nils-herrmann/clean_scirepeval huggingface dataset:
User simulation for spoken dialogue systems: learning and evaluation

We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on “normal” n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The “advanced” n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the C OMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available to train larger ngrams. The linear model produces the best expected accuracy but with respect to expected precision and expected recall it is outperformed by the large n-grams even though it is trained using more information. As a task-based evaluation, we also run each of the user simulation models against a system policy trained on the same corpus. Here the linear feature combination model outperforms the other methods and the advanced n-grams outperform the normal ngrams for all values of n, which again shows their potential. We also calculate the perplexity of the different user models.
Estimating the NDVI from SAR by Convolutional Neural Networks

Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.
Design of simple DC-to-DC Wireless Power Transfer via inductive coupling

This research presents a wireless DC-to-DC power transfer over a short distance. Distance of remotely located target from the source coil and received voltage at the load are the major concern in the Wireless Power Transfer (WPT) applications. The inversely proportional behavior of these parameters degrades the system performance. In order to optimize these values and achieving transfer efficiency, microelectronic circuit is developed and simulated using NI Multisim. The transmitter and receiver modules are constructed and their individual blocks are simulated and analyzed for wireless power transfer. Two inductively coupled coils are designed and used in both transmitter and receiver sections. The inverter is designed using H-bridge with five CMOSFET (IRF520NS, IRF5210S) and its gate signal is generated using NE555 timer IC with two output clock signal where one was inverted using an NMOS inverter. Through simulation, it is observed that with such a design, the power transfer has a limited range, and the range will be smaller for smaller receiving coils and improper alignment. A simulated model is proposed and implemented in this paper. The simulation outputs of each unit is plotted and analyzed separately. In addition, transient plot of load resistance versus output voltage are illustrated to analyze the power of dc-dc power transfer circuit. It is observed that the output voltage is increased correspondingly with increasing load resistance and determined good output voltage is harvested with the optimized load resistance is at 1 kΩ and is 4.25 V. Simulated results shows that the proposed system can transfer power with high efficiency. This proposed system could be made commercially viable through further research work.
Variance Reduction Methods II 1 Importance Sampling

For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of n, however, would almost inevitably result in an estimate, θ̂n = 0, and an approximate confidence interval [L,U ] = [0, 0]! So the naive approach does not work. We could try to use the variance reduction techniques we have seen in the course so far, but they would provide little, if any, help.
Solid State Gas Sensor Research in Germany – a Status Report

This status report overviews activities of the German gas sensor research community. It highlights recent progress in the field of potentiometric, amperometric, conductometric, impedimetric, and field effect-based gas sensors. It is shown that besides step-by-step improvements of conventional principles, e.g. by the application of novel materials, novel principles turned out to enable new markets. In the field of mixed potential gas sensors, novel materials allow for selective detection of combustion exhaust components. The same goal can be reached by using zeolites for impedimetric gas sensors. Operando spectroscopy is a powerful tool to learn about the mechanisms in n-type and in p-type conductometric sensors and to design knowledge-based improved sensor devices. Novel deposition methods are applied to gain direct access to the material morphology as well as to obtain dense thick metal oxide films without high temperature steps. Since conductometric and impedimetric sensors have the disadvantage that a current has to pass the gas sensitive film, film morphology, electrode materials, and geometrical issues affect the sensor signal. Therefore, one tries to measure directly the Fermi level position either by measuring the gas-dependent Seebeck coefficient at high temperatures or at room temperature by applying a modified miniaturized Kelvin probe method, where surface adsorption-based work function changes drive the drain-source current of a field effect transistor.
Clitics in Arabic Language: A Statistical Study

Clitics in Arabic language can be attached to a stem or to each other without orthographic marks such as an apostrophe. In this paper we present a statistical study of clitics and its effect in Arabic language. We tokenize large Arabic text using white-spaces and an automatic clitics tokenizer (AMIRA 2.0) and compare the unique-word count in both cases with English language. We also show the resulted distribution of clitics in Arabic and examine the performance of the used tokenizer. Using a 600 million words Arabic corpus, we report that the corresponding lexicon size could be reduced by 24.54% when applying clitics tokenization.
Fascia lliaca block for analgesia after hip arthroplasty: a randomized double-blind, placebo-controlled trial.

Fascia iliaca block (FIB) is often used to treat pain after total hip arthroplasty (THA), despite a lack of randomized trials to evaluate its efficacy for this indication. The objective of this study was to assess the analgesic benefit of FIB after THA. Our primary hypothesis was administration of FIB decreases the intensity of postoperative pain (numeric rating scale  score) compared with sham block (SB) in patients after THA.



