high level of accuracy. Current space borne sensors, such as IKONOS and QuickBird, lag behind airborne sensors with VNIR bands provided at a much coarser spatial resolution (4m and 2.4m respectively). Techniques must be developed from current airborne sensors that can be applied to new space borne sensors in the future. The ability to generate DSMs from high resolution aerial imagery will afford new insights into the three-dimensional aspects of urban areas which will in turn inform future urban planning.
New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications

Al~traet--In this paper, we propose a new method to obtain the Euclidean distance transformation and the Voronoi diagram based on the exact Euclidean metric for an n-dimensional picture. We present four algorithms to perform the transformation which are constructed by the serial composition of n-dimensional filters. When performed by a general purpose computer, they are faster than the method by H. Yamada for a two-dimensional picture. Those algorithms require only one n-dimensional array for storing input/output pictures and a single one-dimensional array for a work area, if an input picture needs not be preserved.