After institutional review board approval and informed consent, 32 eligible patients having THA were recruited. In the postoperative care unit, although all patients received intravenous morphine sulfate patient-controlled analgesia, patients reporting pain of 3 or greater on the NRS-11 scale were randomized to receive ultrasound-guided fascia iliaca (30 mL 0.5% ropivacaine) or SB (30 mL 0.9% NaCl) using identical technique, below fascia iliaca. The primary outcome was pain intensity (NRS-11) after FIB.



Thirty-two patients (16 in each group) completed the study; all patients received an FIB. There was no difference in pain intensity (NRS-11 = 5.0 ± 0.6 vs 4.7 ± 0.6, respectively) after FIB versus SB or in opioid consumption (8.97 ± 1.6 vs 5.7 ± 1.6 mg morphine, respectively) between the groups at 1 hour. The morphine consumption after 24 hours was similar in both groups (49.0 ± 29.9 vs 50.4 ± 34.5 mg, P = 0.88, respectively).



The evidence in these data suggests that the difference in average pain intensity after FIB versus SB was not significant (95% confidence interval, -2.2-1.4 NRS units).
Efficient and Robust Retrieval by Shape Content through Curvature Scale Space

We introduce a very fast and reliable method for shape similarity retrieval in large image databases which is robust with respect to noise, scale and orientation changes of the objects. The maxima of curvature zero crossing contours of Curvature Scale Space (CSS) image are used to represent the shapes of object boundary contours. While a complex boundary is represented by about ve pairs of integer values, an eeective indexing method based on the aspect ratio of the CSS image , eccentricity and circularity is used to narrow down the range of searching. Since the matching algorithm has been designed to use global information, it is sensitive to major occlusion, but some minor occlusion will not cause any problems. We have tested and evaluated our method on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. The method can either be used in real applications or produce a reliable shape description for more complicated images when other features such as color and texture should also be considered. Since shape similarity is a subjective issue, in order to evaluate the method, we asked a number of volunteers to perform similarity retrieval based on shape on a randomly selected small database. We then compared the results of this experiment to the outputs of our system to the same queries and on the same database. The comparison indicated a promising performance of the system.
Testing the Interactivity Model: Communication Processes, Partner Assessments, and the Quality of Collaborative Work

EE K. BuRGOON IS Professor of Communication and Director of the Center for the Management of Information at the University of Arizona. She is the author or coauthor of seven books and nearly two hundred articles and chapters on topics related to interpersonal and nonverbal communication, deception and credibility, mass media, and new communication technologies. Her current research focuses on interactivity, adaptation, and attunement in communication.
Exploiting the Data Redundancy Locality to Improve the Performance of Deduplication-Based Storage Systems

The chunk-lookup disk bottleneck and the read amplification problems are two great challenges for deduplication-based storage systems and restrict the applicability of data deduplication for large-scale data volumes. Previous studies and our experimental evaluations have shown that the amount of redundant data shared among different types of applications is negligible. Based on the observations, we propose AA-Plus which effectively groups the hash index of the same application together and divides the whole hash index into different groups based on the application types. Moreover, it groups the data chunks of the same application together on the disks. The extensive trace-driven experiments conducted on our lightweight prototype implementation of AA-Plus show that compared with AA-Dedupe, AA-Plus significantly speeds up the write throughput by a factor of up to 6.9 and with an average of 3.1, and speeds up the read throughput by a factor of up to 3.3 and with an average of 1.9.
Design Criteria for Electric Vehicle Fast Charge Infrastructure Based on Flemish Mobility Behavior

This paper studies the technical design criteria for fast charge infrastructure, covering the mobility needs. The infrastructure supplements the residential and public slow charging infrastructure. Two models are designed. The first determines the charging demand, based on current mobility behavior in Flanders. The second model simulates a charge infrastructure that meets the resulting fast charge demand. The energy management is performed by a rule-based control algorithm, that directs the power flows between the fast chargers, the energy storage system, the grid connection, and the photovoltaic installation. There is a clear trade-off between the size of the energy storage system and the power rating of the grid connection. Finally, the simulations indicate that 99.7% of the vehicles visiting the fast charge infrastructure can start charging within 10 minutes with a configuration limited to 5 charging spots, instead of 9 spots when drivers are not willing to wait.
Domain Alignment with Triplets

Deep domain adaptation methods can reduce the distribution discrepancy by learning domain-invariant embedddings. However, these methods only focus on aligning the whole data distributions, without considering the classlevel relations among source and target images. Thus, a target embeddings of a bird might be aligned to source embeddings of an airplane. This semantic misalignment can directly degrade the classifier performance on the target dataset. To alleviate this problem, we present a similarity constrained alignment (SCA) method for unsupervised domain adaptation. When aligning the distributions in the embedding space, SCA enforces a similarity-preserving constraint to maintain class-level relations among the source and target images, i.e., if a source image and a target image are of the same class label, their corresponding embeddings are supposed to be aligned nearby, and vise versa. In the absence of target labels, we assign pseudo labels for target images. Given labeled source images and pseudo-labeled target images, the similarity-preserving constraint can be implemented by minimizing the triplet loss. With the joint supervision of domain alignment loss and similarity-preserving constraint, we train a network to obtain domain-invariant embeddings with two critical characteristics, intra-class compactness and inter-class separability. Extensive experiments conducted on the two datasets well demonstrate the effectiveness of SCA.
Feature reduction and selection for EMG signal classification

0957-4174/$ see front matter 2012  A doi:10.1016/j.eswa.2012.01.102 ⇑ Corresponding author.  angkoon.p@hotmail.com (A. Phiny Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.
Robust Elevator Button Recognition in the Presence of Partial Occlusion and Clutter by Specular Reflections

This paper deals with vision-based elevator button recognition for a robot arm manipulating elevator buttons. The major difficulties in elevator button recognition are the presence of partial occlusion of the target objects and image clutter caused by specular reflection from mirrorlike walls inside the elevator. As a remedy for the elevator button recognition problem in highly complicated settings, we propose a robust button recognition algorithm, which is modularized into feature extraction, initial recognition, and postrefinement modules. In consideration of the diverse button shapes in the form of convex quadrilaterals with rounded button corners, a set of features is specially designed to describe each button contour subject to perspective distortion. A homography-based image transform is also employed for the template matching to achieve a more reliable matching performance. Like a grammar in linguistics, geometric button alignment, plays a key role in the elimination of false positives and the estimation of the missing buttons. The proposed algorithm is extensively tested in a robotic platform to verify effectiveness, robustness, and real-time performance.
Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm.

In this paper we have discussed what appears to be a superior implementation of the Algebraic Reconstruction Technique (ART). The method is based on 1) simultaneous application of the error correction terms as computed by ART for all rays in a given projection; 2) longitudinal weighting of the correction terms back-distributed along the rays; and 3) using bilinear elements for discrete approximation to the ray integrals of a continuous image. Since this implementation generates a good reconstruction in only one iteration, it also appears to have a computational advantage over the more traditional implementation of ART. Potential applications of this implementation include image reconstruction in conjunction with ray tracing for ultrasound and microwave tomography in which the curved nature of the rays leads to a non-uniform ray density across the image.
A Unified Probabilistic Framework for Real-Time Depth Map Fusion

This paper proposes a unified probabilistic framework for real-time depth map fusion. By modeling the depth imaging process as a random experiment, the depth map fusion is converted into probability density function (pdf) estimation. The depth fusion problem is decoupled into four parts: the fusion space, the influence term, the visibility term and the confidence term. We combine these four terms in a unified probabilistic framework, and apply the framework in two cases to evaluate the performance. In the first case, multiple stereo vision cameras are used to acquire multiple depth map streams from multiple viewpoints simultaneously in real time. In the second case, two cameras and a Kinect are combined to provide two depth map streams. In both cases, strategies for each part of the framework are presented to perform real-time depth fusion. Experimental results show that the proposed framework is promising for real-time depth map fusion.
Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays.
Psychosocial functioning and depression: distinguishing among antecedents, concomitants, and consequences.

In this article we attempt to distinguish empirically between psycho-social variables that are concomitants of depression, and variables that may serve as antecedents or sequelae of this disorder. We review studies that investigated the relationship between depression and any of six psychosocial variables after controlling for the effects of concurrent depression. The six variables examined are attributional style, dysfunctional attitudes, personality, social support, marital distress, and coping style. The review suggests that whereas there is little evidence in adults of a cognitive vulnerability to clinical depression, disturbances in interpersonal functioning may be antecedents or sequelae of this disorder. Specifically, marital distress and low social integration appear to be involved in the etiology of depression, and introversion and interpersonal dependency are identified as enduring abnormalities in the functioning of remitted depressives. We attempt to integrate what is known about the relationships among these latter variables, suggest ways in which they may influence the development of depression, and outline specific issues to be addressed in future research.
Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models

Social media has become an inevitable part of individuals personal and business lives. Its benefits come with various negative consequences. One major concern is the prevalence of detrimental online behavior on social media, such as online harassment and cyberbullying. In this study, we aim to address the computational challenges associated with harassment detection in social media by developing a machine learning framework with three distinguishing characteristics. (1) It uses minimal supervision in the form of expert-provided key phrases that are indicative of bullying or non-bullying. (2) It detects harassment with an ensemble of two learners that co-train one another; one learner examines the language content in the message, and the other learner considers the social structure. (3) It incorporates distributed word and graph-node representations by training nonlinear deep models. The model is trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. We evaluate the effectiveness of our approach using post-hoc, crowdsourced annotation of Twitter, Ask.fm, and Instagram data, finding that our deep ensembles outperform previous non-deep methods for weakly supervised harassment detection.